OBJECTIVE: This study aimed to develop and validate a claims-based, machine learning algorithm to predict clinical outcomes across both medical and surgical patient populations.
BACKGROUND: There are few large studies examining and predicting the diversified cardiovascular/noncardiovascular comorbidity relationships with stroke. We investigated stroke risks in a very large prospective cohort of patients with multimorbidity, ...
Biochimica et biophysica acta. Reviews on cancer
May 28, 2021
Current applications of artificial intelligence (AI), machine learning, and deep learning in cancer research and clinical care are highly diverse-from aiding radiologists in reading medical images to predicting oncoprotein folding and dynamics. The l...
The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X...
Journal of managed care & specialty pharmacy
May 25, 2021
Medication therapy management (MTM) and comprehensive medication management (CMM) have been practiced by clinical pharmacists as a predominantly manual activity with interventions documented in a record-keeping system. Program evaluations, largely b...
Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymp...
Journal of evaluation in clinical practice
May 24, 2021
RATIONALE AIMS AND OBJECTIVES: As quality measurement becomes increasingly reliant on the availability of structured electronic medical record (EMR) data, clinicians are asked to perform documentation using tools that facilitate data capture. These t...
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
May 17, 2021
Rare diseases affect between 25 and 30 million people in the United States, and understanding their epidemiology is critical to focusing research efforts. However, little is known about the prevalence of many rare diseases. Given a lack of automated ...
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