| Summary: | 博士 === 元智大學 === 資訊管理學系 === 105 === Intensive care is very important in modern health care. Mortality prediction models are good outcome predictors for intensive care and resources allocation. Many research used the information technologies to construct new mortality prediction models. Healthcare professionals need to utilize intensive care resources effectively. Mortality prediction models help physicians decide which patients require intensive care the most and which do not. There are several approaches in mortality prediction models construction. The first approach is the tendency of mortality. The models of this stage are the tendency of severity for patients. The Glasgow Coma Scale (GCS) is also one of the severe tendency models. These models focused on the tendency of the mortalities which are pure scoring system but not probabilities system. The second approach is the probabilities approach. The APACHE II and SAPS II are two most popular models.
These models were constructed with probity regression and used the probabilities as the outcome description of mortality. The Mortality Probability Model 2nd version (MPM II) is also one of the ICU outcome prediction models with probabilities.
Artificial intelligence technologies have since been built into mortality prediction models, and form the basis of the information technology approach. These models are more accurate than the traditional models. This study retrospectively collected data on 695 patients admitted to intensive care units and constructed a novel mortality prediction model with logarithm least-squares support vector regression (LLS-SVR) and cautious random particle swarm optimization (CRPSO). LLS-SVR-CRPSO was employed to optimally select the parameters of the hybrid system. Logarithm Least-Squares Support Vector Regression (LLS-SVR) has been applied in addressing forecasting problems in various fields, including bioinformatics, financial time series, electronics, plastic injection molding, chemistry and cost estimations. Cautious Random Particle Swarm Optimization (CRPSO) uses random values that allow pbest and gbest to be adjusted to the correct weight using a random value. CRPSO limits the random value to be conditional, to avoid premature convergence into a local optimum. If the random value is greater than 0.8, another random value is chosen. The movement of the range (cautious flow) is controlled to avoid premature convergence. This new mortality model can offer agile support for physicians' intensive care decision-making.
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