Maternal and infant health insights & cognitive intelligence (mihic) system and score to predict the risk of maternal, fetal, and infant morbidity and mortality

Abstract

The MIHIC system in various embodiments described herein helps clinicians predict the risk of maternal mortality by detecting diseases early and identifying possible risks in mothers, fetuses and infants across pre, peri and post-natal stages of pregnancy. The system quantifies risk as a single MIHIC score, which through quantification assigns possible risks to the mother, fetus and infant. The MIHIC score uses a specialized algorithm to derive the individual and overall risk as a value between 0 and 1 and uses the risk scores to stratify the patients into High, Medium and Low risk for preventive intervention and improved pregnancy outcome.

What is claimed is: 
     
         1 . A system comprising:
 a processor executing a machine learning model; and   a database comprising a plurality of patient record data; and   wherein the system is operable to:
 acquire, by the processor, the plurality of patient record data from the database, wherein the patient record data comprises a text data and an image data; 
 identify, by the processor, a data format of the patient record data; 
 segregate, the patient record data into a structured data and an unstructured data; 
 pre-process, by the processor, the structured data and the unstructured data; 
 generate, by the processor, the machine learning model; 
 train, by the processor, the machine learning model with the patient record data; 
 receive, by the machine learning model, a new patient record data associated with a patient, wherein the new patient record data comprises a first clinical data comprising patient data comprising a patient blood pressure, a second clinical data comprising fetal data comprising a fetal heart rate, wherein the patient is a maternal woman; 
 analyze, by the machine learning model, the new patient record data to discover a pattern in the patient record data using the database; 
 predict, by the machine learning model and based on the pattern, a maternal risk score for each health risk factor of a first plurality of health risk factors associated with the patient, a fetal risk score for each health risk factor of a second plurality of health risk factors associated with a fetus of the patient, wherein the maternal risk scores each represent a probability of a maternity-related healthcare event of the patient and the fetal risk scores each represent a probability of a fetus-related healthcare event of the fetus of the patient; and 
 calculate, an overall risk score from the maternal risk score of each of the first plurality of health risk factors and the fetal risk score of each of the second plurality of health risk factors using a statistical technique, wherein the overall risk score is a maternal and infant health insights and cognitive intelligence (MIHIC) score, and wherein MIHIC score represents a quantification of risk for pregnancy outcome. 
   
     
     
         2 . The system of  claim 1 , wherein the patient record data further comprises a demographic data, a medical data, social data, genomic data, omics data, and a genetic data. 
     
     
         3 . The system of  claim 1 , wherein the new patient record data further comprises a patient self-generated data, wherein the patient self-generated data comprises social media data, lifestyle data, and data from wearable devices. 
     
     
         4 . The system of  claim 1 , wherein the machine learning model further enables exploration and correlation of the patient data and the fetal data associated with the first plurality of health risk factors and the second plurality of health risk factors. 
     
     
         5 . The system of  claim 1 , wherein the system is operable to generate the overall risk score for caesarian delivery. 
     
     
         6 . The system of  claim 1 , wherein the system is operable to generate the overall risk score for postpartum depression. 
     
     
         7 . The system of  claim 1 , wherein the system is operable to stratify the plurality of patient record data into a plurality of cohorts based on a risk level and further classify the new patient record data into a cohort from the plurality of cohorts based on the overall risk score; wherein the risk level is determined based on a grouping formed from categorizing values of the overall risk score; and wherein the plurality of cohorts further enable studies for understanding behavior of each health risk factor of the first plurality of health risk factors associated with the patient and each health risk factor of the second plurality of health risk factors associated with the fetus of the patient and various characteristics in the patient record data belonging in each of the plurality of cohorts. 
     
     
         8 . A method comprising:
 receiving, by a processor, a patient record data associated with a first patient, wherein the patient record data comprises a text data and an image data;   identifying, by the processor, a data format of the patient record data;   segregating the patient record data into a structured data and an unstructured data;   pre-processing, by the processor, the structured data and the unstructured data to clean data;   generating a machine learning model, wherein the machine learning model is further trained with the patient record data;   training, by the processor, the machine learning model with the patient record data;   receiving a new patient record data associated with a patient; wherein the new patient record data comprises a first clinical data comprising patient data comprising a patient blood pressure, a second clinical data comprising fetal data comprising a fetal heart rate, wherein the patient is a maternal woman;   analyzing, by the machine learning model, the new patient record data to discover a pattern in the new patient record data using a database;   predicting, by the machine learning model and based on the pattern, a maternal risk score for each health risk factor of a first plurality of health risk factors associated with the patient, and a fetal risk score for each health risk factor of a second plurality of health risk factors associated with a fetus of the patient, wherein the maternal risk scores each represent a probability of a maternity-related healthcare event of the patient and the fetal risk scores each represent a probability of a fetus-related healthcare event of the fetus of the patient;   calculating, by the machine learning model, an overall risk score from the maternal risk score of each of the first plurality of health risk factors and the fetal risk score of each of the second plurality of health risk factors using a statistical technique; and   wherein the overall risk score is a maternal and infant health insights and cognitive intelligence (MIHIC) score, and wherein MIHIC score represents a quantification of risk for pregnancy outcome.   
     
     
         9 . The method of  claim 8 , wherein the patient record data further comprises a demographic data, a medical data, a social data, a genomic data, an omics data, and a genetic data. 
     
     
         10 . The method of  claim 8 , wherein the new patient record data further comprises a patient self-generated data, wherein the patient self-generated data comprises social media data, lifestyle data, and data from wearable devices. 
     
     
         11 . The method of  claim 8 , wherein the machine learning model further enables exploration and correlation of the patient data and the fetal data associated with the first plurality of health risk factors and the second plurality of health risk factors. 
     
     
         12 . The method of  claim 8 , wherein the method is operable to generate the overall risk score for caesarian delivery. 
     
     
         13 . The method of  claim 8 , wherein the method is operable to generate the overall risk score for postpartum depression. 
     
     
         14 . A system comprising:
 a processor that executes computer-executable components stored in a computer-readable memory, the computer-executable components comprising:
 an input module operable to receive a patient record data associated with a patient, wherein the patient record data comprises a first clinical data comprising patient data comprising a patient blood pressure, a second clinical data comprising fetal data comprising a fetal heart rate, wherein the patient is a maternal woman; 
 analyze, by a first model, the patient record data, wherein the first model comprises a machine learning model; 
 predict, by the machine learning model, a maternal risk score for each health risk factor of a first plurality of health risk factors associated with the patient, a fetal risk score for each health risk factor of a second plurality of health risk factors associated with a fetus of the patient, wherein the maternal risk scores each represent a probability of a maternity-related healthcare event of the patient and the fetal risk scores each represent a probability of a fetus-related healthcare event of the fetus of the patient; and 
 compute, an overall risk score using a second model, from the maternal risk score of each of the first plurality of health risk factors and the fetal risk score of each of the second plurality of health risk factors, wherein the second model comprises a statistical technique, wherein the overall risk score is a maternal and infant health insights and cognitive intelligence (MIHIC) score, and wherein MIHIC score represents a quantification of risk for pregnancy outcome. 
   
     
     
         15 . The system of  claim 14 , wherein the machine learning model comprises a relationship derived between inputs in the patient record data and the first plurality of health risk factors associated with the patient, and the second plurality of health risk factors associated with the fetus of the patient. 
     
     
         16 . The system of  claim 14 , wherein the machine learning model is trained using plurality of patient record data, wherein each of the plurality of patient record data comprises a demographic data, a clinical data, a medical data, a social data, a genomic data, an omics data, and a genetic data. 
     
     
         17 . The system of  claim 15 , wherein the machine learning model is a self-learning model comprising a feed-back layer that enables the machine learning model to learn from the patient record data. 
     
     
         18 . The system of  claim 15 , wherein the machine learning model is further operable for: receiving a feed-back relating to an observed healthcare event of one or more of the patient and the fetus of the patient; update the machine learning model with the feed-back; and update a database with the patient record data. 
     
     
         19 . The system of  claim 18 , wherein the machine learning model is further operable to learn from the feed-back and continually improve a prediction of the maternal risk score of each of the first plurality of health risk factors, the fetal risk score of each of the second plurality of health risk factors, and the overall risk score. 
     
     
         20 . The system of  claim 14 , wherein the system is operable to generate the overall risk score for caesarian delivery.