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Poster
Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions
Fenglin Liu · Bang Yang · Chenyu You · Xian Wu · Shen Ge · Zhangdaihong Liu · Xu Sun · Yang Yang · David Clifton

Thu Dec 01 09:00 AM -- 11:00 AM (PST) @ Hall J #706
in Poster Session 5 »
The "Patient Instruction" (PI), which contains critical instructional information provided both to carers and to the patient at the time of discharge, is essential for the patient to manage their condition outside hospital. An accurate and easy-to-follow PI can improve the self-management of patients which can in turn reduce hospital readmission rates. However, writing an appropriate PI can be extremely time consuming for physicians, and is subject to being incomplete or error-prone for (potentially overworked) physicians. Therefore, we propose a new task that can provide an objective means of avoiding incompleteness, while reducing clinical workload: the automatic generation of the PI, which is imagined as being a document that the clinician can review, modify, and approve as necessary (rather than taking the human "out of the loop"). We build a benchmark clinical dataset and propose the Re$^3$Writer, which imitates the working patterns of physicians to first retrieve related working experience from historical PIs written by physicians, then reason related medical knowledge. Finally, it refines the retrieved working experience and reasoned medical knowledge to extract useful information, which is used to generate the PI for previously-unseen patient according to their health records during hospitalization. Our experiments show that, using our method, the performance of 6 different models can be substantially boosted across all metrics, with up to 20%, 11%, and 19% relative improvements in BLEU-4, ROUGE-L, and METEOR, respectively. Meanwhile, we show results from human evaluations to measure the effectiveness in terms of its usefulness for clinical practice. The code is available at https://github.com/AI-in-Health/Patient-Instructions.
Keywords: Natural Language Generation · MIMIC-III · Patient Instruction · AI for Healthcare · Discharge · Medical Text Generation
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Fenglin Liu (University of Oxford)
Bang Yang (Peking University)
Chenyu You (Yale University)

Chenyu You is a Ph.D. student in the Department of Electrical Engineering, at Yale University, working with Professor James Duncan. He obtained his master degree in Electrical Engineering from Stanford University, specializing in Artificial Intelligence (AI) Prior to that, he received his bachelor degree (with highest honors) in Electrical Engineering and Mathematics from Rensselaer Polytechnic Institute (RPI). He is broadly interested in the area of machine learning theory and algorithms intersecting the fields of computer & medical vision, natural language processing, signal processing, and distributed systems.

Xian Wu (Tencent)
Shen Ge (Tencent)
Zhangdaihong Liu (University of Oxford)
Xu Sun (Peking University)
Yang Yang (Shanghai Jiao Tong University)
David Clifton (University of Oxford)

Professor of Clinical Machine Learning Department of Engineering Science University of Oxford

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