Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records
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Abstract
Introduction: Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis. Methods: This is a retrospective cohort study from a SafetyNet hospital’s electronic health records (EHR) from 2003-2016. The study population included 30,601 women aged 18-45 years without concurrent endocrinopathy who had any visit to Boston Medical Center for primary care, obstetrics and gynecology, endocrinology, family medicine, or general internal medicine. Four prediction outcomes were assessed for PCOS. The first outcome was PCOS ICD-9 diagnosis with additional model outcomes of algorithm-defined PCOS. The latter was based on Rotterdam criteria and merging laboratory values, radiographic imaging, and ICD data from the EHR to define irregular menstruation, hyperandrogenism, and polycystic ovarian morphology on ultrasound. Results: We developed predictive models using four machine learning methods: logistic regression, supported vector machine, gradient boosted trees, and random forests. Hormone values (follicle-stimulating hormone, luteinizing hormone, estradiol, and sex hormone binding globulin) were combined to create a multilayer perceptron score using a neural network classifier. Prediction of PCOS prior to clinical diagnosis in an out-of-sample test set of patients achieved an average AUC of 85%, 81%, 80%, and 82%, respectively in Models I, II, III and IV. Significant positive predictors of PCOS diagnosis across models included hormone levels and obesity; negative predictors included gravidity and positive bHCG. Conclusion: Machine learning algorithms were used to predict PCOS based on a large at-risk population. This approach may guide early detection of PCOS within EHR-interfaced populations to facilitate counseling and interventions that may reduce long-term health consequences. Our model illustrates the potential benefits of an artificial intelligence-enabled provider assistance tool that can be integrated into the EHR to reduce delays in diagnosis. However, model validation in other hospital-based populations is necessary.
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Integration of IoT technologies, including AI and machine learning, holds potential to aid PCOS diagnosis and risk prediction, aimed at reducing delays in diagnosis.
“The integration of IoT technologies, including AI and machine learning, holds the potential to aid PCOS diagnosis and risk prediction, aimed at reducing delays in diagnosis [103,104].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [104]
The Abstract Conclusion and Discussion say that an AI-enabled machine-learning tool integrated into electronic health records could predict PCOS risk, aid earlier diagnosis, and reduce diagnostic delays. The paper does not describe this as integration of IoT technologies, so that part of the claim is unsupported. (LLM, conf=0.87)
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- Authors
- Zad, Z., Jiang, V. S., Wolf, A. T., Wang, T., Cheng, J. J., Paschalidis, I. C., & Mahalingaiah, S.
- Affiliations
- Boston University (US)
- Massachusetts General Hospital (US)
- Icahn School of Medicine at Mount Sinai (US)
- University of Wisconsin–Madison (US)
- Harvard University (US)
- Corresponding author
- Mahalingaiah, Shruthi
- Venue
- Frontiers in Endocrinology
- Publisher
- Frontiers Media SA
- Published
- 2024-01-30
- Volume
- 15
- Pages
- 1298628-1298628
- ISSN
- 1664-2392 (ISSN-L)
- PMC
- PMC10866556
- OpenAlex
- W4391360914
- Cited by
- 97
- References
- 36
- Citations by year
- 2026: 49 · 2025: 41 · 2024: 6 · 2023: 1
- FWCI
- 37.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Endocrinology (publishedVersion, cc-by)
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OpenBU (Boston University) (acceptedVersion)
- PubMed Central (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (CCF-2200052, IIS-1914792, DMS-1664644)
- National Institutes of Health (R01 GM135930, UL54 TR004130)
- Topics
- Health Sciences › Medicine › Reproductive Medicine › Ovarian function and disorders
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- polycystic ovary syndrome, early diagnosis, electronic health records, Rotterdam criteria, hyperandrogenism, menstrual irregularity, polycystic ovary morphology, sex hormone-binding globulin, follicle-stimulating hormone, luteinizing hormone, estradiol, obesity
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