Sudden cardiac death (SCD) is one of continuing challenges to the modern clinician. It is responsible for an estimated 400,000 deaths per year in the United States and millions of deaths worldwide. This research developed a personal cardiac homecare system by sensing Lead-I ECG signals for detecting and predicting SCD events, which also builds in ECG identity verification. A MIT/BIH SCD Holter database plus our ECG database were investigated. The system includes a self-made ECG amplifier, a NI DAQ card, a laptop computer, Lab View and MatLab programs. The wavelet analysis was applied to detect SCD and the overall performance is 87.5% correct detection rate. In addition, artificial neural networks (ANN) were used to predict SCD events. The correct prediction rates by applying least mean square (LMS), decision based neural network (DBNN), and back propagation (BP) neural network were 67.44%, 58.14% and 55.81% respectively.

Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69–0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69–0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.

High-throughput microarray experiments now permit researchers to screen thousands of genes simultaneously and determine the different expression levels of genes in normal or cancerous tissues. In this paper, we address the challenge of selecting a relevant and manageable subset of genes from a large microarray dataset. Currently, most gene selection methods focus on identifying a set of genes that can further improve classification accuracy. Few or none of these small sets of genes, however, are biologically relevant (i.e. supported by medical evidence). To deal with this critical issue, we propose two novel methods that can identify biologically relevant genes concerning cancers.