Literature DB >> 34858118

AUTOMATIC ICD-10 CODING USING PRESCRIBED DRUGS DATA.

Alexander Dokumentov, Yassien Shaalan, Piyapong Khumrin, Krit Khwanngern, Anawat Wisetborisut, Thanakom Hatsadeang, Nattapat Karaket, Witthawin Achariyaviriya, Sansanee Auephanwiriyakul, Nipon Theera-Umpon, Terence Siganakis.   

Abstract

This article discusses the emerging trends and challenges related to automatic clinical coding. We introduce an automatic coding system, which assigns short ICD-10 codes (restricted to the first three symbols, which define the category of the disease) based only on drugs prescribed to patients. We show that even with limited input data, the accuracy levels are comparable to those achieved by entry-level clinical coders as depicted by Seyed Nouraei et al.1 We also examine the standard method for performance estimation and speculate that the actual accuracy of our coding system is even higher than estimated.
Copyright © 2021 by the American Health Information Management Association.

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Year:  2021        PMID: 34858118      PMCID: PMC8580462     

Source DB:  PubMed          Journal:  Perspect Health Inf Manag        ISSN: 1559-4122


  7 in total

1.  Accuracy of clinician-clinical coder information handover following acute medical admissions: implication for using administrative datasets in clinical outcomes management.

Authors:  Seyed Ahmad Reza Nouraei; Jagdeep Singh Virk; Anita Hudovsky; Christopher Wathen; Ara Darzi; Darren Parsons
Journal:  J Public Health (Oxf)       Date:  2015-04-23       Impact factor: 2.341

Review 2.  Leaders' experiences and perceptions implementing activity-based funding and pay-for-performance hospital funding models: A systematic review.

Authors:  Pamela E Baxter; Sarah J Hewko; Kathryn A Pfaff; Laura Cleghorn; Barbara J Cunningham; Dawn Elston; Greta G Cummings
Journal:  Health Policy       Date:  2015-05-12       Impact factor: 2.980

Review 3.  Deep learning in neural networks: an overview.

Authors:  Jürgen Schmidhuber
Journal:  Neural Netw       Date:  2014-10-13

4.  Classification in the presence of label noise: a survey.

Authors:  Benoît Frénay; Michel Verleysen
Journal:  IEEE Trans Neural Netw Learn Syst       Date:  2014-05       Impact factor: 10.451

5.  ICD-9-CM to ICD-10-CM Codes: What? Why? How?

Authors:  Donna J Cartwright
Journal:  Adv Wound Care (New Rochelle)       Date:  2013-12       Impact factor: 4.730

6.  Automatic ICD-10 classification of cancers from free-text death certificates.

Authors:  Bevan Koopman; Guido Zuccon; Anthony Nguyen; Anton Bergheim; Narelle Grayson
Journal:  Int J Med Inform       Date:  2015-08-13       Impact factor: 4.046

Review 7.  Comprehensive review of ICD-9 code accuracies to measure multimorbidity in administrative data.

Authors:  Melissa Y Wei; Jamie E Luster; Chiao-Li Chan; Lillian Min
Journal:  BMC Health Serv Res       Date:  2020-06-01       Impact factor: 2.655

  7 in total

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