Using machine learning to predict laboratory test results

Yuan Luo, Peter Szolovits, Anand S. Dighe, Jason M. Baron

Research output: Contribution to journalArticlepeer-review

95 Scopus citations


Objectives: While clinical laboratories report most test results as individual numbers, findings, or observations, clinical diagnosis usually relies on the results of multiple tests. Clinical decision support that integrates multiple elements of laboratory data could be highly useful in enhancing laboratory diagnosis. Methods: Using the analyte ferritin in a proof of concept, we extracted clinical laboratory data from patient testing and applied a variety of machine-learning algorithms to predict ferritin test results using the results from other tests. We compared predicted with measured results and reviewed selected cases to assess the clinical value of predicted ferritin. Results: We show that patient demographics and results of other laboratory tests can discriminate normal from abnormal ferritin results with a high degree of accuracy (area under the curve as high as 0.97, held-out test data). Case review indicated that predicted ferritin results may sometimes better reflect underlying iron status than measured ferritin. Conclusions: These findings highlight the substantial informational redundancy present in patient test results and offer a potential foundation for a novel type of clinical decision support aimed at integrating, interpreting, and enhancing the diagnostic value of multianalyte sets of clinical laboratory test results.

Original languageEnglish (US)
Pages (from-to)778-788
Number of pages11
JournalAmerican Journal of Clinical Pathology
Issue number6
StatePublished - Jun 1 2016


  • Clinical decision support
  • Computational pathology
  • Ferritin
  • Imputation
  • Machine learning
  • Statistical diagnosis

ASJC Scopus subject areas

  • Pathology and Forensic Medicine


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