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ABSTRACT ObjectivesMost bipolar disorder (BD) patients initially present with depressive symptoms, resulting in a delayed diagnosis of BD and poor clinical outcomes. This study aims to identify features predictive of the conversion from Major Depressive Disorder (MDD) to BD by leveraging electronic health record (EHR) data from the Clínica San Juan de Dios Manizales in Colombia. MethodsWe employed a multivariable Cox regression model to identify important predictors of conversion from MDD to BD. ResultsAnalyzing 15 years of EHR data from 13,607 patients diagnosed with MDD, a total of 1610 (11.8%) transitioned to BD. Predictive features of the conversion to BD included severity of the initial MDD episode, presence of psychosis and hospitalization at first episode, family history of BD, and female gender. Additionally, we observed associations with medication classes (positive associations with prescriptions of mood stabilizers, antipsychotics, and negative associations with antidepressants) and a positive association with suicidality, a feature derived from natural language processing (NLP) of clinical notes. Together, these risk factors predicted BD conversion within 5 years of the initial MDD diagnosis, with a recall of 72% and a precision of 38%. ConclusionsOur study confirms previously identified risk factors identified through registry‐based studies (female gender and psychotic depression at the index MDD episode) and identifies novel ones (suicidality extracted from clinical notes). These results simultaneously demonstrate the validity of using EHR data for predicting BD conversion and underscore its potential for the identification of novel risk factors, thereby improving early diagnosis.
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Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records
IntroductionPredictive 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. MethodsThis 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. ResultsWe 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. ConclusionMachine 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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- PAR ID:
- 10489729
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Endocrinology
- Volume:
- 15
- ISSN:
- 1664-2392
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fendo.2024.1298628
