Study analyzes how artificial intelligence system helps diagnose adverse childhood experiences

Children

A paper written by Arash Shaban-Nejad, PhD, MPH, an assistant professor, and Nariman Ammar, PhD, a postdoctoral fellow, both at the Center for Biomedical Informatics in the Department of Pediatrics at the University of Tennessee Health Science Center, was recently published in the Journal of Medical Internet Research – Medical Informatics. The paper discussed how an artificial intelligence system developed by the researchers was used to diagnose and treat children and adults who suffer from Adverse Childhood Experiences (ACEs).

Their research study was named among the Top Milestones on Explainable AI In 2020.

Adverse Childhood Experiences, negative events and processes that an individual might encounter during childhood and adolescence, have been proven to be linked to increased risk of a multitude of negative health outcomes and conditions in adulthood. Drs. Shaban-Nejad and Ammar implemented an artificial intelligence platform called Semantic Platform for Adverse Childhood Experiences Surveillance (SPACES), which assists medical practitioners and health care workers with diagnosing ACEs in the early stages by using real-time data captured through conversations during in-person consultations.

Many AI platforms don’t provide enough explanation to interpret and justify decisions, prolonging the diagnosis and treatment processes. The SPACES platform has functions that can mitigate many of these difficulties. It can detect patient health disparity variants (geographic location, sexual identity, socioeconomic status, etc.), recommend intervention plans to health professionals, and allocate resources as needed.

Current treatment options are long, complex, costly, and most of the time a non-transparent process. We aim not only to assist health care practitioners in extracting, processing, and analyzing the information, but also to inform and empower patients to actively participate in their own health care decision-making process in close cooperation with medical and health care professionals.”

Arash Shaban-Nejad, PhD, MPH, Assistant Professor, Center for Biomedical Informatics, Department of Pediatrics, University of Tennessee Health Science Center

Journal reference:

Ammar, N & Shaban-Nejad, A (2020) Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development. Journal of Medical Internet Research – Medical Informatics. doi.org/10.2196/18752.

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