Intelligent Therapy Assistant (ITA) for cognitive rehabilitation in patients with Acquired Brain Injury

This paper presents the design, development and first evaluation of an algorithm, named Intelligent Therapy Assistant (ITA), which automatically selects, configures and schedules rehabilitation tasks for patients with cognitive impairments after an episode of Acquired Brain Injury. The ITA is integrated in "Guttmann, Neuro Personal Trainer" (GNPT), a cognitive tele-rehabilitation platform that provides neuropsychological services

Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury : Cluster Analysis

Funding: This study was partially funded by the INNOBRAIN project: New Technologies for Innovation in Cognitive Stimulation and Rehabilitation (COMRDI15-1-0017). ACCIÓ-Comunitat RIS3CAT d'innovació en salut NEXTHEALTH (COM15-1-0004) cofinanced this project under the FEDER Catalonia 2014-2020 Operational ProgramTraumatic brain injury (TBI) is a leading cause of disability worldwide. TBI is a highly heterogeneous disease, which makes it complex for effective therapeutic interventions. Cluster analysis has been extensively applied in previous research studies to identify homogeneous subgroups based on performance in neuropsychological baseline tests. Nevertheless, most analyzed samples are rarely larger than a size of 100, and different cluster analysis approaches and cluster validity indices have been scarcely compared or applied in web-based rehabilitation treatments. The aims of our study were as follows: (1) to apply state-of-the-art cluster validity indices to different cluster strategies: hierarchical, partitional, and model-based, (2) to apply combined strategies of dimensionality reduction by using principal component analysis and random forests and perform stability assessment of the final profiles, (3) to characterize the identified profiles by using demographic and clinically relevant variables, and (4) to study the external validity of the obtained clusters by considering 3 relevant aspects of TBI rehabilitation: Glasgow Coma Scale, functional independence measure, and execution of web-based cognitive tasks. This study was performed from August 2008 to July 2019. Different cluster strategies were executed with Mclust, factoextra, and cluster R packages. For combined strategies, we used the FactoMineR and random forest R packages. Stability analysis was performed with the fpc R package. Between-group comparisons for external validation were performed using 2-tailed t test, chi-square test, or Mann-Whitney U test, as appropriate. We analyzed 574 adult patients with TBI (mostly severe) who were undergoing web-based rehabilitation. We identified and characterized 3 clusters with strong internal validation: (1) moderate attentional impairment and moderate dysexecutive syndrome with mild memory impairment and normal spatiotemporal perception, with almost 66% (111/170) of the patients being highly educated (P <.05); (2) severe dysexecutive syndrome with severe attentional and memory impairments and normal spatiotemporal perception, with 49.2% (153/311) of the patients being highly educated (P <.05); (3) very severe cognitive impairment, with 45.2% (42/93) of the patients being highly educated (P <.05). We externally validated them with severity of injury (P =.006) and functional independence assessments: cognitive (P <.001), motor (P <.001), and total (P <.001). We mapped 151,763 web-based cognitive rehabilitation tasks during the whole period to the 3 obtained clusters (P <.001) and confirmed the identified patterns. Stability analysis indicated that clusters 1 and 2 were respectively rated as 0.60 and 0.75; therefore, they were measuring a pattern and cluster 3 was rated as highly stable. Cluster analysis in web-based cognitive rehabilitation treatments enables the identification and characterization of strong response patterns to neuropsychological tests, external validation of the obtained clusters, tailoring of cognitive web-based tasks executed in the web platform to the identified profiles, thereby providing clinicians a tool for treatment personalization, and the extension of a similar approach to other medical conditions

Data mining applied to neurorehabilitation data

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2017Apesar de não serem a principal causa de morte no Mundo, as lesões cerebrais são talvez a principal razão de existirem tantos casos de pessoas que veem a sua vida quotidiana afetada. Tal acontece devido a grandes dificuldades cognitivas que podem ser derivadas de um acidente de automóvel, de uma queda, da presença de um tumor, de um acidente vascular cerebral, da exposição a substâncias tóxicas ou de uma outra qualquer situação que tenha envolvido uma lesão do cérebro. De entre este tipo de lesões podem considerar-se aquelas que são provenientes de traumas por forças externas, ou seja, as chamadas lesões cerebrais traumáticas ou traumatismos crânio-encefálicos. É precisamente em pessoas que sofreram uma lesão desse tipo que se foca este estudo. Em pessoas que, depois dessas lesões, foram sujeitas a um tratamento de neuro reabilitação. Este tratamento, baseado na realização de tarefas especialmente desenhadas para estimular a reorganização das ligações neuronais, permite que os doentes tenham a possibilidade de voltar a conseguir realizar tarefas do dia-a-dia com a menor dificuldade possível. O objetivo da realização destas tarefas é a estimulação da capacidade de plasticidade cerebral, responsável pelo desenvolvimento das conexões sinápticas desde o nascimento e que permite ao cérebro voltar a estabelecer o seu funcionamento normal depois de uma lesão. Naturalmente, o grau de afetação de uma pessoa depende do tipo de lesão e tem uma grande influência não só no tempo de recuperação física e mental, como também no seu estado final. O estudo documentado neste relatório de estágio constitui um meio para atingir um objetivo comum a outros trabalhos de investigação nesta área; pretende-se que os tratamentos de neuro reabilitação possam vir a ser personalizados para cada paciente, para que a sua recuperação seja otimizada. A ideia é que, conhecendo alguns dos dados pessoais de um doente, considerando informação sobre o seu estado inicial e através dos resultados de testes realizados, seja possível associá-lo a um determinado perfil disfuncional, de características bastante específicas, para o terapeuta poder adaptar o seu tratamento. O Institut Guttmann, em Barcelona, foi o primeiro hospital espanhol a prestar cuidados a doentes de lesões medulares. Hoje em dia, um dos seus muitos projetos chama-se GNPT Guttmann NeuroPersonalTrainer e leva a casa dos seus doentes uma plataforma que lhes permite realizar as tarefas definidas pelos terapeutas, no âmbito dos seus tratamentos de neuro reabilitação. Dados desses doentes, incluindo informação démica e resultados de testes realizados antes e depois dos tratamentos, foram cedidos pelo Institut Guttmann ao Grupo de Biomédica e Telemedicina (GBT) sob a forma de bases de dados. Através da sua análise e utilizando ferramentas de Data Mining foi possível obter perfis gerais de disfunção cognitiva e descrever a evolução desses perfis, o principal objetivo desta dissertação. Encontrar padrões em grandes volumes de dados é a principal função de um processo de Data Mining, tratando o assunto de forma muito genérica. Na verdade, é este o conceito utilizado quando são abordados temas de extração de conhecimento a partir de grandes quantidades de dados. Há diversas técnicas que o permitem fazer, que utilizam algoritmos baseados em funções estatísticas e redes neuronais e que têm vindo a ser melhoradas ao longo dos últimos anos, desde que surgiu a primeira necessidade de lidar com grandes conjuntos de elementos. O propósito é sempre o mesmo: que a análise feita a partir destas técnicas permita converter a informação oculta dos dados em informação que pode ser depois utilizada para caracterizar populações, tomar decisões ou para validar resultados. Neste caso, foram utilizados algoritmos de Clustering, um método de Data Mining que permite obter grupos de elementos semelhantes entre si, os clusters, considerando as características de cada um destes elementos. Dados de 698 doentes que sofreram um traumatismo craniano e cuja informação disponível nas bases de dados fornecidas pelo Institut Guttmann satisfazia todas as condições necessárias para serem considerados no estudo, foram integrados num Data Warehouse - um depósito de armazenamento de dados - e depois estruturados. A partir de funções criadas em SQL - a principal linguagem de consultas e organização de bases de dados relacionais - foram obtidas as pontuações correspondentes aos testes realizados pelos doentes, antes do início do tratamento e depois de este ser terminado. Estes testes visaram avaliar, utilizando cinco diferentes níveis de pontuação correspondentes a cada grau de afetação (0 para sem afetação, 1 para afetação suave, 2 para afetação moderada, 3 para afetação severa e 4 para afetação aguda), três funções estritamente relacionadas com o nível cognitivo, a atenção, a memória e algumas funções executivas. As pontuações obtidas para cada uma das funções constituem uma média ponderada da pontuação cada uma das subfunções (atenção dividida, atenção seletiva, memória de trabalho, entre outras), calculadas por pelo menos um dos 24 itens de avaliação a que cada pessoa foi sujeita. De seguida, foram determinados os grupos iniciais e finais, recorrendo a uma ferramenta muito útil para encontrar correlações em grandes conjuntos de dados, o software SPSS. Para determinar a constituição dos clusters iniciais foi aplicado um algoritmo de Clustering designado K-means e, para os finais, um outro denominado TwoStep. A principal característica desta técnica descritiva de Data Mining é a utilização da distância como medida de verificação da proximidade entre dois elementos de um cluster. Os seus algoritmos diferem no tipo de dados a que se aplicam e também na forma como calculam os agrupamentos de elementos. Para cada um dos clusters, e de acordo com cada uma das funções, foi observada a distribuição das pontuações, através de gráficos de barras. Foram também confrontados ambos os conjuntos de clusters para se poder interpretar a relação entre eles. Os clusters, que neste contexto correspondem a perfis de afetação cognitiva, foram validados, e concluiu-se que permitem descrever bem a população em estudo. Por um lado, os seis clusters iniciais determinados representam de uma forma fiel, e com muito sentido do ponto de vista clínico, os conjuntos de pessoas com características suficientemente definidas que os distinguem entre si. Já os três clusters finais, usados para retratar a população no final do tratamento e analisar as evoluções dos pacientes, retratam perfis bastante opostos, o que permitiu, de certa forma interpretar com maior facilidade para que pacientes o efeito da neuro-reabilitação foi mais ou menos positivo. Alguns estudos citados no estado de arte revelaram que algumas variáveis são suscetíveis de influenciar o estado final de um doente. Aproveitando a existência de dados suficientes para tal, foi observado se, tendo em conta os clusters finais, se poderia fazer alguma inferência sobre o efeito de algumas das variáveis – incluindo a idade, o nível de estudos, o intervalo de tempo entre a lesão e o início do tratamento e a sua duração – em cada um destes. No final, considerando apenas as pontuações dos testes em cada função, antes e depois dos tratamentos, foram analisados e interpretados, recorrendo a gráficos, os desenvolvimentos e a evolução global de cada doente. Como desenvolvimentos possíveis, foram tidos em conta os casos em que houve melhorias, agravamentos e também os casos em que os doentes mantiveram o seu estado. Fazendo uso da informação sobre a forma como evoluíram os pacientes, foi possível verificar se, de facto, utilizando apenas os valores das pontuações obtidas nos testes, se poderia ou não confirmar que outras variáveis poderiam ter efeitos na determinação do estado final de um paciente. Os gráficos obtidos demonstraram que há diferenças muito subtis considerando algumas das variáveis, principalmente entre os dos doentes que melhoraram e os dos doentes que viram a sua condição agravada. Concluiu-se que o facto de os clusters agruparem pessoas com tipos de evolução diferentes levou a que o efeito de outras variáveis se mostrasse muito disperso. O tipo de investigação sugerido para futuros desenvolvimentos inclui: (i) o estudo das outras hipóteses de perfis apresentados pelo software usado (SPSS); (ii) considerar os diferentes aspetos das funções avaliadas a um nível mais detalhado; (iii) ter em conta outras variáveis com possíveis efeitos no estado final de um doente.Although they are not the leading cause of death in the world, brain injuries are perhaps the main reason why there are so many cases of people who see their daily lives affected. This is due to the major cognitive difficulties that appear after brain lesion. Brain injuries include those that are derived from traumas due to external forces – the traumatic brain injuries. This study is focused in people who, after these injuries, were subjected to a neuro rehabilitation treatment. The treatment, based on tasks specially designed to stimulate the reorganization of neural connections, allows patients to regain their abilities to perform their everyday tasks with the least possible difficulty. These tasks aim to stimulate the brain plasticity capacity, responsible for the development of synaptic connections which allows the brain to re-establish its normal functioning after an injury. The study documented in this internship report constitutes another step for a major goal, common to other studies in this area: that neuro rehabilitation treatments can be personalized for each patient, so that their recovery is optimized. Knowing some of the personal data of a patient, considering information about their initial state and through the results of tests performed, it is possible to assign a person to a certain dysfunctional profile, with specific characteristics and for the therapist to adapt treatment. One of his many projects of the Institut Guttmann (IG) is called GNPT Guttmann NeuroPersonalTrainer and brings into its patients’ home a platform that allows them to perform the tasks set by the therapists in the context of their neurorehabilitation treatments. Data from these patients, including clinical information and test results performed before and after the treatment, were provided by the IG to the Biomedical and Telemedicine Group (GBT) as databases. Through its analysis and using Data Mining techniques it was possible to obtain general profiles of cognitive dysfunction and to characterize the evolution of these profiles, the objective of this work. Finding patterns and extracting knowledge from large volumes of data are the main functions of a Data Mining process. An analysis performed using these techniques enables the conversion of information hidden in data into information that can later be used to make decisions or to validate results. In this case, Clustering algorithms, which build groups of elements with the similar characteristics called clusters, were used. Also, data from 698 patients who suffered brain trauma and whose information available in the databases provided by the IG satisfied all the conditions considered necessary were integrated into a Data Warehouse and then structured. The scores corresponding to the tests performed before and after the treatment were calculated, for each patient. These tests aimed to evaluate, using five different punctuation levels corresponding to each degree of affectation, three functions strictly related to cognitive level: attention, memory and some executive functions (cognitive processes necessary for the cognitive control of behavior). The initial and final clusters, representing patients’ profiles, were determined, using the SPSS software. The distribution of the scores over the clusters was observed through bar graphs. Both groups of clusters were also confronted to interpret the relationship between them. The clusters, which in this context correspond to profiles of cognitive affectation, were validated, and it was concluded that, at this moment, they represent well the state of patients under study. As some variables, like age and study level, are likely to influence the final state of a patient, it was observed if, given the final clusters, some inference could be made about the effect of those variables. No valuable conclusions were taken from this part. Also, considering the tests scores, patients’ evolution was identified as improvements, aggravations and cases where the conditions is maintained. Using that information, conclusions were extracted, regarding the population and the variables effect. The plots obtained allowed us to correctly describe the patients’ evolution and also to see if the variables considered were good descriptors of that evolution. A simple interpretation from of the facts allows to conclude that the calculated are good general, but not perfect descriptors of the population. The type of research suggested for future developments includes: (i) the study of the other hypothesis of profiles presented by the Data Mining software; (ii) consider the different aspects of the functions evaluated at a more detailed level; (iii) take into account other variables with possible effects on describing the final state of a patient

Artificial Intelligence techniques to support cognitive rehabilitation

In recent years, the Guttmann Institute has incorporated an intelligent assistant as a predicted and personalized decision support system (PPDSS). This PPDSS helps plan rehabilitation sessions for patients suffering from acquired brain injury (ABI). Results show questionable planning when comparing patient profiles and their assigned tasks. The distribution of percentage of effort does not perfectly match the distribution of the cognitive profile. This paper provides a thorough analysis of the patient profiles, showing that a patient’s initial profile and the task execution scores during their first few sessions can be used to better predict their final improvement, to a certain degree of accuracy. Furthermore, results show that more executions of tasks does not automatically lead to improvement. Practice does not seem to make perfect. The proposed technique involves the incorporation of task-weights in the new scheduler

Improving brain injury cognitive rehabilitation by personalized telerehabilitation services: Guttmann neuropersonal trainer

Cognitive rehabilitation aims to remediate or alleviate the cognitive deficits appearing after an episode of acquired brain injury (ABI). The purpose of this work is to describe the telerehabilitation platform called Guttmann Neuropersonal Trainer (GNPT) which provides new strategies for cognitive rehabilitation, improving efficiency and access to treatments, and to increase knowledge generation from the process. A cognitive rehabilitation process has been modeled to design and develop the system, which allows neuropsychologists to configure and schedule rehabilitation sessions, consisting of set of personalized computerized cognitive exercises grounded on neuroscience and plasticity principles. It provides remote continuous monitoring of patient's performance, by an asynchronous communication strategy. An automatic knowledge extraction method has been used to implement a decision support system, improving treatment customization. GNPT has been implemented in 27 rehabilitation centers and in 83 patients' homes, facilitating the access to the treatment. In total, 1660 patients have been treated. Usability and cost analysis methodologies have been applied to measure the efficiency in real clinical environments. The usability evaluation reveals a system usability score higher than 70 for all target users. The cost efficiency study results show a relation of 1-20 compared to face-to-face rehabilitation. GNPT enables brain-damaged patients to continue and further extend rehabilitation beyond the hospital, improving the efficiency of the rehabilitation process. It allows customized therapeutic plans, providing information to further development of clinical practice guidelines

Personalizing paper-and-pencil training for cognitive rehabilitation: a feasibility study with a web-based Task Generator

Cognitive impairments impose important limitations in the performance of activities of daily living. Although there is important evidence on cognitive rehabilitation benefits, its implementation is limited due to the demands in terms of time and human resources. Moreover, many cognitive rehabilitation interventions lack a solid theoretical framework in the selection of paper-and-pencil tasks by the clinicians. In this endeavor, it would be useful to have a tool that could generate standardized paper-and pencil tasks, customized according to patients’ needs. Combining the advantages of information and communication technologies (ICT’s) with a participatory design approach involving 20 health professionals, a novel web-tool for the generation of cognitive rehabilitation training was developed: the Task Generator (TG). The TG is a web-based tool that systematically addresses multiple cognitive domains, and easily generates highly personalized paper and-pencil training tasks. A clinical evaluation of the TG with twenty stroke patients showed that, by enabling the adaptation of task parameters and difficulty levels according to patient cognitive assessment, this tool provides a comprehensive cognitive training.info:eu-repo/semantics/publishedVersio

Capturing expert knowledge for the personalization of cognitive rehabilitation: study combining computational modeling and a participatory design strategy

Background: Cognitive impairments after stroke are not always given sufficient attention despite the critical limitations they impose on activities of daily living (ADLs). Although there is substantial evidence on cognitive rehabilitation benefits, its implementation is limited because of time and human resource’s demands. Moreover, many cognitive rehabilitation interventions lack a robust theoretical framework in the selection of paper-and-pencil tasks by the clinicians. In this endeavor, it would be useful to have a tool that could generate standardized paper-and-pencil tasks, parameterized according to patients' needs. Objective: In this study, we aimed to present a framework for the creation of personalized cognitive rehabilitation tasks based on a participatory design strategy. Methods: We selected 11 paper-and-pencil tasks from standard clinical practice and parameterized them with multiple configurations. A total of 67 tasks were assessed according to their cognitive demands (attention, memory, language, and executive functions) and overall difficulty by 20 rehabilitation professionals. Results: After assessing the internal consistency of the data—that is, alpha values from .918 to .997—we identified the parameters that significantly affected cognitive functions and proposed specific models for each task. Through computational modeling, we operationalized the tasks into their intrinsic parameters and developed a Web tool that generates personalized paper-and-pencil tasks—the Task Generator (TG). Conclusions: Our framework proposes an objective and quantitative personalization strategy tailored to each patient in multiple cognitive domains (attention, memory, language, and executive functions) derived from expert knowledge and materialized in the TG app, a cognitive rehabilitation Web tool.info:eu-repo/semantics/publishedVersio

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

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

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

Accurate early predictions of a patient\u27s 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)