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This study aimed to optimize Fall Risk Appraisal (FRA) graphing for use in intervention programs tailored toward reducing the fall risk of older adults by using computing graphic functions in the R language.

We aimed to develop and validate a novel multimodal framework Hierarchical Multi-task Auxiliary Learning (HiMAL) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD).

During the 2-year maintenance treatment phase (MT) of acute lymphoblastic leukemia (ALL), personalized patient-specified titration of oral antimetabolite drug doses is required to ensure maximum tolerated systemic drug exposure. Drug titration is difficult to implement in practice and insufficient systemic drug exposure resulting from inadequate dose titration increases risk of ALL relapse.

This study uses electronic health record (EHR) data to predict 12 common cancer symptoms, assessing the efficacy of machine learning (ML) models in identifying symptom influencers.

 Health-related social needs (HRSNs) are the unmet social and economic needs (e.g., housing instability) that affect individuals' health and well-being. HRSNs are associated with more emergency department (ED) visits, longer stays, and worse health outcomes. More than a third of ED patients have at least one HRSN, yet patients are rarely screened for HRSNs in the ED. A clinical decision support (CDS) system with predictive modeling offers a promising approach [...]

 Treatment of depressive symptoms in older adults is a growing public health concern. Collecting patient-reported outcomes (PROs) may facilitate efficiently scaling psychotherapy for older adults but user-specific tailoring is needed to improve completion.

 This study explores the results of a rapidly implemented no-cost gratitude intervention designed to address student distress during the coronavirus disease 2019 (COVID-19) pandemic. This intervention focused on shared gratitude journaling with a postimplementation survey of well-being using elements of Seligman's PERMA (Positive emotion, Engagement, Relationships, Meaning, and Accomplishment) model of well-being.

 Evidence supports using patient-generated blood pressure data for better outcomes in hypertension management. However, obstacles like dealing with home-generated paper data sets and questions of validity slowed the meaningful incorporation of home blood pressure into clinical care. As clinicians value patient data more, reliance on digital health solutions for data collection and shared decision-making grows.

The Multiple Chronic Conditions (MCCs) Electronic Care (e-Care) Plan project aims to establish care planning data standards for individuals living with MCCs. This article reports on the portion of the project focused on long COVID and presents the process of identifying and modeling data elements using the HL7 Fast Healthcare Interoperability Resources (FHIR) standard.