TY - GEN
T1 - Discovering interpretable medical workflow models
AU - Li, Jingyuan
AU - Yang, Sen
AU - Chen, Shuhong
AU - Tao, Fei
AU - Marsic, Ivan
AU - Burd, Randall S.
N1 - Publisher Copyright: © 2018 IEEE.
PY - 2018/7/24
Y1 - 2018/7/24
N2 - Medical workflow discovery and analysis can help teams better understand their practice and potentially improve patient outcomes. In the past, medical experts designed hand-made workflow models for this purpose. These models usually needed iterative revision for the experts to reach consensus. Actual practice, however, often deviates from a perceived ideal workflow model. Automatic workflow discovery algorithms have recently been proposed to learn a workflow model from observed activity or event traces. These algorithms, however, can produce spaghetti-like graphical models, with many branches and loops. Although model interpretability is an initial requirement of medical process analysis, it is often difficult or impossible for medical experts to extract knowledge from these chaotic models. We present an algorithm for discovering interpretable medical workflow models that addresses these limitations. We show our preliminary results and evaluate our approach on a real-world medical process.
AB - Medical workflow discovery and analysis can help teams better understand their practice and potentially improve patient outcomes. In the past, medical experts designed hand-made workflow models for this purpose. These models usually needed iterative revision for the experts to reach consensus. Actual practice, however, often deviates from a perceived ideal workflow model. Automatic workflow discovery algorithms have recently been proposed to learn a workflow model from observed activity or event traces. These algorithms, however, can produce spaghetti-like graphical models, with many branches and loops. Although model interpretability is an initial requirement of medical process analysis, it is often difficult or impossible for medical experts to extract knowledge from these chaotic models. We present an algorithm for discovering interpretable medical workflow models that addresses these limitations. We show our preliminary results and evaluate our approach on a real-world medical process.
KW - Medical Process Analysis
KW - Workflow Discovery
UR - https://www.scopus.com/pages/publications/85051112444
UR - https://www.scopus.com/pages/publications/85051112444#tab=citedBy
U2 - 10.1109/ICHI.2018.00089
DO - 10.1109/ICHI.2018.00089
M3 - Conference contribution
T3 - Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018
SP - 437
EP - 439
BT - Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Healthcare Informatics, ICHI 2018
Y2 - 4 June 2018 through 7 June 2018
ER -