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Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.

Konan Hara1, Yasuki Kobayashi1, Jun Tomio1

  • 1Department of Public Health, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.

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Machine learning methods can build effective claims-based algorithms (CBAs) for identifying medical conditions, matching the performance of traditional knowledge-based approaches. This study shows AI can enhance disease identification using health insurance claims data.

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Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Medical Data Analysis

Background:

  • Claims-based algorithms (CBAs) traditionally rely on subject-matter expertise for medical condition identification.
  • These knowledge-dependent CBAs may not be optimally tuned for specific target conditions.
  • Investigating alternative methods is crucial for improving the accuracy and efficiency of claims data analysis.

Purpose of the Study:

  • To evaluate the efficacy of machine learning (ML) methods in supplementing subject-matter knowledge for building CBAs.
  • To compare the performance of various ML algorithms against traditional knowledge-based approaches in identifying hypertension, diabetes, and dyslipidemia.
  • To determine if ML can achieve comparable or superior accuracy in disease identification using claims data.

Main Methods:

  • A retrospective cohort study utilized a Japanese employee health insurance claims database (2016-17).
  • Multiple ML algorithms (logistic regression, k-NN, SVM, penalized logistic regression, tree-based models, neural networks) were employed to construct CBAs.
  • Performance was assessed using the area under the receiver operating characteristic curve (AUC) on a hold-out test set.

Main Results:

  • ML methods, including logistic lasso, logistic elastic-net, and tree-based models, achieved AUCs comparable to knowledge-based logistic regression.
  • For hypertension, AUCs ranged from .923 to .931 (ML) vs. .923 (knowledge-based).
  • For diabetes, AUCs ranged from .958 to .966 (ML) vs. .957 (knowledge-based).
  • For dyslipidemia, AUCs ranged from .747 to .773 (ML) vs. .739 (knowledge-based).

Conclusions:

  • Machine learning methods demonstrate the capability to build claims-based algorithms (CBAs) with performance on par with conventional knowledge-based methods.
  • ML offers a valuable approach to supplement or potentially replace expert knowledge in developing CBAs for chronic disease identification.
  • The findings suggest ML can enhance the accuracy and efficiency of identifying patients with conditions like hypertension, diabetes, and dyslipidemia from claims data.