Early identification of patients at risk for iron-deficiency anemia using deep learning techniques.

Journal: American journal of clinical pathology
PMID: 38642073

Abstract

OBJECTIVES: Iron-deficiency anemia (IDA) is a common health problem worldwide, and up to 10% of adult patients with incidental IDA may have gastrointestinal cancer. A diagnosis of IDA can be established through a combination of laboratory tests, but it is often underrecognized until a patient becomes symptomatic. Based on advances in machine learning, we hypothesized that we could reduce the time to diagnosis by developing an IDA prediction model. Our goal was to develop 3 neural networks by using retrospective longitudinal outpatient laboratory data to predict the risk of IDA 3 to 6 months before traditional diagnosis.

Authors

  • Nelly Estefanie Garduno-Rapp
    Clinical Informatics Center.
  • Yee Seng Ng
    Departments of Radiology (T.J.O., Y.X., E.S., T.B., Y.S.N., R.M.P.) and Health Systems Information Resources (C.B.), University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, 5323 Harry Hines Blvd, Dallas TX 75235.
  • Jenny L Weon
    Clinical Informatics Center.
  • Sameh N Saleh
    Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Christoph U Lehmann
    Department of Biomedical Informatics Vanderbilt University, Nashville, TN; Department of Pediatrics Vanderbilt University, Nashville, TN.
  • Chenlu Tian
    Department of Digestive and Liver Disease, University of Texas Southwestern Medical Center, Dallas, TX, US.
  • Andrew Quinn
    Department of Pathology.

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