Understanding effects of cognitive rehabilitation under a knowledge discovery approach

García-Rudolph, Alejandro; Gibert, Karina

doi:10.1016/j.engappai.2016.06.007

Cited by 8 publications

(3 citation statements)

References 53 publications

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“…Typically, each patient executes a different number of tasks along with treatment and in a different order. For each execution, the patient obtains an immediate result (ranging from 0 to 100, as the percentage of compliance) [ 59 ].…”

Section: Methodsmentioning

confidence: 99%

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

García-Rudolph¹,

García-Molina²,

Opisso³

et al. 2020

JMIR Med Inform

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Background Traumatic 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. Objective 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. Methods 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. Results 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. Conclusions 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.

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Section: Methodsmentioning

confidence: 99%

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

García-Rudolph¹,

García-Molina²,

Opisso³

et al. 2020

JMIR Med Inform

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“…In their research into the personalization of rehabilitation programs, García-Rudolph et al, built various classifiers based upon Knowledge Discovery in Databases (KDD) framework ( 33 ), focusing on pre-processing, patterns, and knowledge extraction. They implemented rehabilitation programs (cognitive computerized tasks) as sequences of sessions with pattern recognition utilizing methodologies such as association rules, classification, clustering and shallow neural models in order to assess patient rehabilitation task execution patterns with positive and negative responses to the rehabilitation treatment ( 34 ). This framework allowed for a finer representation of the design and configuration of successful rehabilitation programs for patients and the identification of small variations within these configurations that can aid in personalizing rehabilitation programs.…”

Section: Related Studymentioning

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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“…It includes programs for training the brain so that it can change its functioning through some processes (21). Cognitive rehabilitation programs consist of a chain of tasks aiming at the achievement of executive functions (22). The key role of cognitive rehabilitation is treating problems caused by brain injury.…”

Section: Introductionmentioning

confidence: 99%

The Effectiveness of Response Inhibition Cognitive Rehabilitation in Improving the Quality of Sleep and Behavioral Symptoms of Children with Attention-Deficit/Hyperactivity Disorder

Yazdanbakhsh

Aivazy

Moradi

2018

J Kermanshah Univ Med Sci

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Background:In children with attention deficit hyperactivity disorder, defects in executive functions and sleep problems are also recognized. Currently, drug interventions and behavioral therapy are used more than any other therapist to treat this disorder. Behavioral or pharmaceutical approaches do not directly target cognitive problems and sleep disorders associated with the disorder. Objectives: The aim of this study was to measure the effectiveness of cognitive rehabilitation of response inhibition in Improving the quality of sleep and behavioral symptom of children with attention deficit/hyperactivity disorder (ADHD). Methods: The present study was semi-experimental design with pre-test and post-test with control group and it was done in the fall of 1396. In this study, children aged 7 to 12 years who were diagnosed with (ADHD) by psychological experts of the counseling center of Kermanshah were selected. The Conners-parent's questionnaire and the Petersburg sleep inventory by parents and the Conners-teacher form by teachers were completed. 20 people were randomly divided into two groups. The experimental group was subjected to 12 sessions of The captain's log mind power builder and the control group did not receive treatment. After the last session of treatment, both groups were re-evaluated. Results: The findings showed that cognitive rehabilitation of response inhibition was effective in Improving the quality of sleep (P < 0.001) and behavioral symptoms of attention deficit/hyperactivity disorder (P < 0.001). Conclusions: Therefore, it can be concluded that cognitive rehabilitation of response inhibition is effective inhibition in improving the quality of sleep and behavioral symptoms in ADHD.

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Understanding effects of cognitive rehabilitation under a knowledge discovery approach

Cited by 8 publications

References 53 publications

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

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

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

The Effectiveness of Response Inhibition Cognitive Rehabilitation in Improving the Quality of Sleep and Behavioral Symptoms of Children with Attention-Deficit/Hyperactivity Disorder

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