Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study

Yuan Ling, Sadid A. Hasan, Vivek Datla, Ashequl Qadir, Kathy Lee, Joey Liu, Oladimeji Farri
Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:271-285, 2017.

Abstract

Clinical diagnostic inferencing is a complex task, which often requires significant medical research and investigation based on an underlying clinical scenario. This paper proposes a novel approach by formulating the task as a reinforcement learning problem such that the system can infer the most probable diagnoses by optimizing clinical concept extraction from a free text case narrative via leveraging relevant external evidence. Such a formulation is deemed to be suitable due to the inherent complexity of the task and unavailability of sufficient annotated data. During training, the agent tries to learn the optimal policy through iterative search and consolidation of the most relevant clinical concepts that best describe a correct diagnosis. A deep Q-network architecture is trained to optimize a reward function that measures the accuracy of the candidate diagnoses and clinical concepts. Our preliminary experiments on the TREC CDS dataset demonstrate the effectiveness of our system over non-reinforcement learning-based strong baselines.

Cite this Paper

BibTeX
@InProceedings{pmlr-v68-ling17a, title = {Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study}, author = {Ling, Yuan and Hasan, Sadid A. and Datla, Vivek and Qadir, Ashequl and Lee, Kathy and Liu, Joey and Farri, Oladimeji}, booktitle = {Proceedings of the 2nd Machine Learning for Healthcare Conference}, pages = {271--285}, year = {2017}, editor = {Doshi-Velez, Finale and Fackler, Jim and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {68}, series = {Proceedings of Machine Learning Research}, month = {18--19 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v68/ling17a/ling17a.pdf}, url = {https://proceedings.mlr.press/v68/ling17a.html}, abstract = {Clinical diagnostic inferencing is a complex task, which often requires significant medical research and investigation based on an underlying clinical scenario. This paper proposes a novel approach by formulating the task as a reinforcement learning problem such that the system can infer the most probable diagnoses by optimizing clinical concept extraction from a free text case narrative via leveraging relevant external evidence. Such a formulation is deemed to be suitable due to the inherent complexity of the task and unavailability of sufficient annotated data. During training, the agent tries to learn the optimal policy through iterative search and consolidation of the most relevant clinical concepts that best describe a correct diagnosis. A deep Q-network architecture is trained to optimize a reward function that measures the accuracy of the candidate diagnoses and clinical concepts. Our preliminary experiments on the TREC CDS dataset demonstrate the effectiveness of our system over non-reinforcement learning-based strong baselines.} }
Endnote
%0 Conference Paper %T Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study %A Yuan Ling %A Sadid A. Hasan %A Vivek Datla %A Ashequl Qadir %A Kathy Lee %A Joey Liu %A Oladimeji Farri %B Proceedings of the 2nd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2017 %E Finale Doshi-Velez %E Jim Fackler %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v68-ling17a %I PMLR %P 271--285 %U https://proceedings.mlr.press/v68/ling17a.html %V 68 %X Clinical diagnostic inferencing is a complex task, which often requires significant medical research and investigation based on an underlying clinical scenario. This paper proposes a novel approach by formulating the task as a reinforcement learning problem such that the system can infer the most probable diagnoses by optimizing clinical concept extraction from a free text case narrative via leveraging relevant external evidence. Such a formulation is deemed to be suitable due to the inherent complexity of the task and unavailability of sufficient annotated data. During training, the agent tries to learn the optimal policy through iterative search and consolidation of the most relevant clinical concepts that best describe a correct diagnosis. A deep Q-network architecture is trained to optimize a reward function that measures the accuracy of the candidate diagnoses and clinical concepts. Our preliminary experiments on the TREC CDS dataset demonstrate the effectiveness of our system over non-reinforcement learning-based strong baselines.
APA
Ling, Y., Hasan, S.A., Datla, V., Qadir, A., Lee, K., Liu, J. & Farri, O.. (2017). Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study. Proceedings of the 2nd Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 68:271-285 Available from https://proceedings.mlr.press/v68/ling17a.html.

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