Artificial intelligence (AI) holds promise for hel** patients access new and individualized
health care pathways while increasing efficiencies for health care practitioners. Radiology …

Abstract Machine learning has increasingly been applied to predict opioid-related harms
due to its ability to handle complex interactions and generating actionable predictions. This …

Background Opioid Use Disorder (OUD) and opioid overdose (OD) impose huge social and
economic burdens on society and health care systems. Research suggests that Medication …

Transparent and FAIR disclosure of meta-information about healthcare data and
infrastructure is essential but has not been well publicized. In this paper, we provide a …

Targeting of location-specific aid for the US opioid epidemic is difficult due to our inability to
accurately predict changes in opioid mortality across heterogeneous communities. AI-based …

Introduction Severe fever with thrombocytopenia syndrome virus (SFTSV) is an emerging
tick-borne virus associated with a high rate of mortality, as well as encephalitis. We aim to …

Background: Information regarding opioid use disorder (OUD) status and severity is
important for patient care. Clinical notes provide valuable information for detecting and …

In this paper, we offer a sociological analysis of early warning and outbreak in the field of
drug policy, focusing on opioid overdose. We trace how 'outbreak'is enacted as a rupturing …

Background Integrating advanced machine-learning (ML) algorithms into clinical practice is
challenging and requires interdisciplinary collaboration to develop transparent …

Objectives To develop and validate a machine-learning algorithm to predict fatal overdose
using Pennsylvania Prescription Drug Monitoring Program (PDMP) data. Methods The …