Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept

Luvizutto, Gustavo José; Silva, Gabrielly Fernanda; Nascimento, Monalisa Resende; Santos, Kelly Cristina; Appelt, Pablo Andrei; Neto, Eduardo de Moura; Souza, Juli Thomaz de; Wincker, Fernanda Cristina; Miranda, Luana Aparecida; Filho, Pedro Tadao Hamamoto; Souza, Luciane Aparecida Pascucci Sande de; Simões, Rafael Plana; Vidal, Edison Iglesias de Oliveira; Bazan, Rodrigo

doi:10.1080/10749357.2021.1926149

Cited by 10 publications

(7 citation statements)

References 83 publications

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“…The application of AI to the management of stroke is a topic that has gained a lot of traction in the general field of health informatics [ 5 ], partly owing to the remarkable impact of stroke in public health and the subsequent high demand for effective and efficient tools to diagnose and treat stroke. Moreover, the complexity and variety of stroke casuistry make it a good target for AI solutions, which are especially suited to process large amounts of data from a wide range of sources, identify patterns and trends in large data sets, and learn and adapt to new data.…”

Section: Introductionmentioning

confidence: 99%

Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review

De Rosario,

Pitarch-Corresa,

Pedrosa

et al. 2023

JMIR Med Inform

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Background Recent advances in natural language processing (NLP) have heightened the interest of the medical community in its application to health care in general, in particular to stroke, a medical emergency of great impact. In this rapidly evolving context, it is necessary to learn and understand the experience already accumulated by the medical and scientific community. Objective The aim of this scoping review was to explore the studies conducted in the last 10 years using NLP to assist the management of stroke emergencies so as to gain insight on the state of the art, its main contexts of application, and the software tools that are used. Methods Data were extracted from Scopus and Medline through PubMed, using the keywords “natural language processing” and “stroke.” Primary research questions were related to the phases, contexts, and types of textual data used in the studies. Secondary research questions were related to the numerical and statistical methods and the software used to process the data. The extracted data were structured in tables and their relative frequencies were calculated. The relationships between categories were analyzed through multiple correspondence analysis. Results Twenty-nine papers were included in the review, with the majority being cohort studies of ischemic stroke published in the last 2 years. The majority of papers focused on the use of NLP to assist in the diagnostic phase, followed by the outcome prognosis, using text data from diagnostic reports and in many cases annotations on medical images. The most frequent approach was based on general machine learning techniques applied to the results of relatively simple NLP methods with the support of ontologies and standard vocabularies. Although smaller in number, there has been an increasing body of studies using deep learning techniques on numerical and vectorized representations of the texts obtained with more sophisticated NLP tools. Conclusions Studies focused on NLP applied to stroke show specific trends that can be compared to the more general application of artificial intelligence to stroke. The purpose of using NLP is often to improve processes in a clinical context rather than to assist in the rehabilitation process. The state of the art in NLP is represented by deep learning architectures, among which Bidirectional Encoder Representations from Transformers has been found to be especially widely used in the medical field in general, and for stroke in particular, with an increasing focus on the processing of annotations on medical images.

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Section: Introductionmentioning

confidence: 99%

Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review

De Rosario,

Pitarch-Corresa,

Pedrosa

et al. 2023

JMIR Med Inform

View full text Add to dashboard Cite

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“…In recent years, researchers have explored their integration into the stroke rehabilitation process, either as prediction tools or as part of larger clinical decision support systems (CDSS) (15)(16)(17). These ML-based tools can provide treatment recommendations and predictions of relevant outcomes such as the length of rehabilitation, the patient's performance throughout therapy, and the patient's cognitive and physical improvement after rehabilitation (18). For the specific case of cognitive improvement, having accurate predictions early in rehabilitation can help clinicians assemble a realistic therapy plan, adjust it to obtain better results, or anticipate additional care after the therapy.…”

Section: Introductionmentioning

confidence: 99%

Understanding and Predicting Cognitive Improvement of Young Adults in Ischemic Stroke Rehabilitation Therapy

et al. 2022

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Accurate early predictions of a patient's likely cognitive improvement as a result of a stroke rehabilitation programme can assist clinicians in assembling more effective therapeutic programs. In addition, sufficient levels of explainability, which can justify these predictions, are a crucial requirement, as reported by clinicians. This article presents a machine learning (ML) prediction model targeting cognitive improvement after therapy for stroke surviving patients. The prediction model relies on electronic health records from 201 ischemic stroke surviving patients containing demographic information, cognitive assessments at admission from 24 different standardized neuropsychology tests (e.g., TMT, WAIS-III, Stroop, RAVLT, etc.), and therapy information collected during rehabilitation (72,002 entries collected between March 2007 and September 2019). The study population covered young-adult patients with a mean age of 49.51 years and only 4.47% above 65 years of age at the stroke event (no age filter applied). Twenty different classification algorithms (from Python's Scikit-learn library) are trained and evaluated, varying their hyper-parameters and the number of features received as input. Best-performing models reported Recall scores around 0.7 and F1 scores of 0.6, showing the model's ability to identify patients with poor cognitive improvement. The study includes a detailed feature importance report that helps interpret the model's inner decision workings and exposes the most influential factors in the cognitive improvement prediction. The study showed that certain therapy variables (e.g., the proportion of memory and orientation executed tasks) had an important influence on the final prediction of the cognitive improvement of patients at individual and population levels. This type of evidence can serve clinicians in adjusting the therapeutic settings (e.g., type and load of therapy activities) and selecting the one that maximizes cognitive improvement.

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“…Applications of HMQA have been used in sports training applications [4], athletic event performance scoring systems [5], motor rehabilitation systems [6], [7], medical diagnostics [8], skill assessment and education [9], [10], and to assess ergonomic risks [11]. Such assessments, in the absence of automated processes, are mostly performed by experts in their respective fields and thus rely on their level of expertise and experience [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

et al. 2022

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The World Health Organization promotes healthy living through regular physical activities, such as exercise and sports, as well as access to healthcare and rehabilitation services for people with motor dysfunctions. However, there is a lack of specialized personnel and increased costs associated with such activities. These have led to the increased use of machine learning for the analysis and evaluation of human motion during exercise. To study the latest advancements in this area, a systematic literature review focusing on publications from 2017 to 2021 was performed. As a result, 88 relevant publications were identified, which developed both shallow machine learning and deep learning algorithms. The results indicated that algorithms for human motion assessment should provide personalized and informative assessments, with explainable and interpretable outcomes, that can be computed in real-time or concurrently with the execution of an exercise. Furthermore, they should be easy to adapt based on the needs of applications and should be able to perform with different motion capture systems. This has been challenging because of the usually small amount of collected data, the lack of large open datasets, and the unique characteristics of exercise motions. Based on the above findings, guidelines for the development of such algorithms are proposed and discussed. They relate to the selection of the type of assessment, handling data imbalances, selecting of motion capture technologies, balancing between accuracy and speed, selecting the right algorithm, performing concurrent assessment during an exercise, personalization and scalability, and evaluation.

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Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept

Cited by 10 publications

References 83 publications

Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review

Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review

Understanding and Predicting Cognitive Improvement of Young Adults in Ischemic Stroke Rehabilitation Therapy

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

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