Narayanan, AjitMagee, Wendy LSiegert, RJ2023-03-062023-03-062023-02-282023-02-282023-02-28BMC Med Inform Decis Mak, ISSN: 1472-6947 (Print); 1472-6947 (Online), Springer Science and Business Media LLC, 23(1), 41-. doi: 10.1186/s12911-023-02128-01472-69471472-6947https://hdl.handle.net/10292/15947BACKGROUND: Prolonged Disorders of Consciousness (PDOC) resulting from severe acquired brain injury can lead to complex disabilities that make diagnosis challenging. The role of machine learning (ML) in diagnosing PDOC states and identifying intervention strategies is relatively under-explored, having focused on predicting mortality and poor outcome. This study aims to: (a) apply ML techniques to predict PDOC diagnostic states from variables obtained from two non-invasive neurobehavior assessment tools; and (b) apply network analysis for guiding possible intervention strategies. METHODS: The Coma Recovery Scale-Revised (CRS-R) is a well-established tool for assessing patients with PDOC. More recently, music has been found to be a useful medium for assessment of coma patients, leading to the standardization of a music-based assessment of awareness: Music Therapy Assessment Tool for Awareness in Disorders of Consciousness (MATADOC). CRS-R and MATADOC data were collected from 74 PDOC patients aged 16-70 years at three specialist centers in the USA, UK and Ireland. The data were analyzed by three ML techniques (neural networks, decision trees and cluster analysis) as well as modelled through system-level network analysis. RESULTS: PDOC diagnostic state can be predicted to a relatively high level of accuracy that sets a benchmark for future ML analysis using neurobehavioral data only. The outcomes of this study may also have implications for understanding the role of music therapy in interdisciplinary rehabilitation to help patients move from one coma state to another. CONCLUSIONS: This study has shown how ML can derive rules for diagnosis of PDOC with data from two neurobehavioral tools without the need to harvest large clinical and imaging datasets. Network analysis using the measures obtained from these two non-invasive tools provides novel, system-level ways of interpreting possible transitions between PDOC states, leading to possible use in novel, next-generation decision-support systems for PDOC.Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.http://creativecommons.org/licenses/by/4.0/AssessmentCRS-RDisorders of consciousnessMachine learningMusic therapyNetwork analysis4201 Allied Health and Rehabilitation Science42 Health SciencesNeurosciencesBioengineeringBrain DisordersClinical Research4 Detection, screening and diagnosis4.2 Evaluation of markers and technologiesMental health0806 Information Systems1103 Clinical SciencesMedical Informatics4203 Health services and systemsHumansComaConsciousness DisordersBenchmarkingCluster AnalysisMachine LearningJournal ArticleOpenAccess10.1186/s12911-023-02128-0