Method and system for predicting the progression of pre-eclampsia.

Abstract

The present invention relates to a method for predicting a patient's health state, the method comprising receiving at least one patient-related kinetic characteristic data, receiving at least one patient-related covariate, processing the at least one patient-related kinetic characteristic data and the at least one patient-related covariate data to generate a patient-related processed data set, generating at least one health state hypothesis based on the patient-related processed data set, and predicting at least one health state based on the at least one health state hypothesis. The present invention also relates to a system for predicting a patient's health state, the system comprising at least one processing component configured to receive at least one patient-related kinetic characteristic data, receive at least one patient-related covariate, and process the at least one patient-related kinetic characteristic data and the at least one patient-related covariate data to generate a patient-related processed data set; and at least one analysis component configured to analyze the patient-related processed data set and generate at least one health state hypothesis based on the patient-related processed data set, wherein the system is configured to predict at least one health state based on the at least one health state hypothesis and to implement the method according to any of the preceding claims.

Description

Technical Field

[0001] The present invention is in the field of predicting the medical condition of patients, and in particular in the field of predicting the progression and / or onset of preeclampsia in pregnant women and the impact thereof on the child thereof. An object of the present invention is to provide a method and system for predicting potential medical outcomes for a pregnant woman and / or the child thereof. More specifically, the present invention relates to a system, a method implemented in such a system, and the use of a corresponding system.

Background Art

[0002] Preeclampsia (PE) is a leading cause of short-term and long-term morbidity and mortality for mothers and perinatal infants worldwide (Tanner, 2022), affecting approximately 5% of all pregnancies (Mol, 2015. This increases the need for better techniques and novel approaches to reduce the burden on affected people. PE is characterized by new onset of gestational hypertension or pre-existing hypertension with at least one new sign of end-organ dysfunction that cannot be explained by causes other than PE.

[0003] PE is one of the most severe complications of pregnancy, bringing serious risks of morbidity and mortality to both the mother and the child thereof. PE is a complex disease, and diagnosis is difficult because pregnant women often have pre-existing morbidity that overlaps with PE, such as pre-existing hypertension or delayed fetal growth. Among Black women, PE affects up to 8% of pregnancies. Despite thorough research, it is still almost impossible to adequately predict, treat, or prevent PE.

[0004] The effects of postnatal pregnancy (PE) on infants and mothers persist for many years after conception, as evidenced by the fetal programming of lifestyle-related diseases and the increased risk of maternal cardiovascular disease (Wellmann, 2014). It is important to understand the three major ongoing pathological stages of PE: (i) placental hypoxia and oxidative stress, (ii) excessive release of anti-angiogenic and pro-inflammatory factors, and (iii) widespread systemic endothelial dysfunction and vasoconstriction (de Alwis, 2020).

[0005] As outlined by Scott et al. in recent years, various guidelines for PE have been developed over the past several decades, representing conventional diagnostic methods and best practices for the early detection of the medical condition (Scott, 2022). Indeed, the noble goal of all research and subsequent clinical care is to identify PE when the mother and child are asymptomatic and to prevent them from reaching the symptomatic stage.

[0006] MacDonald et al. present an up-to-date overview of clinical tools and biomarkers for predicting PE (MacDonald, 2022). Peripheral blood biomarkers, namely soluble Fms-like tyrosine kinase-1 (sFlt1) and placental growth factor (PlGF), have shown promising performance when used as “rule-out” tests, meaning to exclude patients. However, their sensitivity for detecting affected patients is low. This overview describes several placental and cardiovascular biomarkers that could improve future diagnostic performance.

[0007] Jhee et al. (Jhee, 2019) present predictive models for late-onset PE using different predictive models (e.g., logistic regression, decision trees, naive Bayes classifiers, support vector machines, etc.). Prediction of late-onset PE (i.e., after 34 weeks of gestation) was performed on the dataset using maternal characteristics and laboratory parameters in the early stages of second trimester. A detection rate of 77.1% was achieved, while the endpoint of the study was defined as first-onset hypertension with marked proteinuria.

[0008] Maric et al. (Maric, 2020) presented a method focused on statistical analysis. The model is trained on all available clinical and laboratory data, allowing for the inclusion of numerous missing values. Doppler imaging is downplayed as a feature due to the greater difficulty of validation. This significantly increases applicability across different healthcare organizations. Because it is trained on an elastic net, this method has not achieved a performance measure high enough for use in clinical settings. [Overview of the initiative]

[0009] Therefore, in view of the above, an object of the present invention is to overcome or at least mitigate the shortcomings and disadvantages of the prior art. More specifically, an object of the present invention is to provide a method for predicting the health status of a patient, and a corresponding system for the method, which, with improved sensitivity and performance, is less likely to produce false predictions of at least one health status of the patient.

[0010] These objectives are achieved by the present invention.

[0011] In a first embodiment, the present invention relates to a method for predicting a patient's health status, the method comprising the steps of: receiving at least one patient-related dynamic characteristic data; receiving at least one patient-related covariate; processing the at least one patient-related dynamic characteristic data and the at least one patient-related covariate data to generate a processed patient-related dataset; generating at least one health status hypothesis based on the processed patient-related dataset; and predicting at least one health status based on the at least one health status hypothesis.

[0012] In one embodiment, the step of predicting the at least one health condition may be based on a computer-implemented dynamic model. The at least one health condition hypothesis may correlate with at least one medical condition of the patient. It should be understood that the patient may be a female patient, such as a pregnant woman and / or a non-pregnant woman. It should be understood that the term “female” as patient is intended to refer to a female patient of reproductive age and / or childbearing age. Furthermore, the patient may be a fetus and / or a newborn. In one embodiment, the method may further comprise a step of predicting the dynamic behavior of the at least one health condition of the patient.

[0013] In a further embodiment, the method may comprise a step of implementing at least one machine learning technique, wherein the method may comprise a step of performing any of the steps described above using the at least one machine learning technique.

[0014] At least one patient-related covariate may have at least one biomarker. The at least one biomarker may relate to at least one disease condition, and the at least one biomarker may include at least one of the following: soluble Fms-like tyrosine kinase-1 (sFlt-1); placental growth factor (PlGF); neurofilament (NfL); C-terminal portion of arginine vasopressin (copeptin); albumin; hepatic transaminase; urea; hemoglobin; platelets; creatinine; albuminuria; proteinuria; estimated glomerular filtration rate (eGFR); creatinine clearance (CrCl); at least one additional renal function measurement; placental biomarkers such as placental RNA and placental proteins; endothelial / cardiovascular biomarkers such as endothelial RNA and endothelial proteins; or any combination thereof.

[0015] At least one patient-related covariate may include at least one maternal-related covariate, which includes at least one of the following: age; weight; height; body mass index (BMI); pregnancy history; birth history; number of fetuses in the current pregnancy; race; body temperature; heart rate; heart rate variability; respiratory rate; premature rupture of the amniotic membrane; white blood cell count; comorbidities such as history of pre-eclampsia (family and maternal), gestational diabetes, obesity, cardiovascular / renal / liver / thyroid disease, autoimmune disease, anemia, antiphospholipid syndrome, sexually transmitted infections, headaches; smoking habits before and / or during pregnancy; blood oxygen saturation (SpO2); blood pressure (systolic / diastolic); uteroplacental perfusion parameters; Doppler measurements of pulsation index of the umbilical artery, middle cerebral artery, cerebral-placental ratio, uterine artery, fetal descending aorta, ductus venosus, umbilical vein, inferior vena cava, and uterine artery; soft tissue parameters such as partial brachial volume and partial femoral volume; and at least one measurement of the aforementioned at least one biomarker.

[0016] At least one patient-related covariate may include at least one fetal-related covariate, which includes at least one of the following: sex; fetal weight during pregnancy; fetal biometric parameters such as femoral length, abdominal circumference, head circumference, mid-thigh circumference, and transverse diameter; proportion of gestationally undersized; gestational age; heart rate; heart rate variability; respiratory rate; uteroplacental perfusion parameters; and at least one measurement of the said at least one biomarker. Additionally or alternatively, at least one patient-related covariate may include at least one neonatal-related covariate, including at least one of the following: sex; birth weight; body weight; height; gestational age at birth; postnatal age; body temperature; heart rate, heart rate variability; respiratory rate; duration of breastfeeding; duration of exclusive breastfeeding; pH level; respiratory support; oxygen requirements; blood oxygen saturation (SpO2); blood pressure (systolic / diastolic); Apgar score; at least one additional neonatal biometric parameter; and at least one measurement of the said at least one biomarker. Additionally or alternatively, at least one patient-related covariate may include at least one environmental covariate, including at least one of the following: country of residence, country of birth, date and time of birth, humidity conditions at birth, and ambient temperature at birth.

[0017] In one embodiment, the method may comprise a step of generating at least one threshold, where the at least one threshold represents an indicator of at least one potential medical condition. In a further embodiment, the method may comprise a step of outputting at least one potential medical condition, where the at least one potential medical condition may comprise at least one of the following: seizures; respiratory; cardiovascular; hematological dysfunction; endocrine; renal; hepatic; uteroplacental dysfunction; fetal growth restriction; unplanned preterm birth; placental abruption; hemolysis, elevated liver enzymes, thrombocytopenia (HELLP) syndrome; and eclampsia. It should be understood that PE can cause multi-organ complications, including seizures, respiratory, cardiovascular, hematological dysfunction, endocrine, renal, hepatic, and uteroplacental dysfunction, i.e., complications of the central nervous system (CNS). Also, because PE affects the arteries that supply blood to the placenta, complications of PE may include, but are not limited to, fetal growth restriction; and preterm birth. Furthermore, PE can lead to unplanned preterm birth, i.e., delivery before 37 weeks. Therefore, it should be noted that the present invention is applicable to gestational hypertension and gestational diabetes, and relates to maternal, fetal, and neonatal complications of these conditions in pregnant women.

[0018] In a further embodiment, the method may include a step of determining a minimum threshold for the at least one threshold, and a step of determining a maximum threshold for the at least one threshold. Furthermore, at least one patient-related data may be below the minimum threshold, and the method may include a step of outputting a monitoring suggestion. If at least one patient-related data may be above the maximum threshold, the method may include a step of outputting a treatment suggestion. Furthermore, the method may include a step of determining a baseline for the at least one patient-related data. Additionally or alternatively, the method may include a step of determining at least one intermediate threshold, where the at least one intermediate threshold may include at least one value between the minimum threshold and the maximum threshold.

[0019] In one embodiment, the method may comprise the steps of: correlating at least a range of each of the at least one intermediate thresholds with the at least one disease condition; generating an interpreted dataset based on the correlation step; and outputting an automated report indicating the at least one potential disease condition. In a further embodiment, the method may comprise the steps of: determining at least one disease condition change indicator; monitoring changes in the at least one disease condition change indicator; generating a trend for the at least one disease condition change indicator; and predicting the progression of at least one of the at least one disease conditions, the prediction of which may be based on the trend for the at least one disease condition change indicator. The method may also comprise the step of: monitoring changes in the value of at least one of the at least one patient-related traits, the method may comprise the steps of: recording an initial value of the at least one patient-related trait; recording at least one subsequent value of the at least one patient-related trait; comparing the initial value with at least one of the at least one subsequent value; generating comparison value data; and outputting a hypothesis of the patient-related trait based on the comparison value data. The step of recording at least one subsequent value may include a step of recording a current value of one of the at least one patient-related characteristics, the current value may be different from the initial value.

[0020] In one embodiment, the method may be a non-diagnostic method. In another embodiment, the method may be a diagnostic method.

[0021] The method may also include a step of performing a step of the method described herein using data from at least one database. The at least one database may have at least one of the following: a public health database, a patient personal database, a medical professional database, a healthcare worker database, and a private data bank. Additionally or alternatively, the method may include a step of supplying data to the at least one server, a step of training the computer-implemented dynamic model based on the data supplied to the at least one server, and a step of generating a tuning function based on the training data, wherein the tuning function may be suitable for tuning any step of the method described in any of the embodiments of the method described above.

[0022] In one embodiment, the method may include a step of triggering at least one action suggestion based on the at least one health state hypothesis. Additionally or alternatively, the method may include a step of displaying the at least one action suggestion to the user. In another embodiment, the method may include a step of prompting the user to input at least one of the at least one action suggestion to accept and at least one of the at least one action suggestion to reject. The user may reject at least one of the at least one action suggestion, and the method may include a step of prompting the user to provide at least one annotation. Computer implementation dynamic models may be based on: Bayesian statistical methods; artificial neural network (ANN) methods; convolutional neural network (CNN) methods; recurrent neural network (RNN) methods; pharmacokinetic (PMX) modeling and / or simulation methods; supervised learning methods; deep learning (DL) methods, multilayer neural network methods, and / or explainable AI (XAI) concepts.

[0023] At least one medical condition may have at least one of the following: a condition related to fetal growth; a condition related to PE; a condition related to gestational diabetes; a condition related to gestational hypertension; pregnancy medical treatment such as cyclooxygenase inhibitors, e.g., aspirin; at least one related drug treatment; and at least one potential medical condition.

[0024] The method may also comprise a step of correlating at least one biomarker to at least one condition, where the at least one condition may have a potential disease. The method may also comprise a step of predicting the occurrence of at least one hypothesis in a given period, where the method may further comprise a step of recognizing several different periods, including at least one of the prenatal period; pregnancy; labor period and postnatal period. In one embodiment, the method may comprise a step of outputting the likelihood of occurrence correlated to each of the periods. Furthermore, the method may comprise a step of executing at least one machine learning (ML) algorithm. The at least one ML algorithm may have a supervised algorithm architecture, an unsupervised algorithm architecture, or any combination thereof. Additionally or alternatively, the at least one ML algorithm may include at least one artificial deep learning (DL) architecture, where the at least one artificial DL architecture may include at least one of ANN, CNN, and RNN. The unsupervised algorithm architecture may comprise implementing at least one clustering method of at least one cluster. Furthermore, at least one analytical method may include at least one of the following: pattern recognition, probabilistic modeling, Bayesian schemes, reinforcement learning, statistical analysis, statistical models, principal component analysis (PCA), independent component analysis, dynamic time stretching, maximum likelihood estimation (MLE), modeling, estimation, neural networks (NN), CNN, RNN, deep convolutional networks, DL, ultradeep learning, genetic algorithms, Markov models, and / or hidden Markov models.

[0025] In a further embodiment, the method may comprise a step of implementing at least one pharmacokinetic (PMX) model. The at least one PMX model may be a computer-implemented PMX model that includes at least one of the following: a mathematical statistical pharmacokinetic-pharmacodynamic (PK-PD) model; a physiologically based PK (PBPK) model; a physiologically based PK-PD (PBPKPD) model; a drug exposure-efficacy response model; and a drug exposure-safety response model.

[0026] At least one of the patient's health conditions may include PE, gestational diabetes, fetal growth-related conditions, and / or gestational hypertension-related conditions. In one embodiment, the step of predicting at least one health condition based on at least one health condition hypothesis may include the step of using at least one fetal growth-related data. In another embodiment, the step of predicting at least one health condition may include the use of at least one fetal growth-related data, wherein at least one health condition hypothesis may be based on at least one fetal growth-related data. The at least one fetal growth-related data may be obtained from at least one of at least one databases. In a further embodiment, the step of predicting at least one health condition may include the use of at least one PE-related data, wherein at least one health condition hypothesis may be based on at least one PE-related data. The at least one PE-related data may be obtained from at least one of at least one databases.

[0027] Furthermore, the method may include a step of determining at least one drug based on the at least one health condition, wherein the at least one drug may be suitable for preventing the onset and / or recurrence of the at least one medical condition and / or health condition. The method may include a step of determining at least one drug based on the at least one health condition, wherein the at least one drug may be suitable for treating the at least one medical condition and / or health condition. The at least one drug may include at least one of the following: aspirin, ibuprofen; at least one corticosteroid; at least one antihypertensive drug; and at least one cardiovascular drug. The method may also include a step of generating at least one route of drug administration, wherein the at least one route of drug administration may include at least one of the following: intravenous; intramuscular; subcutaneous; inhalation; transdermal; transcutaneous; oral; rectal; and sublingual. The method may also include a step of generating at least one administration regimen of at least one drug. Additionally or alternatively, the method may further comprise a step of optimizing the at least one dosing regimen, wherein the at least one dosing regimen may comprise at least one of the following: the at least one drug; the route of administration of the at least one drug; the at least one dosing scheme; the duration of drug administration; and the frequency of drug administration. The step of optimizing the at least one dosing regimen may be based on at least one health status hypothesis. The method may also comprise a step of implementing at least one optimal control theory, wherein the at least one optimal control theory is computer-implemented. It should be understood that the methods described herein are computer-implemented methods. The method may comprise a step of optimizing at least one ongoing treatment for at least one medical condition. The method may comprise a step of optimizing at least one ongoing treatment for at least one potential medical condition. The optimization step may be based on at least one health status hypothesis and / or at least one health condition.The method may comprise a step of generating at least one treatment proposal, wherein the at least one treatment proposal may be based on at least one health condition hypothesis and / or at least one health condition. The method may comprise a step of optimizing at least one treatment proposal, wherein the method may comprise performing the optimizing step after executing the at least one treatment proposal.

[0028] In one embodiment, the method may comprise adapting any of the foregoing embodiments of the method to a patient, and generating at least one individualized treatment protocol, wherein the at least one individualized treatment protocol may be based on at least one health condition of the patient. The method may comprise a step of optimizing at least one individualized treatment protocol, wherein the method may comprise performing the optimizing step after executing the at least one individualized treatment protocol. Furthermore, the method may comprise implementing any of the foregoing optimizing steps supported by computer-implemented pharmacodynamic methods. The method may comprise performing the method described herein in the absence of a patient. Furthermore, the method may comprise performing the method described herein using at least one piece of historical data. The at least one piece of historical data may be historical data of the patient. The at least one piece of historical data may comprise data from at least one of the following: public health databases, patient personal databases, medical professional databases, medical personnel databases, and private data banks.

[0029] In another embodiment, the method may comprise at least one of the following: capturing at least one image data of the patient; and receiving at least one image data of the patient, wherein the at least one image data may comprise data related to at least one medical condition and / or at least one potential medical condition of the patient. Also, the method is suitable for being implemented on at least one medical device such as an ultrasound device.

[0030] In a second aspect, the present invention relates to a system for predicting a patient's health condition, the system comprising: at least one processing component configured to receive at least one piece of patient-related dynamic characteristic data, receive at least one patient-related covariate, and process said at least one piece of patient-related dynamic characteristic data and said at least one piece of patient-related covariate data to generate a patient-related processed dataset; and at least one analysis component configured to analyze the patient-related processed dataset and generate at least one health condition hypothesis based on the patient-related processed dataset, wherein the system is configured to predict at least one health condition based on said at least one health condition hypothesis.

[0031] Furthermore, the system may comprise at least one storage component configured to store data related to said at least one health condition of said patient. The system may also comprise at least one computing component configured to implement a dynamic model for predicting said at least one health condition. Said at least one health condition hypothesis may have a correlation with at least one medical condition of said patient.

[0032] The patient may be a neonate, a fetus, and / or a woman, wherein the woman may be at least one of a pregnant woman or a non-pregnant woman.

[0033] The system may be configured to predict the dynamic behavior of at least one health condition of the patient. The system may be configured to perform any of the steps described in any of the embodiments of the aforementioned method by means of at least one machine learning technique.

[0034] At least one patient-related covariate may have at least one biomarker. The at least one biomarker may relate to at least one disease condition, and the at least one biomarker may include at least one of the following: soluble Fms-like tyrosine kinase-1 (sFlt-1); placental growth factor (PlGF); neurofilament (NfL); C-terminal portion of arginine vasopressin (copeptin); albumin; hepatic transaminase; urea; hemoglobin; platelets; creatinine; albuminuria; proteinuria; estimated glomerular filtration rate (eGFR); creatinine clearance (CrCl); at least one additional renal function measurement; placental biomarkers such as placental RNA and placental proteins; endothelial / cardiovascular biomarkers such as endothelial RNA and endothelial proteins; or any combination thereof.

[0035] At least one patient-related covariate may have at least one neonatal-related covariate, including at least one of the following: sex; race; birth weight; gestational age; mode of birth such as vaginal, vacuum extraction, or cesarean section; body temperature; heart rate; respiratory rate; pH level; umbilical cord pH level; respiratory support; oxygen requirements; blood oxygen saturation (SpO2); blood pressure (systolic / diastolic); Apgar score; and at least one measurement of at least one biomarker. Additionally, at least one patient-related covariate may have at least one maternal-related covariate, including at least one of the following: age; race; premature rupture of the amniotic membranes; body temperature; risk factors such as diabetes, steatosis, pregnancy history, birth history, and leukocyte count; and at least one measurement of at least one biomarker. Additionally or alternatively, at least one patient-related covariate may include at least one fetal-related covariate, including at least one of the following: sex; fetal weight during pregnancy; fetal biometric parameters such as femoral length, abdominal circumference, head circumference, mid-thigh circumference, and biparietal diameter; percentage of gestationally undersized fetuses; gestational age; heart rate; heart rate variability; respiratory rate; uteroplacental perfusion parameters; and at least one measurement of the aforementioned at least one biomarker. Furthermore, at least one patient-related covariate may include at least one environmental covariate, including at least one of the following: country of residence; country of birth; date and time of birth; humidity conditions at birth; and ambient temperature at birth.

[0036] The system may be configured to generate at least one threshold, where the at least one threshold represents an indicator of at least one potential medical condition. The system may be configured to output at least one potential medical condition, where the at least one potential medical condition may include at least one of the following: seizures; respiratory; cardiovascular; hematological dysfunction; endocrine; renal; hepatic; uteroplacental dysfunction; fetal growth restriction; unplanned preterm birth; placental abruption; hemolysis, elevated liver enzymes, thrombocytopenia (HELLP) syndrome; and eclampsia. At least one analysis component may be configured to determine a minimum threshold for the at least one threshold and to determine a maximum threshold for the at least one threshold. If at least one patient-related data may be below the minimum threshold, at least one analysis component outputs a monitoring suggestion. If at least one patient-related data may be above the maximum threshold, at least one analysis component outputs a treatment suggestion. At least one analysis component may be configured to determine a baseline for at least one patient-related data. At least one analysis component may be configured to determine at least one intermediate threshold, which may include at least one value between the minimum threshold and the maximum threshold. At least one analysis component may be configured to correlate at least one range of each of the at least one intermediate thresholds with the at least one disease condition, generate an interpreted dataset based on the correlation steps, and output an automated report showing at least one potential disease condition. At least one analysis component may be configured to determine at least one disease condition change indicator, monitor changes in the at least one disease condition change indicator, generate at least one disease condition change indicator trend, and predict the progression of at least one of the at least one disease condition based on the at least one disease condition change indicator trend.

[0037] In a further embodiment, the system may include at least one monitoring component configured to monitor a change in the value of at least one of the at least one patient-related characteristics, the at least one monitoring component may further be configured to record an initial value of the at least one patient-related characteristic, record at least one subsequent value of the at least one patient-related characteristic, compare the initial value with at least one of the at least one subsequent value to generate comparison value data, and output a hypothesis of the patient-related characteristic based on the comparison value data. The at least one monitoring component may be configured to record a current value of one of the at least one patient-related characteristics, the current value may be different from the initial value. The system may be a non-diagnostic system. In another embodiment, the system may be a diagnostic system.

[0038] The system may be configured to perform steps of the method described in any of the embodiments of the method described above using data from at least one database. The at least one database may have at least one of the following: a public health database, a patient's personal database, a medical professional's database, a healthcare worker's database, and a private data bank. Furthermore, the system may be configured to supply data to the at least one server, to train the computer-implemented dynamic model based on the data supplied to the at least one server, and to generate a tuning function based on the training data, the tuning function may be suitable for tuning any configuration of the system described in any of the embodiments of the system described above. The system may be configured to trigger at least one action suggestion based on the at least one health condition hypothesis and / or at least one health condition. The system may be configured to display the at least one action suggestion to the user.

[0039] In one embodiment, the system may be configured to prompt the user to input at least one acceptance of at least one of the at least one action proposals and at least one rejection of the at least one action proposal. If the user rejects at least one of the at least one action proposals, the system may be configured to prompt the user to provide at least one annotation. The computer implementation dynamics model may be based on: Bayesian statistical methods, artificial neural network (ANN) methods, convolutional neural network (CNN) methods, recurrent neural network (RNN) methods, pharmacokinetic (PMX) methods, supervised learning methods, deep learning (DL) and / or multilayer neural network methods, and / or explainable AI (XAI) concepts.

[0040] At least one medical condition may include at least one of the following: a fetal growth-related condition; neonatal thyroid dysfunction; PE-related conditions; gestational diabetes-related conditions; gestational hypertension-related conditions; and gestational thyroid dysfunction. The system may be configured to correlate at least one biomarker with at least one medical condition, where the at least one medical condition may have a potential disease. The system may also be configured to predict the occurrence of at least one hypothesis in a given period, where the system may further recognize several different periods, including at least one of the prenatal period, pregnancy, labor, and postnatal period. The system may be configured to output the likelihood of occurrence correlated with each period. Furthermore, the system may be configured to run at least one machine learning (ML) algorithm, where at least one ML algorithm may have a supervised algorithm architecture, an unsupervised algorithm architecture, or any combination thereof. At least one ML algorithm may include at least one artificial deep learning (DL) architecture, where at least one artificial DL architecture may include at least one of ANN, CNN, and RNN. An unsupervised algorithmic architecture may be configured to implement at least one clustering method for at least one cluster.

[0041] The system may also be configured to perform at least one analytical technique, where at least one analytical technique may include at least one of the following: pattern recognition, probabilistic modeling, Bayesian schemes, reinforcement learning, statistical analysis, statistical models, principal component analysis (PCA), independent component analysis, dynamic time stretching, maximum likelihood estimation (MLE), modeling, estimation, neural networks (NN), convolutional neural networks (CNN), recurrent neural networks (RNN), deep convolutional networks, deep learning (DL), ultradeep learning, genetic algorithms, Markov models, and / or hidden Markov models. The system may also be configured to implement at least one pharmacokinetic (PMX) model, where at least one PMX model may include at least one of the following: mathematical statistical pharmacokinetic-pharmacodynamic (PK-PD) models; physiologically based PK (PBPK) models; physiologically based PK-PD (PBPKPD) models; drug exposure-efficacy response models; and drug exposure-safety response models.

[0042] The system may be configured to predict at least one health state based on at least one health state hypothesis and to use at least one fetal growth-related data. The system may be configured to predict at least one health state using at least one fetal growth-related data, wherein at least one health state hypothesis may be based on at least one fetal growth-related data. The at least one fetal growth-related data may be obtained from at least one of at least one databases. The system may be configured to predict at least one health state and may have the use of at least one PE-related data, wherein at least one health state hypothesis may be based on at least one PE-related data. The at least one PE-related data may be obtained from at least one of at least one databases.

[0043] The system may comprise at least one imaging component configured to acquire at least one image data of the patient and at least one image data of the patient, wherein the at least one image data may include data relating to at least one medical condition and / or at least one potential medical condition of the patient.

[0044] Furthermore, the system is configured to carry out any of the steps described herein.

[0045] The system may comprise at least one implementation component configured to connect to at least one medical device, such as an ultrasound device, where the system, once connected to at least one medical device, is configured to perform any of the steps described herein. The system may be configured to operate without a patient.

[0046] Furthermore, the method comprises a step of using the system described herein to carry out any of the steps of the method described herein.

[0047] In a third embodiment, the present invention relates to a method of treatment for treating a patient's medical condition, wherein the treatment comprises the step of generating a treatment protocol comprising at least one therapeutic agent and a treatment regimen, the treatment regimen being based on at least one health condition hypothesis. The at least one health condition hypothesis may be provided by the method described herein. The at least one agent may be provided by the method described herein. The at least one health condition of the patient may include PE, gestational diabetes, and / or fetal growth problems.

[0048] Treatment may further include a stage in which the patient is treated for at least one potential condition prior to the onset of at least one condition. The patient may be at least one of pregnant or non-pregnant women, a fetus, and / or a newborn.

[0049] In a fourth embodiment, the present invention relates to a diagnostic method for diagnosing a patient's medical condition, wherein the diagnosis comprises the step of generating at least one diagnostic finding that includes at least one medical condition of the patient, wherein the at least one diagnostic finding is based on at least one health condition hypothesis. The diagnostic method may further comprise the step of generating at least one method of treatment, wherein the at least one method of treatment is for treating at least one medical condition of the patient. The diagnostic method may further comprise the step of generating at least one diagnostic finding, wherein the at least one diagnostic finding includes at least one medical condition of the patient prior to the onset of at least one medical condition. The diagnostic method may further comprise at least one preventive method of treatment, wherein the at least one preventive method of treatment is for treating at least one medical condition of the patient prior to the onset of at least one medical condition. At least one health condition hypothesis may be provided by the method described in any of the embodiments of the method described above.

[0050] The diagnostic method may comprise a step of providing at least one drug, the at least one drug may be provided by the method of any of the embodiments of the method described above. At least one of the patient's health conditions may include PE, gestational hypertension, gestational diabetes, and / or fetal growth problems. The patient may be at least one of pregnant women and non-pregnant women, a fetus and / or a newborn. The diagnostic method may comprise a step of proposing a method of treatment as described in any of the embodiments of the method described above.

[0051] In a fifth aspect, the present invention relates to the use of a system described herein for carrying out the method described herein. The method may include a step of prompting the system described herein to carry out a step of the method described herein. Use of the method described herein for implementing the treatment method described herein. Use of the method described herein for implementing the diagnostic method described herein. Use of the method described herein for implementing the diagnostic method and the treatment method described herein, wherein implementing the diagnostic method precedes implementing the treatment method.

[0052] In short, the present invention relates to disease prediction in the field of perinatal pharmaceuticals. More specifically, the method of the present invention allows for the combination of different components such as machine learning, data augmentation, artificial intelligence, dynamic pharmacodynamics, and pharmaceutics, which are suitable for neonatology and obstetrics. The present invention also allows for the use of multiple PE-related biomarkers to detect and monitor maternal, fetal, and neonatal stressors over time, for example, the past 15 years, in clinical research. Since PE is a progressive, multisystem disease, the present invention enables the examination of multiple PE-related biomarkers, such as (i) cardiovascular markers in triage (Wellmann, 2014), (ii) biomarkers for detecting and monitoring quasi-clinical maternal terminal organ dysfunction such as copeptins in the renal system (Wellmann, 2014) and NfL for the central nervous system (Evers, 2018), and (iii) biomarkers for diagnosing and monitoring fetal stress responses (Burkhardt, 2012) and adverse fetal outcomes (Letzner, 2011), (Depoorter, 2018). This is particularly advantageous because the combined analysis of such biomarkers enables the prediction of a patient's health status. It should be understood that the prediction of health status may include the patient's current, future, and / or past health status. That is, the present invention may enable the prediction of a patient's future health status before the onset of the disease, and may also enable the prediction of a patient's current health status before the onset of the disease. In addition, the present invention provides an integrated method which includes combining and utilizing multidimensional time-series data, processing data supported by computer implementation methods, and leveraging AI and PMX-based computer implementation models to personalize and optimize the prevention, diagnosis, management, and treatment of PE. The present invention is also advantageous because it can avoid PE-related complications in a patient or group of patients, such as complications in the mother and her unborn and newborn children.Thus, the present invention improves the perinatal prevention, diagnosis, and management of PE and its complications by combining available multi-source inputs, and the methods of the present invention enable such combinations to be carried out without human intervention. This is because the computer implementation method makes it possible to utilize sequential multi-source data, providing solutions that can optimize disease prevention, diagnosis, and management, reduce morbidity not only prenatally (i.e., mother and fetus) but also postnatally (i.e., mother and newborn), achieve data integration at all levels by utilizing the intelligent integration of concepts of multiple components, including clinical data, biomarkers, uteroplacental perfusion, and fetal growth data, along with time-series measurements, and combining ML and other AI methods with pharmacological principles and innovative pharmacokinetic computer models, and leveraging pharmacokinetic computer implementation simulation techniques, so that administration is optimized and personalized to maximize the efficacy / safety balance not only for the mother but also for her unborn and newborn children. This method is particularly advantageous because, with improved sensitivity and performance, it provides a more accurate, effective, and efficient method, corresponding system, or method for predicting a patient's health status, and is less likely to produce false predictions of at least one of the patient's health conditions.

[0053] This technology is also described by the following numbered embodiments.

[0054] The following describes embodiments of the method. These embodiments are abbreviated by the letter "M" followed by a number. When an embodiment of the method is referred to herein, it refers to these embodiments.

[0055] M1. A method for predicting a patient's health status, wherein the method is: The step of receiving at least one patient-related dynamic characteristics data, The stage of receiving at least one patient-related covariate, A step of processing the at least one patient-related dynamic characteristics data and the at least one patient-related covariate data to generate a processed patient-related dataset, A step of generating at least one health status hypothesis based on the patient-related processed dataset, and A step in which at least one health condition is predicted based on the aforementioned at least one health condition hypothesis. A method that includes [a certain feature].

[0056] M2. The step of predicting at least one health condition is the method according to the above embodiment, based on a computer-implemented dynamic model.

[0057] M3. The method according to any embodiment of the above-described method, wherein the at least one health status hypothesis is correlated with at least one medical condition of the patient.

[0058] M4. The method according to any embodiment of the above-described method, wherein the patient is a female patient.

[0059] M5. The method according to the above embodiment, wherein the female patient is a pregnant woman.

[0060] M6. The method according to either of the two embodiments described above, wherein the female patient includes a non-pregnant woman.

[0061] M7. The method according to any embodiment of the above-described method, wherein the patient is a fetus.

[0062] M8. The method according to any of the embodiments of the above-described method, wherein the patient is a neonatal.

[0063] M9. The method according to any of the embodiments of the above-described method, further comprising the step of predicting the dynamic behavior of at least one health condition of a patient.

[0064] M10. A method according to any embodiment of the above-described method, comprising the step of implementing at least one machine learning technique, wherein the method comprises the step of using the at least one machine learning technique to perform any of the above-described steps.

[0065] M11. The method according to any embodiment of the above-described method, wherein at least one patient-related covariate comprises at least one biomarker.

[0066] M12. The method according to the above embodiment, wherein at least one biomarker is associated with at least one pathological condition.

[0067] M13. The method of any of the three embodiments described above, wherein at least one biomarker comprises soluble Fms-like tyrosine kinase-1 (sFlt-1); placental growth factor (PlGF); neurofilament (NfL); C-terminal portion of arginine vasopressin (copeptin); albumin; hepatic transaminase; urea; hemoglobin; platelets; creatinine; albuminuria; proteinuria; estimated glomerular filtration rate (eGFR); creatinine clearance (CrCl); at least one additional renal function measurement; placental biomarkers such as placental RNA and placental proteins; endothelial / cardiovascular biomarkers such as endothelial RNA and endothelial proteins; or at least one of any combination thereof.

[0068] M14. At least one patient-related covariate is age; weight; height; body mass index (BMI); pregnancy history; birth history; number of fetuses in the current pregnancy; race; body temperature; heart rate; heart rate variability; respiratory rate; premature rupture of the amniotic membranes; white blood cell count; history of PE (family and maternal); comorbidities such as gestational diabetes, obesity, cardiovascular / renal / liver / thyroid disease, autoimmune disease, anemia, antiphospholipid syndrome, sexually transmitted infections, headaches; smoking habits before and / or during pregnancy; blood oxygen saturation (SpO2); blood pressure (systolic / diastolic); A method having the features of embodiments M4 to M6 as described in any embodiment of the above method, comprising: tension period; uteroplacental perfusion parameters; Doppler measurements of pulsation indices of the umbilical artery, middle cerebral artery, cerebral-placental ratio, uterine artery, fetal descending aorta, ductus venosus, umbilical vein, inferior vena cava, and uterine artery; soft tissue parameters such as brachial portion volume and femoral portion volume; and at least one maternal-related covariate including at least one measurement of at least one of the at least one biomarkers.

[0069] M15. A method having the features of Embodiment M7, as described in any embodiment of the above-described method, wherein at least one patient-related covariate includes sex; fetal weight during pregnancy; fetal biometric parameters such as femoral length, abdominal circumference, head circumference, mid-thigh circumference, and transverse diameter; proportion of gestationally undersized; gestational age; heart rate; heart rate variability; respiratory rate; uteroplacental perfusion parameters; and at least one of the measurements of at least one of the at least one biomarkers.

[0070] M16. A method having the features of Embodiment M8, as described in any embodiment of the above-described method, wherein at least one patient-related covariate includes sex; birth weight; body weight; height; gestational age at birth; age postpartum; body temperature; heart rate, heart rate variability; respiratory rate; breast milk; duration of exclusive breastfeeding; pH value; respiratory support; oxygen requirements; blood oxygen saturation (SpO2); blood pressure (systolic / diastolic); Apgar score; at least additional neonatal biometric parameters; and at least one of the measurements of at least one of the at least one biomarkers.

[0071] M17. The method according to any embodiment of the above-described method, wherein at least one patient-related covariate is at least one environmental covariate, which includes at least one of the following: country of residence, country of birth, date and time of birth, humidity conditions at birth, and ambient temperature at birth.

[0072] M18. The method according to any embodiment of the above-described method, comprising the step of generating at least one threshold, wherein the at least one threshold represents an indicator of at least one potential disease condition.

[0073] M19. The method according to any embodiment of the above-described method, comprising a step of outputting at least one potential medical condition, wherein the at least one potential medical condition includes at least one of the following: seizures; respiratory; cardiovascular; hematological; endocrine; renal; hepatic; uteroplacental; fetal growth restriction; unplanned preterm birth; placental abruption; hemolysis, elevated liver enzymes, thrombocytopenia (HELLP) syndrome; and eclampsia.

[0074] M20. The method is, A step of determining a minimum threshold for at least one of the thresholds, and Step of determining the maximum threshold for at least one of the aforementioned thresholds. A method according to any of the above-described embodiments of the method, comprising:

[0075] M21. The method according to the above embodiment, wherein the method comprises a step of outputting a monitoring suggestion if at least one patient-related data is below a minimum threshold.

[0076] M22. The method according to either of the two embodiments described above, wherein the method comprises a step of outputting a treatment suggestion if at least one patient-related data is above a maximum threshold.

[0077] M23. The method according to any of the three embodiments described above, comprising the step of determining a baseline for at least one patient-related data.

[0078] M24. The method according to any of the four embodiments described above, comprising the step of determining at least one intermediate threshold, wherein the at least one intermediate threshold includes at least one value between the minimum threshold and the maximum threshold.

[0079] M25. The above method is A step of correlating at least the range of each of the at least one intermediate thresholds with the at least one disease condition. Based on the aforementioned correlation step, the step of generating an interpreted dataset, and Step 1: Outputting an automated report indicating at least one potential medical condition. The method according to the above-described embodiment, comprising:

[0080] M26. The method is, The step of determining at least one indicator of disease progression, A step of monitoring changes in at least one of the disease condition indicators, A step of generating at least one disease condition change indicator trend, and A step of predicting the progression of at least one of the aforementioned at least one medical condition. The method comprising, wherein the prediction is based on the trend of at least one disease change indicator, according to any embodiment of the method described above.

[0081] M27. The method comprises the step of monitoring a change in the value of at least one of the at least one patient-related characteristics, and the method is A step of recording the initial value of at least one patient-related characteristic, A step of recording at least one subsequent value of the at least one patient-related characteristic, A step of comparing the initial value with at least one of the at least one subsequent value, The stage of generating comparative value data, and Based on the aforementioned comparative data, the next step is to generate hypotheses about patient-related characteristics. A method according to any of the above-described embodiments of the method, comprising:

[0082] M28. The method according to the above embodiment, wherein the step of recording at least a subsequent value includes a step of recording a current value of one of the at least one patient-related characteristics, the current value being different from the initial value.

[0083] M29. A method according to any of the embodiments of the method described above, wherein the method is a non-diagnostic method.

[0084] M30. The method is a diagnostic method, as described in any of the embodiments of the method described above.

[0085] M31. The method according to any embodiment of the method described above, further comprising the step of performing a step of the method according to any of the embodiments described above using data from at least one database.

[0086] M32. The method according to the above embodiment, wherein at least one database comprises at least one of the following: a public health database, a patient's personal database, a medical professional's database, a healthcare worker's database, and a private data bank.

[0087] M33. The method is, The step of supplying data to at least one of the aforementioned servers, The steps include training the computer-implemented dynamic model based on data supplied to at least one of the servers, and The step of generating an adjustment function based on the aforementioned training data. The method according to any embodiment of the aforementioned method, wherein the adjustment function is suitable for adjusting any step of the method described in any embodiment of the aforementioned method.

[0088] M34. A method according to any embodiment of the above-described method, comprising a step of triggering at least one action proposal based on the at least one health condition hypothesis.

[0089] M35. The method according to the above embodiment, further comprising the step of displaying the at least one action suggestion to the user.

[0090] M36. The method is, Acceptance of at least one of the aforementioned at least one proposed action, and Rejection of at least one of the aforementioned at least one proposed action The method of any one of the two embodiments described above, further comprising a step of prompting the user to enter at least one of the following.

[0091] M37. The method according to the above embodiment, wherein if the user rejects at least one of the at least one action proposals, the method further comprises a step of prompting the user to provide at least one annotation.

[0092] M38. A computer-implemented dynamic model is a method according to any embodiment of the method described above, based on Bayesian statistical methods.

[0093] M39. A computer-implemented dynamic model is a method according to any embodiment of the above-described method, based on an ANN, CNN, or RNN technique.

[0094] M40. A computer-implemented dynamic model is a method according to any embodiment of the aforementioned method, based on pharmacodynamic modeling and / or simulation techniques.

[0095] M41. A computer-implemented dynamic model is a method according to any embodiment of the method described above, based on a supervised learning technique.

[0096] M42. A computer-implemented dynamic model is a method according to any embodiment of the aforementioned method, based on deep learning and / or multilayer neural network techniques.

[0097] M43. A computer-implemented dynamic model is a method according to any embodiment of the method described above, based on the explainable AI concept (XAI).

[0098] M44. The method according to any embodiment of the above-described method, wherein at least one medical condition is at least one of the following: a fetal growth-related condition; a PE-related condition; a gestational diabetes-related condition; a gestational hypertension-related condition; pregnancy medical treatment such as a cyclooxygenase inhibitor, e.g., aspirin; at least one related drug treatment; and at least one potential medical condition.

[0099] M45. A method comprising the step of correlating at least one biomarker to at least one disease condition, wherein the at least one disease condition has the characteristics of Embodiment M13 as described in any of the embodiments of the above-described method.

[0100] M46. The method according to any embodiment of the method described above, comprising the step of predicting the occurrence of at least one hypothesis during a given period, wherein the method further comprises the step of recognizing a number of different periods, including at least one of the prenatal period, pregnancy, labor period, and postnatal period.

[0101] M47. The method according to the embodiment described above, further comprising the step of outputting the likelihood of occurrence correlated with each period.

[0102] M48. A method according to any of the embodiments of the above-described methods, comprising a step of executing at least one machine learning algorithm.

[0103] M49. The method according to the above embodiments, wherein at least one machine learning algorithm has a supervised algorithmic architecture, an unsupervised algorithmic architecture, or any combination thereof.

[0104] M50. The method of any of the embodiments of the above-described method, wherein at least one machine learning algorithm comprises at least one artificial deep learning (DL) architecture.

[0105] M51. The method according to the above embodiment, wherein at least one artificial deep learning architecture includes at least one of ANN, CNN, and RNN.

[0106] M52. An unsupervised algorithmic architecture is a method having the features of Embodiment M49, as described in any of the embodiments of the above-described methods, which includes implementing at least one clustering technique for at least one cluster.

[0107] M53. The method according to any embodiment of the above-described method, comprising a step of performing at least one analytical technique, wherein the at least one analytical technique is at least one of pattern recognition, probabilistic modeling, Bayesian scheme, reinforcement learning, statistical analysis, statistical model, principal component analysis, independent component analysis, dynamic time stretching, maximum likelihood estimation, modeling, estimation, neural network, convolutional network, recurrent network, deep convolutional network, deep learning, ultradeep learning, genetic algorithm, Markov model, and / or hidden Markov model.

[0108] M54. A method according to any embodiment of the above-described method, comprising the step of implementing at least one pharmacokinetic model.

[0109] M55. The method according to the above embodiment, wherein at least one pharmacokinetic model is a computer-implemented pharmacokinetic model comprising at least one of the following: a mathematical statistical PKPD model; a physiologically based PK (PBPK) model; a physiologically based PKPD (PBPKPD) model; a drug exposure-efficacy response model; and a drug exposure-safety response model.

[0110] M56. The method according to any of the embodiments of the above-described method, wherein at least one of the patient's health conditions is PE.

[0111] M57. The method according to any embodiment of the above-described method, in which at least one health condition of the patient is gestational diabetes.

[0112] M58. The method of any embodiment of the above-described method, wherein at least one of the patient's health conditions includes a condition related to fetal growth.

[0113] M59. The method according to any of the embodiments of the above-described method, wherein at least one of the patient's health conditions is a condition related to pregnancy-induced hypertension.

[0114] M60. The method of any embodiment of the preceding method, wherein the step of predicting at least one health condition based on at least one health condition hypothesis is the step of using at least one fetal growth-related data.

[0115] M61. A method according to any embodiment of the preceding method, wherein the step of predicting at least one health condition includes a step of using at least one fetal growth-related data, and the at least one health condition hypothesis is based on at least one fetal growth-related data.

[0116] M62. A method having the features of Embodiment M32 described in the preceding embodiment, wherein at least one fetal growth-related data is obtained from at least one of at least one databases.

[0117] M63. The method according to any of the embodiments of the preceding method, wherein the step of predicting at least one health condition includes the step of using at least one PE-related data, and the step of predicting at least one health condition hypothesis is based on at least one PE-related data.

[0118] M64. A method having the features of embodiment M32 described in the above embodiment, wherein at least one PE-related data is obtained from at least one of at least one databases.

[0119] M65. The method according to any of the above embodiments, comprising the step of determining at least one drug based on the at least one health condition, wherein the at least one drug is suitable for preventing the onset of the at least one disease and / or the at least one health condition.

[0120] M66. The method according to any of the above embodiments, comprising the step of determining at least one drug based on the at least one health condition, wherein the at least one drug is suitable for treating the at least one medical condition and / or the at least one health condition.

[0121] M67. The method according to either of the two embodiments described above, wherein at least one agent comprises at least one of aspirin; ibuprofen; at least one corticosteroid; at least one antihypertensive agent; and at least one cardiovascular agent.

[0122] M68. The method according to any of the three embodiments described above, comprising the step of generating at least one drug delivery route, wherein the at least one drug delivery route includes at least one of intravenous; intramuscular; subcutaneous; inhalation; transdermal; transcutaneous; oral; rectal; and sublingual.

[0123] M69. A method having the features of embodiments 45M and 65M to 68M described in any of the above embodiments, comprising the step of generating at least one administration regimen of at least one drug.

[0124] M70. The method according to the above embodiment, further comprising the step of optimizing the at least one dosing regimen, wherein the at least one dosing regimen comprises at least one of the at least one drug, the route of administration of the at least one drug, the administration scheme, the duration of drug administration, and the frequency of drug administration.

[0125] M71. The method according to the above embodiment, comprising the step of optimizing at least one dosing regimen based on the at least one health condition hypothesis.

[0126] M72. The method according to one of the two embodiments described above, comprising the step of implementing at least one optimal control theory, where at least one optimal control theory is computer-implemented.

[0127] M73. A method according to any embodiment of the above-described method, which is a computer implementation method.

[0128] M74. A method according to any embodiment of the above-described method, comprising a step of optimizing the ongoing treatment of at least one medical condition.

[0129] M75. A method according to any embodiment of the above-described method, comprising a step of optimizing the ongoing treatment of at least one potential medical condition.

[0130] M76. The optimization step is based on at least one health status hypothesis and / or at least one health status, according to the method of either of the two embodiments described above.

[0131] M77. A method comprising the step of generating at least one treatment proposal, wherein the at least one treatment proposal is based on at least one health condition hypothesis and / or at least one health condition, according to any embodiment of the aforementioned method.

[0132] M78. The method according to the above embodiment, comprising at least a step of optimizing a treatment proposal, wherein the method comprises a step of performing an optimization step after performing at least one treatment proposal.

[0133] M79. The method is, The steps of adapting one of the embodiments of the method described above to a patient, and A method according to any of the embodiments of the aforementioned method, comprising the step of generating at least one individualized treatment protocol, wherein the at least one individualized treatment protocol is based on at least one health condition of the patient.

[0134] M80. The method according to the embodiment described above, comprising at least a step of optimizing an individualized treatment protocol, wherein the step of performing the optimization step is performed after performing at least one individualized treatment protocol.

[0135] M81. A method according to any embodiment of the aforementioned method, comprising the step of implanting one of the aforementioned optimization steps supported by a computer-implemented pharmacokinetic technique.

[0136] M82. A method according to any of the embodiments described above, comprising the step of performing any of the embodiments of the described method in the absence of a patient.

[0137] M83. A method according to any of the embodiments described above, further comprising the step of performing any of the embodiments of the described method using at least one historical data.

[0138] M84. The method according to the above embodiment, wherein at least one historical data is patient historical data.

[0139] M85. At least one historical data point is Public health database, Patient personal database, Database of medical professionals, A database of medical professionals, and Private Data Bank The method according to either of the two embodiments described above, wherein the data is from at least one of the following.

[0140] M86. The method is, The step of acquiring at least one image data of the patient; and The stage of receiving at least one image data of the patient. The method of any of the embodiments of the method described above, comprising at least one of the following, wherein at least one image data has data relating to at least one medical condition and / or at least one potential medical condition of the patient.

[0141] M87. The method according to any of the embodiments described above, which is preferred to be implemented in at least one medical device, such as an ultrasound device.

[0142] M88. The method according to any of the embodiments of the above-described method, comprising the step of using a system described in any of the embodiments of the system for carrying out any of the steps described in any of the embodiments of the method.

[0143] The following describes embodiments of the system. These embodiments are abbreviated by the letter "S" followed by a number. Whenever embodiments of the system are referred to in this specification, these embodiments are meant.

[0144] S1. A system for predicting a patient's health status, wherein the system is Receive at least one patient-related dynamic characteristics data, At least one patient-related covariate was received, The at least one patient-related dynamic characteristics data and the at least one patient-related covariate data are processed to generate a processed patient-related dataset. At least one processing component configured as follows: The processed dataset related to the aforementioned patients was analyzed, Based on the aforementioned patient-related processed dataset, generate at least one health status hypothesis. At least one analytical component configured in such a way Equipped with, Herein, the system is configured to predict at least one health condition based on the at least one health condition hypothesis.

[0145] S2. The system according to the above embodiment, comprising at least one memory component configured to store data relating to the at least one health condition of the patient.

[0146] S3. The system according to the above embodiment, comprising at least one computing component configured to implement a dynamic model for predicting the at least one health condition.

[0147] S3. The system according to any of the embodiments of the system described above, wherein the at least one health condition hypothesis is correlated with at least one medical condition of the patient.

[0148] S4. The patient is a female patient, as described in any of the embodiments of the system described above.

[0149] S5. The system described in the above embodiment, wherein the female patient is a pregnant woman. S6. The system according to either of the two embodiments described above, wherein the female patient includes a non-pregnant woman. S7. The system according to any of the embodiments of the system described above, wherein the patient is a fetus.

[0150] S8. The method according to any of the embodiments of the above-described method, wherein the patient is a neonatal.

[0151] S9. A system according to any of the embodiments of the system described above, configured to predict the dynamic behavior of at least one health condition of a patient.

[0152] S10. The system is configured to implement at least one machine learning technique, wherein the system is configured to perform any of the steps described in any of the embodiments of the aforementioned system by the at least one machine learning technique.

[0153] S11. A system according to any of the embodiments of the system described above, wherein at least one patient-related covariate includes at least one biomarker.

[0154] S12. The system according to the above embodiment, wherein at least one biomarker is associated with at least one pathological condition.

[0155] S13. The system according to any of the three embodiments described above, wherein at least one biomarker comprises at least one of the following: soluble Fms-like tyrosine kinase-1 (sFlt-1); placental growth factor (PlGF); neurofilamentous light chain (NfL); copeptin; placental biomarkers such as placental RNA and placental proteins; endothelial / cardiovascular biomarkers such as endothelial RNA and endothelial proteins; or any combination thereof.

[0156] S14. A system having the features of Embodiment S4 as described in any of the embodiments of the system described above, wherein at least one patient-related covariate is sex; race, birth weight, gestational age, mode of birth such as vaginal, vacuum extraction, or cesarean section, body temperature, heart rate, respiratory rate, pH value; umbilical cord pH value; respiratory support; oxygen requirements; blood oxygen saturation (SpO2); blood pressure (systolic / diastolic); Apgar score; and at least one of the measurements of at least one of the biomarkers.

[0157] S15. A system having the features of Embodiment S5 as described in any of the embodiments of the system described above, wherein at least one patient-related covariate is a risk factor such as age, race, premature rupture of the amniotic membrane, body temperature, diabetes, steatosis, pregnancy history, birth history, leukocyte count, and at least one maternal-related covariate is at least one of at least one measurement of at least one biomarker.

[0158] S16. A system having the features of Embodiment S6 as described in any of the embodiments of the system described above, wherein at least one patient-related covariate includes sex; fetal weight during pregnancy; fetal biometric parameters such as femoral length, abdominal circumference, head circumference, mid-thigh circumference, and transverse diameter; proportion of gestationally undersized; gestational age; heart rate; heart rate variability; respiratory rate; uteroplacental perfusion parameters; and at least one of the measurements of at least one of the at least one biomarkers.

[0159] S17. The system according to any of the embodiments of the system described above, wherein at least one patient-related covariate includes at least one environmental covariate, which includes at least one of the following: country of residence, country of birth, date and time of birth, humidity conditions at birth, and ambient temperature at birth.

[0160] S18. The system is configured to generate at least one threshold, wherein the at least one threshold represents at least one potential disease indicator, as described in any of the embodiments of the system described above.

[0161] S19. The system according to any embodiment of the system described above, wherein the system is configured to output at least one potential medical condition, wherein the at least one potential medical condition includes at least one of the following: seizures; respiratory; cardiovascular; hematological; endocrine; renal; hepatic; uterine and placental dysfunction; fetal growth restriction; unplanned preterm birth; placental abruption; hemolysis, elevated liver enzymes, thrombocytopenia (HELLP) syndrome; and eclampsia.

[0162] S20. At least one analytical component is: Determine the minimum threshold for at least one of the aforementioned thresholds, Determine the maximum threshold for at least one of the aforementioned thresholds. A system as described in any of the aforementioned embodiments of the system.

[0163] S21. The system according to the above embodiment, wherein at least one analytical component outputs a monitoring suggestion if at least one patient-related data point is below a minimum threshold.

[0164] S22. The system according to either of the two embodiments described above, wherein at least one analytical component outputs a treatment suggestion if at least one patient-related data point is above a maximum threshold.

[0165] S23. The system according to any of the three embodiments described above, wherein at least one analytical component is configured to determine a baseline for the at least one patient-related data.

[0166] S24. The system according to any of the four embodiments described above, wherein at least one analytical component is configured to determine at least one intermediate threshold, wherein the at least one intermediate threshold includes at least one value between the minimum threshold and the maximum threshold.

[0167] S25. At least one analytical component is: Each of the at least one intermediate thresholds is correlated with the at least one disease condition. Based on the aforementioned correlation step, an interpreted dataset is generated, Generates an automated report showing at least one potential medical condition. The system described in the above-described embodiment, configured as such.

[0168] S26. At least one analytical component is: Determine at least one indicator of disease progression, The changes in at least one of the disease condition indicators are monitored, Generate at least one disease condition change indicator trend, Based on the trend of the at least one disease condition change indicator, predict the progression of at least one of the at least one disease conditions. A system as described in any of the aforementioned embodiments of the system.

[0169] S27. The system comprises at least one monitoring component configured to monitor a change in the value of at least one of the at least one patient-related characteristics, and the at least one monitoring component further comprises The initial value of at least one patient-related characteristic is recorded, Record the subsequent value of at least one of the aforementioned patient-related characteristics, The initial value is compared with at least one of the subsequent values, Generate comparative value data, Based on the aforementioned comparative data, hypotheses regarding patient-related characteristics are output. A system as described in any of the aforementioned embodiments of the system.

[0170] S28. The system according to the above embodiment, wherein at least one monitoring component is configured to record a current value of one of the at least one patient-related characteristics, the current value being different from the initial value.

[0171] S29. The system is a non-diagnostic system, as described in any of the embodiments of the system described above.

[0172] S30. The system is a diagnostic system, as described in any of the embodiments of the system described above.

[0173] S31. The system according to one of the embodiments of the system described above, configured to perform a step of the method described in any of the embodiments of the method described above using data from at least one database.

[0174] S32. The system according to the above embodiment, wherein at least one database comprises at least one of the following: a public health database, a patient's personal database, a medical professional's database, a healthcare worker's database, and a private data bank.

[0175] S33. The system is The data is supplied to at least one of the aforementioned servers, The computer implementation dynamic model is trained based on the data supplied to the at least one server. Based on the aforementioned training data, generate an adjustment function. The system is configured such that the adjustment function is suitable for adjusting any configuration of the system described in any of the embodiments of the system described above.

[0176] S34. The system according to any of the embodiments of the system described above, wherein the system is configured to trigger at least one action suggestion based on the at least one health condition hypothesis.

[0177] S35. The system according to the above embodiment, configured to display the at least one action suggestion to the user.

[0178] S36. The system is Acceptance of at least one of the aforementioned at least one proposed action, and Rejection of at least one of the aforementioned at least one proposed action The system according to either of the two embodiments described above, configured to prompt the user to enter at least one of the following.

[0179] S37. The system according to the above embodiment, configured to prompt the user to provide at least one annotation if the user rejects at least one of the at least one action proposals.

[0180] S38. The computer-implemented dynamic model is a system based on Bayesian statistical methods, as described in any of the embodiments of the system described above.

[0181] S39. The computer-implemented dynamic model is a system based on any of the above-described embodiments of the system, which is based on an ANN, CNN, or RNN method.

[0182] S40. The computer-implemented dynamic model is a system described in any of the above-described embodiments of the system, based on pharmacokinetic methods.

[0183] S41. The computer-implemented dynamic model is a system described in any of the above-described embodiments of the system, based on a supervised learning method.

[0184] S42. The computer-implemented dynamic model is a system described in any of the above-described embodiments of the system, based on deep learning and / or multilayer neural network techniques.

[0185] S43. A computer-implemented dynamic model is a system described in any of the above-mentioned embodiments of the system, based on the explainable AI concept (XAI).

[0186] S44. The system according to any of the embodiments of the system described above, wherein at least one medical condition includes at least one of the following: a fetal growth-related condition; neonatal thyroid dysfunction; PE-related conditions; gestational diabetes-related conditions; gestational hypertension-related conditions; and gestational thyroid dysfunction.

[0187] S45. The system is configured to correlate at least one biomarker to at least one disease condition, where the at least one disease condition is having a potential disease, and the system has the features of Embodiment S13 as described in any of the embodiments of the system described above.

[0188] S46. The system according to any embodiment of the system described above, wherein the system is configured to predict the occurrence of at least one hypothesis during a given period, wherein the system is configured to recognize a plurality of different periods, including at least one of the prenatal period, pregnancy, labor period, and postnatal period.

[0189] S47. The system according to the above embodiment, configured to output the likelihood of occurrence correlated with each period.

[0190] S48. The system is one of the embodiments of the system described above, configured to run at least one machine learning algorithm.

[0191] S49. The system according to the above embodiment, wherein at least one machine learning algorithm has a supervised algorithm architecture, an unsupervised algorithm architecture, or any combination thereof.

[0192] S50. A system according to any of the embodiments of the system described above, comprising at least one machine learning algorithm and at least one artificial deep learning (DL) architecture.

[0193] S51. The system according to the above embodiment, wherein at least one artificial deep learning architecture includes at least one of ANN, CNN, and RNN.

[0194] S52. An unsupervised algorithm architecture is configured to implement at least one clustering method for at least one cluster, and the system has the features of embodiment S47 as described in any of the embodiments of the system described above.

[0195] S53. The system is configured to perform at least one analytical method, the system being any of the embodiments of the system described above, wherein the system is configured to perform at least one analytical method, the system being at least one of pattern recognition, probabilistic modeling, Bayesian schemes, reinforcement learning, statistical analysis, statistical models, principal component analysis, independent component analysis, dynamic time stretching, maximum likelihood estimation, modeling, estimation, neural networks, convolutional networks, recurrent networks, deep convolutional networks, deep learning, ultradeep learning, genetic algorithms, Markov models, and / or hidden Markov models.

[0196] S54. The system is configured to implement at least one pharmacokinetic model, as described in any of the embodiments of the system described above.

[0197] S55. The system described in the above embodiment, wherein at least one pharmacokinetic model includes a PKPD model.

[0198] S56. At least one health condition of a patient is a system according to any of the embodiments of the system described above, including PE.

[0199] S57. The system described in any of the embodiments of the system described above, in which at least one of the patient's health conditions is gestational diabetes.

[0200] S58. A system according to any of the embodiments of the system described above, wherein at least one of the patient's health conditions includes a fetal growth-related problem.

[0201] S59. A system according to any of the embodiments of the system described above, wherein at least one of the patient's health conditions is a condition related to pregnancy-induced hypertension. S60. The system according to any of the embodiments of the system described above, wherein the system is configured to predict at least one health condition based on at least one health condition hypothesis and to use at least one fetal growth-related data.

[0202] S61. The system is configured to predict at least one health condition using at least one fetal growth-related data, wherein the at least one health condition hypothesis is based on at least one fetal growth-related data, as described in any of the embodiments of the system described above.

[0203] S62. A system having the features of embodiment S30 described in the preceding embodiment, wherein at least one fetal growth-related data is obtained from at least one of at least one databases.

[0204] S63. The system is configured to predict at least one health condition using at least one PE-related data, and at least one health condition hypothesis is based on at least one PE-related data, as described in any of the embodiments of the system described above.

[0205] S64. A system having the features of embodiment S30 described above, wherein at least one PE-related data is obtained from at least one of at least one databases.

[0206] S65. The aforementioned system is To acquire at least one image data of the aforementioned patient, and To receive at least one image data of the aforementioned patient. The imaging component comprises at least one configured to perform at least one of the following, where The system according to any of the embodiments of the system described above, wherein the at least one image data includes data relating to at least one medical condition and / or at least one potential medical condition of the patient.

[0207] S66. A system according to any of the embodiments of the system described above, configured to perform any of the steps described in any of the embodiments of the method.

[0208] S67. The system according to any embodiment of the system described above, comprising at least one implementation component configured to connect the system to at least one medical device, such as an ultrasound device, wherein the system, once connected to at least one medical device, is configured to perform any of the steps described in any of the embodiments of the method described above.

[0209] S68. The system according to any of the embodiments of the system described above, configured to operate in the absence of a patient.

[0210] The following describes embodiments of the treatment method. These embodiments are abbreviated by the letter "T" followed by a number. In this specification, when referring to embodiments of the treatment method, these embodiments are meant.

[0211] T1. A method of treatment for treating a patient’s medical condition, wherein the treatment comprises the step of generating a treatment protocol comprising at least one therapeutic agent and a treatment regimen, the treatment regimen being based on at least one health condition hypothesis.

[0212] T2. A treatment according to the above embodiment, provided by the method described in any of the embodiments of the above-described method, wherein at least one health condition hypothesis is provided by the method described in any of the embodiments of the above-described method.

[0213] T3. The treatment according to any of the two embodiments described above, wherein at least one agent is provided by the method described in any of the embodiments of the above-described method.

[0214] T4. At least one of the patient's health conditions is treated with any of the embodiments of the treatment described above, including PE.

[0215] T5. At least one of the patient's health conditions is a condition requiring treatment as described in any of the embodiments of the treatment described above, including gestational diabetes and / or gestational hypertension.

[0216] T6. At least one of the patient's health conditions is a fetal growth problem, and treatment is required for any of the embodiments of treatment described above.

[0217] T7. The treatment further comprises treating the patient for at least one potential medical condition of the patient prior to the onset of at least one medical condition, as described in any of the embodiments of the treatment described above.

[0218] T8. The treatment described in any of the embodiments of the treatment described above, wherein the patient is at least one of a pregnant woman and a non-pregnant woman.

[0219] T9. The patient is a fetus, and the treatment is one of the embodiments of the treatment described above.

[0220] T10. The patient is a newborn, and the treatment is as described in any of the embodiments of the treatment described above.

[0221] The following describes embodiments of the diagnostic method. These embodiments are abbreviated by the letter "D" followed by a number. In this specification, when referring to embodiments of the diagnostic method, these embodiments are meant.

[0222] D1. A diagnostic method for diagnosing a patient's medical condition, wherein the diagnosis comprises the step of generating at least one diagnostic finding that includes at least one medical condition of the patient, wherein the at least one diagnostic finding is based on at least one health condition hypothesis.

[0223] D2. The diagnosis according to the above embodiment, comprising the step of generating at least one method of treatment, wherein the at least one method of treatment is for treating at least one medical condition of a patient.

[0224] D3. The method according to either of the two embodiments described above, comprising a step of generating at least one diagnostic finding, the diagnostic finding comprising at least one medical condition of the patient prior to the onset of at least one medical condition.

[0225] D4. The diagnosis according to the above embodiment, wherein the diagnosis comprises at least one preventive method of treatment, the at least one preventive method of treatment being for treating at least one condition in the patient prior to the onset of at least one condition.

[0226] D5. At least one health status hypothesis is provided by the method described in any of the embodiments of the above-described method, as described in any of the embodiments of the above-described method.

[0227] D6. The diagnosis according to any of the embodiments of the diagnosis described above, comprising providing at least one drug, wherein the at least one drug is provided by the method described in any of the embodiments of the method described above.

[0228] D7. At least one of the patient's health conditions is a diagnosis as described in any of the embodiments of the diagnosis described above, including PE.

[0229] D8. At least one of the patient's health conditions is a diagnosis as described in any of the embodiments of the diagnosis described above, including gestational diabetes and / or gestational hypertension.

[0230] D9. At least one of the patient's health conditions is a diagnosis described in any of the embodiments of the diagnosis described above, including a fetal growth problem.

[0231] D10. The diagnosis described in any of the embodiments of the diagnosis described above, wherein the patient is at least one of a pregnant woman and a non-pregnant woman.

[0232] D11. The patient is a fetus, as described in any of the embodiments of the diagnosis described above.

[0233] D12. The patient is a newborn, as described in any of the diagnostic embodiments described above.

[0234] D13. A diagnosis according to any of the embodiments of the diagnosis described above, comprising proposing a treatment method according to any of the embodiments of the treatment method described above.

[0235] The following describes embodiments of use. These embodiments are abbreviated by the letter "U" followed by a number. When embodiments of the system are referred to in this specification, these embodiments are meant.

[0236] U1. Use of the system described in any of the embodiments of the system described above to carry out the method described in any of the embodiments of the method described above.

[0237] U2. A use of the method of any of the embodiments of the method described above, comprising the step of prompting the system described in any of the embodiments described above to carry out a step of the method described in any of the embodiments described above.

[0238] U3. Use of the method described in any of the embodiments of the aforementioned method to implement the method described in any of the embodiments of the aforementioned method.

[0239] U4. Use of the method described in any of the embodiments of the aforementioned method to implement the diagnostic method described in any of the embodiments of the aforementioned method.

[0240] U5. Use of the method described in any of the embodiments of the aforementioned methods for implementing the diagnostic method described in any of the embodiments of the aforementioned diagnostic method and the treatment method described in any of the embodiments of the aforementioned treatment method, wherein the implementation of the diagnostic method precedes the implementation of the treatment method. [Brief explanation of the drawing]

[0241] The present invention will now be described with reference to the accompanying drawings illustrating embodiments of the present invention. These embodiments are merely illustrative and should not limit the present invention.

[0242] [Figure 1] A schematic diagram of a system according to an embodiment of the present invention for predicting a patient's health status is shown. [Figure 2] A schematic representation of the layered structure of the implementation of the present invention, according to an embodiment of the present invention, is shown. [Figure 3] A flowchart illustrating an embodiment according to the present invention is provided in a schematic manner. [Figure 4] This shows a comparison of pregnancy progression between two types of patients.

[0243] Please note that not all reference numerals are shown in all drawings. Instead, some reference numerals have been omitted in some drawings for the sake of brevity and ease of illustration. Embodiments of the present invention are described here with reference to the accompanying drawings. [Modes for carrying out the invention]

[0244] Figure 1 schematically illustrates a system 1000 for predicting a patient's health status. In short, the system 1000 comprises a processing component 1100, an analysis component 1200, a computing component 1300, a storage component 1400, and a monitoring component 1500. It should be understood that in some embodiments, the system 1000 may comprise one or more of these components.

[0245] In one embodiment, the storage component 1400 may be an external component, such as a remote component. In Figure 1, this is indicated by a dashed line. However, it should be understood that any other component of system 1000 may also be external, for example, the monitoring component 1500 may be a remote component. If a component of system 1000 is an external component, it should be understood that it may be assigned to a server (remote or local) or even located in the cloud.

[0246] The processing component 1100 may be configured to receive at least one patient-related dynamics data, receive at least one patient-related covariate, and process the at least one patient-related dynamics data and the at least one patient-related covariate data to generate a patient-related processed dataset. In other words, the processing component 1100 is tasked with receiving data, such as raw or unprocessed data, from different systems such as databases, manual input by users, or automated input performed by another device or system. Once the processing component 1100 receives data, it can process the data autonomously or at least partially autonomously to generate a patient-related processed dataset.

[0247] The analysis component 1200 may be configured to analyze a patient-related processed dataset and generate at least one health status hypothesis based on the patient-related processed dataset.

[0248] In one embodiment of system 1000, the processing component 1100 and the analysis component 1200 may represent an integrated component.

[0249] System 1000 is configured to take multiple different data as inputs. In particular, but not limited to, System 1000 may receive, process, and / or analyze data provided by: multiple biomarkers such as parental, fetal, and / or neonatal biomarkers; multiple clinical parameters such as parental, fetal, and / or neonatal clinical parameters; demographic, lifestyle, and psychometric scores relating to the patient and / or group of patients; multiple environmental parameters; drug therapies such as current drug therapies and / or drug therapies recommended in effective or ongoing guidelines for the patient; administration regimens and medical histories relating to the patient or group of patients; heart rate recording data (CTG); electroencephalogram (EEG); electrocardiogram (ECG), pulse and / or oxygen measurements; sound and modified forms such as Doppler and duplex; magnetic resonance imaging (MRI); and X-rays.

[0250] In one embodiment, the system 1000 may also include one or more imaging components (not shown) configured to capture images of a patient that may be associated with at least one health condition and / or medical condition.

[0251] The monitoring component 1500 is configured to monitor the system 1000, that is, the components of the system 1000. The monitoring component 1500 may also be configured to monitor the change in value of at least one of at least one patient-related characteristics; record the initial value of at least one patient-related characteristic; record at least one subsequent value of at least one patient-related characteristic; compare the initial value with at least one of the at least one subsequent value; generate comparison value data; and output a hypothesis of the patient-related characteristic based on the comparison value data.

[0252] Additionally or alternatively, the monitoring component 1500 may also be configured to record a current value of at least one patient-related characteristic, where the current value is different from the initial value. That is, the monitoring component 1500 is configured to monitor changes in the value of at least one patient-related characteristic over time. For this purpose, it should be understood that the monitoring component 1500 or the system 1000 or a component of the system 1000 may record and / or determine an initial value. However, it should also be understood that this initial value may already be included in the received data. In some embodiments, the initial value may be called a baseline.

[0253] Furthermore, system 1000 is configured to predict at least one health state of a patient based on at least one state hypothesis.

[0254] The computing component 1300 is configured to implement a dynamic model for predicting at least one health condition. In one embodiment, the computing component 1300 is also configured to implement multiple models for predicting at least one health condition, improving findings, and / or proposing, generating, and / or improving drugs for the treatment of at least one health condition.

[0255] Furthermore, the memory component 1400 is configured to store data related to at least one health condition of the patient. In one embodiment, the memory component 1400 may also comprise a server having a plurality of computer implementation modules. In a further embodiment, the memory component 1400 may also comprise at least partially a processing component 1100, an analysis component 1200, a computing component 1400, and / or a monitoring component 1500.

[0256] In one embodiment, the computing component 1300 may also include a computing device further described in Figure 3.

[0257] The system is further configured to output multiple data, including patient information such as PE. This information may include, among other things, disease onset data, severity data scoring and prediction, disease dynamics analysis and interpretation, and patient risk assessment, such as maternal, fetal, or neonatal risk assessment. The risk assessment may further include prediction and / or estimation of maternal, fetal, and / or neonatal complications, types of complications, and / or levels of complications. The system 1000 is also configured to output at least one treatment suggestion and / or treatment protocol and / or optimization of an ongoing treatment protocol.

[0258] In one embodiment, system 1000 may also include a signal processing component (not shown) configured to process a plurality of signals supplied from one or more devices external to and / or independent of system 1000. The signal processing component may also be included in the processing component 1100 and may be configured to process data received as signal data.

[0259] Figure 2 schematically shows a layered representation of an implementation of the method according to an embodiment of the present invention. The method is a computer implementation method. The method is carried out by system 1000. Briefly, the layered representation has three layers L1, L2, and L3. Layer L1 may also be called the input layer, L2 may be called the model layer, modeling layer, processing layer, and / or analysis layer, and L3 may be called the output layer and / or results layer.

[0260] The input layer L1 may receive a number of inputs 210, 220, 230, which may include, but are not limited to, biomarkers such as parental, fetal, and / or neonatal biomarkers; multiple clinical parameters such as parental, fetal, and / or neonatal clinical parameters; demographic, lifestyle, and psychometric scores relating to the patient and / or group of patients; multiple environmental parameters; drug therapies such as current drug therapies and / or drug therapies recommended in effective or ongoing guidelines for the patient; administration regimens and medical history relating to the patient or group of patients; heart rate recording data (CTG); electroencephalogram (EEG); electrocardiogram (ECG), pulse and / or oxygen measurements; sound and modified forms such as Doppler and duplex; magnetic resonance imaging (MRI); and data provided by X-rays.

[0261] These inputs may be processed within the Modeling L2 layer, where multiple computer-implemented dynamic models 310, 320, 330 may be applied to the input data to generate processed data which can be further analyzed and interpreted to produce at least one finding, which can be represented by the computer-implemented prediction stage as at least one hypothesis regarding at least one health condition of a patient or group of patients. The multilayer computer implementation method may further utilize the Output Layer L3, where the interpreted data may be provided to a user such as a physician. Such outputs may include, among other things, PE-related predictions, assessments 410, risk assessments 420 of PE or any other medical condition of a patient or group of patients, which may include, for example, maternal-related risk assessments S1, fetal-related risk assessments S2, and / or neonatal-related risk assessments S3. The Output Layer L3 may also include one or more treatments 430, e.g., suggestions for treatment protocols and / or treatment methods, as well as optimization of current and / or future treatments.

[0262] This is particularly advantageous because the multi-layer computer-implemented method provides at least one hypothesis regarding at least one health condition of a patient or a group of patients by the system 1000, wherein the hypothesis is based on individual available data that may include current data and / or historical data. That is, the computer-implemented method allows the system 1000 to process, analyze, and interpret data relating to, for example, PE during pregnancy and its influence on fetal development during pregnancy shown in FIG. 4, and FIG. 4 shows the progression for a healthy pregnant woman 100A and a pregnant woman 100B suffering from PE.

[0263] FIG. 3 provides a schematic diagram of the computing device 100. The computing device 100 may include a computing unit 35, a first data storage unit 30A, a second data storage unit 30B, and a third data storage unit 30C.

[0264] The computing device 100 may be a single computing device or an assembly composed of a plurality of computing devices. The computing device 100 may be disposed locally, or may be disposed remotely such as a cloud solution.

[0265] Different data may be stored on different data storage units 30. Additional data storage units may also be provided, and / or those mentioned above may be at least partially combined.

[0266] The computing unit 35 can access the first data storage unit 30A, the second data storage unit 30B, and the third data storage unit 30C through an internal communication path 160, which may include a bus connection 160.

[0267] The computing unit 30 may be a single processor or a plurality of processors, and may be, but is not limited to, a CPU (Central Processing Unit), a GPU (Graphical Processing Unit), a DSP (Digital Signal Processor), an APU (Accelerator Processing Unit), an ASIC (Application Specific Integrated Circuit), an ASIP (Application Specific Instruction Set Processor), or an FPGA (Field Programmable Gate Array). The first data storage unit 30A may be single or plural, and may be, but is not limited to, a volatile or non-volatile memory such as random access memory (RAM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), static RAM (SRAM), flash memory, magnetoresistive RAM (MRAM), ferroelectric RAM (F-RAM), or parameter RAM (P-RAM).

[0268] The second data storage unit 30B may be single or plural, and may be, but is not limited to, a volatile or non-volatile memory such as random access memory (RAM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), static RAM (SRAM), flash memory, magnetoresistive RAM (MRAM), ferroelectric RAM (F-RAM), or parameter RAM (P-RAM).

[0269] The third data storage unit 30C may be single or plural, and may be, but is not limited to, a volatile or non-volatile memory such as random access memory (RAM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), static RAM (SRAM), flash memory, magnetoresistive RAM (MRAM), ferroelectric RAM (F-RAM), or parameter RAM (P-RAM).

[0270] It should be understood that the first data storage unit 30A (also referred to as the encryption key storage unit 30A), the second data storage unit 30B (also referred to as the data share storage unit 30B), and the third data storage unit 30C (also referred to as the decryption key storage unit 30C) may generally be parts of the same memory. In other words, each device may be provided with only one general data storage unit 30, which may be configured to store each encryption key (so that the part of the data storage unit 30 that stores the encryption key becomes the encryption key storage unit 30A), each data element share (so that the part of the data storage unit 30 that stores the data element share becomes the data share storage unit 30B), and each decryption key (so that the part of the data storage unit 30 that stores the decryption key becomes the decryption key storage unit 30A).

[0271] In some embodiments, the third data storage unit 30C may be a secure memory device 30C such as a self-encrypting memory, hardware-based full-disk encrypted memory, and the like, which is capable of automatically encrypting all stored data. The data can only be decrypted from the memory components if the authentication of a party requiring access to the third data storage unit 30C is successful, where the party can be a user, a computing device, a processing unit, and the like. In some embodiments, the third data storage unit 30C may only be connected to a computing unit 35, and the computing unit 35 may be configured never to output data received from the third data storage unit 30C. This ensures the secure storage and handling of encryption keys (i.e., private keys) stored in the third data storage unit 30C.

[0272] In some embodiments, a second data storage unit 30B may not be provided, and instead, the computing device 100 may be configured to receive the corresponding encrypted shares from the database 60. In some embodiments, the computing device 100 may include a second data storage unit 30B and be configured to receive the corresponding encrypted shares from the database 60.

[0273] The computing device 100 may include further memory components 140, which may be one or more, and may be, but are not limited to, volatile or non-volatile memory, such as random access memory (RAM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), static RAM (SRAM), flash memory, magnetoresistive RAM (MRAM), ferroelectric RAM (F-RAM), or parameter RAM (P-RAM). The memory components 140 may also be connected to other components of the computing device 100 (such as computing components 35) via an internal communication channel 160.

[0274] Furthermore, the computing device 100 may include an external communication component 130. The external communication component 130 can be configured to facilitate the sending and receiving of data to and from external devices (e.g., backup devices, recovery devices, databases). The external communication component 130 may have an antenna (e.g., a Wi-Fi® antenna, an NFC antenna, a 2G / 3G / 4G / 5G antenna, and similar), a USB port / plug, a LAN port / plug, a contact pad providing an electrical connection, and similar. The external communication component 130 can send and / or receive data based on a communication protocol that includes instructions for sending and / or receiving data. Such instructions can be stored in a memory component 140 and executed by the computing unit 35 and / or the external communication component 130. The external communication component 130 can be connected to an internal communication channel 160. Thus, the data received by the external communication component 130 can be provided to the memory component 140, the computing unit 35, the first data storage unit 30A and / or the second data storage unit 30B and / or the third data storage unit 30C. Similarly, the data stored in the memory component 140, the first data storage unit 30A and / or the second data storage unit 30B and / or the third data storage unit 30C, and / or the data generated by the computing unit 35 can be provided to the external communication component 130 for transmission to an external device.

[0275] Furthermore, the computing device 100 may include an input user interface 110 that enables a user of the computing device 100 to provide at least one input (e.g., a command) to the computing device 100. For example, the input user interface 110 may include buttons, a keyboard, a trackpad, a mouse, a touchscreen, a joystick, and the like.

[0276] Furthermore, the computing device 100 may include an output user interface 120 that enables the computing device 100 to provide metrics to the user. For example, the output user interface 110 may be an LED, a display, a speaker, and the like.

[0277] The output and input user interfaces 100 may also be connected to the internal components of the device 100 through the internal communication component 160.

[0278] The processor may be one or more, and may be a CPU, GPU, DSP, APU, or FPGA. The memory may be one or more, and may be volatile or non-volatile memory, such as SDRAM, DRAM, SRAM, flash memory, MRAM, F-RAM, or P-RAM. The data processing device may include data processing means such as a processor unit, hardware accelerator, and / or microcontroller. The data processing device 20 may have memory components, such as main memory (e.g., RAM), cache memory (e.g., SRAM), and / or secondary memory (e.g., HDD, SSD). The data processing device may include a bus configured to facilitate data exchange between components of the data processing device, such as communication between memory components and processing components. The data processing device may include a network interface card, which can be configured to connect the data processing device to a network such as the Internet. The data processing device may include, for example, the following user interfaces: (1) Output user interface, e.g.: A screen or monitor configured to display visual data (for example, to display a graphical user interface for a survey to a user), A speaker configured to transmit audio data (for example, to play audio data to a user). (2) Input user interface, for example: A camera configured to capture visual data (e.g., capturing images and / or videos of the user), A microphone configured to capture audio data (for example, to record voice from a user), A keyboard configured to enable text insertion and / or other keyboard commands (for example, allowing the user to input text data and / or other keyboard commands by having the user type on the keyboard) and / or a trackpad, mouse, touchscreen, or joystick configured to facilitate navigation of different graphical user interfaces of the survey.

[0279] A data processing device can be a processing unit configured to execute program instructions. A data processing device can be a system-on-a-chip comprising a processing unit, memory components, and a bus. A data processing device can be a personal computer, laptop, pocket computer, smartphone, or tablet computer. A data processing device can be a server, either local or remote. A data processing device can be a processing unit or system-on-a-chip that can interface with a personal computer, laptop, pocket computer, smartphone, tablet computer, and / or a user interface (such as the user interface described above).

[0280] Please note that not all reference numerals are shown in all drawings. Instead, some reference numerals have been omitted in some drawings for the sake of brevity and ease of illustration. Embodiments of the present invention are described here with reference to the accompanying drawings.

[0281] The reference numerals and letters appearing in parentheses within the claims identify features described in the embodiments and shown in the accompanying drawings, and are provided to the reader as examples of the claimed subject matter. Such reference numerals and letters are not to be construed as limiting the claims in any way.

[0282] The phrase "at least one of the first option and the second option" is intended to mean the first option, or the second option, or the first option and the second option.

[0283] While preferred embodiments have been described above with reference to the attached drawings, those skilled in the art will understand that these embodiments are provided for illustrative purposes only and should not be construed as limiting the scope of the invention as defined by the claims.

[0284] Whenever relative terms such as “about,” “substantially,” or “almost” are used in this specification, such terms should be interpreted as including exact terms as well. That is, for example, “substantially straight” should be interpreted as including “(completely) straight.”

[0285] Whenever steps are described above or in the attached claims, please note that the order of steps described in this document may be incidental. That is, unless otherwise specified or unless it is obvious to those skilled in the art, the order of steps described may be incidental. For example, when this document presents a method comprising steps (A) and (B), this does not necessarily mean that step (A) precedes step (B), and that step (A) may be performed simultaneously with (at least partially) step (B), or that step (B) may precede step (A). Furthermore, when it is said that step (X) precedes another step (Z), this does not imply that there is no step between steps (X) and (Z). That is, step (X) preceding step (Z) includes situations in which step (X) is performed immediately before step (Z), but also includes situations in which one or more steps (Y1), (Y2) are performed after (X), followed by step (z). If terms such as "after" or "before" are used, the corresponding considerations apply.

Claims

1. A method for predicting a patient's health status, wherein the method is The stage of receiving at least one patient-related dynamic characteristics data, The stage of receiving at least one patient-related covariate, A step of processing the at least one patient-related dynamic characteristics data and the at least one patient-related covariate data to generate a processed patient-related dataset, A step of generating at least one health status hypothesis based on the patient-related processed dataset, and A step in which at least one health condition is predicted based on the aforementioned at least one health condition hypothesis. Equipped with, The aforementioned at least one patient-related covariate is, At least one biomarker, wherein the at least one biomarker relates to at least one disease, wherein the at least one biomarker includes at least one of the following: soluble Fms-like tyrosine kinase (sFlt-1); placental growth factor (PLGF); neurofilament (NfL); C-terminal portion of arginine vasopressin (copeptin); albumin; hepatic transaminase; urea; hemoglobin; platelets; creatinine; albuminuria; proteinuria; estimated glomerular filtration rate (eGFR); creatinine clearance (CrCl); at least one additional renal function measurement; placental biomarkers such as placental RNA and placental proteins; endothelial / cardiovascular biomarkers such as endothelial RNA and endothelial proteins; or any combination thereof; At least one maternal covariate, including at least one of the following: age; weight; height; body mass index (BMI); pregnancy history; birth history; number of fetuses in the current pregnancy; race; body temperature; heart rate; heart rate variability; respiratory rate; premature rupture of the amniotic membranes; white blood cell count; history of pre-eclampsia (family and mother), gestational diabetes, obesity, cardiovascular / renal / hepatic / thyroid disease, autoimmune disease, anemia, antiphospholipid syndrome, sexually transmitted infections, headaches, and other comorbidities; smoking habits before and / or during pregnancy; blood oxygen saturation (SpO2); blood pressure (systolic / diastolic); uteroplacental perfusion parameters; Doppler measurements of pulsation index of the umbilical artery, middle cerebral artery, cerebral-placental ratio, uterine artery, fetal descending aorta, ductus venosus, umbilical vein, inferior vena cava, and uterine artery; soft tissue parameters such as partial brachial volume and partial femoral volume; and at least one measurement of the aforementioned biomarkers; At least one fetal-related covariate, including at least one of the following: sex; fetal weight during pregnancy; fetal biometric parameters such as femoral length, abdominal circumference, head circumference, mid-thigh circumference, and transverse diameter; percentage of gestationally undersized fetuses; gestational age; heart rate; heart rate variability; respiratory rate; uteroplacental perfusion parameters; and at least one measurement of the aforementioned biomarkers; At least one neonatal-related covariate, including at least one of the following: sex; birth weight; body weight; height; gestational age at birth; age postpartum; body temperature; heart rate, heart rate variability; respiratory rate; breast milk; duration of exclusive breastfeeding; pH level; respiratory support; oxygen requirements; blood oxygen saturation (SpO2); blood pressure (systolic / diastolic); Apgar score; at least additional neonatal biometric parameters; and at least one measurement of the said at least one biomarker; and At least one environmental covariate, including at least one of the following: country of residence, country of birth, date and time of birth, humidity conditions at birth, and ambient temperature at birth. A method having at least one of the following.

2. The method according to claim 1, wherein the step of predicting the at least one health state is based on a computer-implemented dynamic model, the at least one health state hypothesis has a correlation with at least one medical condition of the patient, and the method further comprises the step of predicting the dynamic behavior of the at least one health state of the patient.

3. The aforementioned method, A step of generating at least one threshold, wherein the at least one threshold represents an indicator of at least one potential medical condition; A stage in which at least one potential medical condition is produced, wherein the at least one potential medical condition includes at least one of the following: seizures; respiratory; cardiovascular; hematological dysfunction; endocrine; renal; hepatic; uterine and placental dysfunction; fetal growth restriction; unplanned premature birth; placental abruption; hemolysis, elevated liver enzymes, thrombocytopenia (HELLP) syndrome; and eclampsia; A step of determining a minimum threshold for at least one of the thresholds, and A step of determining the maximum threshold for at least one of the aforementioned thresholds, With respect to at least one patient-related data, the steps include determining a baseline and A step of determining at least one intermediate threshold, wherein the at least one intermediate threshold includes at least one value between the minimum threshold and the maximum threshold. The method according to claim 1, comprising:

4. The aforementioned method, A step of correlating at least the range of each of the at least one intermediate thresholds with the at least one disease condition, Based on the aforementioned correlation step, the step of generating an interpreted dataset, and Step 1: Outputting an automated report indicating at least one potential medical condition. The method according to claim 3, comprising:

5. The aforementioned method, The step of determining at least one indicator of disease progression, A step of monitoring changes in at least one of the disease condition indicators, A step of generating at least one disease condition change indicator trend, and A step of predicting the progression of at least one of the aforementioned at least one disease condition, wherein the prediction is based on the trend of the indicator of change in the at least one disease condition. Equipped with, Herein, the method comprises the step of monitoring a change in the value of at least one of the at least one patient-related characteristics, and the method is A step of recording the initial value of at least one patient-related characteristic, A step of recording at least one subsequent value of the at least one patient-related characteristic, A step of comparing the initial value with at least one of the at least one subsequent value, The stage of generating comparative value data, and Based on the aforementioned comparative data, the next step is to generate hypotheses about patient-related characteristics. The method according to claim 1, wherein the step of recording at least one subsequent value comprises the step of recording a current value of one of the at least one patient-related characteristics, the current value being different from the initial value.

6. The aforementioned method, The step of supplying data to the at least one server, The steps include training a computer-implemented dynamic model based on data supplied to at least one of the servers, and A step of generating an adjustment function based on the training data, wherein the adjustment function is suitable for adjusting any step of the method according to claim 1. A step that triggers at least one action proposal based on the aforementioned at least one health condition hypothesis, The step of displaying at least one of the aforementioned action suggestions to the user, A step in which the user is prompted to input at least one acceptance of at least one of the at least one action proposals and at least one rejection of the at least one action proposal, The method according to claim 1, comprising:

7. A method for predicting a patient's health status, wherein the method is The stage of receiving at least one patient-related dynamic characteristics data, The stage of receiving at least one patient-related covariate, A step of processing the at least one patient-related dynamic characteristics data and the at least one patient-related covariate data to generate a processed patient-related dataset, A step of generating at least one health status hypothesis based on the patient-related processed dataset, and A step in which at least one health condition is predicted based on the aforementioned at least one health condition hypothesis. Equipped with, The aforementioned method, A step of determining at least one drug based on the aforementioned at least one health condition, wherein the aforementioned at least one drug is To prevent the occurrence of the aforementioned at least one medical condition and / or the aforementioned at least one health condition, Treating the aforementioned at least one medical condition and / or the aforementioned at least one health condition. Preferably to be at least one of the following: A step of generating at least one administration regimen for the at least one drug; A step of optimizing the at least one administration regimen, wherein the at least one administration regimen comprises at least one of the at least one drug, the route of administration of the at least one drug, the administration regimen, the duration of drug administration, and the frequency of drug administration, wherein the step of optimizing the at least one administration regimen is based on the at least one health condition hypothesis. A method that includes [a certain feature].

8. A system for predicting a patient's health status, wherein the system is Receive at least one patient-related dynamic characteristics data, At least one patient-related covariate was received, The data of at least one patient-related dynamic characteristics and the data of at least one patient-related covariate are processed to generate a processed patient-related dataset. At least one processing component configured as follows: The processed dataset related to the aforementioned patients was analyzed, Based on the processed patient-related dataset, generate at least one health status hypothesis. at least one analytical component configured in such a way Equipped with, The system is configured to predict at least one health condition based on the at least one health condition hypothesis and to carry out the method according to any one of claims 1 to 7.

9. The aforementioned system, At least one memory component configured to store data relating to the at least one health condition of the patient; At least one computing component configured to implement a dynamic model for predicting the at least one health condition, wherein the at least one health condition hypothesis has a correlation with at least one medical condition of the patient. Equipped with, The system according to claim 8, wherein the system is configured to predict the dynamic behavior of the patient’s at least one health condition.

10. The aforementioned system, Generate at least one threshold, where the at least one threshold represents an indicator of at least one potential medical condition; Outputs at least one potential medical condition. The system according to claim 8, wherein the at least one potential medical condition is at least one of the following: seizures; respiratory; cardiovascular; hematological; endocrine; renal; hepatic; uteroplacental; fetal growth restriction; unplanned premature birth; placental abruption; hemolysis, elevated liver enzymes, thrombocytopenia (HELLP) syndrome; and eclampsia.

11. The aforementioned at least one analytical component is Determine the minimum threshold for at least one of the aforementioned thresholds, Determine the maximum threshold for at least one of the aforementioned thresholds, Each of the aforementioned at least one intermediate thresholds is correlated with at least one disease condition. Based on the aforementioned correlation step, an interpreted dataset is generated, It generates an automated report showing at least one potential medical condition, Determine at least one indicator of disease progression, The changes in at least one of the disease condition indicators are monitored, Generate at least one disease condition change indicator trend, Based on the trend of the at least one disease condition change indicator, predict the progression of at least one of the at least one disease conditions. The system according to claim 8, configured as described above.

12. The system comprises at least one monitoring component configured to monitor a change in the value of at least one of the at least one patient-related characteristics, and the at least one monitoring component further comprises The initial value of at least one patient-related characteristic is recorded, Record at least one subsequent value of the aforementioned at least one patient-related characteristic, The initial value is compared with at least one of the at least one subsequent value, Generate comparative value data, Based on the aforementioned comparative data, hypotheses regarding patient-related characteristics are output. Record one current value of at least one patient-related characteristic, where the current value is different from the initial value. The system according to claim 8, configured as described above.

13. The aforementioned system, The data is supplied to the aforementioned at least one server, A computer-implemented dynamic model is trained based on the data supplied to at least one of the servers. Based on the training data, an adjustment function is generated, wherein the adjustment function is suitable for adjusting any configuration of the system described in claim 8 above; Based on the aforementioned at least one health condition hypothesis, trigger at least one action proposal. The system according to claim 8, wherein the system is configured to display the at least one action proposal to the user and to prompt the user to input at least one of the at least one acceptance of the at least one action proposal and at least one rejection of the at least one action proposal.

14. The aforementioned system, To acquire at least one image data of the aforementioned patient, and To receive at least one image data of the aforementioned patient. The imaging component comprises at least one configured to perform at least one of the following, where The system according to claim 8, wherein the at least one image data includes data relating to at least one medical condition and / or at least one potential medical condition of the patient.

Images