A Machine Learning Model for Evaluating Imported Disease Screening Strategies in Immigrant Populations.

Journal: The American journal of tropical medicine and hygiene
PMID: 34544039

Abstract

Given the high prevalence of imported diseases in immigrant populations, it has postulated the need to establish screening programs that allow their early diagnosis and treatment. We present a mathematical model based on machine learning methodologies to contribute to the design of screening programs in this population. We conducted a retrospective cross-sectional screening program of imported diseases in all immigrant patients who attended the Tropical Medicine Unit between January 2009 and December 2016. We designed a mathematical model based on machine learning methodologies to establish the set of most discriminatory prognostic variables to predict the onset of the: HIV infection, malaria, chronic hepatitis B and C, schistosomiasis, and Chagas in immigrant population. We analyzed 759 patients. HIV was predicted with an accuracy of 84.9% and the number of screenings to detect the first HIV-infected person was 26, as in the case of Chagas disease (with a predictive accuracy of 92.9%). For the other diseases the averages were 12 screenings to detect the first case of chronic hepatitis B (85.4%), or schistosomiasis (86.9%), 23 for hepatitis C (85.6%) or malaria (93.3%), and eight for syphilis (79.4%) and strongyloidiasis (88.4%). The use of machine learning methodologies allowed the prediction of the expected disease burden and made it possible to pinpoint with greater precision those immigrants who are likely to benefit from screening programs, thus contributing effectively to their development and design.

Authors

  • Juan L Fernández-Martínez
    Symptom Management Branch, Division of Intramural Research, National Institute of Nursing Research, Building 3, Room 5E14 3 Center Drive Bethesda, MD 20892, USA. Electronic address: guillermina.bea@icloud.com.
  • José A Boga
    Department of Microbiology, Hospital Universitario Central de Asturias (HUCA), Oviedo, Spain.
  • Enrique de Andrés-Galiana
    Group of Inverse Problems, Optimization and, Machine Learning, University of Oviedo, Asturias, Spain.
  • Luis Casado
    Internal Medicine Department, Hospital de la Cruz Roja, Gijón, Spain.
  • Jonathan Fernández
    Department of Microbiology, Hospital Universitario Central de Asturias (HUCA), Oviedo, Spain.
  • Candela Menéndez
    Internal Medicine Department, Hospital Universitario Central de Asturias (HUCA), Oviedo, Spain.
  • Alicia García-Pérez
    Internal Medicine Department, Hospital Universitario Central de Asturias (HUCA), Oviedo, Spain.
  • Noelia Moran-Suarez
    Internal Medicine Department, Hospital Universitario Central de Asturias (HUCA), Oviedo, Spain.
  • María Martinez-Sela
    Internal Medicine Department, Hospital Universitario Central de Asturias (HUCA), Oviedo, Spain.
  • Fernando Vázquez
    Department of Microbiology, Hospital Universitario Central de Asturias (HUCA), Oviedo, Spain.
  • Azucena Rodríguez-Guardado
    Tropical Medicine Unit, Hospital Universitario Central de Asturias (HUCA), Oviedo, Spain.

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