Analyzing electronic medical records to extract prepregnancy morbidities and pregnancy complications: Toward a learning health system

Abstract

Introduction: Preexisting and pregnancy-related medical conditions frequently co-occur, leading to multimorbidity (≥2 morbidities) in pregnant women, and much of this information is in semi-structured format in electronic medical records (EMRs). The aim was to advance the learning health system as a platform for automating information extraction from EMRs and to uncover the prevalence of common morbidities during pregnancy and their association with pregnancy-related complications.

Methods: This study included 48 502 pregnant women attending Monash Health maternity hospitals from 2016 to 2021. Natural language processing (NLP) was used to extract morbidities from semi-structured text in EMRs. Chi-squared tests were used to assess the association between morbidities of gestational diabetes mellitus (GDM) and other pregnancy complications. The k-means clustering algorithm identified clusters of comorbid conditions associated with GDM.

Results: The most common comorbidities during pregnancy were vitamin deficiency (14 019; 28.9%), overweight (13 918; 28.7%), obesity (11 026; 22.7%), anemia and other blood-related disorders (4821; 9.9%), mental health disorders (4314; 9.8%), asthma (4126; 8.5%), thyroid diseases (3576; 7.4%), endometrial disease (1927; 3.9%), cardiovascular disease (1525; 3.1%), and polycystic ovary syndrome (PCOS) (1464; 3.0%). While 22.5% of women had no medical conditions, 77.5% had one or more. Multimorbidity was associated with conditions including overweight, obesity, vitamin deficiency, thyroid disease, substance use, PCOS, GDM, and endometrial diseases. On cluster analysis, aged 35 years or older, overweight, vitamin deficiency, obesity, thyroid disease, asthma, uterine disease, other blood disorders, mental disorders, and PCOS were associated with GDM.

Conclusions: More than three-quarters of pregnant women in the Australian urban setting experienced one or more morbidities during pregnancy, which can be associated with adverse pregnancy outcomes. This project contributes to developing a learning health system infrastructure to deliver high-value maternal health care while reducing costs.

Keywords: comorbidities; gestational diabetes mellitus; learning health system; natural language processing.

FIGURE 1

Common pregnancy comorbidities. (A) Top panel: WordCloud of common pregnancy comorbidities. (B) Bottom panel: Top 25 commonly occurring medical condition tokens.

FIGURE 2

Top 40 associations based on p‐value (Chi‐squared test). The level of statistical significance decreases from bottom to top. CVD, cardiovascular disease; D.M., diabetes mellitus; GDM, gestational diabetes mellitus; PCOS, polycystic ovary syndrome; PIH, pregnancy‐induced hypertension; SCN_NICU, admission of newborn to special care nursery or antenatal care unit.

FIGURE 3

The top three complications associated with each comorbidity. CVD, cardiovascular disease; D.M., diabetes mellitus; GDM, gestational diabetes mellitus; PCOS, polycystic ovary syndrome; PIH, pregnancy‐induced hypertension; SCN_NICU, admission of newborn to special care nursery or antenatal care unit.

FIGURE 4

The cluster analysis results scatter plot shows those who share similar comorbidities during their pregnancies (each point representing a pregnant woman). Cluster 0: obesity, vitamin deficiency, asthma, other blood disorders, advanced age, mental disorders, thyroid disease, PCOS, migraine, urinary tract infection; Cluster 1: mental disorders, other blood disorders, asthma, thyroid disease, overweight, vitamin deficiency, uterine disease, PCOS, iron deficiency anemia, obesity; Cluster 2: advanced age, overweight, vitamin deficiency, obesity, thyroid disease, asthma uterine disease, other blood disorders, mental disorders, PCOS; Cluster 3: overweight, vitamin deficiency, thyroid disease, other blood disorders, asthma, mental disorders, PCOS, uterine disease, migraine, cardiovascular disease; Cluster 4: vitamin deficiency, other blood disorders, thyroid disease, asthma, mental disorders.