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Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems
While the use of machine learning methods in clinical decision support has great potential for improving patient care, acquiring standardized, complete, and sufficient training data presents a major challenge for methods relying exclusively on machine learning techniques. Domain experts possess know...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Medical Informatics Association
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4525246/ https://www.ncbi.nlm.nih.gov/pubmed/26306246 |
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author | Kuusisto, Finn Dutra, Inês Elezaby, Mai Mendonça, Eneida A. Shavlik, Jude Burnside, Elizabeth S. |
author_facet | Kuusisto, Finn Dutra, Inês Elezaby, Mai Mendonça, Eneida A. Shavlik, Jude Burnside, Elizabeth S. |
author_sort | Kuusisto, Finn |
collection | PubMed |
description | While the use of machine learning methods in clinical decision support has great potential for improving patient care, acquiring standardized, complete, and sufficient training data presents a major challenge for methods relying exclusively on machine learning techniques. Domain experts possess knowledge that can address these challenges and guide model development. We present Advice-Based-Learning (ABLe), a framework for incorporating expert clinical knowledge into machine learning models, and show results for an example task: estimating the probability of malignancy following a non-definitive breast core needle biopsy. By applying ABLe to this task, we demonstrate a statistically significant improvement in specificity (24.0% with p=0.004) without missing a single malignancy. |
format | Online Article Text |
id | pubmed-4525246 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | American Medical Informatics Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-45252462015-08-24 Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems Kuusisto, Finn Dutra, Inês Elezaby, Mai Mendonça, Eneida A. Shavlik, Jude Burnside, Elizabeth S. AMIA Jt Summits Transl Sci Proc Articles While the use of machine learning methods in clinical decision support has great potential for improving patient care, acquiring standardized, complete, and sufficient training data presents a major challenge for methods relying exclusively on machine learning techniques. Domain experts possess knowledge that can address these challenges and guide model development. We present Advice-Based-Learning (ABLe), a framework for incorporating expert clinical knowledge into machine learning models, and show results for an example task: estimating the probability of malignancy following a non-definitive breast core needle biopsy. By applying ABLe to this task, we demonstrate a statistically significant improvement in specificity (24.0% with p=0.004) without missing a single malignancy. American Medical Informatics Association 2015-03-25 /pmc/articles/PMC4525246/ /pubmed/26306246 Text en ©2015 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose |
spellingShingle | Articles Kuusisto, Finn Dutra, Inês Elezaby, Mai Mendonça, Eneida A. Shavlik, Jude Burnside, Elizabeth S. Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems |
title | Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems |
title_full | Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems |
title_fullStr | Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems |
title_full_unstemmed | Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems |
title_short | Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems |
title_sort | leveraging expert knowledge to improve machine-learned decision support systems |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4525246/ https://www.ncbi.nlm.nih.gov/pubmed/26306246 |
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