AIMC Topic: Medical History Taking
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Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study.
International journal of environmental research and public health
Feb 21, 2021
BACKGROUND: The efficacy of artificial intelligence (AI)-driven automated medical-history-taking systems with AI-driven differential-diagnosis lists on physicians' diagnostic accuracy was shown. However, considering the negative effects of AI-driven ...
Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints.
Acta orthopaedica
Feb 12, 2021
Background and purpose - Machine learning (ML) techniques are a form of artificial intelligence able to analyze big data. Analyzing the outcome of (digital) questionnaires, ML might recognize different patterns in answers that might relate to differe...
[Machine learning is unlikely to improve the diagnostic performance of medical history taking].
Nederlands tijdschrift voor geneeskunde
Jan 7, 2021
Use of machine learning has been proposedtoimprovethediagnostic performance of medicalhistorytaking, whichwould first have tobestandardized. Thiscommentary reviews theoretical, practical andethicalconsiderationswithregardtothisproposal.
Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records.
Diabetes care
Apr 29, 2020
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.
Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia.
EBioMedicine
Apr 10, 2020
BACKGROUND: We developed and validated an artificial intelligence (AI)-assisted prediction of preeclampsia applied to a nationwide health insurance dataset in Indonesia.
Towards Reliable ARDS Clinical Decision Support: ARDS Patient Analytics with Free-text and Structured EMR Data.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
In this work, we utilize a combination of free-text and structured data to build Acute Respiratory Distress Syndrome(ARDS) prediction models and ARDS phenotype clusters. We derived 'Patient Context Vectors' representing patientspecific contextual ARD...
A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data.
PloS one
Dec 30, 2019
'Asthma' is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previously been based on longitudinal wheeze patterns or hypothesis-driven observational criteria. The aim ...
Family history information extraction via deep joint learning.
BMC medical informatics and decision making
Dec 27, 2019
Family history (FH) information, including family members, side of family of family members (i.e., maternal or paternal), living status of family members, observations (diseases) of family members, etc., is very important in the decision-making proce...
Family member information extraction via neural sequence labeling models with different tag schemes.
BMC medical informatics and decision making
Dec 27, 2019
BACKGROUND: Family history information (FHI) described in unstructured electronic health records (EHRs) is a valuable information source for patient care and scientific researches. Since FHI is usually described in the format of free text, the entire...
Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data.
PloS one
Aug 22, 2019
Colorectal cancer (CRC) is third in prevalence and mortality among all cancers in the US. Currently, the United States Preventative Services Task Force (USPSTF) recommends anyone ages 50-75 and/or with a family history to be screened for CRC. To impr...
Cardiovascular Diseases
Middle Aged
Health Surveys
United States
Diabetes Mellitus
Health Status
Early Detection of Cancer
Models, Statistical
Risk Factors
Aged
Colorectal Neoplasms
Male
Neural Networks, Computer
Prognosis
Female
Self Report
Practice Guidelines as Topic
Medical History Taking
Humans
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