2,912 research outputs found

    An Investigation on Disease Diagnosis and Prediction by Using Modified K-Mean clustering and Combined CNN and ELM Classification Techniques

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    Data analysis is important for managing a lot of knowledge in the healthcare industry. The older medical study favored prediction over processing and assimilating a massive volume of hospital data. The precise research of health data becomes advantageous for early disease identification and patient treatment as a result of the tremendous knowledge expansion in the biological and healthcare fields. But when there are gaps in the medical data, the accuracy suffers. The use of K-means algorithm is modest and efficient to perform. It is appropriate for processing vast quantities of continuous, high-dimensional numerical data. However, the number of clusters in the given dataset must be predetermined for this technique, and choosing the right K is frequently challenging. The cluster centers chosen in the first phase have an impact on the clustering results as well. To overcome this drawback in k-means to modify the initialization and centroid steps in classification technique with combining (Convolutional neural network) CNN and ELM (extreme learning machine) technique is used. To increase this work, disease risk prediction using repository dataset is proposed. We use different types of machine learning algorithm for predicting disease using structured data. The prediction accuracy of using proposed hybrid model is 99.8% which is more than SVM (support vector machine), KNN (k-nearest neighbors), AB (AdaBoost algorithm) and CKN-CNN (consensus K-nearest neighbor algorithm and convolution neural network)

    Network analysis of unstructured EHR data for clinical research.

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    In biomedical research, network analysis provides a conceptual framework for interpreting data from high-throughput experiments. For example, protein-protein interaction networks have been successfully used to identify candidate disease genes. Recently, advances in clinical text processing and the increasing availability of clinical data have enabled analogous analyses on data from electronic medical records. We constructed networks of diseases, drugs, medical devices and procedures using concepts recognized in clinical notes from the Stanford clinical data warehouse. We demonstrate the use of the resulting networks for clinical research informatics in two ways-cohort construction and outcomes analysis-by examining the safety of cilostazol in peripheral artery disease patients as a use case. We show that the network-based approaches can be used for constructing patient cohorts as well as for analyzing differences in outcomes by comparing with standard methods, and discuss the advantages offered by network-based approaches

    Methodologies of Legacy Clinical Decision Support System -A Review

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    Information technology playing a prominent role in the field of medical by incorporating the Clinical Decision Support System(CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at the right time. Now a day's Clinical decision support system is a dynamic research area in the field of computer, but the lack of the knowledge of the understanding as well as the functioning of the system ,make the adoption slow by the physician and patient. The literature review of this paper will focus on the overview of legacy CDSS, the kind of methodologies and classifier employed to prepare such decision support system using a non-technical approach to the physician and the strategy- makers . This study will provide the scope of understanding the clinical decision support along with the gateway to physician ,policy-makers to develop and deploy the decision support system as a healthcare service to make the quick, agile and right decision. Future direction to handle the uncertainties along with the challenges of clinical decision support system are also enlightened in this study

    Early hospital mortality prediction using vital signals

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    Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the ICU, achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE 2018 and published in Smart Health journa
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