TY - GEN
T1 - Automatic identification of comparative effectiveness research from medline citations to support clinicians' treatment information needs
AU - Zhang, Mingyuan
AU - Del Fiol, Guilherme
AU - Grout, Randall W.
AU - Jonnalagadda, Siddhartha
AU - Medlin, Richard
AU - Mishra, Rashmi
AU - Weir, Charlene
AU - Liu, Hongfang
AU - Mostafa, Javed
AU - Fiszman, Marcelo
PY - 2013
Y1 - 2013
N2 - Online knowledge resources such as Medline can address most clinicians' patient care information needs. Yet, significant barriers, notably lack of time, limit the use of these sources at the point of care. The most common information needs raised by clinicians are treatment-related. Comparative effectiveness studies allow clinicians to consider multiple treatment alternatives for a particular problem. Still, solutions are needed to enable efficient and effective consumption of comparative effectiveness research at the point of care. Objective: Design and assess an algorithm for automatically identifying comparative effectiveness studies and extracting the interventions investigated in these studies. Methods: The algorithm combines semantic natural language processing, Medline citation metadata, and machine learning techniques. We assessed the algorithm in a case study of treatment alternatives for depression. Results: Both precision and recall for identifying comparative studies was 0.83. A total of 86% of the interventions extracted perfectly or partially matched the gold standard. Conclusion: Overall, the algorithm achieved reasonable performance. The method provides building blocks for the automatic summarization of comparative effectiveness research to inform point of care decision-making.
AB - Online knowledge resources such as Medline can address most clinicians' patient care information needs. Yet, significant barriers, notably lack of time, limit the use of these sources at the point of care. The most common information needs raised by clinicians are treatment-related. Comparative effectiveness studies allow clinicians to consider multiple treatment alternatives for a particular problem. Still, solutions are needed to enable efficient and effective consumption of comparative effectiveness research at the point of care. Objective: Design and assess an algorithm for automatically identifying comparative effectiveness studies and extracting the interventions investigated in these studies. Methods: The algorithm combines semantic natural language processing, Medline citation metadata, and machine learning techniques. We assessed the algorithm in a case study of treatment alternatives for depression. Results: Both precision and recall for identifying comparative studies was 0.83. A total of 86% of the interventions extracted perfectly or partially matched the gold standard. Conclusion: Overall, the algorithm achieved reasonable performance. The method provides building blocks for the automatic summarization of comparative effectiveness research to inform point of care decision-making.
KW - Comparative effectiveness research
KW - computer assisted decision making
KW - information needs
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=84894356704&partnerID=8YFLogxK
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U2 - 10.3233/978-1-61499-289-9-846
DO - 10.3233/978-1-61499-289-9-846
M3 - Conference contribution
C2 - 23920677
AN - SCOPUS:84894356704
SN - 9781614992882
T3 - Studies in Health Technology and Informatics
SP - 846
EP - 850
BT - MEDINFO 2013 - Proceedings of the 14th World Congress on Medical and Health Informatics
PB - IOS Press
T2 - 14th World Congress on Medical and Health Informatics, MEDINFO 2013
Y2 - 20 August 2013 through 23 August 2013
ER -