title: Using Machine Learning to Predict Laboratory Test Results.
abstract: OBJECTIVE
  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.
authors.affiliation: From the Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Luo
authors.affiliation: From the Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge.
authors.forename: Peter
authors.initials: P
authors.lastname: Szolovits
authors.affiliation: Department of Pathology, Massachusetts General Hospital, Boston Harvard Medical School, Boston, MA.
authors.forename: Anand S
authors.initials: AS
authors.lastname: Dighe
authors.affiliation: Department of Pathology, Massachusetts General Hospital, Boston Harvard Medical School, Boston, MA. jmbaron@partners.org.
authors.forename: Jason M
authors.initials: JM
authors.lastname: Baron
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D005260:Female; D005293:Ferritins / Q000097:blood*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged
chemical_list: D005293:Ferritins
keywords: Clinical decision support; Computational pathology; Ferritin; Imputation; Machine learning; Statistical diagnosis