Threatened abortion patient drug curative effect monitoring and personalized drug recommendation system

Abstract

The invention relates to the technical field of curative effect monitoring, in particular to a threatened abortion patient drug curative effect monitoring and personalized medication recommendation system, which comprises a data acquisition module for acquiring physiological data and fetal heart rate information of a threatened abortion patient in real time; the microbiome analysis module is used for analyzing the intestinal microbiome composition of the patient, detecting the microflora change of the endocrine system by collecting the excrement sample of the patient, and predicting the metabolic effect of the medicine on the patient; the drug curative effect evaluation module comprehensively analyzes the data from the data acquisition module and the microbiome analysis module, evaluates the curative effect of the threatened abortion patient after using the drug, and evaluates the influence of the drug on the fetus health in combination with the fetal heart rate and the heart rate variability data; the personalized drug use recommendation module is used for providing personalized drug dosage and use scheme recommendation according to an evaluation result of the drug curative effect evaluation module; the treatment scheme is automatically adjusted, and it is ensured that the medicine plays a role in the optimal treatment range, so that the treatment effect is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of efficacy monitoring, and particularly to a drug efficacy monitoring and personalized medication recommendation system for patients with threatened abortion. Background Art

[0002] Threatened abortion refers to symptoms that endanger the survival of pregnancy in the early stage of pregnancy, manifested as vaginal bleeding, abdominal pain, etc., and accompanied by clinical manifestations such as uterine contractions. If not treated in time, it may lead to abortion. With the development of obstetric technology, the treatment of threatened abortion has gradually turned to comprehensive intervention, including drug treatment, rest and lifestyle adjustment, etc. Drug treatment plays a crucial role in the prevention and treatment of threatened abortion. However, due to individual differences among patients, such as hormone levels, immune function, drug metabolism ability, etc., traditional "one-size-fits-all" treatment plans often fail to meet the personalized needs of patients. How to scientifically and reasonably recommend the dosage and usage plan of drugs according to the individual characteristics and treatment responses of patients has become an important issue in clinical treatment.

[0003] Currently, the treatment methods and drug use for threatened abortion mostly rely on doctors' experience and general treatment plans, lacking a comprehensive assessment of patients' individual differences. Traditional treatment models usually fail to fully consider factors such as gut microbiota, hormone fluctuations, and fetal health, which may lead to poor drug efficacy or increased side effects. For example, changes in gut microbiota can significantly affect the metabolic rate and efficacy of drugs, but there are few systems for in-depth analysis of microbiome data and personalized drug recommendation in the existing technology. In addition, the existing technology has not fully integrated physiological data such as fetal heart rate and heart rate variability (HRV) for real-time dynamic monitoring, thus lacking the ability to timely adjust the drug treatment effect. Therefore, there are certain deficiencies in the existing technology in terms of drug efficacy monitoring and personalized medication recommendation. Summary of the Invention

[0004] The present invention provides a drug efficacy monitoring and personalized medication recommendation system for patients with threatened abortion.

[0005] A drug efficacy monitoring and personalized medication recommendation system for patients with threatened abortion, characterized by including:

[0006] A data acquisition module: real-time collects the physiological data and fetal heart rate information of patients with threatened abortion, where the physiological data includes blood pressure and contraction frequency, and the fetal heart rate information includes fetal heart rate and fetal heart rate variability;

[0007] A microbiome analysis module: analyzes the composition of the gut microbiome of patients, detects the changes in the microbial community of the endocrine system by collecting the fecal samples of patients, and predicts the metabolic effects of drugs on patients;

[0008] Drug Efficacy Evaluation Module: Comprehensively analyze the data from the Data Acquisition Module and the Microbiome Analysis Module, evaluate the efficacy of drugs used by patients with threatened abortion, and combine fetal heart rate and fetal heart rate variability data to evaluate the impact of drugs on fetal health;

[0009] Personalized Medication Recommendation Module: Provide recommendations on personalized drug dosage and usage regimens based on the evaluation results of the Drug Efficacy Evaluation Module.

[0010] Optionally, the Data Acquisition Module includes:

[0011] Use a blood pressure monitor to measure the patient's blood pressure;

[0012] Use a uterine contraction sensor to detect the pressure changes of the uterine muscles and record the uterine contraction frequency in real time;

[0013] Use a wireless fetal heart rate monitor to monitor the fetal heart rate changes;

[0014] Based on a fetal electrocardiogram monitor, monitor the fetal heart rate variability.

[0015] Optionally, the Microbiome Analysis Module includes:

[0016] Stool Sample Collection and Processing: Collect the stool samples of patients with threatened abortion through a dedicated sampling tool and send them to the laboratory for processing;

[0017] Microbiome Relative Abundance Analysis: Use metagenomic sequencing technology to perform high-throughput sequencing analysis on the microbial communities in the patient's stool samples, and identify the composition of the microbial flora and its relative abundance in the samples;

[0018] Monitoring of Changes in the Microbiome of the Endocrine System: Combine the microbiome data to monitor the changes in the intestinal flora of the endocrine system, and analyze the impact of changes in intestinal microorganisms on the hormone fluctuations in threatened abortion;

[0019] Drug Metabolism Prediction: Based on the microbiome data and the patient's drug use history, combine the changes in intestinal microorganisms with the hormone fluctuations in threatened abortion to construct a drug metabolism prediction model to predict the metabolic effects of drugs on the patient.

[0020] Optionally, the Microbiome Relative Abundance Analysis includes:

[0021] Identifying the Composition of the Microbial Community: Obtain the sequence data of each sample through metagenomic sequencing technology, perform clustering and annotation on the sequence data, and identify the bacterial species in the samples by comparing with the database to obtain the occurrence frequency of each species in the samples;

[0022] Calculating the Relative Abundance of Microorganisms: The proportion of each identified microbial species in the entire sample, that is, the relative abundance, and the calculation formula is:

[0023] Among them, is the relative abundance of the i-th microbial species, C i is the number of sequence reads of the i-th microorganism, and T is the total number of all microbial sequences in the sample.

[0024] Optionally, the monitoring of the changes in the endocrine system microbial community includes:

[0025] The impact of intestinal microbial changes on the hormone fluctuations in threatened abortion is expressed as:

[0026]

[0027] Among them, H hormone,k is the concentration of the k-th hormone, α 0 is the constant term, α i is the regression coefficient of the i-th microbial population, indicating the impact of this microbial population on the hormone level, is the relative abundance of the i-th microbial population, ∈ k is the model error term.

[0028] Optionally, the drug metabolism prediction includes:

[0029] Based on the microbiome data and the patient's drug use history, combined with the impact of intestinal microbial changes on the hormone fluctuations in threatened abortion, a drug metabolism prediction model is constructed, and the predicted metabolic effect of the drug on the patient is expressed as:

[0030]

[0031] Among them, is the predicted metabolic effect, β 0 is the constant term, indicating the baseline metabolic effect, RA i is the relative abundance of the i-th microbial population, reflecting the contribution of the microbial population to the metabolic effect, H k is the concentration of the k-th hormone, reflecting the impact of hormone fluctuations on drug metabolism, D j is the drug use history characteristic, β i 、δ j are the regression coefficients, indicating the impact of different variables on the drug metabolic effect, γ k is the impact coefficient of the k-th hormone on the drug efficacy, and is the model error term.

[0032] Optionally, by comprehensively analyzing the data from the data acquisition module and the microbiome analysis module, the drug efficacy after a threatened abortion patient uses the drug is evaluated and expressed as:

[0033]

[0034] Among them, Edrug is the drug efficacy, representing the effect of the drug in the patient's body, β 0 is the constant term, representing the baseline efficacy before drug use, RA i is the relative abundance of the i-th microbial population, β i is the regression coefficient of the i-th microbial population on the drug efficacy, H k is the concentration of the k-th hormone, γ k is the influence coefficient of the k-th hormone on the drug efficacy, representing the regulatory effect of the change in hormone concentration on the drug efficacy, D j is the usage history information of the j-th drug, δ j is the regression coefficient of the j-th drug on the drug efficacy, representing the influence of drug use on the efficacy, ∈ is the error term, representing the random error or measurement error of the model.

[0035] Optionally, combining fetal heart rate and fetal heart rate variability data, the assessment of the impact of the drug on fetal health is expressed as:

[0036]

[0037] where, F fetus is the fetal health indicator, usually measured by the stability or change of the fetal heart rate, α 0 is the constant term, representing the baseline value of fetal health, HR is the fetal heart rate, HRV is the fetal heart rate variability, representing the amplitude and regularity of fetal heartbeat fluctuations, α 1 is the regression coefficient, representing the impact of the fetal heart rate on fetal health, α 2 is the regression coefficient, representing the impact of heart rate variability on fetal health,, RA i is the relative abundance of the i-th microbial population, reflecting the potential impact of microorganisms on fetal health, β i is the regression coefficient, representing the impact of the i-th microbial population on fetal health, ∈ is the error term, representing the random error of the model.

[0038] Optionally, the personalized medication recommendation module includes:

[0039] Collect and integrate the drug efficacy assessment results;

[0040] Adjust the drug dosage based on the assessment results.

[0041] Advantages of the present invention:

[0042] In the present invention, by collecting the physiological data, fetal heart rate, and heart rate variability information of patients in real time, the changes in the patient's condition can be tracked in a timely manner. This ability of real-time data collection and dynamic monitoring enables the system to accurately evaluate the immediate efficacy of drugs during the treatment process and automatically adjust the treatment plan according to the specific physiological state of the patient, ensuring that the drugs play their role within the optimal treatment range, thereby optimizing the treatment effect and minimizing the adverse reactions of the drugs.

[0043] In the present invention, through the microbiome analysis module, the changes in the intestinal microbial community of patients are deeply analyzed. Due to the key role of the gut microbiota in drug metabolism, the system can provide accurate data support for drug efficacy evaluation based on the metabolic effects of microorganisms. The integration of microbiome data makes the adjustment of personalized drug doses more scientific. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1 It is a schematic diagram of the modules of an embodiment of the present invention;

[0046] Figure 2 It is a schematic diagram of the microbiome analysis of an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The present invention will be described in detail below in conjunction with the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; and the drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0048] It should be pointed out that in the specification, when referring to "an embodiment", "embodiments", "exemplary embodiments", "some embodiments", etc., it indicates that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment includes such specific features, structures, or characteristics. In addition, when combining embodiments to describe specific features, structures, or characteristics, the implementation of such features, structures, or characteristics in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0049] Generally, terms can be understood at least in part from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or property in the singular sense, or can be used to describe a combination of features, structures, or properties in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but rather can alternatively, depending at least in part on the context, allow for the existence of other factors that are not necessarily explicitly described.

[0050] As Figure 1 - Figure 2 shown, a drug efficacy monitoring and personalized medication recommendation system for threatened abortion patients, characterized in that it includes:

[0051] A data acquisition module: Real-time acquisition of the physiological data and fetal heart rate information of threatened abortion patients, the physiological data including blood pressure and uterine contraction frequency, and the fetal heart rate information including fetal heart rate and fetal heart rate variability (HRV);

[0052] A microbiome analysis module: Analyze the composition of the patient's gut microbiome, detect changes in the microbial community of the endocrine system by collecting the patient's stool samples, and predict the metabolic effects of drugs on the patient;

[0053] A drug efficacy evaluation module: Comprehensively analyze the data from the data acquisition module and the microbiome analysis module, evaluate the efficacy of the drug used by threatened abortion patients, and evaluate the impact of the drug on fetal health in combination with fetal heart rate and fetal heart rate variability (HRV) data;

[0054] A personalized medication recommendation module: Provide recommendations on personalized drug dosage and usage regimens according to the evaluation results of the drug efficacy evaluation module.

[0055] The data acquisition module includes:

[0056] Use a blood pressure monitor to measure the patient's blood pressure;

[0057] Use a uterine contraction sensor to detect the pressure change of the uterine muscle and record the uterine contraction frequency in real time;

[0058] Use a wireless fetal heart rate monitor to monitor the fetal heart rate changes;

[0059] Based on a fetal electrocardiogram monitor, monitor the fetal heart rate variability.

[0060] The microbiome analysis module includes:

[0061] Stool sample collection and processing: Collect the stool samples of threatened abortion patients through a dedicated sampling tool and send them to the laboratory for processing;

[0062] Microbiome relative abundance analysis: High-throughput sequencing analysis of the microbial community in the patient's stool samples was performed using metagenomic sequencing technology to identify the composition of the microbial flora in the samples and their relative abundances;

[0063] Monitoring of changes in the microbial community of the endocrine system: Combining microbiome data, monitoring the changes in the gut microbiota of the endocrine system, and analyzing the impact of gut microbiota changes on the hormonal fluctuations in threatened abortion;

[0064] Drug metabolism prediction: Based on the microbiome data and the patient's drug use history, combined with the impact of gut microbiota changes on the hormonal fluctuations in threatened abortion, a drug metabolism prediction model was constructed to predict the metabolic effects of drugs on the patient.

[0065] The microbiome relative abundance analysis includes:

[0066] Identifying the composition of the microbial community: Through metagenomic sequencing technology, sequence data for each sample was obtained, the sequence data was clustered and annotated, and the bacterial species in the samples were identified by comparing with databases to obtain the occurrence frequency of each species in the samples;

[0067] Calculating the relative abundance of microorganisms: The proportion of each identified microbial species in the entire sample, i.e., the relative abundance, is calculated using the formula:

[0068] Where, is the relative abundance of the i-th microbial species, C i is the number of sequence reads of the i-th microorganism (i.e., the occurrence frequency of the species), and T is the total number of all microbial sequences in the sample (i.e., the sum of the reads of all microorganisms).

[0069] The monitoring of changes in the microbial community of the endocrine system includes:

[0070] The impact of gut microbiota changes on hormonal fluctuations in threatened abortion is expressed as:

[0071]

[0072] Where, H hormone,k is the concentration of the k-th hormone (such as estrogen, progesterone, etc.), α 0 is a constant term, taking any real value, α i is the regression coefficient of the i-th microbial population, indicating the impact of this microbial population on the hormone level, and the value of α i can be positive, negative, or zero. If the microbial population has a positive regulatory effect on the hormone level (such as some probiotics promoting estrogen metabolism through their products), then α i is positive. If the microbial population has a negative regulatory effect on the hormone level (such as some anaerobic bacteria may inhibit progesterone levels through their metabolites), then αi is negative. If the impact of a certain microbial population on hormone levels is not significant, then α i may be zero, and RA i is the relative abundance of the i-th microbial population, and ∈ k is the model error term, theoretically ranging from -∞ to +∞.

[0073] Drug metabolism prediction includes:

[0074] Based on the microbiome data and the patient's drug use history, combined with the impact of changes in gut microbiota on the hormone fluctuations in threatened abortion, a drug metabolism prediction model is constructed. The predicted metabolic effect of the drug on the patient is expressed as:

[0075]

[0076] Among them, is the predicted metabolic effect (such as drug concentration change, metabolite concentration), and β 0 is the constant term, representing the baseline metabolic effect, is the relative abundance of the i-th microbial population, reflecting the contribution of the microbial population to the metabolic effect, and H k is the concentration of the k-th hormone, reflecting the impact of hormone fluctuations on drug metabolism (such as estrogen, progesterone, etc.), and D j is the drug use history feature (such as drug dose, usage frequency, etc.), and β i and δ j are the regression coefficients, representing the impact of different variables on the drug metabolic effect, and γ k is the impact coefficient of the k-th hormone on the drug efficacy. If the microbial population, hormone, or drug concentration increases, it can promote drug metabolism or metabolite generation, and the regression coefficient is positive. If the microbial population, hormone, or drug concentration increases and inhibits drug metabolism or metabolite generation, the regression coefficient is negative. If the impact of a certain variable on drug metabolism is not significant, the regression coefficient is zero, and it is the model error term.

[0077] By comprehensively analyzing the data from the data collection module and the microbiome analysis module, the drug efficacy after a threatened abortion patient uses the drug is evaluated and expressed as:

[0078]

[0079] Among them, E drug is the drug efficacy, representing the effect of the drug in the patient's body, usually the drug metabolism rate or the amount of metabolite generated, and β 0 is the constant term, representing the baseline efficacy before drug use, and the value range is real numbers (such as 0 to 1000 ng / mL), and RA i is the relative abundance of the i-th microbial population, and β iis the regression coefficient of the i-th microbial population on drug efficacy (which can be positive or negative, reflecting the impact of microorganisms on drug metabolism or efficacy), H k is the concentration of the k-th hormone, γ k is the influence coefficient of the k-th hormone on drug efficacy, indicating the regulatory effect of hormone concentration changes on drug efficacy, which may be positive or negative, D j is the usage history information of the j-th drug, δ j is the regression coefficient of the j-th drug on drug efficacy, indicating the impact of drug use on efficacy, ∈ is the error term, representing the random error or measurement error of the model.

[0080] Combining fetal heart rate and heart rate variability (HRV) data, the assessment of the impact of drugs on fetal health is expressed as:

[0081]

[0082] where, F fetus is the fetal health index, usually measured by the stability or change of fetal heart rate, α 0 is the constant term, representing the baseline value of fetal health (which may be a real number, usually related to the normal range of fetal heart rate), HR is the fetal heart rate, usually between 120 bpm and 160 bpm, HRV is the fetal heart rate variability, indicating the amplitude and regularity of fetal heartbeat fluctuations, usually in the range of 10 ms to 50 ms, α 1 is the regression coefficient, indicating the impact of fetal heart rate on fetal health, α 2 is the regression coefficient, indicating the impact of heart rate variability on fetal health, and its value range can be determined according to clinical data, usually positive, RA i is the relative abundance of the i-th microbial population, reflecting the potential impact of microorganisms on fetal health, β i is the regression coefficient, indicating the impact of the i-th microbial population on fetal health, which may be positive or negative, ∈ is the error term, representing the random error of the model.

[0083] The personalized medication recommendation module includes:

[0084] Collect and integrate the results of drug efficacy assessment;

[0085] The metabolic rate of the drug: Evaluate the absorption, distribution, metabolism, and excretion processes of the drug in the patient's body, determine whether the drug reaches the expected blood drug concentration, and evaluate whether the dose needs to be adjusted by calculating the difference between the drug concentration and the target concentration;

[0086] Hormone level changes: Pregnant women with threatened abortion usually experience fluctuations in hormone levels (such as progesterone, estrogen, etc.), and these hormone changes may affect drug metabolism. By real-time monitoring of hormone concentration changes, evaluate the interaction between drugs and hormone fluctuations;

[0087] Fetal heart rate and HRV: Changes in fetal health, especially fluctuations in heart rate and heart rate variability, are important indicators for evaluating the impact of drugs on fetal health. By combining these data, the potential impact of drugs on the fetus can be evaluated to help adjust the drug usage plan;

[0088] Impact of the microbiome: The composition of the gut microbiota affects drug metabolism. By analyzing the abundance of the microbial community and its impact on drug metabolism, it can be determined whether the drug dosage needs to be adjusted according to the microbial community characteristics of the patient;

[0089] Adjust the drug dosage based on the evaluation results;

[0090] Adjusting the drug dose: According to the evaluation results of the drug metabolism rate, if it is found that the drug is metabolized too quickly or too slowly in the body, the dose can be adjusted in the following ways:

[0091] Metabolized too quickly: The drug concentration in the patient's body is low, and the drug dose needs to be increased;

[0092] Metabolized too slowly: The drug concentration in the patient's body is too high, and there may be a risk of drug accumulation, so the drug dose needs to be reduced;

[0093] Consider hormonal fluctuations: If changes in hormone levels have a significant impact on drug metabolism, the drug efficacy can be optimized by adjusting the drug dose or drug type. For example, when the progesterone concentration is high, the dose of certain drugs may need to be increased;

[0094] Adjustment of the impact of the microbiome: According to the results of the microbiome analysis, determine which microbial populations' abundance is related to the drug metabolism rate, and then adjust the drug dose. For example, if certain probiotics enhance drug metabolism, the drug dose can be increased accordingly; if anaerobic bacteria in the intestine inhibit drug metabolism, the dose needs to be reduced;

[0095] Combined with fetal health assessment: According to the changes in fetal heart rate and HRV, evaluate whether the drug has a negative impact on fetal health. If the drug affects fetal health, the drug type or dose should be adjusted, or a safer treatment plan should be adopted.

[0096] This invention covers any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To enable the public to have a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments of this invention. However, those skilled in the art can fully understand this invention without the description of these details. Additionally, well-known methods, processes, procedures, elements, and circuits, etc. are not described in detail to avoid unnecessary confusion to the essence of this invention.

[0097] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A drug efficacy monitoring and personalized medication recommendation system for patients with threatened abortion, characterized by: include: Data acquisition module: real-time acquisition of physiological data and fetal heart rate information of patients with threatened abortion, wherein the physiological data include blood pressure and uterine contraction frequency, and the fetal heart rate information includes fetal heart rate and fetal heart rate variability; Microbiome analysis module: Analyze the composition of the patient's intestinal microbiome, collect stool samples from patients, detect changes in the microbial community in the endocrine system, and predict the metabolic effects of drugs on patients; Drug efficacy evaluation module: Comprehensively analyze the data from the data acquisition module and the microbiome analysis module to evaluate the efficacy of drugs used by patients with threatened abortion, and combine the fetal heart rate and fetal heart rate variability data to evaluate the impact of drugs on fetal health; Personalized medication recommendation module: Provides personalized drug dosage and usage plan recommendations based on the evaluation results of the drug efficacy evaluation module.

2. The drug efficacy monitoring and personalized medication recommendation system for patients with threatened abortion according to claim 1, characterized in that: The data acquisition module comprises: Use a blood pressure measuring device to measure the patient's blood pressure; Use uterine contraction sensors to detect changes in uterine muscle pressure and record the frequency of uterine contractions in real time; Use a wireless fetal heart rate monitor to monitor fetal heart rate changes; Based on the fetal electrocardiogram monitor, fetal heart rate variability is monitored.

3. The drug efficacy monitoring and personalized medication recommendation system for patients with threatened abortion according to claim 1, characterized in that: The microbiome analysis module includes: Stool sample collection and processing: Stool samples from patients with threatened abortion are collected using a special sampling tool and sent to the laboratory for processing; Relative abundance analysis of the microbiome: high-throughput sequencing analysis of the microbial community in the patient's stool samples was performed using metagenomic sequencing technology to identify the bacterial community composition and relative abundance in the samples; Monitoring of changes in endocrine system microbial communities: Combined with microbiome data, monitor changes in the intestinal flora of the endocrine system and analyze the effects of intestinal microbial changes on hormone fluctuations that may cause threatened abortion; Prediction of drug metabolism: Based on microbiome data and the patient's history of drug use, combined with the changes in intestinal microorganisms and the fluctuations in hormones that cause threatened abortion, a drug metabolism prediction model is constructed to predict the metabolic effects of drugs on patients.

4. The drug efficacy monitoring and personalized medication recommendation system for threatened abortion patients according to claim 1, characterized in that: The microbiome relative abundance analysis includes: Identify the composition of the microbial community: Use metagenomic sequencing technology to obtain sequence data for each sample, cluster and annotate the sequence data, identify the bacterial species in the sample by comparing the database, and obtain the frequency of occurrence of each species in the sample; Calculate the relative abundance of microorganisms: The proportion of each identified microbial species in the entire sample, that is, the relative abundance, is calculated as follows: Among them, RA i is the relative abundance of the i-th microbial species, C i is the number of sequence reads of the i-th microorganism, and T is the total number of all microbial sequences in the sample.

5. The drug efficacy monitoring and personalized medication recommendation system for patients with threatened abortion according to claim 4, characterized in that: The monitoring of changes in the endocrine system microbial community includes: The effect of intestinal microbial changes on hormone fluctuations in threatened abortion is expressed as: Among them, H hormone,k is the concentration of the kth hormone, α0 is a constant term, α i is the regression coefficient of the ith microbial population, indicating the effect of the microbial population on hormone levels, RA i is the relative abundance of the i-th microbial population, ∈ k is the model error term.

6. The drug efficacy monitoring and personalized medication recommendation system for threatened abortion patients according to claim 5, characterized in that: The drug metabolism prediction includes: Based on the microbiome data and the patient's history of drug use, combined with the effect of intestinal microbial changes on hormone fluctuations in threatened abortion, a drug metabolism prediction model was constructed to predict the metabolic effect of the drug on the patient as follows: in, is the predicted metabolic effect, β0 is a constant term representing the baseline metabolic effect, and RA i is the relative abundance of the ith microbial population, reflecting the contribution of the microbial population to the metabolic effect, H k is the concentration of the kth hormone, reflecting the effect of hormone fluctuations on drug metabolism, D j is the history of drug use, β i , δ j is the regression coefficient, which indicates the influence of different variables on drug metabolism effect, γ k is the influence coefficient of the kth hormone on drug efficacy and is the model error term.

7. The drug efficacy monitoring and personalized medication recommendation system for threatened abortion patients according to claim 1, characterized in that: Comprehensive analysis of the data from the data acquisition module and the microbiome analysis module to evaluate the efficacy of drugs used by patients with threatened abortion is expressed as follows: Among them, E drug is the drug efficacy, which indicates the effect of the drug in the patient, β0 is a constant term, which indicates the baseline efficacy before the drug is used, and RA i is the relative abundance of the i-th microbial population, β i is the regression coefficient of the i-th microbial population on drug efficacy, H k is the concentration of the kth hormone, γ k is the influence coefficient of the kth hormone on the drug efficacy, indicating the regulatory effect of the change in hormone concentration on the drug efficacy. j is the usage history information of the jth drug, δ j is the regression coefficient of the jth drug on drug efficacy, indicating the effect of drug use on efficacy, and ∈ is the error term, indicating the random error or measurement error of the model.

8. The drug efficacy monitoring and personalized medication recommendation system for patients with threatened abortion according to claim 1, characterized in that: Combining fetal heart rate and fetal heart rate variability data, the impact of drugs on fetal health is evaluated as follows: Among them, F fetus It is an indicator of fetal health, usually measured by the stability or change of fetal heart rate. α0 is a constant term, which indicates the baseline value of fetal health. HR is the fetal heart rate. HRV is the fetal heart rate variability, which indicates the amplitude and regularity of fetal heartbeat fluctuations. α1 is the regression coefficient, which indicates the effect of fetal heart rate on fetal health. α2 is the regression coefficient, which indicates the effect of heart rate variability on fetal health. RA i is the relative abundance of the i-th microbial population, reflecting the potential impact of microorganisms on fetal health, β i is the regression coefficient, which indicates the effect of the ith microbial population on fetal health, and ∈ is the error term, which indicates the random error of the model.

9. The drug efficacy monitoring and personalized medication recommendation system for threatened abortion patients according to claim 1, characterized in that: The personalized medication recommendation module includes: Collect and integrate drug efficacy evaluation results; Adjust medication dosage based on the evaluation results.

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