IMPORTANCE: Quality improvement programs for colorectal cancer surgery have been introduced with benchmarking based on quality indicators, such as mortality. Detailed (pre)operative characteristics may offer relevant information for proper case-mix c...
BACKGROUND: The clinical impact of postoperative opioid use requires accurate prediction strategies to identify at-risk patients. We utilize preoperative claims data to predict postoperative opioid refill and new persistent use in opioid-naïve patien...
BACKGROUND: Postoperative acute kidney injury is common after major vascular surgery and is associated with increased morbidity, mortality, and cost. High-performance risk stratification using a machine learning model can inform strategies that mitig...
BACKGROUND: For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a l...
BACKGROUND: Identifying individuals at risk for future hospitalization or death has been a major priority of population health management strategies. High-risk individuals are a heterogeneous group, and existing studies describing heterogeneity in hi...
Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic m...
COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 ...
BACKGROUND: Pediatric myocarditis is a rare disease with substantial mortality. Little is known regarding its prognostic factors. We hypothesize that certain comorbidities and procedural needs may increase risks of poor outcomes. This study aims to i...
Clinical journal of the American Society of Nephrology : CJASN
Oct 8, 2020
BACKGROUND AND OBJECTIVES: Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records.