AIMC Topic: Information Seeking Behavior
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I sympathise with both the supporters and naysayers of hierarchical communication rules.
BMJ (Clinical research ed.)
Sep 8, 2020
Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study.
Journal of medical Internet research
Apr 2, 2020
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
Oct 29, 2019
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
Sep 19, 2019
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
Dec 5, 2018
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...
Safety of patient-facing digital symptom checkers.
Lancet (London, England)
Nov 6, 2018
Qcorp: an annotated classification corpus of Chinese health questions.
BMC medical informatics and decision making
Mar 22, 2018
BACKGROUND: Health question-answering (QA) systems have become a typical application scenario of Artificial Intelligent (AI). An annotated question corpus is prerequisite for training machines to understand health information needs of users. Thus, we...
Resource Classification for Medical Questions.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Feb 10, 2017
We present an approach for manually and automatically classifying the resource type of medical questions. Three types of resources are considered: patient-specific, general knowledge, and research. Using this approach, an automatic question answering...
Combining Open-domain and Biomedical Knowledge for Topic Recognition in Consumer Health Questions.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Feb 10, 2017
Determining the main topics in consumer health questions is a crucial step in their processing as it allows narrowing the search space to a specific semantic context. In this paper we propose a topic recognition approach based on biomedical and open-...
Recognizing Question Entailment for Medical Question Answering.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Feb 10, 2017
With the increasing heterogeneity and specialization of medical texts, automated question answering is becoming more and more challenging. In this context, answering a given medical question by retrieving similar questions that are already answered b...
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