Description:

Professor Yuan Luo
Northwestern University
Feinberg School of Medicine

Title: Using machine learning to predict laboratory test results

Abstract: 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. 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 result using the results from other tests. We compared predicted to measured results and reviewed selected cases to assess the clinical value of predicted ferritin. We show that patient demographics and results of other laboratory tests can discriminate normal from abnormal ferritin results with a high degree of accuracy (AUC 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. Our next step is to integrate temporality into predicting multi-variate analytes. We devise an algorithm alternating between multiple imputation based cross sectional prediction and stochastic process based auto regressive prediction. We show modest performance improvement of the combined algorithm compared to either component alone. 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 multi-analyte sets of clinical laboratory test results.

Bio: Yuan Luo is an Assistant Professor at Department of Preventive Medicine, Division of Health & Biomedical Informatics with courtesy appointments in IEMS and EECS. He earned his PhD degree from MIT EECS. His research interests include machine learning, natural language processing, time series analysis, computational genomics and big data analytics, with a focus on medical applications. He proposed Subgraph Augmented Non-negative Tensor Factorization (SANTF) for building a clinical model that improves both accuracy and interpretability, by turning narrative text into graph representations and applying tensor factorization to mining graph features. This work was awarded the first prize at NLP Doctoral Consortium in 2013 Annual Symposium of the American Medical Informatics Association. He has extended the subgraph mining and factorization models to time series analysis and computational genomics. He was also a member of the Student Editorial Board for Journal of the American Medical Informatics Association.