AIMC Topic: Information Seeking Behavior

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What is the impact of artificial intelligence-based chatbots on infodemic management?

Frontiers in public health
Artificial intelligence (AI) chatbots have the potential to revolutionize online health information-seeking behavior by delivering up-to-date information on a wide range of health topics. They generate personalized responses to user queries through t...

Health Information Seeking From an Intelligent Web-Based Symptom Checker: Cross-sectional Questionnaire Study.

Journal of medical Internet research
BACKGROUND: The ever-growing amount of health information available on the web is increasing the demand for tools providing personalized and actionable health information. Such tools include symptom checkers that provide users with a potential diagno...

Healthcare data integration using machine learning: A case study evaluation with health information-seeking behavior databases.

Research in social & administrative pharmacy : RSAP
BACKGROUND: The amount of data in health care is rapidly rising, leading to multiple datasets generated for any given individual. Data integration involves mapping variables in different datasets together to form a combined dataset which can then be ...

Information seeking criteria: artificial intelligence, economics, psychology, and neuroscience.

Reviews in the neurosciences
There has been an enormous amount of interest in how the brain seeks information. The study of this issue is a rapidly growing field in neuroscience. Information seeking is to make informative choices among multiple alternatives. A central issue in i...

Patients' Convergence of Mass and Interpersonal Communication on an Online Forum: Hybrid Methods Analysis.

Journal of medical Internet research
BACKGROUND: Patients are increasingly taking an active role in their health. In doing so, they combine both mass and interpersonal media to gratify their cognitive and affective needs (ie, convergence). Owing to methodological challenges when studyin...

Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study.

Journal of medical Internet research
BACKGROUND: Electronic medical record (EMR) systems capture large amounts of data per patient and present that data to physicians with little prioritization. Without prioritization, physicians must mentally identify and collate relevant data, an acti...

Using machine learning to selectively highlight patient information.

Journal of biomedical informatics
BACKGROUND: Electronic medical record (EMR) systems need functionality that decreases cognitive overload by drawing the clinician's attention to the right data, at the right time. We developed a Learning EMR (LEMR) system that learns statistical mode...

[E-health and "Cancer outside the hospital walls", Big Data and artificial intelligence].

Bulletin du cancer
To heal otherwise in oncology has become an imperative of Public Health and an economic imperative in France. Patients can therefore receive live most of their care outside of hospital with more ambulatory care. This ambulatory shift will benefit fro...

Using Machine Learning to Predict the Information Seeking Behavior of Clinicians Using an Electronic Medical Record System.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Poor electronic medical record (EMR) usability is detrimental to both clinicians and patients. A better EMR would provide concise, context sensitive patient data, but doing so entails the difficult task of knowing which data are relevant. To determin...