@inproceedings{05fafaad726a4ceb8f37c3f700329a9e,
title = "Representation and Normalization of Complex Interventions for Evidence Computing",
abstract = "Complex interventions are ubiquitous in healthcare. A lack of computational representations and information extraction solutions for complex interventions hinders accurate and efficient evidence synthesis. In this study, we manually annotated and analyzed 3,447 intervention snippets from 261 randomized clinical trial (RCT) abstracts and developed a compositional representation for complex interventions, which captures the spatial, temporal and Boolean relations between intervention components, along with an intervention normalization pipeline that automates three tasks: (i) treatment entity extraction; (ii) intervention component relation extraction; and (iii) attribute extraction and association. 361 intervention snippets from 29 unseen abstracts were included to report on the performance of the evaluation. The average F-measure was 0.74 for treatment entity extraction on an exact match and 0.82 for attribute extraction. The F-measure for relation extraction of multi-component complex interventions was 0.90. 93\% of extracted attributes were correctly attributed to corresponding treatment entities.",
keywords = "complex intervention, evidence-based medicine, Knowledge representation, natural language processing",
author = "Zhehuan Chen and Hao Liu and Stan Liao and Marguerite Bernard and Tian Kang and Stewart, \{Latoya A.\} and Chunhua Weng",
note = "Publisher Copyright: {\textcopyright} 2022 International Medical Informatics Association (IMIA) and IOS Press.; 18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021 ; Conference date: 02-10-2021 Through 04-10-2021",
year = "2022",
month = jun,
day = "6",
doi = "10.3233/SHTI220146",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "592--596",
editor = "Paula Otero and Philip Scott and Martin, \{Susan Z.\} and Elaine Huesing",
booktitle = "MEDINFO 2021",
}