DataSheet_1_Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records.docx

التفاصيل البيبلوغرافية
العنوان: DataSheet_1_Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records.docx
المؤلفون: Zahra Zad, Victoria S. Jiang, Amber T. Wolf, Taiyao Wang, J. Jojo Cheng, Ioannis Ch. Paschalidis, Shruthi Mahalingaiah
سنة النشر: 2024
مصطلحات موضوعية: Endocrinology, Reproduction, Cell Metabolism, polycystic ovary syndrome (PCOS), disease prediction, predictive model, machine learning, artificial intelligence
الوصف: 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 ...
نوع الوثيقة: dataset
اللغة: unknown
Relation: https://figshare.com/articles/dataset/DataSheet_1_Predicting_polycystic_ovary_syndrome_with_machine_learning_algorithms_from_electronic_health_records_docx/25109345
DOI: 10.3389/fendo.2024.1298628.s001
الاتاحة: https://doi.org/10.3389/fendo.2024.1298628.s001
https://figshare.com/articles/dataset/DataSheet_1_Predicting_polycystic_ovary_syndrome_with_machine_learning_algorithms_from_electronic_health_records_docx/25109345
Rights: CC BY 4.0
رقم الانضمام: edsbas.7AE99DBD
قاعدة البيانات: BASE
الوصف
DOI:10.3389/fendo.2024.1298628.s001