Informing the development of an outcome set and banks of items to measure mobility among individuals with acquired brain injury using natural language processing

• Published in: BMC neurology Vol. 22; no. 1; p. 464
• Main Authors: Alhasani, Rehab, Godbout, Mathieu, Durand, Audrey, Auger, Claudine, Lamontagne, Anouk, Ahmed, Sara
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 09.12.2022; BioMed Central; BMC
• Subjects: Acquired brain injury; Activities of Daily Living; Artificial intelligence; Balance; Brain; Brain Injuries; Brain injury; Brain research; Cognition; Computational linguistics; Core Outcome Set; Disability Evaluation; Disabled Persons; Embedding; Health aspects; Humans; Injuries; Item banks; Language; Language processing; Machine Learning; Methods; Mobility; Motor task performance; Natural language interfaces; Natural Language Processing; Outcome and process assessment (Health Care); Patients; Quality of life; Rehabilitation; Social interactions; Stroke; Subject heading schemes; Traumatic brain injury; Canada; Mobility; Core Outcome Set; Acquired brain injury; Item banks; Machine Learning; Natural Language Processing
• ISSN: 1471-2377; 1471-2377
• DOI: 10.1186/s12883-022-02938-1

The sheer number of measures evaluating mobility and inconsistencies in terminology make it challenging to extract potential core domains and items. Automating a portion of the data synthesis would allow us to cover a much larger volume of studies and databases in a smaller fraction of the time compared to the usual process. Thus, the objective of this study was to identify a comprehensive outcome set and develop preliminary banks of items of mobility among individuals with acquired brain injury (ABI) using Natural Language Processing (NLP). An umbrella review of 47 reviews evaluating the content of mobility measures among individuals with ABI was conducted. A search was performed on 5 databases between 2000 and 2020. Two independent reviewers retrieved copies of the measures and extracted mobility domains and items. A pre-trained BERT model (state-of-the-art model for NLP) provided vector representations for each sentence. Using the International Classification of Functioning, Disability, and Health Framework (ICF) ontology as a guide for clustering, a k-means algorithm was used to retrieve clusters of similar sentences from their embeddings. The resulting embedding clusters were evaluated using the Silhouette score and fine-tuned according to expert input. The study identified 246 mobility measures, including 474 domains and 2109 items. Encoding the clusters using the ICF ontology and expert knowledge helped in regrouping the items in a way that is more closely related to mobility terminology. Our best results identified banks of items that were used to create a 24 comprehensive outcome sets of mobility, including Upper Extremity Mobility, Emotional Function, Balance, Motor Control, Self-care, Social Life and Relationships, Cognition, Walking, Postural Transition, Recreation, and Leisure Activities, Activities of Daily Living, Physical Functioning, Communication, Work/Study, Climbing, Sensory Functions, General Health, Fatigue, Functional Independence, Pain, Alcohol and Drugs Use, Transportation, Sleeping, and Finances. The banks of items of mobility domains represent a first step toward establishing a comprehensive outcome set and a common language of mobility to develop the ontology. It enables researchers and healthcare professionals to begin exposing the content of mobility measures as a way to assess mobility comprehensively.