BMC medical informatics and decision making
May 31, 2024
BACKGROUND: Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated using anti-epileptic drugs (AEDs) as the first-line therapy. However, due to the variations in their...
Computer methods in biomechanics and biomedical engineering
Jan 4, 2024
Breast cancer poses a significant global health challenge, demanding enhanced diagnostic accuracy and streamlined medical history documentation. This study presents a holistic approach that harnesses the power of artificial intelligence (AI) and mach...
Medical AI is increasingly being developed and tested to improve medical diagnosis, prediction and treatment of a wide array of medical conditions. Despite worries about the explainability and accuracy of such medical AI systems, it is reasonable to ...
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 ...
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...
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.
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.
BACKGROUND: We developed and validated an artificial intelligence (AI)-assisted prediction of preeclampsia applied to a nationwide health insurance dataset in Indonesia.
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...
'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 ...
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