Performance Verification of a Text Analyzer Using Machine Learning for Radiology Reports Toward Phenotyping

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The medical field is embracing the information age, and the rapidly increasing medical data generated from hospital information system signified the advent of Big Data in the healthcare arena, such that real-time data are now available to assist many clinical decisions. Real World Data (RWD) from hospital information system structured numerical data and unstructured text data, and it is imperative that phenotyping reproducibly extracts patients with an accurate phenotype from RWD using a rule-based approach. In this study, of sampling computed tomography reports from 100 patients, 48 were diagnosed with interstitial pneumonia. Three machine learning methods (Support Vector Machine, Feature Selection and Gradient Boosting Decision Tree (GBDT)) were combined for development of a text phenotyping method, which was applied for the analysis to achieve prediction with good performance. We extracted several feature words to predict true cases of interstitial pneumonia and recognized that the effect of feature selection was identified from a good performance of GBDT’s AUC. We also identified that while applying machine learning to text-based RWD, variables have to be narrowed down.

Original languageEnglish
Title of host publicationInnovation in Medicine and Healthcare - Proceedings of 9th KES-InMed 2021
EditorsYen-Wei Chen, Yen-Wei Chen, Satoshi Tanaka, Robert J. Howlett, Robert J. Howlett, Robert J. Howlett, Lakhmi C. Jain, Lakhmi C. Jain
PublisherSpringer Science and Business Media Deutschland GmbH
Pages171-182
Number of pages12
ISBN (Print)9789811630125
DOIs
Publication statusPublished - 2021
Event9th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2021 - Virtual, Online
Duration: Jun 14 2021Jun 16 2021

Publication series

NameSmart Innovation, Systems and Technologies
Volume242
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference9th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2021
CityVirtual, Online
Period6/14/216/16/21

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Computer Science(all)

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