Machine learning for predicting cognitive deficits using auditory and demographic factors.

Journal: PloS one
PMID: 38743715

Abstract

IMPORTANCE: Predicting neurocognitive deficits using complex auditory assessments could change how cognitive dysfunction is identified, and monitored over time. Detecting cognitive impairment in people living with HIV (PLWH) is important for early intervention, especially in low- to middle-income countries where most cases exist. Auditory tests relate to neurocognitive test results, but the incremental predictive capability beyond demographic factors is unknown.

Authors

  • Christopher E Niemczak
    Geisel School of Medicine at Dartmouth, Space Medicine Innovations Laboratory, Lebanon, NH, United States of America.
  • Basile Montagnese
    Geisel School of Medicine at Dartmouth, Space Medicine Innovations Laboratory, Lebanon, NH, United States of America.
  • Joshua Levy
    Department of Pathology & Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California.
  • Abigail M Fellows
    Dartmouth Health, Department of Medicine, Division of Hyperbaric Medicine, Lebanon, NH, United States of America.
  • Jiang Gui
    Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
  • Samantha M Leigh
    Dartmouth Health, Department of Medicine, Division of Hyperbaric Medicine, Lebanon, NH, United States of America.
  • Albert Magohe
    Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania.
  • Enica R Massawe
    Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania.
  • Jay C Buckey
    Geisel School of Medicine at Dartmouth, Space Medicine Innovations Laboratory, Lebanon, NH, United States of America.

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