Management of miscarriage for increased odds of subsequent ovulation

Abstract

A method for managing miscarriage in a female individual suffering from a pregnancy loss, comprising at least: a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a βhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss, b) determining one or more management score based on said set of data comprising at least a first management score predictive of a most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, c) predicting the most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said first management score, and d) removing said foetus and / or foetal material according to said most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score; wherein said most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score increases the most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.

Description

FIELDDespite one in four pregnancies ending with a pregnancy loss, there is limited knowledge about when to expect reproductive recovery. Stagnating ẞhCG limits chances of ovulation, thus inhibiting the onset of menstrual bleeding, however with great individual variability creating a need for a more patient-specific approach.BACKGROUNDPregnancy loss, the spontaneous demise of a pregnancy before 22 weeks of gestation,¹ affects at least 25% of all pregnancies.2 Despite associated physical and psychological comorbidities and implications for future pregnancies and maternal health, 3,4 research in the field of pregnancy loss is scarce. This is stressed by the Lancet Series Miscarriage Matters, imploring for further research to catalyse development of health evaluation after miscarriage, as well as guidance regarding reproductive recovery and perspectives of a subsequent pregnancy.5 A recent study by Tessema et al. challenges the World Health Organization's recommendation of a minimum interpregnancy interval of six months after pregnancy loss by finding no evidence linking conception within three months of a pregnancy loss to increased risk of adverse obstetric complications. This suggests that patients can attempt subsequent conception when they feel ready and offering accurate guidance on reoccurrence of ovulation becomes even more valuable.Donnet et al. followed 18 women experiencing first trimester pregnancy loss and found that participants experienced ovulation at a mean of 29 days (min-max: 13-103 days) post pregnancy loss, 7 hinting at substantial individuality between patients, as well as involvement of other factors.Furthermore, Hallet's synthesis of various smaller studies indicates that in 67-90% of cases, initial menstrual bleeding following spontaneous miscarriage is preceded by an ovulation.8Amongst others, a contributing factor to delay of a subsequent ovulation is thought to be stagnating levels of the glycoprotein hormone, human chorionic gonadotropin (hCG) from the lost pregnancy. 9ẞhCG is a heterodimeric molecule, where the alpha subunit is structurally equivalent to that of luteinizing hormone (LH), follicle-stimulating hormone (FSH) and thyroid-stimulating hormone (TSH) and the beta subunit is unique for ẞhCG.10 Because of this, as well as a shared LH / hCG receptor, ẞhCG levels following a pregnancy loss induce negative feedback in the pituitary, thus decreasing transcription of gonadotropins FSH and LH, potentially hindering ovulation. 9 In a study, Stier et al. investigated the time from 35 surgical treatments of spontaneous miscarriages to complete clearance of ẞhCG to be, however with an unknown cut-off level, 9 to 35 days (median = 19 days) post treatment¹¹. Experiencing retained product of conception after treatment is correlated with a delayed decline of ẞhCG levels,12-14 whereas other factors such as higher initial ẞhCG level are expected to have same effect11. To our knowledge, no studies investigated the decline in ẞhCG concentrations after pregnancy loss in a population of this magnitude and none analysed maternal factors affecting this.Patients experiencing pregnancy loss are typically advised to anticipate their next menstrual bleeding within 4-8 weeks. Nonetheless, the absence of clear scientific backing or uniform guidelines creates a vagueness that does not take individual factors into account. The aim of this study was to investigate the ẞhCG levels at time of pregnancy loss and six weeks after and factors affecting these levels. With these results we can provide patients with more personalized information and care regarding pregnancy loss and subsequent reproductive prospects, and we can improve understanding of return of menstrual bleeding, catering to an unmet need.SUMMARYWe found that 6-7 weeks after a pregnancy loss, ẞhCG levels were negatively correlated with return of menstrual bleeding and thereby a reproductive recovery. Surgical compared with both medical and expectant treatment, maternal age, and gestational age were associated with higher ẞhCG. Maternal BMI, number of prior livebirths or pregnancy losses, and intrauterine insemination compared to spontaneous conception were associated with lower ẞhCG.These findings contribute towards a more personalized approach to supporting women who are experiencing or have experienced pregnancy loss, facilitating their physical and reproductive recovery. An improved understanding of the return of fecundity following a pregnancy loss will facilitate appropriate and early intervention alongside expectation management between patient and provider. This could lower time to a subsequent cycle and potential pregnancy, hereby possibly increasing chances of a livebirth in such.It is an object of the present disclosure to provide estimation or prediction of the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of a pregnancy loss in a female individual.In one aspect, the present disclosure relates to a method for managing miscarriage in a female individual suffering from a pregnancy loss, comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) determining one or more management score based on said set of data, wherein the management score is predictive of the most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,c) predicting the most suitable type of management of miscarriage to be received by said female individual based on said one or more management score, andd) removing said foetus and / or foetal material according to the most suitable type of management of miscarriage predicted based on said one or more management score;wherein the most suitable type of management of miscarriage predicted based on said one or more management score increases the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.In another aspect, the present disclosure relates to a method for increasing the likelihood of occurrence of an ovulation during a period of 6 weeks after the determination of a pregnancy loss in a female individual, comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) determining one or more management score based on said set of data, wherein the management score is predictive of the most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,c) predicting the most suitable type of management of miscarriage to be received by said female individual based on said one or more management score, andd) removing said foetus and / or foetal material according to the most suitable type of management of miscarriage predicted based on said one or more management score,wherein the most suitable type of management of miscarriage predicted based on said one or more management score increases the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.In another aspect, the present disclosure relates to a method to assess the suitability of a type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) determining one or more management score based on said set of data, wherein the management score is predictive of the most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, andc) predicting the most suitable type of management of miscarriage to be received by said female individual based on said one or more management score,wherein the most suitable type of management of miscarriage predicted based on said one or more management score increases the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.In a further aspect, the present disclosure relates to a method further comprising:determining the one or more management score based on said set of data comprising a second management score predictive of the second most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, andpredicting the second most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said second management score,wherein said second most suitable type of management of miscarriage predicted based on said one or more management score comprising said second management score increases the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.In another further aspect, the present disclosure relates to a method further comprising:determining the one or more management score based on said set of data comprising a third management score predictive of the least most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, andpredicting the least most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said second management score,wherein said least suitable type of management of miscarriage predicted based on said one or more management score comprising said least management score increases the least the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.In another aspect, the present disclosure relates to a computer-implemented method for identifying the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) determining one or more management score based on said set of data, wherein the management score is predictive of the most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,c) predicting the most suitable type of management of miscarriage to be received by said female individual based on said one or more management score, andd) providing an output including a first output associated with the most suitable type of management of miscarriage predicted based on said one or more management score,wherein the most suitable type of management of miscarriage predicted based on said one or more management score increases the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.In another aspect, the present disclosure relates to an electronic device for identifying the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, the electronic device comprising an interface, one or more processors, and a memory, wherein the one of more processors are configured to:a) obtain a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) determine one or more management scores based on the set of data, the management score being indicative of the most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, andc) provide an output including a first output associated with the one or more management scores.wherein the most suitable type of management of miscarriage increases the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.In a further aspect, the present disclosure relates to a computer-implemented method further comprising, or an electronic device further configured to:determining the one or more management score based on said set of data comprising a second management score predictive of the second most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,predicting the second most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said second management score, andproviding an output including a second output associated with the second most suitable type of management of miscarriage predicted based on said one or more management score comprising said second management score,wherein the second most suitable type of management of miscarriage predicted based on said one or more management score comprising said second management score increases the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.In another further aspect, the present disclosure relates to a computer-implemented method further comprising, or an electronic device further configured to:determining the one or more management score based on said set of data comprising a third management score predictive of the least suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,predicting the least suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said second management score, andproviding an output including a third output associated with the least suitable type of management of miscarriage predicted based on said one or more management score comprising said third management scorewherein the least suitable type of management of miscarriage predicted based on said one or more management score comprising said third management score increases the least the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individualIn another aspect, the present disclosure relates to a computer-implemented method for training a machine learning model, such as a neural network, to process as inputs a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue and provide as output one or more management scores associated with the most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,, the method comprising:a) obtaining, using at least one processor, a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) performing, using the at least one processor, a training comprising:i) generating, using the at least one processor and a machine-learning model, management data based on the set of data,ii) obtaining, using the at least one processor, training data,iii) determining, using the at least one processor and one or more management functions, one or more management score based on the set of data and the training data; andiv) training, using the at least one processor, the machine learning model based on one or more management score,wherein the most suitable type of management of miscarriage increases the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.In a further aspect, the present disclosure relates to a computer-implemented method for training a machine learning model, such as a neural network, to process as inputs a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue and provide as output one or more management scores, further comprising a second management score associated with the second most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, the method further comprisingdetermining the one or more management score based on said set of data and the training data comprising said second management score, andtraining, using the at least one processor, the machine learning model based said one or more management score, comprising said second management score,wherein the second most suitable type of management of miscarriage increases the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.In another further aspect, the present disclosure relates to a computer-implemented method for training a machine learning model, such as a neural network, to process as inputs a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue and provide as output one or more management scores, further comprising a third management score associated with the least suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, the method further comprisingdetermining the one or more management score based on said set of data and the training data comprising said third management score, andtraining, using the at least one processor, the machine learning model based said one or more management score, comprising said third management score,wherein the least suitable type of management of miscarriage increases the least the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.In a further aspect, the most suitable type of management of miscarriage for increasing the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said individual is chosen from a group comprising at least the surgical management of miscarriage, the medical management of miscarriage, and / or the expectant management of miscarriage.In a further aspect, the most suitable type of management of miscarriage chosen for increasing the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said individual is:a) the surgical management of miscarriage if the ẞhCG value is below or above a certain threshold,b) the medical management of miscarriage if the ẞhCG value is below or above a certain threshold, and / orc) the expectant management of miscarriage if the ẞhCG value is below or above a certain threshold.BRIEF DESCRIPTION OF THE FIGURESFig. 1 schematically illustrates an example system according to the present disclosure,Fig. 2 is a flow chart of an example computer implemented method according to the present disclosure,Fig. 3 is a flow chart of an example computer implemented method according to the present disclosure,Fig. 4 illustrates an example implementation of a machine learning model according to the disclosure.Fig. 5 illustrates the ẞhCG levels after pregnancy loss.Fig. 6 illustrates the odds-ratios for seven variables robustly predicting the ẞhCG value at follow-up, namely: ẞhCG at pregnancy loss, days since pregnancy loss, gestational age at pregnancy loss (calculated from last menstrual period), prior number of live births, creatine kinase, the ASAT / ALAT ratio (De Ritis ratio), and the treatment choiceFig. 7 illustrates the effect of the ASAT / ALAT ratio on the ẞhCG value.Fig. 8 illustrates the difference of performance between models using all variables, and models using a reduced number of variables.Fig. 9 illustrates the difference of performance between models using all variables, and models using a reduced number of variables, with external data sets from CUH Herlev.Fig. 10 illustrates the difference of performance between models using all variables, and models using a reduced number of variables, with external data sets from CUH North Zealand.Fig. 11 summarizes the risk of excessive ẞhCG levels post pregnancy loss depending on a selection of preferred variables.Fig. 12 illustrates the probability of return of menstrual cycle within 8 weeks after a pregnancy loss, depending on the ẞhCG levels measured at follow-up visit.Figure 13 illustrates the treatments yielding the highest probability of having a BETAHCG < 3 IU / L at follow-up, depending on the ẞhCG level measured at the time of pregnancy loss.DETAILED DESCRIPTIONThe present disclosure present outcome prediction models for managing miscarriage in a female individual suffering from a pregnancy loss comprising identifying the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss to increase the likelihood of occurrence of an ovulation during a period of 6 weeks after the determination of a pregnancy loss, using biochemistry and clinical variables. The model demonstrated strong performance metrics, that aligned well with the results from two external validation sites (Herlev University Hospital and Northern Zealand Hospital), underlining the model's generalizability. Furthermore, our association studies and machine learning models implicate not only the placenta-produced hormone ẞ-hCG and gestational age, but also maternal levels of enzymes and lipids, thyroid function, and liver.Taken together, this demonstrates that a blood sample can be used to estimate the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss; and can be a powerful method to plan subsequent reproductive prospects by increasing the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss and said management of miscarriage, which women should be offered when experiencing a pregnancy loss.The external validation performed on an independent dataset further substantiates the robustness and generalizability of our model. Despite these strengths, the cohort includes pregnancy losses, and the models may need to be further validated in ongoing pregnancies, but the present data teach that the concept is applicable in ongoing pregnancies.Various examples and details are described hereinafter, with reference to the figures when relevant. It should be noted that the figures may or may not be drawn to scale and that elements of similar structures or functions are represented by reference numerals throughout the figures. It should also be noted that the figures are only intended to facilitate the description of the examples. They are not intended as an exhaustive description of the disclosure or as a limitation on the scope of the disclosure. In addition, an illustrated example needs not have all the aspects or advantages shown. An aspect or an advantage described in conjunction with a particular example is not necessarily limited to that example and can be practiced in any other examples even if not so illustrated, or if not so explicitly described.The figures are schematic and simplified for clarity, and they merely show details which aid understanding the disclosure, while other details have been left out. Throughout, the same reference numerals are used for identical or corresponding parts.It is to be understood that a description of a feature in relation to the system / electronic device is also applicable to the corresponding feature in the method(s) of operating a system / electronic device as disclosed herein and vice versa.The present disclosure relates to tools and methods for analysis, classification, monitoring, and / or prediction for managing miscarriage in a female individual suffering from a pregnancy loss, according to the most suitable type of management of miscarriage predicted, to increase the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.The present disclosure also relates to tools and methods for analysis, classification, monitoring, and / or prediction for increasing the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual, by removing said foetus and / or foetal material according to the most suitable type of management of miscarriage predicted to increase said likelihood.The present disclosure also relates to tools and methods for analysis, classification, monitoring, and / or prediction for identifying the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.An electronic device for identifying the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss, is disclosed. The electronic device comprises an interface, one or more processors, and a memory. The one or more processors are configured to obtain, e.g. retrieve from a database and / or receive via the interface, clinical data including one or more of, such as two or all of, female data associated with a female; and pregnancy loss data, e.g. associated with a foetus and / or foetal tissue and / or pregnancy tissue. The one or more processors are configured to determine one or more management scores based on one or more of the female data, and the pregnancy loss data.The one or more management scores can comprise a first management score associated with the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.The one or more management scores can comprise a second management score associated with the second most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.The one or more management scores can comprise a third management score associated with the least suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.The one or more processors are configured to provide an output associated with the one or more management scores, such as an output including a first output associated with the first management score, a second output associated with the second management score, and / or a third output associated with the third management score.The one or more management scores may comprise a first management score, e.g. indicative of a status of the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the most likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The first output may be associated with, such as representative or indicative of, the first management score.The one or more management scores may comprise a second management score, e.g. indicative of a status of the second most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The second output may be associated with, such as representative or indicative of, the second management score.The one or more management scores may comprise a third management score, e.g. indicative of a status of the least suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The third output may be associated with, such as representative or indicative of, the third management score.In one or more example electronic devices, the one or more processors are configured to obtain female data associated with a female; obtain pregnancy loss data associated with a foetus and / or foetal tissue; determine one or more management scores based on one or more of the female data, and the pregnancy loss data, the one or more management scores comprising a first management score associated with the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss; and provide an output including a first output associated with the one or more management scores comprising the first management score.In one or more example electronic devices, the one or more processors are configured to obtain female data associated with a female; obtain pregnancy loss data associated with a foetus and / or foetal tissue; determine one or more management scores based on one or more of the female data, and the pregnancy loss data, the one or more management scores comprising a second management score associated with the second most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss; and provide an output including a second output associated with the one or more management scores comprising the second management score.In one or more example electronic devices, the one or more processors are configured to obtain female data associated with a female; obtain pregnancy loss data associated with a foetus and / or foetal tissue; determine one or more management scores based on one or more of the female data, and the pregnancy loss data, the one or more management scores comprising a third management score associated with the least suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss; and provide an output including a third output associated with the one or more management scores comprising the third management score.In one or more examples, to provide an output including a first output associated with the one or more management scores comprises to display, on a display of the interface, one or more user interface elements indicative of, e.g. showing the value of, one or more management scores, such as first user interface element indicative of the first management score, such as second user interface element indicative of the second management score, and / or such as third user interface element indicative of the third management score.For example, a first primary user interface element may be color-coded based on a first management score, e.g. indicative of the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.For example, a second user interface element may be color-coded based on a second management score, e.g. indicative of the second most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.For example, a third user interface element may be color-coded based on a third management score, e.g. indicative of the least suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.In one or more examples, to provide an output including a first output, a second output, and / or a third output associated with the one or more management scores comprises to transmit the one or more management scores or a subset thereof to a remote server or database, e.g. via a network.In one or more examples, to provide an output including a first output, a second output, and / or a third output associated with the one or more management scores comprises to store the one or more management scores or a subset thereof in the memory or a database.Female dataFemale data also denoted maternal data are data indicative and / or associated with the female or mother of the foetus.In one or more example electronic devices, the female data comprises female blood data also denoted FBD of the female, e.g. first female blood data of a first female blood sample taken at a first time and / or second female blood data of a second female blood sample taken at a second time. The first time may be less than 48 hours, such as less than 24 hours, after detection of pregnancy loss. The second time may be larger than 1 week after detection of pregnancy loss, such as in the range from 2 weeks to 8 weeks, e.g. 4 weeks to 6 weeks, after detection of pregnancy loss. In one or more example electronic devices, to determine the one or more management scores comprises to determine the one or more management scores, such as the first management score, based on the female blood data, such as the first female blood data and / or the second female blood data.The female blood data may comprise or be indicative of one or more of Whole Genome Sequencing (WGS), Cell-Free Fetal DNA (cffDNA), proteomics, metabolomics immunological profile, metabolic profile, inflammatory markers, blood coagulation status, and RNA sequencing.The female blood data may comprise or be indicative of one or more of the creatine kinase level, the Aspartate Transaminase (ASAT) / Alanine Transaminase (ALAT) ratio (De Ritis ratio), the C-Reactive Protein (CRP) level, the thyroid Peroxidase antibody level, the thyroglobulin antibody level, the Gamma-Glutamyl Transferase (GGT) level, the Aspartate Transaminase (ASAT) level, the Alanine Transaminase (ALAT) level, the triglycerides level, the LDL Cholesterol level, the apolipoprotein B level, the creatinine level, the thyroid-Stimulating Hormone (TSH) level, the uric acid level, the rheumatoid Factor level, the transferrin level, the iron level, the alipoprotein(a) level, the albumin level, the bilirubin level, the urea level, the HDL Cholesterol level, the TG Rich Lipoprotein Cholesterol (TRL-C) level, the lipoprotein(a) level, the triacylglycerol lipase level, and / or the Lactate Dehydrogenase (LDH) level.The first female blood sample may be taken at a first time within a first time period, e.g. within 48 hours or within 24 hours of detection of pregnancy loss.In one or more example electronic devices, the female data comprises female biosample data also denoted FBSD of the female. The female biosample data FBSD may comprise one or more of vaginal data associated with a vaginal (microbiome) sample, rectal data associated with a rectal sample, and urine data associated with a urine sample. In one or more example electronic devices, to determine the one or more management scores comprises to determine the one or more management scores, such as the first management score, based on the female biosample data, such as one or more of the vaginal data, the rectal data, and the urine data of the female biosample data.The vaginal data may comprise one or more vaginal parameters indicative of vaginal sample. The vaginal data of the female biosample data may comprise or be indicative of vaginal microbiome, such as 16S rRNA sequencing and / or shotgun-sequencing and / or vaginal immunology.The rectal data of the female data may comprise one or more rectal parameters indicative of rectal sample. The rectal data of the female biosample data may comprise or be indicative of Gut microbiome, such as 16S rRNA sequencing and / or shotgun-sequencing and / or rectal immunology.The urine data of the female biosample data may comprise or be indicative of one or more of endocrine disrupters, infections, cannabinoids, illegal drugs, and cotinine.In one or more example electronic devices, the female data comprises ultrasound image data of an ultrasound scanning of the uterus of the female. To determine the one or more management scores may comprise to determine the one or more management scores, such as the first, second, and / or third management score, based on the ultrasound image data.The female data may comprise one or more of type of pregnancy loss, selected evacuation treatment (or management of miscarriage), and if and / or which pregnancy complications occurred. A type of pregnancy loss may be selected from one or more of spontaneous complete miscarriage, spontaneous incomplete miscarriage, missed abortion, and anembryonic pregnancy.The female data may comprise one or more of age, Body Mass Index (BMI) (kg.m2), weight, height, hip-waist ratio, pulse and blood pressure (systolic and / or diastolic) of the female.The female data may comprise one or more of the number of days since pregnancy loss, the number of prior live births, the presence or absence of vaginal bleeding at inclusion, the number of prior pregnancy loss, and / or the cycle duration prior to the pregnancy.The female data may comprise health data, such as one or more health parameters indicative of female health. In one or more examples, the health data of the female data may comprise a health parameter, e.g. a first health parameter, indicative of whether the female has, has been operated for, or has been diagnosed with endometriosis. In one or more examples, the health data of the female data may comprise a health parameter, e.g. a second health parameter, indicative of whether the female has or has been diagnosed with fibrom, such as uterine fibroms. In one or more examples, the health data of the female data may comprise a health parameter, e.g. a third health parameter, indicative of whether the female has, has been operated for, or has been diagnosed with hernia. In one or more examples, the health data of the female data may comprise a health parameter, e.g. a fourth health parameter, indicative of whether the female has, has been operated for, or has been diagnosed with ovarian cyst. In one or more examples, the health data of the female data may comprise a health parameter, e.g. a fifth health parameter, indicative of whether the female has, has been operated for, or has been diagnosed with Appendicitis. In one or more examples, the health data of the female data may comprise a health parameter, e.g. a sixth health parameter, indicative of whether the female has or has been diagnosed with genital infection. In one or more examples, the health data of the female data may comprise a health parameter, e.g. a seventh health parameter, indicative of whether the female has, has been operated for, or has been diagnosed with Ectopic pregancy. In one or more examples, the health data of the female data may comprise a health parameter, e.g. an eighth health parameter, indicative of whether the female has, has been operated for, or has been diagnosed with Polyscystiuc Ovarian Syndrome. In one or more examples, the health data of the female data may comprise a health parameter, e.g. a ninth health parameter, indicative of whether the female has had a Caesarean section. In one or more examples, the health data of the female data may comprise a health parameter, e.g. a tenth health parameter, indicative of whether the female has had vaginal bleeding during pregnancy. The health data may be obtained as questionnaire data via a questionnaire and / or via a patient record.The female data may comprise information or data on one or more of current medications, reproductive history, lifestyle, physical health, and mental health of the female. For example, the female data may comprise female questionnaire data also denoted FQD comprising or indicative of one or more of general information, reproductive history, health, medication and dietary supplements, communication, relationship, staff satisfaction, well-being, sexuality, health behavior, family, childhood, sociodemographic factors, and work. The questionnaire data of the female data may comprise questionnaire data from different times, such first questionnaire data from questionnaire answers at a first time and / or second questionnaire data from questionnaire answers at a second time. The questionnaire data may comprise third questionnaire data from questionnaire answers at a third time.The questionnaire data of the female data may comprise a parameter indicative of alcohol intake during pregnancy. The questionnaire data of the female data may comprise a parameter indicative of smoking during pregnancy. The female data, such as questionnaire data, may comprise a partner parameter indicative of the number of sexual partners.The female data, such as questionnaire data, may comprise a first dietary parameter, e.g. indicative of whether the female has taken vitamin(s) prior to and / or during the pregnancy. For example, the first dietary parameter may be a vector or matrix and may be indicative of type(s) and / or amount(s) of vitamin supplements, such as vitamin D and / or vitamin E, taken prior to and / or during pregnancy.The female data, such as questionnaire data, may comprise a second dietary parameter, e.g. indicative of whether the female has taken fish oil prior to and / or during the pregnancy. For example, the second dietary parameter may be a vector or matrix and may be indicative of type(s) and / or amount(s) of fish oil taken prior to and / or during pregnancy.Pregnancy loss dataThe pregnancy loss data may be associated with or indicative of one or more of a foetus, foetal tissue, pregnancy tissue, and product of conception and / or tissue thereof.The pregnancy loss data may comprise the ẞhCG values (IU / L) at the time of detection of the pregnancy loss.Said ẞhCG values may be measured from a first female blood sample taken at a first time and / or second female blood data of a second female blood sample taken at a second time. The first time may be less than 48 hours, such as less than 24 hours, after detection of pregnancy loss. The second time may be larger than 1 week after detection of pregnancy loss, such as in the range from 2 weeks to 8 weeks, e.g. 4 weeks to 6 weeks, after detection of pregnancy loss. In one or more example electronic devices, to determine the one or more management scores comprises to determine the one or more management scores, such as the first management score, based on the female blood data, such as the first female blood data and / or the second female blood data. Said first and second blood samples may be the same or different blood samples as the first and second blood samples used for determination of the female data.The pregnancy loss data may comprise one or more parameter of the gestational age at pregnancy loss (calculated from the last menstruation of said individual), or the diagnosis of the pregnancy loss (such as missed abortion, blighted ovum, spontaneous ongoing pregnancy loss, etc.).The pregnancy loss data may comprise one or more parameters indicative of one or more of method or mode of conception, such as natural, IVF, or insemination, gestational age(s), e.g. based on last menstrual period also denoted GA_LM and / or findings from diagnostic ultrasound (e.g. multiple gestations) also denoted GA_UL, and phenotype.The pregnancy loss data may comprise one or more parameters indicative whether a gestational age estimated from crown-rump length could have been obtained and / or was not obtainable.The pregnancy loss data may comprise one or more parameters indicative of whether donor sperm and / or donor oocyte was used for conception of the foetus.The pregnancy loss data may comprise one or more parameters indicative of whether donor sperm and / or donor oocyte was used for conception of the foetus.The pregnancy loss data may comprise one or more of type of pregnancy loss, and if and / or which complications occurred. A type of pregnancy loss may be selected from one or more of spontaneous complete miscarriage, spontaneous incomplete miscarriage, missed abortion, and anembryonic pregnancy,The pregnancy loss data may comprise foetal biosample data of the foetus. The fetal biosample data may be associated with or be determined from one or more of pregnancy tissue, atretic embryos, and oocytes. The foetal biosample data may comprise one or more of Whole Genome Sequencing (WGS) and Single-cell shallow sequencing.Machine learning modelIn one or more example electronic devices, to determine the one or more management scores, such as the first, second, and / or third management score, comprises to apply a machine learning model, e.g. to one or more of the female data FD, and the pregnancy loss data PLD. In other words, female data, and / or pregnancy loss data may be fed to a machine learning model providing as output one or more management scores, e.g. including first, second, and / or third management score. The female data, such as one or more of female blood data, female biosample data, and health data of the female as described herein, may be fed to the machine learning model for provision of one or more management scores, e.g. including first, second, and / or third management score(s), as output. The pregnancy loss data, may be fed to the machine learning model for provision of one or more management scores, e.g. including first, second, and / or third management score(s), as outputIn one or more example electronic devices, the first management score also denoted RS_1 is indicative of the suitability of a first type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The first management score may be a single value, e.g. indicative of the suitability of the first type of management of miscarriage.In one or more example electronic devices, the first management score also denoted RS_1 comprises or is a first primary management score also denoted RS_1_1, e.g. indicative of the suitability of a first type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The first primary management score may be a single value, e.g. indicative of the suitability of the first type of management of miscarriage.In one or more example electronic devices, the first management score RS_1 comprises or is a first secondary management score also denoted RS_1_2, e.g. indicative of the unsuitability of a first type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The first secondary management score may be a single value, e.g. indicative of the unsuitability of the first type of management of miscarriage.In other words, the first management score may comprise a plurality of first management scores including a first primary management score indicative of the suitability of first type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss; and including a first secondary management score indicative of the unsuitability of a first type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.The one or more management scores may comprise a second management score also denoted RS_2 and / or a third management score also denoted RS_3.The same way, the second management score denoted RS_2 may comprise a first primary management score denoted RS_2_1, and / or a first secondary management score denoted RS_2_2.The same way, the second management score denoted RS_3 may comprise a first primary management score denoted RS_3_1, and / or a first secondary management score denoted RS_3_2.In other words, to determine one or more management scores based on one or more of the female data, and the pregnancy loss data, may comprise to determine a second management score and / or a third management score.To provide an output may comprise to provide an output including a second output associated with the second management score. To provide an output may comprise to provide an output including a third output associated with the third management score.In one or more example electronic devices, the second management score also denoted RS_2 is indicative of the suitability of a second type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The seocnd management score may be a single value, e.g. indicative of the suitability of the second type of management of miscarriage.In one or more example electronic devices, the second management score also denoted RS_2 comprises or is a second primary management score also denoted RS_2_1, e.g. indicative of the suitability of a second type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The second primary management score may be a single value, e.g. indicative of the suitability of the second type of management of miscarriage.In one or more example electronic devices, the second management score RS_2 comprises or is a second secondary management score also denoted RS_2_2, e.g. indicative of the unsuitability of a second type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The second secondary management score may be a single value, e.g. indicative of the unsuitability of the second type of management of miscarriage.In one or more example electronic devices, the third management score also denoted RS_3 is indicative of the suitability of a third type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The third management score may be a single value, e.g. indicative of the suitability of the third type of management of miscarriage.In one or more example electronic devices, the third management score also denoted RS_3 comprises or is a third primary management score also denoted RS_3_1, e.g. indicative of the suitability of a third type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The third primary management score may be a single value, e.g. indicative of the suitability of the third type of management of miscarriage.In one or more example electronic devices, the third management score RS_3 comprises or is a third secondary management score also denoted RS_3_2, e.g. indicative of the unsuitability of a third type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The third secondary management score may be a single value, e.g. indicative of the unsuitability of the third type of management of miscarriage.Computer-implemented methodA computer-implemented method for predicting the suitability of the type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss is disclosed.The method comprises obtaining female data associated with a female. The method comprises obtaining pregnancy loss data associated with a foetus and / or foetal tissue. The method comprises determining one or more management scores based on one or more of the female data, and the pregnancy loss data, the one or more management scores associated with the suitability of the type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss; and including at least a first, second, and / or third management score indicative of a suitability of a first, second, and / or third type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The method comprises providing an output including at least a first, second, and / or third output associated with the one or more management scores.In one or more example computer-implemented methods, the female data FD comprises female blood data FBD of the female. In one or more example methods for pregnancy loss classification, determining the one or more management scores comprises determining the one or more management scores based on the female blood data.In one or more example computer-implemented methods, the female data FD comprises female biosample data FBSD of the female. In one or more example methods for pregnancy loss classification, determining the one or more management scores comprises determining the one or more management scores based on the female biosample data.In one or more example computer-implemented methods, the first management score is indicative of the suitability of a first type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.In one or more example computer-implemented methods, the first management score comprises a first primary management score indicative of the suitability of a first type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss; and / or a first secondary management score indicative of the unsuitability of a first type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.In one or more example computer-implemented methods, the second management score is indicative of the suitability of a second type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.In one or more example computer-implemented methods, the second management score comprises a second primary management score indicative of the suitability of a second type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss; and / or a second secondary management score indicative of the unsuitability of a second type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.In one or more example computer-implemented methods, the third management score is indicative of the suitability of a third type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.In one or more example computer-implemented methods, the third management score comprises a third primary management score indicative of the suitability of a third type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss; and / or a third secondary management score indicative of the unsuitability of a third type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.In the context of the present disclosure, increasing "the least" can mean increasing the "last most" (i.e. increasing the most after the first most and the second most) the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss; or can mean not increasing the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.A computer-implemented method for training a neural network to process as inputs one or more, such as one or all of female data (FD) associated with a female; and pregnancy loss data (PLD) associated with a foetus and / or foetal tissue, and to provide as output one or more management scores associated with the suitability of a type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The method comprises obtaining, using at least one processor, female data associated with a female; and pregnancy loss data associated with a foetus and / or foetal tissue. The method comprises performing, using the at least one processor, a training, wherein performing the training comprises generating, using the at least one processor and a machine-learning model, likelihood data based on the female data, and the pregnancy loss data. Performing the training comprises obtaining, using the at least one processor, training data; determining, using the at least one processor and one or more likelihood functions, one or more likelihood parameters based on the female data, the pregnancy loss data, and the training data; and training, using the at least one processor, the machine learning model based on the one or more likelihood parameters.It is to be understood that a description of a feature in relation to the electronic device is also applicable to the corresponding feature in the method(s) of operating an electronic device as disclosed herein.Fig. 1 illustrates a system implementing the electronic device and / or methods according to the present disclosure. The system 2 comprises one or more databases 4 storing clinical data including one or more of female data FD, and pregnancy loss data PLD. The system 2 comprises an electronic device 6 for classification of a likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of a pregnancy loss, the electronic device comprising an interface 8, one or more processors including processor 10, and a memory 12, wherein the one of more processors are configured to obtain, via the interface 8, female data FD associated with a female; and obtain, via the interface 8, pregnancy loss data PLD associated with a fetus and / or fetal tissue. The one or more databases 4 communicates with the electronic device 6 over network. The electronic device 6 may implement database 4A in memory 12.The one of more processors are configured to determine one or more management scores based on one or more of the female data FD, and the pregnancy loss data PLD. The one or more management scores are associated with the suitability of a type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. It includes a first management score RS_1 indicative of a suitability of a type of management of miscarriage. The one of more processors are configured to provide, via the interface 8, an output including a first output associated with one or more management scores, such as the first management score RS_1. The interface 8 of the electronic device optionally comprises a display 14, wherein to provide an output comprises to display a first user interface element 30 as a first output associated or representing the first management score (RS_1=0.8 in the illustrated example). For example, to provide an output may comprise to display a first primary user interface element as a first primary output associated or representing the first primary management score. For example, to provide an output may comprise to display a first secondary user interface element as a first secondary output associated or representing the first secondary management score.The electronic device 6 may be configured to perform any of the methods disclosed herein, such as the method in any of Fig. 2 and / or Fig. 3. In other words, the electronic device 6 is configured for analysis, classification, monitoring, and / or prediction of the suitability of a type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.The processor circuitry 302 is optionally configured to perform any of the operations disclosed in Figs. 2-3 (such as any one or more of: S104, S106, S112, S112A, S112B, S112C, S112D, S112E, 114, S114A, S114B, S202, S204, S204A, S204B, S204C, S204D). The operations of the electronic device 6 may be embodied in the form of executable logic routines (e.g., lines of code, software programs, etc.) that are stored on a non-transitory computer readable medium (e.g., the memory 12) and are executed by the one or more processors / processor 10.Furthermore, the operations of the electronic device 6 may be considered a method that the electronic device 6 is configured to carry out. Also, while the described functions and operations may be implemented in software, such functionality may as well be carried out via dedicated hardware or firmware, or some combination of hardware, firmware and / or software.The memory 12 may be or comprise one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, a random access memory (RAM), or other suitable device. In a typical arrangement, the memory 12 may include a non-volatile memory for long term data storage and a volatile memory that functions as system memory for the processor 10. The memory 12 may exchange data with the processor 10 over a data bus. Control lines and an address bus between the memory 12 and the processor 10 also may be present (not shown in Fig. 1). The memory 12 is considered a non-transitory computer readable medium.The memory 12 may be configured to store one or more of female data, such as female blood data and / or, female biosample data, pregnancy loss data, one or more management scores, e.g. including a first management score, an output, e.g. including a first output, a female output, machine learning model, first primary management score, first secondary management score, risk data, training data and / or one or more likelihood parameters in a part of the memory 12.Fig. 2 shows a flow chart of an example computer-implemented method for management of miscarriage classification. The method comprises obtaining S104 female data associated with a female. The method comprises obtaining S106 pregnancy loss data associated with a foetus and / or foetal tissue. The method comprises determining S112 one or more management scores based on one or more of the female data, and the pregnancy loss data, the one or more management scores associated with the management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss and including a first management score indicative of a likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of a pregnancy loss in a female individual. The method comprises providing S114 an output including providing S114A a first output associated with the first management score.Fig. 3 shows a flow chart of an example computer-implemented method for training a machine learning model, such as a neural network, to process as inputs female data associated with a female; and pregnancy loss data associated with a foetus and / or foetal tissue, and for providing as output one or more management scores associated with associated with the suitability of a type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss. The method 200 comprises obtaining S202, using at least one processor, female data associated with a female; and pregnancy loss data associated with a foetus and / or foetal tissue. The method comprises performing S204, using the at least one processor, a training. Performing S204 the training comprises generating S204A, using the at least one processor and a machine-learning model, such as a neural network, risk data based on the female data, and the pregnancy loss data. Performing S204 the training comprises obtaining S204B, using the at least one processor, training data. Performing S204 the training comprises determining S204C, using the at least one processor and one or more management functions, one or more management parameters based on the female data, the pregnancy loss data, and the training data. Performing S204 the training comprises training S204D, using the at least one processor, the machine learning model / neural network based on the one or more management parameters.Fig. 4 illustrates an example implementation of a machine learning model according to the present disclosure. The machine learning model 10A takes as input female data, and pregnancy loss data PLD, the female data FD optionally including one or more of FBD, FBSD, and FQD.The machine learning model 10A provides as output a first management score RS_1, such as one or both of RS_1_1 indicative of a suitability of a first type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss, and RS_1_2 indicative of an unsuitability of a first type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.Optionally, the machine learning model 10A provides as output a first management score RS_2, such as one or both of RS_2_1 indicative of a suitability of a second type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss, and RS_2_2 indicative of an unsuitability of a second type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.Optionally, the machine learning model 10A provides as output a first management score RS_3, such as one or both of RS_3_1 indicative of a suitability of a third type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss, and RS_3_2 indicative of an unsuitability of a third type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, to increase the least the likelihood of occurrence of an ovulation during a period of 6 weeks after the detection of said pregnancy loss.DEFINITIONSIndividualIn the present context the term "individual" relates to the mother. The individual is preferably a person in experiencing miscarriage / pregnancy loss. Though the present examples describe the measurements in a maternal sample, the present disclosure can be adapted to measurements direct on the foetus.Management of miscarriageWhen detecting a pregnancy loss, if there's still the foetus or foetal tissue in the womb, the options to remove the foetus / foetal tissue are:- expectant management, i.e. waiting for the foetus / foetal tissue to pass out of the womb naturally,- medical management, i.e. taking medicine that causes the foetus / foetal tissue to pass out of the womb- surgical management, i.e. having the foetus / foetal tissue surgically removed surgically.Method of conceptionThe term "method of conception" in the present disclosure may encompass natural methods of conception, such as mating; or artificial methods of conception, such as IUI (Intrauterine insemination) or IVF (in vitro fertilization). IVF may consist of ICSI (Intra cytoplasmic sperm infection), donor sperm, or donor oocyte.Diagnosis of the pregnancy lossThe term "Diagnosis of the pregnancy loss" in the present disclosure may encompass e.g. missed abortion, blighted ovum, or spontaneous ongoing pregnancy loss.Last menstruationThe time period since the last menstruation is calculated from the first day of the last menstrual period.Cycle durationThe term "cycle duration" in the present disclosure may encompass short cycle (<21 days), regular cycle (21-35 days), long cycle (>35 days), no cycle, or unknown cycle.IsoformsTwo isoenzymes are present in humans:GOT1 / cAST, the cytosolic isoenzyme derives mainly from red blood cells and heart.GOT2 / mAST, the mitochondrial isoenzyme is present predominantly in liver.These isoenzymes have evolved from a common ancestral AST via gene duplication, and they share a sequence homology of approximately 45%. In the Examples below, both isoforms are measured via the Siemens Atellica technology, but any means for identifying the ratio can be used including means that can differentiate between the two isoforms.SampleIn the Examples below, blood samples were collected at various times like for example either prior to the removal of pregnancy tissue. The plasma component was separated through a dual-stage centrifugation process and then preserved at low temperatures for subsequent analysis.cfDNA can be extracted from the plasma and added to library construction via PCR and sequenced and bioinformatics processing was performed as described by Hartwig et al (Schlaikjær Hartwig, T. et al. Cell-free foetal DNA for genetic evaluation in Copenhagen Pregnancy Loss Study (COPL): a prospective cohort study. Lancet Lond. Engl. 401, 762-771 (2023).Briefly, reads across lanes were merged into one fastq file. Reads were aligned using bowtie2. Aligned reads with a quality < 1 were removed, and the remaining reads were sorted and deduplicated using samtools. The foetal fraction was determined using SeqFF (Kim, S. K. et al. Determination of foetal DNA fraction from the plasma of pregnant women using sequence read counts. Prenat. Diagn. 35, 810-815 (2015)) by which small differences of sequencing behaviour for maternal and foetal cell-free DNA and read counts were used for estimation.Maternal whole blood was collected in EDTA tubes or serum clot activator tubes and separated into plasma and serum. The serum samples were used for biochemical analysis at the Department of Clinical Biochemistry, at Copenhagen University Hospital Herlev, measured using standard assays, such as ẞ-hCG (sandwich immunoassay by Siemens Atellica IM Analyzer), creatinine and cholesterol (Enzymatic reaction and absorbance by Siemens Atellica CH 930).In the present context, the term "sample" relates to any liquid or solid sample collected from an individual to be analysed. Preferably, the sample is liquefied at the time of assaying.In one or more exemplary embodiments, the sample is selected from the group consisting of blood, serum, plasma, urine, faeces, rectal swab, rectal microbiome, vaginal microbiome, vaginal discharge, vaginal secretion, cervical discharge, cervical swab, vaginal swab, amniotic fluid and other secreted fluids from the vagina and / or uterus.In one or more exemplary embodiments, the sample is selected from the group consisting of blood, urine, faeces, rectal swab, rectal microbiome, vaginal microbiome, and vaginal discharge samples.In one or more exemplary embodiments, the sample is selected from the group consisting of blood, serum, plasma, and urine.The sample taken may be dried for transport and future analysis. Thus, the method of the present disclosure includes the analysis of both liquid and dried samples.To increase detection efficiency, the sample data and the gestational age may be compared to a set of reference data to determine whether the individual is at increased risk of for example pregnancy loss or carrying a foetus with e.g. Down syndrome.BloodIn one or more exemplary embodiments, the sample is a blood sample.In one or more exemplary embodiments, the blood sample is separated into plasma and serum samples.PlasmaIn one or more exemplary embodiments, the sample is a plasma sample.SerumIn one or more exemplary embodiments, the sample is a serum sample.UrineIn one or more exemplary embodiments, the sample is a urine sample.FaecesIn one or more exemplary embodiments, the sample is a faeces sample.Rectal microbiomeIn one or more exemplary embodiments, the sample is a rectal microbiome sample.Rectal swabIn one or more exemplary embodiments, the sample is a rectal swab sample.Vaginal dischargeIn one or more exemplary embodiments, the sample is a vaginal discharge sample.Vaginal microbiomeIn one or more exemplary embodiments, the sample is a vaginal microbiome sample.Vaginal secretionIn one or more exemplary embodiments, the sample is a vaginal secretion sample.Cervical dischargeIn one or more exemplary embodiments, the sample is a cervical discharge sample.Cervical swabIn one or more exemplary embodiments, the sample is a cervical swab sample.Vaginal swabIn one or more exemplary embodiments, the sample is a vaginal swab sample.Amniotic fluidIn one or more exemplary embodiments, the sample is an amniotic fluid sample.Other secreted fluids from the vagina and / or uterusIn one or more exemplary embodiments, the sample is a secreted fluid from the vagina and / or uterus.Maternal and / or a paternal sample.In one or more exemplary embodiments, the sample is a maternal and / or a paternal sample.Timing of the sample takingThe sample can be preferably taken at the time of the detection of the pregnancy loss, or during a follow-up visit.In one or more exemplary embodiments, the sample is taken within 96 hours after detection of the pregnancy loss, such as but not limited to within 90 hours, 84 hours, 78 hours, 72 hours, 66 hours, 60 hours, 54 hours, 48 hours, 42 hours, 36 hours, 30 hours, 24 hours, 18 hours, 12 hours, 11, hours, 10 hours, 9 hours, 8 hours, 7, hours, 6 hours, 5 hours, 4 hours, 3 hours, 2 hours or 1 hour after of a pregnancy loss.Thus, the sample can be taken at any time after the detection of the pregnancy loss, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, or 96 hours after the detection of the pregnancy loss.In one or more exemplary embodiments, another sample can be taken during a follow-up visit that can happen anytime during a period of 10 weeks, such as 4 to 8 weeks, such as 6 to 8 weeks after the detection of the pregnancy loss, preferably 6 to 8 weeks, preferably 6 or 8 weeks after detection of the pregnancy loss.Thus, the follow-up sample can be taken at any time after the detection of the pregnancy loss, such as within 1 to 10 weeks after the detection of the pregnancy loss, such as 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, or 70 days after the detection of the pregnancy loss; preferably within 8 weeks after the detection of the pregnancy loss, such as within 6 to 8 weeks, such as 6 weeks or 8 weeks after the detection of the pregnancy loss.BiomarkersBeta Human chorionic gonadotropin (BhCG)ẞhCG is a heterodimeric molecule, where the alpha subunit is structurally equivalent to that of luteinizing hormone (LH), follicle-stimulating hormone (FSH) and thyroid-stimulating hormone (TSH) and the beta subunit is unique for ẞhCG.Measurement of serum-ẞhCGBlood was drawn in 9 mL EDTA tubes (Greiner Bio-One, Austria) and centrifuged by the Blood Bank at Hvidovre Hospital, Denmark. Hereafter serum was pipetted into 2 x 1 mL matrix tubes (Fisher Scientific, Denmark) per participant and frozen to -80 °C until further analysis. Analysis of serum was performed at the Department of Clinical Biochemistry at Herlev Hospital, Denmark through a Siemens Atellica Solution Immunoassay & Clinical Chemistry Analyzer in the period January 2023 to February 2024. The Atellica IM Total hCG assay implemented a 2-site sandwich immunoassay which utilized direct chemiluminometric technology. This involved two specific antibodies, goat polyclonal anti-hCG antibody labeled with acridinium ester and mouse monoclonal anti-hCG bound to paramagnetic particles. The assay detected both free ẞ-subunit and intactẞhCG with a measuring interval between 2-0-200,000-0 IU / L. According to the laboratory specific reference interval, ẞhCG ≥ 5IU / L were indicative of a pregnancy.Circulating cell free foetal DNA (cffDNA)Cell-free DNA are short fragments of DNA released into the bloodstream through a natural process of cell death. During pregnancy, maternal blood contains cell-free DNA (cfDNA), both from her own tissue, and from the foetus via the placenta (cffDNA).Approximately 2-20% of total cfDNA in maternal blood is foetal, of placental origin. cfDNA derived from the placenta can be detected as early as 4+ weeks gestation and is undetectable within hours postpartum. A non-invasive prenatal test (NIPT) analyses cfDNA from a maternal blood sample to screen for common chromosomal conditions in the foetus.The percentage of total cell-free DNA (cfDNA) in a sample derived from the foetus or placenta (cffDNA) is called the foetal fraction-which can affect the ability of NIPT to detect foetal aneuploidy. When considering various cfDNA and / or cffDNA NIPT technologies, it's important to understand how foetal fraction is used and how it can affect test results.The skilled addressee knows that foetal DNA fraction from the plasma of pregnant women can be determined using sequence read counts. Thus, the cell-free foetal DNA was determined from the read counts of the total cfDNA, using a previously established algorithm, SeqFF. Read counts across the genome are binned and used as input features to a penalized regression model, which then predicts the foetal fraction.Aspartate transaminase (AST)Aspartate transaminase (AST) or aspartate aminotransferase, also known as AspAT / ASAT / AAT or (serum) glutamic oxaloacetic transaminase (GOT, SGOT), is a pyridoxal phosphate (PLP)-dependent transaminase enzyme (EC 2.6.1.1).AST catalyses the reversible transfer of an a-amino group between aspartate and glutamate and, as such, is an important enzyme in amino acid metabolism. AST is found in the liver, heart, skeletal muscle, kidneys, brain, red blood cells and gall bladder. Serum AST level, serum ALT (alanine transaminase) level, and their ratio (AST / ALT ratio) are commonly measured clinically as biomarkers for liver health. The tests are part of blood panels.AST is similar to alanine transaminase (ALT) in that both enzymes are associated with liver parenchymal cells. The difference is that ALT is found predominantly in the liver, with clinically negligible quantities found in the kidneys, heart, and skeletal muscle, while AST is found in the liver, heart (cardiac muscle), skeletal muscle, kidneys, brain, and red blood cells.Alanine aminotransferase (ALT)Alanine transaminase (ALT) is a transaminase enzyme (EC 2.6.1.2). It is also called alanine aminotransferase (ALT or ALAT) and was formerly called serum glutamate-pyruvate transaminase or serum glutamic-pyruvic transaminase (SGPT).ALT is found in plasma and in various body tissues but is most common in the liver. It catalyses the two parts of the alanine cycle. Serum ALT level, serum AST (aspartate transaminase) level, and their ratio (AST / ALT ratio) are routinely measured clinically as biomarkers for liver health.When used in diagnostics, AST and ALT are almost always measured in international units / litre (IU / L or U / L) or ukat. While sources vary on specific reference range values for patients, 0-40 IU / L is the standard reference range for experimental studies. In a clinical setting, the reference ranges for pregnant women are 16-40 U / L in gestational week 13-40 for AST and 8-36 U / L in gestational week 13 to 35 for ALT. Both AST and ALT may be elevated during pregnancy.Fluctuation of ALT levels are normal over the course of the day, and they can also increase in response to strenuous physical exercise. When elevated ALT levels are found in the blood, the possible underlying causes can be further narrowed down by measuring other enzymes.In 2000, the American Association for Clinical Chemistry determined that the appropriate terminology for AST and ALT are aspartate aminotransferase and alanine aminotransferase. The term transaminase is outdated and no longer used.AST / ALT ratioThe AST / ALT ratio or De Ritis ratio is the ratio between the concentrations of the two enzymes aspartate transaminase and alanine aminotransferase in the blood of a human or an animal. The AST / ALT ratio is measured by conventional analytical methods, such as immunological methods known to the art. It is traditionally used as one of several liver function tests, and typically measured with a blood test. AST and ALT as determined for this disclosure are analysed at a Siemens Atellica CH 930 machine by enzyme spectrophotometry but can be determined by any means known to the skilled addressee.In one or more exemplary embodiments, the AST / ALT ratio is determined by measuring RNA (cell-free or intracellular), DNA, protein, and / or metabolite levels of the aspartate transaminase and alanine aminotransferase enzymes in a sample.In one or more exemplary embodiments, the AST / ALT ratio is determined by measuring cell-free RNA levels of the aspartate transaminase and alanine aminotransferase enzymes in a sample.In one or more exemplary embodiments, the AST / ALT ratio is determined by measuring intracellular RNA levels of the aspartate transaminase and alanine aminotransferase enzymes in a sample.In one or more exemplary embodiments, the AST / ALT ratio is determined by measuring DNA levels of the aspartate transaminase and alanine aminotransferase enzymes in a sample.In one or more exemplary embodiments, the AST / ALT ratio is determined by measuring protein levels of the aspartate transaminase and alanine aminotransferase enzymes in a sample.In one or more exemplary embodiments, the AST / ALT ratio is determined by measuring metabolite levels of the aspartate transaminase and alanine aminotransferase enzymes in a sample.To determine the clinical severity variations of the AST / ALT ratio in the present context, means for evaluating the detectable signal of the AST / ALT ratio measured involves a reference or reference means. The reference also makes it possible to consider assay, kit and method variations, handling variations and other variations not related directly or indirectly to the AST / ALT ratio.In the context of the present invention, the term "reference" relates to a standard in relation to quantity, quality or type, against which other values or characteristics can be compared, such as e.g. a standard curve.The reference data presented in the Examples below reflects the maternal blood AST / ALT ratio from confirmed intrauterine pregnancy loss before 22 weeks of gestation but could equally be maternal blood AST / ALT ratio from all pregnant women carrying viable foetuses.As will be generally understood by those of skill in the art, methods for screening for foetal abnormalities are processes of decision making by comparison. For any decision-making process, reference values based on individuals having the disease or condition of interest and / or individuals not having the disease or condition of interest are needed.In the present disclosure, the reference values are the maternal blood level of the measured marker or markers, for example, the AST / ALT ratio, in both pregnant women carrying for example genetically abnormal foetuses, pregnant women carrying viable foetuses, and women having a pregnancy loss. A set of reference data is established by collecting the reference values for a number of samples. As will be obvious to those of skill in the art, the set of reference data will improve by including increasing numbers of reference values.In one or more exemplary embodiments, the reference means is an internal reference means and / or an external reference means.In the present context the term "internal reference means" relates to a reference which is not handled by the user directly for each determination, but which is incorporated into a device for the determination of the biomarker of interest, like for example the AST / ALT ratio, whereby only the 'final result' or the 'final measurement' is presented. The terms the "final result" or the "final measurement" relates to the result presented to the user when the reference value has been considered. In one or more exemplary embodiments, the internal reference means is provided in connection to a device used for the determination of the biomarker in question.In the present context, the term "external reference means" relates to a reference which is handled directly by the user to determine the biomarker, before obtaining the 'final result' or the 'final measurement'. In one or more exemplary embodiments, the external reference means are selected from the group consisting of a table, a diagram and similar reference means where the user can compare the measured signal to selected reference means. The external reference means relates to a reference used as a calibration, value reference, information object, etc. for the AST / ALT ratio and which has been excluded from the device used.In one or more exemplary embodiments, the reference level / predetermined value is indicative of a normal physiological condition of said individual.In one or more exemplary embodiments, the reference level / predetermined value is indicative of a condition is a foetal abnormality.Although any of the known analytical methods for measuring the AST / ALT ratio will function in the present invention, as obvious to one skilled in the art, the analytical method used for the AST / ALT ratio must be the same method used to generate the reference data for the AST / ALT ratio. If a new analytical method is used for the AST / ALT ratio, a new set of reference data, based on data developed with the method, must be generated.In one or more exemplary embodiments, methods described herein is combined with levels of further biomarkers selected from the group consisting of gestational age, beta-Human Chorionic Gonadotropin (β-hCG), Pregnancy-associated plasma protein A (PAPP-A), Disintegrin and metalloproteinase domain-containing protein 12 (ADAM12), ISM2, TFPI2, ERVV-1, LYPD3, EBIB3_IL27, CSH1, GDF15, ANGPT2, FBN2, PRG2, INSL4, LAIR2, TSHB, C1QTNF6, LHB, SIGLEC6, MMP12, Placental Growth Factor (PLGF1), Alanine Transaminase (ALT), Aspartattransaminase (AST), High-density lipoprotein cholesterol (HDL), Low-density lipoprotein cholesterol (LDL), Apolipoprotein B, Uric Acid, Transferrin, Bilirubin, Creatin kinase and Lipoprotein A alpha feto-protein (AFP), unconjugated oestrol (uE3), human chorionic gonadotrophin (hCG), free alpha sub-unit of hCG (free a-hCG), free beta sub-unit of hCG (free ẞ-hCG), beta-core hCG, hyperglycosylated hCG (ITG), placental growth hormonre (PGH), inhibin, preferably dimeric inhibin-A (inhibin A), pregnancy-associated plasma protein A (PAPP-A), Complexes of PAPP-A with proMBP (proform of major basic protein), ProMBP, ProMBP complexes with angiotensinogen and / or complement factors and split products Schwangerschaftsprotein 1 (SP1), Cancer antigen 125(CA125), Prostate specific antigen (PSA), Leukocyte enzymes, foetal DNA, foetal RNA, foetal cells, stem cells, oestradiol, ultrasound markers, nuchal translucency, femur length, absence of nasal bone, hyperechogenic bowel, echogenic foci in the heart, choroids plexus cysts, hydronephrosis, foetal malformations, steroids, peptides, chemokines, interleukins (e.g. IL-6, IL-4, IL-1), tumour necrosis factor, transforming growth factor alpha and beta, acute phase reactants, C-reactive protein, Fibronectin, maternal or foetal single nucleotide polymorphisms, e.g. promoter region polymorphisms in TNFbeta and mannan- binding lectin, complement components, HLA-G, and / or HLA molecules in the sample.Thus, in one embodiment, the present disclosure relates to a method as described herein, wherein the AST / ALT ratio directly and / or use the AST / ALT ratio estimating the amount of circulating cell free foetal DNA (cffDNA) amount is combined with values from at least one marker selected from the group defined above.In one or more exemplary embodiments, the AST / ALT ratio is combined with levels of further biomarkers selected from the group consisting of gestational age, beta-Human Chorionic Gonadotropin (β-hCG), Pregnancy-associated plasma protein A (PAPP-A), vascular endothelial growth factor (VEGF), soluble fms-like tyrosine kinase-1 (sFlt-1), Alpha-Fetoprotein (AFP), Disintegrin and metalloproteinase domain-containing protein 12 (ADAM12), ISM2, TFPI2, ERVV-1, LYPD3, EBIB3_IL27, CSH1, GDF15, ANGPT2, FBN2, PRG2, INSL4, LAIR2, TSHB, C1QTNF6, LHB, SIGLEC6, MMP12, Placental Growth Factor (PLGF1), Alanine Transaminase (ALT), Aspartattransaminase (ASAT), High-density lipoprotein cholesterol (HDL), Low-density lipoprotein cholesterol (LDL), Apolipoprotein B, Uric Acid, Transferrin, Bilirubin, Creatin kinase and Lipoprotein A. in the sample.In one or more exemplary embodiments, the AST / ALT ratio is combined with levels of further biomarkers selected from the group consisting of gestational age, β-hCG, Bilirubin, Creatin kinase and Lipoprotein A.C-Reactive Protein (CRP)In one or more exemplary embodiments, the ẞhCG level is combined with the CRP level in the sample.Thyroid Peroxidase AntibodyIn one or more exemplary embodiments, the ẞhCG level is combined with the Thyroid Peroxidase Antibody level in the sample.Thyroglobulin AntibodyIn one or more exemplary embodiments, the ẞhCG level is combined with the Thyroglobulin Antibody level in the sampleGamma-Glutamyl Transferase (GGT)In one or more exemplary embodiments, the ẞhCG level is combined with the GGT level in the sampleTriglyceridesIn one or more exemplary embodiments, the ẞhCG level is combined with the Triglycerides level in the sampleLDL CholesterolIn one or more exemplary embodiments, the ẞhCG level is combined with the LDL Cholesterol level in the sampleApolipoprotein BIn one or more exemplary embodiments, the ẞhCG level is combined with the Apolipoprotein B level in the sampleCreatinineIn one or more exemplary embodiments, the ẞhCG level is combined with the Creatinine level in the sampleThyroid-Stimulating Hormone (TSH)In one or more exemplary embodiments, the ẞhCG level is combined with the TSH level in the sampleUric AcidIn one or more exemplary embodiments, the ẞhCG level is combined with uric acid level in the sampleRheumatoid FactorIn one or more exemplary embodiments, the ẞhCG level is combined with the Rheumatoid Factor level in the sampleCreatine KinaseIn one or more exemplary embodiments, the ẞhCG level is combined with the Creatine Kinase level in the sampleTransferrinIn one or more exemplary embodiments, the ẞhCG level is combined with the Transferrin level in the sampleIronIn one or more exemplary embodiments, the ẞhCG level is combined with the iron level in the sampleAlbuminIn one or more exemplary embodiments, the ẞhCG level is combined with the Albumin level in the sampleBilirubinIn one or more exemplary embodiments, the ẞhCG level is combined with the Bilirubin level in the sampleUreaIn one or more exemplary embodiments, the ẞhCG level is combined with the urea level in the sampleHDL CholesterolIn one or more exemplary embodiments, the ẞhCG level is combined with the HDL Cholesterol level in the sampleTG Rich Lipoprotein Cholesterol (TRL-C)In one or more exemplary embodiments, the ẞhCG level is combined with the TRL-C level in the sampleLipoprotein(a)In one or more exemplary embodiments, the ẞhCG level is combined with the Lipoprotein(a) level in the sampleTriacylglycerol LipaseIn one or more exemplary embodiments, the ẞhCG level is combined with the Triacylglycerol Lipase level in the sampleLactate Dehydrogenase (LDH)In one or more exemplary embodiments, the ẞhCG level is combined with the LDH level in the sampleStatistical analysisWe evaluated the predictiveness of using collected clinical and biochemistry data. Due to the extreme skew of the outcome variables and many censored values, i.e. below the detection limit 2 IU / L we used a Bayesian ordinal regression model, assuming proportional odds and a logistic link function (ref). The parameters of the model were determined using Hamiltonian Monte Carlo (HMC) sampling, as implemented in Stan. The sampler was run for 4,000 iterations (2,000 warmup iterations), with default settings for the HMC sampler. Convergence was assessed by inspection of R-hat values (all R-hat < 1.01), treedepth (treedepth not exceeding 10 post warm-up), and divergences (no divergences).We included clinical and biochemistry data, as defined in the prior paragraphs. Variables with a high skew were log2 transformed (see Table 1). The transformation of variables was done irrespective of the association with the outcome to avoid overfitting.To identify the most influential predictors, we employed a backward feature selection procedure with a bootstrap-resampling approach to enhance the robustness of the selected variables. Specifically, we generated 500 bootstrap samples from the original dataset and fit an ordinal regression model to each sample. Backward feature selection was then applied iteratively within each bootstrap sample, removing features until the Akaike Information Criterion (AIC) no longer decreased. At each iteration, the feature resulting in the largest AIC reduction was eliminated. It would be too computationally expensive to use the Bayesian model, and we instead used a maximum likelihood ordinal regression model. The final subset of predictors was determined by selecting those features that were retained in at least 75% of the bootstrap samples.Only data from the Copenhagen University Hospital Hvidovre was used for model fitting and feature selection. The fitted models, and the comparison of the full feature set and reduced feature set, was compared using the ROC-AUC and calibration at two thresholds, namely ẞhCG ≥ 3 and ẞhCG ≥ 5. The first threshold represents the low bound for what would be expected in a pre-menopausal non-pregnancy woman, and the second threshold is indicative of a pregnancy. Calibration was evaluated by inspection of calibration curves, and we report the slope and intercept as quantitative measures of calibration, along with the 95% confidence interval. Accuracy, sensitivity, specificity, and the F1 score was evaluated by thresholding the predicted probabilities into "yes" or "no”, based on the prevalence in the training cohort (Copenhagen University Hospital Hvidovre). The F1 score is the harmonic mean of the precision and recall.The models were externally validated using data collected from the Copenhagen University Hospital Herlev and North Zealand Hospital.Missing values were imputed as the mode. We report the 95% Bayesian Credible Interval for all estimates, unless elsewhere noted.EXAMPLESClinical dataClinical data was collected from the electronic health records, questionnaires, and through interviews with clinical staff. For this project, we included 13 variables, described in Supplementary Table 1. The variables consist of both data describing the maternal, fetal, and paternal characteristics.Biochemistry dataBiochemistry data was generated using the serum sample collected at the inclusion to the Copenhagen Pregnancy Loss Study. More than 30 biomarkers were measured, covering the major organ systems and placental / fetal growth. The variables are described in the file "Features_new.xlsx".Statistical AnalysisWe evaluated the predictiveness of using collected clinical and biochemistry data. Due to the extreme skew of the outcome variables and many censored values, i.e. below the detection limit 2 IU / L we used a Bayesian ordinal regression model, assuming proportional odds and a logistic link function (ref). The parameters of the model were determined using Hamiltonian Monte Carlo (HMC) sampling, as implemented in Stan. The sampler was run for 4,000 iterations (2,000 warmup iterations), with default settings for the HMC sampler. Convergence was assessed by inspection of R-hat values (all R-hat < 1.01), treedepth (treedepth not exceeding 10 post warm-up), and divergences (no divergences).We included clinical and biochemistry data, as defined in the prior paragraphs. Variables with a high skew were log2 transformed (see Table 1). The transformation of variables was done irrespective of the association with the outcome to avoid overfitting.To identify the most influential predictors, we employed a backward feature selection procedure with a bootstrap-resampling approach to enhance the robustness of the selected variables. Specifically, we generated 500 bootstrap samples from the original dataset and fit an ordinal regression model to each sample. Backward feature selection was then applied iteratively within each bootstrap sample, removing features until the Akaike Information Criterion (AIC) no longer decreased. At each iteration, the feature resulting in the largest AIC reduction was eliminated. It would be too computationally expensive to use the Bayesian model, and we instead used a maximum likelihood ordinal regression model. The final subset of predictors was determined by selecting those features that were retained in at least 75% of the bootstrap samples.Only data from the Copenhagen University Hospital Hvidovre was used for model fitting and feature selection. The fitted models, and the comparison of the full feature set and reduced feature set, was compared using the ROC-AUC and calibration at two thresholds, namely ẞhCG ≥ 3 and ẞhCG ≥ 5. The first threshold represents the low bound for what would be expected in a pre-menopausal non-pregnancy woman, and the second threshold is indicative of a pregnancy. Calibration was evaluated by inspection of calibration curves, and we report the slope and intercept as quantitative measures of calibration, along with the 95% confidence interval. Accuracy, sensitivity, specificity, and the F1 score was evaluated by thresholding the predicted probabilities into "yes" or "no", based on the prevalence in the training cohort (Copenhagen University Hospital Hvidovre). The F1 score is the harmonic mean of the precision and recall.The models were externally validated using data collected from the Copenhagen University Hospital Herlev and North Zealand Hospital.Missing values were imputed as the mode. We report the 95% Bayesian Credible Interval for all estimates, unless elsewhere noted.RESULTSThe multivariable regression model follows that described in Juul et al. When doing feature selection, we identified seven variables robustly predicting the ẞhCG value at follow-up, namely: ẞhCG at pregnancy loss, days since pregnancy loss, gestational age at pregnancy loss (calculated from last menstrual period), prior number of live births, creatine kinase, the ASAT / ALAT ratio (De Ritis ratio), and the treatment choice (Figure 6). The effect sizes, summarized as the odds-ratios, are shown in Figure 6, and in the table 4.We evaluated the model's capability to predict elevated ẞhCG levels at two levels, namely 3 IU / L and 5 IU / L. We observed that the models utilizing all variables, and the model that only utilized the seven variables, had no difference in performance (Figure 8). When evaluating it on two external data sets from CUH Herlev and CUH North Zealand, respectively, the performance replicated nicely. Calibration did decrease slightly (Figure 9 + Figure 10).The accuracy, sensitivity, specificity, and F1 score were highly concordant between all data sets, and using only the seven most important features did not affect predictive capability (Table 1 + Table 2). This indicates that the model generalize well and can be applied to new patient populations, using only the seven proposed variables.We present the findings as a nomogram, which can be efficiently used for summarizing the risk of excessive ẞhCG levels post pregnancy loss (Figure 11). Following are three examples of using the invention in practice.ẞhCG cutoff values are also used for determining best treatment yielding the highest probability of having a ẞhCG < 3 IU / L at follow-up (see figure 13).These data show that medical treatment is preferred over surgical treatment, if the ẞhCG levels are < 58560 IU / L.Conversely, medical treatment should be favored over ẞhCG values are < 5572 IU / L.These values were determined using data strictly from one site, namely Hvidovre Hospital.Two external data sets were further used to confirm the good replication of these findings (see Table 6). The conclusion is that these findings replicate well to external data, with acceptable accuracy, sensitivity, and specificity.Example 1:Patient prognosis. The "average patient" (defined in Table 3) has a 38.8% chance (33.6%; 43.9% 95% Bayesian Credible Interval) of a ẞhCG >= 5 IU / L, after 6 weeks. This can be calculated directly using the nomogram. Furthermore, a forecasting curve can be created by varying the number of weeks since pregnancy loss (Figure 5). The "average patient" can be replaced with the exact patient characteristics to provide an individualized prediction and curve.Example 2:Choice of treatment. By varying the choice of treatment in the nomogram, different possibilities can be considered. For example, for the "average patient":1. Surgical treatment has a 38.8% chance (33.6%; 43.9% 95% Bayesian Credible Interval) of a ẞhCG >= 5 IU / L, after 6 weeks2. Medical treatment has a 28.8% chance (24.0%; 33.6% 95% Bayesian Credible Interval) of a ẞhCG >= 5 IU / L, after 6 weeks3. Expectant management has a 37.3% chance (26.9%; 48.0% 95% Bayesian Credible Interval) of a ẞhCG >= 5 IU / L, after 6 weeksConsequently, for the "average patient", medical treatment would enable the fastest return of ovulation.Example 3:Women with elevated ASAT / ALAT ratio are poor metabolizers of ẞhCG, reflected by an increased odds for each doubling in the ASAT / ALAT Ratio (odds ratio = 1.71, 1.39; 2.16 95% Bayesian Credible Interval). An example is also given in Figure 7.We identified all women that had been to follow-up within 8 weeks (see table 5 and Figure 12). Here, the threshold refers to a specific ẞhCG value at follow-up, and the probability of having a return of menstrual bleeding for the two groups. The two groups are defined as either having a ẞhCG below the threshold, or above-equal to the threshold.For instance, for the group with ẞhCG >= 3, 73% report return of menstrual bleeding. Conversely, for the group with ẞhCG < 3, 82% report return of menstrual bleeding. The low and high columns are the 95% confidence interval.For instance, for the group with ẞhCG >= 5, 65% report return of menstrual bleeding. Conversely, for the group with ẞhCG < 5, 81% report return of menstrual bleeding. The low and high columns are the 95% confidence intervalConsequently, a reduction in ẞhCG was associated with a 9.5 percentage point increase in the probability of ovulation within six weeks of the pregnancy loss (ovulation occurs 14 days prior to menstrual bleeding).Table 1: Performance for predicting ẞhCG >= 3 IU / LDataset Hospital Accuracy sensitivity Specificity F1Full CUH Hvidovre 0.784 0.776 0.789 0.716Reduced CUH Hvidovre 0.768 0.764 0.769 0.697Full CUH Herlev 0.747 0.701 0.778 0.688Reduced CUH Herlev 0.727 0.701 0.744 0.671Full CUH North Zealand 0.725 0.839 0.607 0.756Reduced CUH North Zealand 0.702 0.793 0.607 0.730Table 2: Performance for predicting ẞhCG >= 5 IU / LDataset Hospital Accuracy Sensitivity specificity F1Full CUH Hvidovre 0.785 0.771 0.819 0.836Reduced CUH Hvidovre 0.777 0.769 0.797 0.830Full CUH Herlev 0.711 0.718 0.692 0.785Reduced CUH Herlev 0.691 0.676 0.731 0.762Full CUH North Zealand 0.801 0.839 0.545 0.880Reduced CUH North Zealand 0.789 0.819 0.591 0.871Table 3: The "Average patient"Variable ValueTreatment Surgical treatmentDays since pregnancy loss 44 daysPrior number of live births 0ẞhCG 21920 IU / LCreatine kinase 59 U / LASAT / ALAT Ratio 0.86Table 4Name Median Low HighDays since pregnancy loss -0.12 -0.14 -0.11Gestational age, last menstruation (days) 0.02 0.02 0.03Number of prior live births -0.32 -0.49 -0.15Medical treatment vs Expectant management -0.39 -0.86 0.1Surgical treatment vs Expectant management 0.07 -0.39 0.55HCGBETA at time of pregnancy loss, log2 transformed (IU / L) 0.61 0.53 0.69ASAT / ALAT Ratio, log2 transformed (De Ritis ratio) 0.55 0.34 0.78Creatinkinase, log2 transformed (U / L) -0.21 -0.41 -0.02Medical vs surgical treatment -0.45 -0.69 -0.2Table 5:Threshold Probability low highBETAHCG >= 3 0.73 0.68 0.77BETAHCG <3 0.82 0.77 0.87BETAHCG >= 4 0.7 0.65 0.75BETAHCG < 4 0.81 0.76 0.84BETAHCG >= 5 0.65 0.58 0.72BETAHCG <5 0.81 0.77 0.84BETAHCG >= 6 0.65 0.57 0.72BETAHCG <6 0.79 0.76 0.83BETAHCG >= 7 0.63 0.54 0.72BETAHCG <7 0.79 0.75 0.82BETAHCG >= 8 0.61 0.51 0.7BETAHCG < 8 0.79 0.76 0.82BETAHCG >= 9 0.61 0.5 0.71BETAHCG <9 0.79 0.75 0.82BETAHCG >= 10 0.58 0.47 0.69BETAHCG < 10 0.79 0.75 0.82BETAHCG >= 11 0.6 0.48 0.71BETAHCG < 11 0.78 0.75 0.81BETAHCG >= 12 0.6 0.47 0.72BETAHCG < 12 0.78 0.75 0.81BETAHCG >= 13 0.63 0.49 0.76BETAHCG < 13 0.77 0.74 0.8BETAHCG >= 14 0.6 0.45 0.74BETAHCG < 14 0.78 0.74 0.81BETAHCG >= 15 0.6 0.44 0.74BETAHCG < 15 0.77 0.74 0.81BETAHCG >= 16 0.58 0.42 0.72BETAHCG < 16 0.78 0.74 0.81BETAHCG >= 17 0.63 0.47 0.78BETAHCG < 17 0.77 0.74 0.8BETAHCG >= 18 0.58 0.41 0.74BETAHCG < 18 0.77 0.74 0.8BETAHCG >= 19 0.56 0.38 0.73BETAHCG < 19 0.77 0.74 0.8BETAHCG >= 20 0.56 0.38 0.73BETAHCG < 20 0.77 0.74 0.8Table 6Metric Hvidovre Hospital Herlev Hospital North Zealand HospitalSensitivity 0.66 0.7 0.53Specificity 0.61 0.62 0.77Pos Pred Value 0.76 0.75 0.7Neg Pred Value 0.49 0.57 0.61Precision 0.76 0.75 0.7Recall 0.66 0.7 0.53F1 0.71 0.72 0.61Accuracy 0.64 0.67 0.65ITEMS1. A method for managing miscarriage in a female individual suffering from a pregnancy loss, comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) determining one or more management score based on said set of data comprising at least a first management score predictive of a most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,c) predicting the most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said first management score, andd) removing said foetus and / or foetal material according to said most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score;wherein said most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score increases the most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.2. A method for increasing the likelihood of occurrence of an ovulation during a period of 6 weeks after the determination of a pregnancy loss in a female individual, comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) determining one or more management score based on said set of data comprising at least a first management score predictive of a most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,c) predicting the most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said first management score, andd) removing said foetus and / or foetal material according to the most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score,wherein said most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score increases the most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.3. A method to assess the suitability of a type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) determining one or more management score based on said set of data comprising at least a first management score predictive of a most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, andc) predicting the most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said first management score,wherein said most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score increases the most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.4. The method according to any of the preceding items, further comprising:determining the one or more management score based on said set of data comprising a second management score predictive of the second most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, andpredicting the second most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said second management score,wherein said second most suitable type of management of miscarriage predicted based on said one or more management score comprising said second management score increases the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.5. The method according to any of the preceding items, further comprising:determining the one or more management score based on said set of data comprising a third management score predictive of the least most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, andpredicting the least most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said second management score,wherein said least suitable type of management of miscarriage predicted based on said one or more management score comprising said least management score increases the least the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.6. A computer-implemented method for identifying the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) determining one or more management score based on said set of data comprising at least a first management score predictive of a most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, andc) predicting the most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said first management score, andd) providing an output including a first output associated with the most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score,wherein said most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score increases the most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.7. An electronic device for identifying the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, the electronic device comprising an interface, one or more processors, and a memory, wherein the one of more processors are configured to:a) obtain a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) determining one or more management score based on said set of data comprising at least a first management score predictive of a most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,c) predicting the most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said first management score, andd) providing an output including a first output associated with the most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score;wherein said most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score increases the most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.8. The computer-implemented method according to item 6, or the electronic device according to item 7, further comprising:determining the one or more management score based on said set of data comprising a second management score predictive of the second most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,predicting the second most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said second management score, andproviding an output including a second output associated with the second most suitable type of management of miscarriage predicted based on said one or more management score comprising said second management score,wherein the second most suitable type of management of miscarriage predicted based on said one or more management score comprising said second management score increases the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.9. The computer-implemented method according to any of items 6 or 8, or the electronic device according to item 7 or 8, further comprising:determining the one or more management score based on said set of data comprising a third management score predictive of the least suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,predicting the least suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said second management score, andproviding an output including a third output associated with the least suitable type of management of miscarriage predicted based on said one or more management score comprising said third management scorewherein the least suitable type of management of miscarriage predicted based on said one or more management score comprising said third management score increases the least the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.10. A computer-implemented method for training a machine learning model, such as a neural network, to process as inputs a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue and provide as output one or more management scores, comprising a first management score associated with the most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, the method comprising:a) obtaining, using at least one processor, a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) performing, using the at least one processor, a training comprising:i) generating, using the at least one processor and a machine-learning model, management data based on the set of data,ii) obtaining, using the at least one processor, training data,iii) determining one or more management score based on the set of data and the training data, comprising a first management score, using the at least one processor and one or more management functions; andiv) training, using the at least one processor, the machine learning model based said one or more management score, comprising a first management score,wherein the most suitable type of management of miscarriage increases the most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.11. A computer-implemented method for training a machine learning model, such as a neural network, according to item 10, to process as inputs a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue and provide as output one or more management scores, further comprising a second management score associated with the second most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, the method further comprisingdetermining the one or more management score based on said set of data and the training data comprising said second management score, andtraining, using the at least one processor, the machine learning model based said one or more management score, comprising said second management score,wherein the second most suitable type of management of miscarriage increases the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.12. A computer-implemented method for training a machine learning model, such as a neural network, according to item 10 or 11, to process as inputs a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue and provide as output one or more management scores, further comprising a third management score associated with the least suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, the method further comprisingdetermining the one or more management score based on said set of data and the training data comprising said third management score, andtraining, using the at least one processor, the machine learning model based said one or more management score, comprising said third management score,wherein the least suitable type of management of miscarriage increases the least the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.13. The method, computer-implemented method, or electronic device according to any of the preceding items, wherein the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual is increased when having a ẞhCG value inferior to 10 IU / L, such as inferior to 5 IU / L, such as inferior to 3 IU / L at a follow-up visit.14. The method, computer-implemented method, or electronic device according to any of the preceding items, wherein the most suitable type of management of miscarriage yields the highest probability of having a ẞhCG inferior to 10 IU / L, such as inferior to 5 IU / L, such as inferior to 3 IU / L at a follow-up visit.15. The method, computer-implemented method, or electronic device according to item 13 or 14, wherein the follow-up visit occurs during a period of 10 weeks, such as within 4 to 8 weeks, such as within 6 to 8 weeks after the detection of the pregnancy loss, such as 6 or 8 weeks after detection of the pregnancy loss.16. The method, computer-implemented method, or electronic device according to any of the preceding items, wherein the most suitable, second most suitable, and / or least suitable type of management of miscarriage is chosen from a group comprising at least the surgical management of miscarriage, the medical management of miscarriage, and the expectant management of miscarriage.17. The method, computer-implemented method, or electronic device according to any of the preceding items, wherein the most suitable type of management of miscarriage chosen for increasing the most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said individual is:a) medical management over surgical management, if the ẞhCG levels are inferior or equal to 58560 IU / L, and / ormedical management over expectant management, if the ẞhCG levels are inferior or equal to 5572 IU / L.18. The method, computer-implemented method, or electronic device according to any one of the preceding items, wherein the ẞhCG value (IU / L) is obtained via a blood sample of said female individual.19. The method, computer-implemented method, or electronic device according to item 18, wherein the blood sample is taken at the time of detection of the pregnancy loss.20. The method, computer-implemented method, or electronic device according to any one of the preceding items, wherein the set of data comprises at least one further foetal data selected from the group consisting of:i) the gestational age at pregnancy loss, calculated from the last menstruation of said individualii) the method of conception,iii) the diagnosis of the pregnancy loss, andpreferably the gestational age at pregnancy loss, calculated from the last menstruation of said individual21. The method, computer-implemented method, or electronic device according to any one of the preceding items, wherein the set of data further comprises at least one female data selected from the group consisting of:i) the number of days since pregnancy loss,ii) the number of prior live births,iii) the creatine kinase level,iv) the Aspartate Transaminase (ASAT) / Alanine Transaminase (ALAT) ratio (De Ritis ratio).v) the age of said female individual (in years),vi) the body mass index (BMI) of said female individual (kg.m2),vii) the presence or absence of vaginal bleeding at inclusion,viii) the number of prior pregnancy loss,ix) the cycle duration prior to the pregnancy,x) the C-Reactive Protein (CRP) level,xi) the thyroid Peroxidase antibody level,xii) the thyroglobulin antibody level,xiii) the Gamma-Glutamyl Transferase (GGT) level,xiv) the Aspartate Transaminase (ASAT) level,xv) The Alanine Transaminase (ALAT) level,xvi) the triglycerides level,xvii) the LDL Cholesterol level,xviii) the apolipoprotein B level,xix) the creatinine level,xx) the thyroid-Stimulating Hormone (TSH) level,xxi) the uric acid level,xxii) the rheumatoid Factor level,xxiii) the transferrin level,xxiv) the iron level,xxv) the alipoprotein(a) level,xxvi) the albumin level,xxvii) the bilirubin level,xxviii) the urea level,xxix) the HDL Cholesterol level,xxx) the TG Rich Lipoprotein Cholesterol (TRL-C) level,xxxi) the lipoprotein(a) level,xxxii) the triacylglycerol lipase level, and / orxxxiii) the Lactate Dehydrogenase (LDH) level,preferably:i) the number of days since pregnancy loss,ii) the number of prior live births,iii) the creatine kinase level, and / oriv) the Aspartate Transaminase (ASAT) / Alanine Transaminase (ALAT) ratio (De Ritis ratio).22. The method, computer-implemented method, or electronic device according to item 21, wherein the following data are obtained via a blood sample of said female individual.i) the creatine kinase level,ii) the Aspartate Transaminase (ASAT) / Alanine Transaminase (ALAT) ratio (De Ritis ratio),iii) the C-Reactive Protein (CRP) level,iv) the thyroid Peroxidase antibody level,v) the thyroglobulin antibody level,vi) the Gamma-Glutamyl Transferase (GGT) level,vii) the Aspartate Transaminase (ASAT) level,viii) The Alanine Transaminase (ALAT) level,ix) the triglycerides level,x) the LDL Cholesterol level,xi) the apolipoprotein B level,xii) the creatinine level,xiii) the thyroid-Stimulating Hormone (TSH) level,xiv) the uric acid level,xv) the rheumatoid Factor level,xvi) the transferrin level,xvii) the iron level,xviii) the alipoprotein (a) level,xix) the albumin level,xx) the bilirubin level,xxi) the urea level,xxii) the HDL Cholesterol level,xxiii) the TG Rich Lipoprotein Cholesterol (TRL-C) level,xxiv) the lipoprotein(a) level,xxv) the triacylglycerol lipase level, and / orxxvi) the Lactate Dehydrogenase (LDH) level.23. The method, computer-implemented method, or electronic device according to item 22, wherein the blood sample is taken at the time of detection of the pregnancy loss.24. The method, computer-implemented method, or electronic device according to any one of the preceding items, wherein the set of data comprises at least one further foetal data and / or female data selected from the group consisting of:i) the number of days since pregnancy loss,ii) the gestational age at pregnancy loss, calculated from the last menstruation of said individualiii) the number of prior live births,iv) the creatine kinase level, and / orv) the Aspartate Transaminase (ASAT) / Alanine Transaminase (ALAT) ratio (De Ritis ratio).25. The method, computer-implemented method, or electronic device according to any one of the preceding items, wherein the management score is determined using a non-linear model or a linear model, preferably a linear model, such as a Bayesian partial odds ordinal regression model or a Bayesian logistic regression model.26. The method, computer-implemented method, or electronic device according to item 25, wherein the management score is determined using a Bayesian ordinal regression model.27. The method, computer-implemented method, or electronic device according to item 26, wherein the parameters of the model were determined using Hamiltonian Monte Carlo (HMC) sampling.REFERENCES1 Bender Atik R, Christiansen OB, Elson J, et al. ESHRE guideline: recurrent pregnancy loss. Hum Reprod Open 2018; 2018: 1-12.2 Macklon NS, Geraedts JPM, Fauser BCJM. Conception to ongoing pregnancy: the 'black box' of early pregnancy loss. Hum Reprod Update 2002; 8: 333-43.3 Egerup P, Mikkelsen AP, Kolte AM, et al. Pregnancy loss is associated with type 2 diabetes: a nationwide case-control study. Diabetologia 2020; 63: 1521-9.4 Westergaard D, Nielsen AP, Mortensen LH, Nielsen HS, Brunak S. Phenome-wide analysis of short-and long-run disease incidence following recurrent pregnancy loss using data from a 39-year period. J Am Heart Assoc 2020; 9: 15069.5 The Lancet. Miscarriage: worldwide reform of care is needed. The Lancet 2021; 397: 1597.6 Tessema GA, Håberg SE, Pereira G, Regan AK, Dunne J, Magnus MC. Interpregnancy interval and adverse pregnancy outcomes among pregnancies following miscarriages or induced abortions in Norway (2008-2016): A cohort study. PLoS Med 2022; 19: e1004129.7 Donnet ML, Howie PW, Marnie M, Cooper W, Lewis M. Return of ovarian function following spontaneous abortion. Clinical endocrinology (Oxford) 1991; 46: 63-5.8 Hallet RL. Cyclic ovarian function following spontaneous abortions. Am J Obstet Gynecol 1954; 67:52-5.9 Heffner LJ. The reproductive system at a glance, Fourth edi. Chichester, West Sussex: John Wiley & Sons, Inc., 2014.10 Cole LA. HCG, the wonder of today's science. Reproductive Biology and Endocrinology 2012; 10:1-18.11 STEIER JA, BERGSJOS P, MYKING OL. Human Chorionic Gonadotropin in Maternal Plasma After Induced Abortion, Spontaneous Abortion, and Removed Ectopic Pregnancy. The American College of Obstetricians and Gynecologists 1984; 64: 391-4.12 Rørbye C, Nørgaard M, Nilas L. Prediction of late failure after medical abortion from serial b-hCG measurements and ultrasonography. DOI:10.1093 / humrep / deh041.13 Serdinšek T, Reljič M, Kovač V. Medical management of first trimester missed miscarriage: the efficacy and complication rate. J Obstet Gynaecol (Lahore) 2019; 39: 647-51.14 Petersen SG, Perkins AR, Gibbons KS, Bertolone JI, Mahomed K. Utility of ẞhCG monitoring in the follow-up of medical management of miscarriage. Aust N Z J Obstet Gynaecol 2017; 57: 358-65.15 Braunstein GD. False-positive serum human chorionic gonadotropin results: Causes, characteristics, and recognition. Am J Obstet Gynecol 2002; 187: 217-24.16 Gnoth C, Johnson S. Strips of hope: Accuracy of home pregnancy tests and new developments. Geburtshilfe Frauenheilkd 2014; 74: 661-9.17 Melmed S, Kleinberg D, Ho K. Pituitary Physiology and Diagnostic Evaluation. Williams Textbook of Endocrinology, Twelfth Edition 2011;: 175-228.18 Schwartz MW, Seeley RJ, Zeltser LM, et al. Obesity Pathogenesis: An Endocrine Society Scientific Statement. Endocr Rev 2017; 38: 267.19 Alpert MA, Hashimi MW. Obesity and the Heart. Am J Med Sci 1993; 306: 117-23.20 Nwabuobi C, Arlier S, Schatz F, Guzeloglu-Kayisli O, Lockwood CJ, Kayisli UA. hCG: Biological Functions and Clinical Applications. Int J Mol Sci 2017; 18. DOI:10.3390 / IJMS18102037.21 Nisula BC, Blithe DL, Akar A, Lefort G, Wehmann RE. Metabolic fate of human choriogonadotropin. J Steroid Biochem 1989; 33: 733-7.22 Piantanida E, Ippolito S, Gallo D, et al. The interplay between thyroid and liver: implications for clinical practice. J Endocrinol Invest 2020; 43: 885-99.23 Ticconi C, Giuliani E, Veglia M, Pietropolli A, Piccione E, Di Simone N. Thyroid autoimmunity and recurrent miscarriage. Am J Reprod Immunol 2011; 66: 452-9.

Claims

1. A method to assess the suitability of a type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) determining one or more management score based on said set of data comprising at least a first management score predictive of a most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, andc) predicting the most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said first management score,wherein said most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score increases the most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.

2. The method according to claim 1, further comprising:determining the one or more management score based on said set of data comprising a second management score predictive of the second most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, andpredicting the second most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said second management score,wherein said second most suitable type of management of miscarriage predicted based on said one or more management score comprising said second management score increases the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.

3. The method according to any of the preceding claims, further comprising:determining the one or more management score based on said set of data comprising a third management score predictive of the least most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, andpredicting the least most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said second management score,wherein said least suitable type of management of miscarriage predicted based on said one or more management score comprising said least management score increases the least the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.

4. A computer-implemented method for identifying the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, comprising at least:a) obtaining a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) determining one or more management score based on said set of data comprising at least a first management score predictive of a most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, andc) predicting the most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said first management score, andd) providing an output including a first output associated with the most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score,wherein said most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score increases the most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.

5. An electronic device for identifying the most suitable type of management of miscarriage for removing the foetus and / or foetal material in a female individual suffering from a pregnancy loss, the electronic device comprising an interface, one or more processors, and a memory, wherein the one of more processors are configured to:a) obtain a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) determining one or more management score based on said set of data comprising at least a first management score predictive of a most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,c) predicting the most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said first management score, andd) providing an output including a first output associated with the most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score;wherein said most suitable type of management of miscarriage predicted based on said one or more management score comprising said first management score increases the most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.

6. The computer-implemented method according to claim 4, or the electronic device according to claim 5, further comprising:determining the one or more management score based on said set of data comprising a second management score predictive of the second most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,predicting the second most suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said second management score, andproviding an output including a second output associated with the second most suitable type of management of miscarriage predicted based on said one or more management score comprising said second management score,wherein the second most suitable type of management of miscarriage predicted based on said one or more management score comprising said second management score increases the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.

7. The computer-implemented method according to any of claims 4 to 6, or the electronic device according to claim 7 or 8, further comprising:determining the one or more management score based on said set of data comprising a third management score predictive of the least suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue,predicting the least suitable type of management of miscarriage to be received by said female individual based on said one or more management score comprising said second management score, andproviding an output including a third output associated with the least suitable type of management of miscarriage predicted based on said one or more management score comprising said third management scorewherein the least suitable type of management of miscarriage predicted based on said one or more management score comprising said third management score increases the least the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.

8. A computer-implemented method for training a machine learning model, such as a neural network, to process as inputs a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue and provide as output one or more management scores, comprising a first management score associated with the most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, the method comprising:a) obtaining, using at least one processor, a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue, said pregnancy loss data comprising at least a ẞhCG value (IU / L) in said female individual, measured at the time of the determination of the pregnancy loss,b) performing, using the at least one processor, a training comprising:i) generating, using the at least one processor and a machine-learning model, management data based on the set of data,ii) obtaining, using the at least one processor, training data,iii) determining one or more management score based on the set of data and the training data, comprising a first management score, using the at least one processor and one or more management functions; andiv) training, using the at least one processor, the machine learning model based said one or more management score, comprising a first management score,wherein the most suitable type of management of miscarriage increases the most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.

9. A computer-implemented method for training a machine learning model, such as a neural network, according to item 8, to process as inputs a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue and provide as output one or more management scores, further comprising a second management score associated with the second most suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, the method further comprisingdetermining the one or more management score based on said set of data and the training data comprising said second management score, andtraining, using the at least one processor, the machine learning model based said one or more management score, comprising said second management score,wherein the second most suitable type of management of miscarriage increases the second most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.

10. A computer-implemented method for training a machine learning model, such as a neural network, according to claim 8 or 9, to process as inputs a set of data comprising pregnancy loss data associated with a foetus and / or foetal tissue and provide as output one or more management scores, further comprising a third management score associated with the least suitable type of management of miscarriage to be received by said female individual at the time of the pregnancy loss, for removing the foetus and / or foetal tissue, the method further comprisingdetermining the one or more management score based on said set of data and the training data comprising said third management score, andtraining, using the at least one processor, the machine learning model based said one or more management score, comprising said third management score,wherein the least suitable type of management of miscarriage increases the least the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said female individual.

11. The method, computer-implemented method, or electronic device according to any of the preceding claims, wherein the most suitable, second most suitable, and / or least suitable type of management of miscarriage is chosen from a group comprising at least the surgical management of miscarriage, the medical management of miscarriage, and the expectant management of miscarriage.

12. The method, computer-implemented method, or electronic device according to claim 11, wherein the most suitable type of management of miscarriage chosen for increasing the most the likelihood of occurrence of an ovulation during a period of 6 weeks after said pregnancy loss in said individual is:a) medical management over surgical management, if the ẞhCG levels are inferior or equal to 58560 IU / L, and / orb) medical management over expectant management, if the ẞhCG levels are inferior or equal to 5572 IU / L13. The method, computer-implemented method, or electronic device according to any one of the preceding claims, wherein the ẞhCG value (IU / L) is obtained via a blood sample of said female individual.

14. The method, computer-implemented method, or electronic device according to any one of the preceding items, wherein the set of data comprises at least one further foetal data selected from the group consisting of:i) the gestational age at pregnancy loss, calculated from the last menstruation of said individualii) the method of conception,iii) the diagnosis of the pregnancy loss, andpreferably the gestational age at pregnancy loss, calculated from the last menstruation of said individual15. The method, computer-implemented method, or electronic device according to any one of the preceding items, wherein the management score is determined using a non-linear model or a linear model, preferably a linear model, such as a Bayesian partial odds ordinal regression model or a Bayesian logistic regression model.

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