Abstract-Clinical Decision Support Systems (CDSS) have been used widely since 2000s to improve the healthcare quality. CDSS can be utilized to support healthcare services as a tool to diagnose, predict, as well as to provide clinical interpretation, alert, and reminder. There are many researches of CDSS implementation on literatures but not many of them present the evidence of CDSS successful implementation. In spite of the potential use of CDSS, there are some researches that reveal the failures of CDSS implementation. This paper contributes to CDSS development by investigating and exploring CDSS success factors with usability testing. The testing involves participants from different types of backgrounds (physicians, IT developers, and students). The participants are being asked to experience three different CDSS to predict cardiovascular risk factors. The result of the research shows that involving different type of users give more insight to design process. It can be concluded that user center design is very critical to produce successful CDSS.
Abstract-Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks (BP ANN) has some limitations. There are parameters to be set in the beginning, long time for training process, and possibility to be trapped in local minima. In this research, we implemented ANN with extreme learning techniques for diagnosing breast cancer based on Breast Cancer Wisconsin Dataset. Results showed that Extreme Learning Machine Neural Networks (ELM ANN) has better generalization classifier model than BP ANN. The development of this technique is promising as intelligent component in medical decision support systems.
Malaria is a communicable disease caused by a plasmodium parasite and transmitted among human by Anopheles mosquitoes. Late medication of this disease can cause a death of patients. Indonesia has many endemic areas with a high volume of patients diagnosed by Malaria. Currently, this incidences data is stored in Microsoft Excel files. We need to build a data warehouse to easily manage these data. Here, we create ontology of Malaria's incidence data to figure out the important information in Malaria data warehouse that we want to build. We identify entities, classes, subclasses, and relationships between these entities. We employed Protégé to build and visualize the ontology of Malaria incidence data. Keywords: ontology, data warehouse, malaria, visualization, Protégé AbstrakMalaria adalah penyakit menular yang disebabkan oleh parasite plasmodium dan dipindahkan ke tubuh manusian oleh Nyamuk Anopheles. Penanganan yang lambat terhadap penyakit malaria dapat menyebabkan kematian pasien. Indonesia adalah salah satu negara yang memiliki banyak wilayah endemik dengan volume yang cukup tinggi terhadap pasien didiagnosis penyakit malaria. Saat ini, data dari kasus-kasus malaria di berbagai wilayah endemic di Indonesia masih disimpan dalam banyak file excel. Akibatnya, terdapat kesulitan untuk memperoleh informasi yang cepat dalam pengambilan keputusan untuk penanganan kasus malaria. Oleh karena itu, perlu dibangun sebuah data warehouse untuk mengatur data tersebut secara terpusat. Pada artikel ini, dibuat ontology untuk mengidentifikasi informasi dan parameter penting dari elemen-elemen yang harus ada dalam data warehouse, seperti: entitas, kelas, sub-kelas, dan hubungan antar entitas. Protégé digunakan untuk membangun visualisasi dan memudahkan pemahaman terhadap hubungan antar entitas dalam data kasus malaria.
Statistical Downscaling modeling is a technique in climatology that uses statistical modeling to analyze the relationship between large-scale data (global) and small-scale data (local). General Circulation Model is a numerical model that produces many data from various climate parameters such as precipitation, temperature, and humidity for the need for climate forecasting. Statistical Downscaling modeling to estimate monthly rainfall in areas that have a monsoon rainfall pattern in Indonesia had been carried out using the L1/Lasso Regulation and Principal Component Analysis, Spatio Temporal Bayesian Regression, Spatio Temporal Generalized Linear Mixed Model, and Geographically and Temporally Weighted Regression. Monthly rainfall data are spatial and temporal heterogeneity and are not normally distributed because of non-negative values and skew to the right. One approach to analyze the data is using the Geographically and Temporally Weighted Gamma Regression method that was developed from Geographically and Temporally Weighted Regression using Gamma distribution and parameter estimation using the Maximum Likelihood Estimation method. This study will conduct this modelling using response variables of monthly rainfall data from 35 stations in West Java Province from January 2010 to December 2012, and predictor variables are monthly rainfall of the previous period, monthly precipitation from the General Circulation Model from the National Centers for Environmental Prediction in the form of a Climate Forecast System Reanalysis model. The study results show that Geographically and Temporally Weighted Gamma Regression modelling using the Gaussian kernel function and fixed bandwidth on Statistical Downscaling can predict monthly rainfall in a location in West Java Province for a certain period. Based on this model generated at a location, changes in a predictor variable can be seen in the value of monthly rainfall in a period. The combination of this model with the Kriging Spherical interpolation can estimate the monthly rainfall value in locations that are not observed.
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