Diagnóstico de TDAH con Machine Learning y Sensores: Un Mapeo Sistemático
Palabras clave:
TDAH, Aprendizaje Maquina, Diagnostico, Trastorno por déficit de atención con hiperactividad, SensoresResumen
El trastorno de déficit de atención con hiperactividad (TDAH) es un trastorno del neurodesarrollo que tiene como características principales la hiperactividad y la falta concentración en actividades cotidianas. Es un trastorno conocido por darse a tempranas edades y afectar a el desempeño escolar. El diagnóstico del TDAH ha sido un procedimiento complejo, al necesitar una gran recolección y análisis de datos de manera manual. Por esto mismo, se ha propuesto el uso de las herramientas de machine learning (ML) para mejorar la precisión y el tiempo del diagnóstico. Actualmente, existen una proliferación de las metodologías que reconocen diferentes tipos de datos de pacientes para el diagnóstico del TDAH. El objetivo de este estudio es revisar dentro del estado del arte las investigaciones previas sobre está área y responder los cuestionamientos planteados. En el artículo se discutirán temas sobre los modelos de machine learning y los diferentes datos recolectados. Entre los resultados se ve como modelos de redes neuronales y Support Vector Machine (SVM) son los de mayor frecuencia. Además, que los datos de movimiento y aceleración tienen un mayo desempeño a comparación de las implementaciones por procesos neuronales a contra posición de lo esperado. Podemos concluir que los estudios relacionados a la adquisición de datos de movimiento tienen una gran promesa en su implementación y desarrollo en tiempos futuros para una mayor precisión en los resultados clínicos.Citas
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