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Using machine learning to detect sarcopenia from electronic health records.

Xiao Luo1, Haoran Ding1, Andrea Broyles2

  • 1School of Engineering and Technology, Indiana University Purdue University at Indianapolis, Indianapolis, IN, USA.

Digital Health
|September 1, 2023
PubMed
Summary

Machine learning models can predict sarcopenia using electronic health records (EHR). This approach aids in early detection of sarcopenia, improving patient outcomes and independence.

Keywords:
Sarcopeniahealth informaticsmachine learningmusculoskeletal

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Area of Science:

  • Gerontology
  • Biomedical Informatics
  • Musculoskeletal Health

Background:

  • Sarcopenia, characterized by low muscle mass and strength, leads to mobility issues and loss of independence.
  • Sarcopenia is frequently underdiagnosed and not explicitly coded in electronic health records (EHR).

Purpose of the Study:

  • To enhance sarcopenia detection by utilizing structured data from electronic health records (EHR).
  • To evaluate the efficacy of machine learning models in predicting sarcopenia from EHR data.

Main Methods:

  • Adults were categorized into control, Sarcopenia-1 (≥1 sarcopenia test), and Sarcopenia-2 (≥2 sarcopenia tests) groups.
  • Machine learning models were applied to EHR data, including diagnoses, medications, and lab tests, to predict sarcopenia.
  • Performance was assessed using area under the curve (AUC) for logistic regression, multi-layer perceptron, and support vector machine models.

Main Results:

  • Sarcopenic participants were older, had higher fat mass, more comorbidities, and chronic diseases.
  • Models demonstrated higher performance for Sarcopenia-2 (Logistic Regression AUC: 91.44%) compared to Sarcopenia-1 (Logistic Regression AUC: 71.59%).
  • Key predictors for sarcopenia included diabetes, digestive issues, nervous/musculoskeletal/respiratory symptoms, metabolic and kidney disorders, and use of opioids, corticosteroids, and antihyperlipidemics.

Conclusions:

  • Machine learning models can effectively predict sarcopenia from structured EHR data.
  • This predictive capability can facilitate large-scale, early detection and intervention strategies for sarcopenia in clinical settings.
  • Further research can refine these models for broader clinical application.