EMR data drive based GDM forecasting method

Abstract

The invention discloses an EMR (Electronic Medical Record) data drive based GDM (Gestational Diabetes Mellitus) forecasting method playing an increasing important role in smart medical service. The invention proposes a machine learning based GMD forecasting frame and constructs three forecasting frames including a full-domain data forecasting model, a staging data forecasting model and a weekly data forecasting model according different time window division methods for collected data. After a forecasting item is identified, high-dimensional EMR data digging is implemented through seven steps including input and ETL data cleaning, correlation of a medical record code and feature data, EMR data pre-treatment, secondary data treatment, feature engineering, machine learning and forecasting application. A mark data set related to definite diagnosis is constructed by using clinic data and is divided into two sub sets used for model training and testing. The method performs forecasting through supporting a support vector machine, a Bayesian network, a decision making tree and an integration based hybrid model and GDM mode classification is realized.

Description

technical field [0001] The invention relates to the field of diabetes prediction, in particular to a method for predicting gestational diabetes driven by electronic medical record data. Background technique [0002] In disease prediction, take gestational diabetes mellitus (GDM) as an example. According to the International Diabetes Federation survey, although more and more women receive prenatal care, it is still the most common pregnancy complication, which is defined as Diabetes with normal glucose metabolism or potential impaired glucose tolerance before pregnancy, or diagnosed during pregnancy. The serious consequences of GDM have made the medical community attach great importance to its early diagnosis and prevention. Risks associated with gestational diabetes include type 2 diabetes in both mother and child, fetal overgrowth and related short-term adverse prognosis risks, and long-term obesity in offspring. GDM predictive diagnosis and prevention are important issue...

AI Technical Summary

Problems solved by technology

[0004] In view of the deficiencies of existing research, the present invention provides a method for predicting gestational diabetes driven by electronic medical records; it plays an increasingly important role in regional medical services; using clinical data, combined with methods such as artificial intelligence and machine learning, provides An intelligent decision support system for diseases, which helps to solve the problems of repeated inspections and repeated diagnosis and treatment, improves the work efficiency of doctors and reduces the labor load, strengthens the control of medical errors, enriches the service methods of the regional medical information sharing platform and improves the application value

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