OBJECTIVES: Given widespread interest in applying artificial intelligence (AI) to health data to improve patient care and health system efficiency, there is a need to understand the perspectives of the general public regarding the use of health data ...
BACKGROUND: Complex electronic medical records (EMRs) presenting large amounts of data create risks of cognitive overload. We are designing a Learning EMR (LEMR) system that utilizes models of intensive care unit (ICU) physicians' data access pattern...
BACKGROUND: Incidental radiographic findings, such as adrenal nodules, are commonly identified in imaging studies and documented in radiology reports. However, patients with such findings frequently do not receive appropriate follow-up, partially due...
OBJECTIVE: The use of poorly designed and improperly implemented health information technology (HIT) may compound risks because it can disrupt established work patterns and encourage workarounds. Analyzing and learning from HIT events could reduce th...
BACKGROUND: Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictiv...
Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) an...
OBJECTIVES: Conduct a survey of the literature for advancements in cancer informatics over the last three years in three specific areas where there has been unprecedented growth: 1) digital health; 2) machine learning; and 3) precision oncology. We a...
OBJECTIVES: We survey recent developments in medical Information Extraction (IE) as reported in the literature from the past three years. Our focus is on the fundamental methodological paradigm shift from standard Machine Learning (ML) techniques to ...
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