BACKGROUND: The US population is becoming more racially and ethnically diverse. Research suggests that cultural diversity within organizations can increase team potency and performance, yet this theory has not been explored in the field of surgery. F...
BACKGROUND: Patients with mental health disorders often have difficulty perceiving associations between multiple symptoms, such as inter-relations between somatic and psychological symptoms. This difficulty may be particularly challenging in patients...
BACKGROUND: Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that f...
BACKGROUND: For many children, visiting the hospital can lead to a state of increased anxiety. Social robots are being explored as a possible tool to reduce anxiety and distress in children attending a clinical or hospital environment. Social robots ...
Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a ...
The prediction of outcomes is a critical part of the clinical surveillance for hospitalized patients. Here, we present Timesias, a machine learning pipeline which predicts outcomes from real-time sequential clinical data. The strategy implemented in ...
Journal of the American Medical Informatics Association : JAMIA
Jul 14, 2021
OBJECTIVE: Claims-based algorithms are used in the Food and Drug Administration Sentinel Active Risk Identification and Analysis System to identify occurrences of health outcomes of interest (HOIs) for medical product safety assessment. This project ...
BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine le...
Depression symptom heterogeneity limits the identifiability of treatment-response biomarkers. Whether improvement along dimensions of depressive symptoms relates to separable neural networks remains poorly understood. We build on work describing thre...