BACKGROUND: Although dialysis patients are at a high risk of death, it is difficult for medical practitioners to simultaneously evaluate many inter-related risk factors. In this study, we evaluated the characteristics of hemodialysis patients using m...
International journal of medical informatics
May 21, 2020
BACKGROUND: Severe sepsis and septic shock are still the leading causes of death in Intensive Care Units (ICUs), and timely diagnosis is crucial for treatment outcomes. The progression of electronic medical records (EMR) offers the possibility of sto...
Current stroke risk assessment tools presume the impact of risk factors is linear and cumulative. However, both novel risk factors and their interplay influencing stroke incidence are difficult to reveal using traditional additive models. The goal of...
European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
May 14, 2020
At present, risk assessment for alcohol withdrawal syndrome relies on clinical judgment. Our aim was to develop accurate machine learning tools to predict alcohol withdrawal outcomes at the individual subject level using information easily attainable...
Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are ...
BACKGROUND: The decision to treat unruptured intracranial aneurysms (UIAs) or not is complex and requires balancing of risk factors and scores. Machine learning (ML) algorithms have previously been effective at generating highly accurate and comprehe...
Identifying patients eligible for clinical trials using electronic health records (EHRs) is a challenging task usually requiring a comprehensive analysis of information stored in multiple EHRs of a patient. The goal of this study is to investigate di...
OBJECTIVE: We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms.
OBJECTIVE: Individuals with immune-mediated inflammatory disease (IMID) have a higher prevalence of psychiatric disorders than the general population. We utilized machine-learning to identify patient-reported outcome measures (PROMs) that accurately ...
International journal of medical informatics
Apr 23, 2020
BACKGROUND: Emergency department (ED) overcrowding has been a serious issue and demands effective clinical decision-making of patient disposition. In previous studies, emergency clinical narratives provide a rich context for clinical decisions. We ai...
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