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Full-Text Articles in Computer Sciences
A Multi-Model Approach In Developing An Intelligent Assistant For Diagnosis Recommendation In Clinical Health Systems, Christian E. Pulmano, Ma. Regina Justina E. Estuar
A Multi-Model Approach In Developing An Intelligent Assistant For Diagnosis Recommendation In Clinical Health Systems, Christian E. Pulmano, Ma. Regina Justina E. Estuar
Department of Information Systems & Computer Science Faculty Publications
Clinical health information systems capture massive amounts of unstructured data from various health and medical facilities. This study utilizes unstructured patient clinical text data to develop an intelligent assistant that can identify possible related diagnoses based on a given text input. The approach applies a one-vs-rest binary classification technique wherein given an input text data, it is identified whether it can be positively or negatively classified for a given diagnosis. Multi-layer Feed-Forward Neural Network models were developed for each individual diagnosis case. The task of the intelligent assistant is to iterate over all the different models and return those that …
Rice Blast Disease Forecasting For Northern Philippines, Proceso L. Fernandez Jr, Alvin R. Malicdem
Rice Blast Disease Forecasting For Northern Philippines, Proceso L. Fernandez Jr, Alvin R. Malicdem
Department of Information Systems & Computer Science Faculty Publications
Rice blast disease has become an enigmatic problem in several rice growing ecosystems of both tropical and temperate regions of the world. In this study, we develop models for predicting the occurrence and severity of rice blast disease, with the aim of helping to prevent or at least mitigate the spread of such disease. Data from 2 government agencies in selected provinces from northern Philippines were gathered, cleaned and synchronized for the purpose of building the predictive models. After the data synchronization, dimensionality reduction of the feature space was done, using Principal Component Analysis (PCA), to determine the most important …