Modelo predictivo de mineria de datos de apoyo a la gestion hospitalaria sobre la morbilidad de pacientes hospitalizados

La minería de datos en el sector salud permite identificar los perfiles de salud en los pacientes, ayuda a comprender el patrón de comportamiento, a través del historial de información almacenada que hace posible su gestión cotidiana, siendo así esta información diversa y compleja. El presente trabajo de investigación, propone aplicar un marco estándar de actividades de minería datos, creando un modelo predictivo, que sirva de apoyo a la Gestión Hospitalaria sobre la morbilidad con pacientes hospitalizados, basado en el algoritmo de análisis de serie de tiempo, Modelo ARIMA (AutoRegresive Integrated Moving Average) de Box y Jenkis (Box G.E.P. & Jenkins, 1973), con información histórica de los últimos 7 años de los pacientes del Hospital Víctor Ramos Guardia. En la investigación, se tomó como referencia la metodología CRISP-DM (Cross Industry Standard Process For Data Mining), que consiste en la comprensión del negocio, comprensión de los datos, preparación de los datos, modelado, evaluación y despliegue. Por lo tanto se realizó la extracción de los datos, transformación de los datos, carga de datos, limpieza de datos, diseño del datamart “HEALTHMINING”, la selección y creación de variables que sirvieron como datos de entrada para mi modelo, para posteriormente crear un modelo de pronósticos, que me permitió conocer los casos de morbilidad en pacientes hospitalizados del hospital VRG para los próximos tres años.Data mining in the health care sector identifies the health profiles of patients, helps to understand the pattern of behavior of patients through history stored information from your transactional system. This research proposes to apply a standard framework for data mining, creating a predictive model, which supports the Hospital Management on morbidity in hospitalized patients, based algorithm for time series analysis, ARIMA Model (AutoRegresive Integrated Moving Average) of Box and Jenkins (Box GEP & Jenkins, 1973), based on historical data to predict future or unknown values. In research, reference was made to the CRISP-DM methodology (Cross Industry Standard Process for Data Mining), which consists of business understanding, data understanding, data preparation, modeling, evaluation and deployment. Therefore the data extraction , data transformation , data loading , data cleansing , data mart design "" HEALTHMINING "" the selection and creation of variables that were used as input to my model, was performed to create later forecasting model , which allowed me to hear cases of morbidity in hospitalized patients in the hospital VRG for the next three years.Tesi

Integrated Framework of Knowledge Discovery and Knowledge Management for E-health In Saudi Arabia: Supporting Citizens with Diabetes Mellitus

Saudi Arabia experiences insufficient effort in terms of patients’ education in relation to a number of prevalent diseases, including diabetes mellitus, musculoskeletal disorders and upper respiratory tract infections. In addition, the number of studies related to e-health initiatives to support patients in the Kingdom are limited and only benefit patients of a few hospitals. This situation leads to deficient application of self-management and education strategies to empower patients to manage their diseases. Unfortunately, such a deficiency can affect the health status in the Kingdom negatively as diabetes mellitus is reported as the first cause of death in the Kingdom among all other prevalent diseases. Although knowledge management has been proven to be a valuable approach to sharing knowledge and educating users to manage their illnesses, it has not been implemented appropriately to support the increasing number of diabetic citizens in Saudi Arabia. In this research, knowledge management is integrated with knowledge discovery to support specific needs of the diabetic community in the Kingdom. Such an integration constitutes an e-health initiative to support diabetic citizens and healthcare professionals to manage this expanding illness in Saudi Arabia. Knowledge discovery is implemented through data mining to elicit useful knowledge related to specific diabetes complications encountered by diabetic citizens in the Kingdom. The integrated framework applies the SECI model to capture and disseminate useful diabetes self-management and educational expertise to support the management of diabetes complications. This integrated approach to knowledge management and knowledge discovery has provided a valuable tool implemented in terms of a web portal. This has facilitated the exchange and dissemination of tacit and explicit knowledge of the diabetic community in the forms of strategies, guidelines and best practices. It has also overcome the issues faced by the organisational and national cultures affecting knowledge management practice in Saudi Arabia