Identifying main finding sentences in clinical case reports.

Database : the Journal of Biological Databases and Curation
Mengqi LuoNeil R Smalheiser

Abstract

Clinical case reports are the 'eyewitness reports' of medicine and provide a valuable, unique, albeit noisy and underutilized type of evidence. Generally, a case report has a single main finding that represents the reason for writing up the report in the first place. However, no one has previously created an automatic way of identifying main finding sentences in case reports. We previously created a manual corpus of main finding sentences extracted from the abstracts and full text of clinical case reports. Here, we have utilized the corpus to create a machine learning-based model that automatically predicts which sentence(s) from abstracts state the main finding. The model has been evaluated on a separate manual corpus of clinical case reports and found to have good performance. This is a step toward setting up a retrieval system in which, given one case report, one can find other case reports that report the same or very similar main findings. The code and necessary files to run the main finding model can be downloaded from https://github.com/qi29/main_ finding_recognition, released under the Apache License, Version 2.0.

References

Aug 18, 1999·Journal of the Royal Society of Medicine·J P Vandenbroucke
Mar 4, 2008·Otolaryngology--head and Neck Surgery : Official Journal of American Academy of Otolaryngology-Head and Neck Surgery·J Gail NeelyJason T Rich
Oct 31, 2014·Clinical Case Reports·Debra JacksonDeborah C Saltman
Oct 30, 2015·Journal of the Medical Library Association : JMLA·Neil R SmalheiserPhilip S Yu
Sep 9, 2017·Bioinformatics·Gizem SoganciogluArzucan Özgür
Jun 27, 2018·BMC Medical Informatics and Decision Making·Matthew ShardlowSophia Ananiadou
Jul 7, 2018·Anesthesia and Analgesia·Michelle H Vu, Guy Weinberg
Jan 19, 2019·Database : the Journal of Biological Databases and Curation·Neil R SmalheiserXiaokai Cui

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Software Mentioned

Scikit
Apache
Stanford Parser
NLTK
Natural Language Toolkit ( NLTK )
Python
Java Archive
learn

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