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

• Published in: American journal of clinical pathology Vol. 162; no. 3; pp. 243 - 251
• Main Authors: Garduno-Rapp, Nelly Estefanie, Ng, Yee Seng, Weon, Jenny L, Saleh, Sameh N, Lehmann, Christoph U, Tian, Chenlu, Quinn, Andrew
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 03.09.2024
• Subjects: Adult; Aged; Anemia; Anemia, Iron-Deficiency - diagnosis; Deep Learning; Diagnosis; Early Diagnosis; Electronic Health Records; Electronic medical records; Female; Humans; Iron deficiency; Laboratories; Long short-term memory; Male; Memory cells; Middle Aged; Neural networks; Neural Networks, Computer; Patients; Prediction models; Retrospective Studies; Sensitivity analysis; Sensitivity and Specificity; early diagnosis; deep learning; artificial neural networks; iron-deficiency anemia; diagnosis; gated recurrent unit; machine learning; long short-term memory
• ISSN: 0002-9173; 1943-7722; 1943-7722
• DOI: 10.1093/ajcp/aqae031

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. Methods We analyzed retrospective outpatient electronic health record data between 2009 and 2020 from an academic medical center in northern Texas. We included laboratory features from 30,603 patients to develop 3 types of neural networks: artificial neural networks, long short-term memory cells, and gated recurrent units. The classifiers were trained using the Adam Optimizer across 200 random training-validation splits. We calculated accuracy, area under the receiving operating characteristic curve, sensitivity, and specificity in the testing split. Results Although all models demonstrated comparable performance, the gated recurrent unit model outperformed the other 2, achieving an accuracy of 0.83, an area under the receiving operating characteristic curve of 0.89, a sensitivity of 0.75, and a specificity of 0.85 across 200 epochs. Conclusions Our results showcase the feasibility of employing deep learning techniques for early prediction of IDA in the outpatient setting based on sequences of laboratory data, offering a substantial lead time for clinical intervention.