Utilizing clinical observational data to estimate individualized treatment effects (ITE) is a challenging task, as confounding inevitably exists in clinical data. Most of the existing models for ITE estimation tackle this problem by creating unbiased...
BACKGROUND: Using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) complication status of patients who underwent an operation at the University of Colorado Hospital, we developed a machine learning algorithm for ...
International journal of medical informatics
Oct 5, 2019
CONTEXT: Adverse events in healthcare are often collated in incident reports which contain unstructured free text. Learning from these events may improve patient safety. Natural language processing (NLP) uses computational techniques to interrogate f...
International journal of medical informatics
Oct 5, 2019
BACKGROUND AND OBJECTIVE: Healthcare systems deal with multiple challenges in releasing information from data silos, finding it almost impossible to be implemented, maintained and upgraded, with difficulties ranging in the technical, security and hum...
OBJECTIVE: There is a lot of information about cancer in Electronic Health Record (EHR) notes that can be useful for biomedical research provided natural language processing (NLP) methods are available to extract and structure this information. In th...
International journal of medical informatics
Oct 1, 2019
BACKGROUND AND PURPOSE: Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely and accurate prediction of post-stroke pneumonia would be highly valuable in clinical practice. Pre...
BACKGROUND: Current modes of identifying alcohol misuse in hospitalized patients rely on self-report questionnaires and diagnostic codes that have limitations, including low sensitivity. Information in the clinical notes of the electronic health reco...
International journal of medical informatics
Sep 26, 2019
OBJECTIVE: Recognizing frailty, also known as clinical geriatric syndrome in the elderly that is characterized by high vulnerability and low resilience, and its extensive influence in clinical practice is challenging. This study aims to develop a soc...
Electronic medical records (EMRs) support the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events. But so far most algorithms have been centralized, taking little acc...
Background and Purpose- The availability of and expertise to interpret advanced neuroimaging recommended in the guideline-based endovascular stroke therapy (EST) evaluation are limited. Here, we develop and validate an automated machine learning-base...
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