[HTML][HTML] A review of challenges and opportunities in machine learning for health
Modern electronic health records (EHRs) provide data to answer clinically meaningful
questions. The growing data in EHRs makes healthcare ripe for the use of machine learning …
questions. The growing data in EHRs makes healthcare ripe for the use of machine learning …
[HTML][HTML] Barriers to achieving economies of scale in analysis of EHR data
MP Sendak, S Balu, KA Schulman - Applied clinical informatics, 2017 - thieme-connect.com
Signed in 2009, the Health Information Technology for Economic and Clinical Health Act
infused $28 billion of federal funds to accelerate adoption of electronic health records …
infused $28 billion of federal funds to accelerate adoption of electronic health records …
[HTML][HTML] Predicting emergency department utilization among children with asthma using deep learning models
Pediatric asthma is a leading cause of emergency department (ED) utilization, which is
expensive and often preventable. Therefore, development of ED utilization predictive …
expensive and often preventable. Therefore, development of ED utilization predictive …
[HTML][HTML] Forecasting future asthma hospital encounters of patients with asthma in an academic health care system: predictive model development and secondary …
Background Asthma affects a large proportion of the population and leads to many hospital
encounters involving both hospitalizations and emergency department visits every year. To …
encounters involving both hospitalizations and emergency department visits every year. To …
Considerations for addressing bias in artificial intelligence for health equity
MD Abràmoff, ME Tarver, N Loyo-Berrios… - NPJ digital …, 2023 - nature.com
Health equity is a primary goal of healthcare stakeholders: patients and their advocacy
groups, clinicians, other providers and their professional societies, bioethicists, payors and …
groups, clinicians, other providers and their professional societies, bioethicists, payors and …
Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework
The emergence and increasing use of artificial intelligence and machine learning (AI/ML) in
healthcare practice and delivery is being greeted with both optimism and caution. We focus …
healthcare practice and delivery is being greeted with both optimism and caution. We focus …
Discrimination by artificial intelligence in a commercial electronic health record—a case study
SG Murray, RM Wachter, RJ Cucina - Health Affairs Forefront, 2020 - healthaffairs.org
When artificial intelligence (AI) is built into electronic health record (EHR) software, who is
responsible for the consequences? Does responsibility lie exclusively with the hospital or …
responsible for the consequences? Does responsibility lie exclusively with the hospital or …
[HTML][HTML] Interpretation of machine learning predictions for patient outcomes in electronic health records
Electronic health records are an increasingly important resource for understanding the
interactions between patient health, environment, and clinical decisions. In this paper we …
interactions between patient health, environment, and clinical decisions. In this paper we …
Mitigating health disparities in ehr via deconfounder
Health disparities, or inequalities between different patient demographics, are becoming a
crucial issue in medical decision-making, especially in Electronic Health Record (EHR) …
crucial issue in medical decision-making, especially in Electronic Health Record (EHR) …
Predicting frequent emergency department visits among children with asthma using EHR data
Objective For children with asthma, emergency department (ED) visits are common,
expensive, and often avoidable. Though several factors are associated with ED use …
expensive, and often avoidable. Though several factors are associated with ED use …