Utilizing Artificial Intelligence: Machine Learning Algorithms to Develop a Preoperative Endometriosis Prediction Model.

Objective: To evaluate the predictive value of clinical features in the diagnosis of endometriosis by utilizing machine learning algorithms (MLAs), aiming to develop an accurate, explainable prediction model.

Design: Retrospective case-control study from 2011 to 2022.

Setting: Tertiary referral center specializing in pelvic pain and minimally invasive gynecologic surgery.

AI in Hypertensive Disorders of Pregnancy: Review.

Background: Hypertensive disorders of pregnancy (HDP) are a leading cause of maternal and fetal mortality worldwide. Early detection and risk stratification are critical for timely intervention to prevent severe complications such as eclampsia, stroke, and preterm delivery. However, traditional clinical methods often lack the precision needed to identify high-risk individuals effectively.

Enhancing EHR Systems with data from wearables: An end-to-end Solution for monitoring post-Surgical Symptoms in older adults.

Mobile health (mHealth) apps have gained popularity over the past decade for patient health monitoring, yet their potential for timely intervention is underutilized due to limited integration with electronic health records (EHR) systems. Current EHR systems lack real-time monitoring capabilities for symptoms, medication adherence, physical and social functions, and community integration. Existing systems typically rely on static, in-clinic measures rather than dynamic, real-time patient data.

Machine learning-based prediction models for home discharge in patients with COVID-19: Development and evaluation using electronic health records.

Objective: This study aimed to develop and validate predictive models using electronic health records (EHR) data to determine whether hospitalized COVID-19-positive patients would be admitted to alternative medical care or discharged home.

Methods: We conducted a retrospective cohort study using deidentified data from the University of Florida Health Integrated Data Repository. The study included 1,578 adult patients (≥18 years) who tested positive for COVID-19 while hospitalized, comprising 960 (60.