Document Detail


Collaborative knowledge acquisition for the design of context-aware alert systems.
MedLine Citation:
PMID:  22744961     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
OBJECTIVE: To present a framework for combining implicit knowledge acquisition from multiple experts with machine learning and to evaluate this framework in the context of anemia alerts.
MATERIALS AND METHODS: Five internal medicine residents reviewed 18 anemia alerts, while 'talking aloud'. They identified features that were reviewed by two or more physicians to determine appropriate alert level, etiology and treatment recommendation. Based on these features, data were extracted from 100 randomly-selected anemia cases for a training set and an additional 82 cases for a test set. Two staff internists assigned an alert level, etiology and treatment recommendation before and after reviewing the entire electronic medical record. The training set of 118 cases (100 plus 18) and the test set of 82 cases were explored using RIDOR and JRip algorithms.
RESULTS: The feature set was sufficient to assess 93% of anemia cases (intraclass correlation for alert level before and after review of the records by internists 1 and 2 were 0.92 and 0.95, respectively). High-precision classifiers were constructed to identify low-level alerts (precision p=0.87, recall R=0.4), iron deficiency (p=1.0, R=0.73), and anemia associated with kidney disease (p=0.87, R=0.77).
DISCUSSION: It was possible to identify low-level alerts and several conditions commonly associated with chronic anemia. This approach may reduce the number of clinically unimportant alerts. The study was limited to anemia alerts. Furthermore, clinicians were aware of the study hypotheses potentially biasing their evaluation.
CONCLUSION: Implicit knowledge acquisition, collaborative filtering and machine learning were combined automatically to induce clinically meaningful and precise decision rules.
Authors:
Erel Joffe; Ofer Havakuk; Jorge R Herskovic; Vimla L Patel; Elmer Victor Bernstam
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2012-06-28
Journal Detail:
Title:  Journal of the American Medical Informatics Association : JAMIA     Volume:  19     ISSN:  1527-974X     ISO Abbreviation:  J Am Med Inform Assoc     Publication Date:    2012 Nov-Dec
Date Detail:
Created Date:  2012-10-18     Completed Date:  2013-04-25     Revised Date:  2013-11-15    
Medline Journal Info:
Nlm Unique ID:  9430800     Medline TA:  J Am Med Inform Assoc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  988-94     Citation Subset:  IM    
Affiliation:
Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
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MeSH Terms
Descriptor/Qualifier:
Anemia / prevention & control*
Artificial Intelligence*
Decision Support Systems, Clinical*
Diagnosis, Computer-Assisted*
Electronic Health Records
Humans
Internal Medicine
Israel
Physician's Practice Patterns
Grant Support
ID/Acronym/Agency:
1RC1RR028254/RC/CCR NIH HHS; 3UL1RR024148/RR/NCRR NIH HHS; UL1 RR024148/RR/NCRR NIH HHS
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