ABSTRACT
El amplio acceso a computadoras de alto desempeño y dispositivos electrónicos de gran almacenamiento, entre otros, ha permitido en los últimos años la generación de cantidades masivas de datos, concepto que puede ser representado por Velocidad, Volumen y Variabilidad. La Minería de Datos es un proceso que permite descubrir patrones o asociaciones relevantes, no plenamente descubiertas en principio con los métodos tradicionales de análisis, en grandes bases de datos y generar modelos. Para ello, usa herramientas de áreas tales como Sistemas de Bases de Datos, Almacenamiento, Aprendizaje Automático, Estadística, Visualización de la Información y Computación de Alto Desempeño. En las últimas décadas, la biología molecular ha pasado del análisis de genes individuales a estudios más complejos que abarcan el genoma completo de un individuo. El desarrollo de tecnologías genómicas de alto desempeño, como los microarrays y la secuenciación de próxima generación (NGS), ha hecho posible producir de manera exponencial información, con la expansión de nuestro conocimiento de las bases genéticas de varias enfermedades. En la Medicina Genómica, el uso de la Minería de Datos para el análisis de la información genómica se está convirtiendo en una necesidad cada vez más buscada, contribuyendo así hacia una medicina personalizada tal que permite inferir modelos clínicamente relevantes y definir estrategias terapéuticas individualizadas a partir de datos moleculares de pacientes. (AU)
The availability of use of high-performance computers and large-storage electronic devices, among others, has allowed the generation of a huge masses of digital data, an idea that can be represented by velocity, volume and variety. Data mining is a process that permits to discover relevant patterns or relations, not previously seen with traditional methods of analysis, in large databases and generate models. It uses tools from Database Systems, Data Warehouse, Machine Learning, Statistics, Information Visualization and High-Performance Computing. In the last decades, molecular biology has moved from individual gene analysis to more complex studies that involve the complete genome. The development of high-throughput genomic technologies, such as microarrays and next-generation sequencing, has promoted the exponential growth of a huge amount of information, expanding our knowledge on the genetic basis of various diseases. In genomics medicine, the application of data mining techniques has become an increasingly important process that contributes towards a personalized medicine, that involves the inference of clinically relevant models and defines individualized therapeutic strategies based on the molecular data of patients. (AU)
Subject(s)
Humans , Genomics/methods , Data Mining , Autistic Disorder/genetics , Computers, Mainframe , Information Management , Machine Learning , Data Warehousing , Data AnalysisABSTRACT
Hospitals are experiencing an increasing amount of financial difficulty due to government control of hospital rates since national health insurance has been implemented. The decision support system(DSS) was developed to provide cost and revenue information for the services rendered by each department in an effect to reduce costs. This information may be used to identify the causes of financial loss if cost exceeds revenue and to conduct variance analysis or portfolio analysis to improve financial situation of hospitals. The DSS was developed using a micro-mainframe interface approach where the mainframe computer collects and summarizes daily cost and revenue data and the micro computer computes the cost for each department. The significances of this paper are to determine the cost allocation basis and methods which are suitable to Korean situation and to apply DSS technology to the cost analysis.