Prediction of Outcome in the Vegetative State by Machine Learning Algorithms: A Model for Clinicians?

Pignolo, Loris; Lagan, Vincenzo i

doi:10.4236/jsea.2011.46044

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…First and foremost, the straight relapse technique is utilized to ascertain the connection between the public bound together assessment results and the exhaustive assessment consequences of the framework, and Table 1 is gotten. In Figure 4, there is a huge straight connection between the consequences of the National Physical Education bound together assessment and the thorough assessment worth of the framework, R 2 = 0:9509, and on the grounds, the extensive assessment cycle of the framework does not present the aftereffects of the National Physical Education brought together assessment; however, as per the aftereffects of the physical education homeroom showing test, it is very well may be viewed as that the framework has a specific prescient incentive for the aftereffects of the National Physical Education bound together assessment [31]. The R 2 esteem estimation plot is the proportion of relapse change to direct fluctuation, which can compute the contrast between the relapse result and the first outcome.…”

Section: Artificial Intelligence Evaluation and National Physical Edu...mentioning

confidence: 99%

Applications of Artificial Intelligence and IoT in the Development of Sports Training Education Management

Tang

Lü

2022

Wireless Communications and Mobile Computing

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With the persistent improvement of information technology, regular physical education showing techniques are presently not pertinent. To guarantee the objectivity of school physical education assessment, this paper plans a bunch of school physical education the board information framework in view of artificial intelligence technology. The weighting calculation in the understudy execution assessment module and educator execution assessment module in the framework takes on the canny calculation in light of FNN brain organization. In this review, the artificial intelligence and current technology of physical education have explored and talked about which to give the best hypothetical premise to the advanced technology of physical development and improvement. Obviously, artificial intelligence has a place with serious areas of strength for a disciplines which is growing yet in addition one of the principal heading of the PC and its connected advancement interdisciplinary examination, impacting the whole educating progress. Artificial intelligence enjoys many benefits in the use of educating technology.

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Section: Artificial Intelligence Evaluation and National Physical Edu...mentioning

confidence: 99%

Applications of Artificial Intelligence and IoT in the Development of Sports Training Education Management

Tang

Lü

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…These studies focus on determining survival, predicting gross outcome, and/or identifying predictive factors of a patient's condition after TBI (usually acute TBI). Recent studies (Pignolo Pignolo and Lagani, 2011) compare different machine learning classifiers (C4.5, Support Vector Machine, Naive Bayes, K-NN) in the early prediction of outcome of the subjects in vegetative state due to TBI. As previously mentioned, neural networks have also been applied e.g.…”

Section: Classificationmentioning

confidence: 99%

Understanding effects of cognitive rehabilitation under a knowledge discovery approach

García-Rudolph

Gibert

2016

Engineering Applications of Artificial Intelligence

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“…In (Pignolo & Lagani, 2011) compared four different machine learning methods (C4.5, SVM, Naïve Bayes and K-NN) to identify the most suitable algorithm in the prognostic evaluation of subjects in a vegetative state. They concluded that all tested algorithms are usable in this respect.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Supporting the design of sequences of cumulative activities impacting on multiple areas through a data mining approach : application to design of cognitive rehabilitation programs for traumatic brain injury patients

García Rudolph

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Traumatic brain injury (TBI) is a leading cause of disability worldwide. It is the most common cause of death and disability during the first three decades of life and accounts for more productive years of life lost than cancer, cardiovascular disease and HIV/AIDS combined. Cognitive Rehabilitation (CR), as part of Neurorehabilitation, aims to reduce the cognitive deficits caused by TBI. CR treatment consists of sequentially organized tasks that require repetitive use of impaired cognitive functions. While task repetition is not the only important feature, it is becoming clear that neuroplastic change and functional improvement only occur after a number of specific tasks are performed in a certain order and repetitions and does not occur otherwise. Until now, there has been an important lack of well-established criteria and on-field experience by which to identify the right number and order of tasks to propose to each individual patient. This thesis proposes the CMIS methodology to support health professionals to compose CR programs by selecting the most promising tasks in the right order. Two contributions to this topic were developed for specific steps of CMIS through innovative data mining techniques SAIMAP and NRRMR methodologies. SAIMAP (Sequence of Activities Improving Multi-Area Performance) proposes an innovative combination of data mining techniques in a hybrid generic methodological framework to find sequential patterns of a predefined set of activities and to associate them with multi-criteria improvement indicators regarding a predefined set of areas targeted by the activities. It combines data and prior knowledge with preprocessing, clustering, motif discovery and classes` post-processing to understand the effects of a sequence of activities on targeted areas, provided that these activities have high interactions and cumulative effects. Furthermore, this work introduces and defines the Neurorehabilitation Range (NRR) concept to determine the degree of performance expected for a CR task and the number of repetitions required to produce maximum rehabilitation effects on the individual. An operationalization of NRR is proposed by means of a visualization tool called SAP. SAP (Sectorized and Annotated Plane) is introduced to identify areas where there is a high probability of a target event occurring. Three approaches to SAP are defined, implemented, applied, and validated to a real case: Vis-SAP, DT-SAP and FT-SAP. Finally, the NRRMR (Neurorehabilitation Range Maximal Regions) problem is introduced as a generalization of the Maximal Empty Rectangle problem (MER) to identify maximal NRR over a FT-SAP. These contributions combined together in the CMIS methodology permit to identify a convenient pattern for a CR program (by means of a regular expression) and to instantiate by a real sequence of tasks in NRR by maximizing expected improvement of patients, thus provide support for the creation of CR plans. First of all, SAIMAP provides the general structure of successful CR sequences providing the length of the sequence and the kind of task recommended at every position (attention tasks, memory task or executive function task). Next, NRRMR provides specific tasks information to help decide which particular task is placed at each position in the sequence, the number of repetitions, and the expected range of results to maximize improvement after treatment. From the Artificial Intelligence point of view the proposed methodologies are general enough to be applied in similar problems where a sequence of interconnected activities with cumulative effects are used to impact on a set of areas of interest, for example spinal cord injury patients following physical rehabilitation program or elderly patients facing cognitive decline due to aging by cognitive stimulation programs or on educational settings to find the best way to combine mathematical drills in a program for a specific Mathematics course. El traumatismo craneoencefálico (TCE) es una de las principales causas de morbilidad y discapacidad a nivel mundial. Es la causa más común de muerte y discapacidad en personas menores de 30 años y es responsable de la pérdida de más años de vida productiva que el cáncer, las enfermedades cardiovasculares y el SIDA sumados. La Rehabilitación Cognitiva (RC) como parte de la Neurorehabilitación, tiene como objetivo reducir el impacto de las condiciones de discapacidad y disminuir los déficits cognitivos causados (por ejemplo) por un TCE. Un tratamiento de RC está formado por un conjunto de tareas organizadas de forma secuencial que requieren un uso repetitivo de las funciones cognitivas afectadas. Mientras que el número de ejecuciones de una tarea no es la única característica importante, es cada vez más evidente que las transformaciones neuroplásticas ocurren cuando se ejecutan un número específico de tareas en un cierto orden y no ocurren en caso contrario. Esta tesis propone la metodología CMIS para dar soporte a los profesionales de la salud en la composición de programas de RC, seleccionando las tareas más prometedoras en el orden correcto. Se han desarrollado dos contribuciones para CMIS mediante las metodologías SAMDMA y RNRRM basadas en técnicas innovadoras de minería de datos. SAMDMA (Secuencias de Actividades que Mejoran el Desempeño en Múltiples Áreas) propone una combinación de técnicas de minería de datos y un marco de trabajo genérico híbrido para encontrar patrones secuenciales en un conjunto de actividades y asociarlos con indicadores de mejora multi-criterio en relación a un conjunto de áreas hacia las cuales las actividades están dirigidas. Combina el uso de datos y conocimiento experto con técnicas de pre-procesamiento, clustering, descubrimiento de motifs y post procesamiento de clases. Además, se introduce y define el concepto de Rango de NeuroRehabilitación (RNR) para determinar el grado de performance esperado para una tarea de RC y el número de repeticiones que debe ejecutarse para producir mayores efectos rehabilitadores. Se propone una operacionalización del RNR por medio de una herramienta de visualización llamada Plano Sectorizado Anotado (PAS). PAS permite identificar áreas en las que hay una alta probabilidad de que ocurra un evento. Tres enfoques diferentes al PAS se definen, implementan, aplican y validan en un caso real : Vis-PAS, DT-PAS y FT-PAS. Finalmente, el problema RNRRM (Rango de NeuroRehabilitación de Regiones Máximas) se presenta como una generalización del problema del Máximo Rectángulo Vacío para identificar RNR máximos sobre un FT-PAS. La combinación de estas dos contribuciones en la metodología CMIS permite identificar un patrón conveniente para un programa de RC (por medio de una expresión regular) e instanciarlo en una secuencia real de tareas en RNR maximizando las mejoras esperadas de los pacientes, proporcionando soporte a la creación de planes de RC. Inicialmente, SAMDMA proporciona la estructura general de secuencias de RC exitosas para cada paciente, proporcionando el largo de la secuencia y el tipo de tarea recomendada en cada posición. RNRRM proporciona información específica de tareas para ayudar a decidir cuál se debe ejecutar en cada posición de la secuencia, el número de veces que debe ser repetida y el rango esperado de resultados para maximizar la mejora. Desde el punto de vista de la Inteligencia Artificial, ambas metodologías propuestas, son suficientemente generales como para ser aplicadas a otros problemas de estructura análoga en que una secuencia de actividades interconectadas con efectos acumulativos se utilizan para impactar en un conjunto de áreas de interés. Por ejemplo pacientes lesionados medulares en tratamiento de rehabilitación física, personas mayores con deterioro cognitivo debido al envejecimiento y utilizan programas de estimulación cognitiva, o entornos educacionales para combinar ejercicios de cálculo en un programa específico de Matemáticas.

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Prediction of Outcome in the Vegetative State by Machine Learning Algorithms: A Model for Clinicians?

Abstract: ABSTRACT

Cited by 8 publications

References 21 publications

Applications of Artificial Intelligence and IoT in the Development of Sports Training Education Management

Applications of Artificial Intelligence and IoT in the Development of Sports Training Education Management

Understanding effects of cognitive rehabilitation under a knowledge discovery approach

Supporting the design of sequences of cumulative activities impacting on multiple areas through a data mining approach : application to design of cognitive rehabilitation programs for traumatic brain injury patients

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