Chapters authored
Temporal Clustering for Behavior Variation and Anomaly Detection from Data Acquired Through IoT in Smart Cities
In this chapter, we propose a methodology for behavior variation and anomaly detection from acquired sensory data, based on temporal clustering models. Data are collected from five prominent European smart cities, and Singapore, that aim to become fully “elderly-friendly,” with the development and deployment of ubiquitous systems for assessment and prediction of early risks of elderly Mild Cognitive Impairments (MCI) and frailty, and for supporting generation and delivery of optimal personalized preventive interventions that mitigate those risks, utilizing smart city datasets and IoT infrastructure. Low level data collected from IoT devices are preprocessed as sequences of activities, with temporal and causal variations in sequences classified as normal or anomalous behavior. The goals of proposed methodology are to (1) recognize significant behavioral variation patterns and (2) support early identification of pattern changes. Temporal clustering models are applied in detection and prediction of the following variation types: intra-activity (single activity, single citizen) and inter-activity (multiple-activities, single citizen). Identified behavioral variations and anomalies are further mapped to MCI/frailty onset behavior and risk factors, following the developed geriatric expert model.
Part of the book: Recent Applications in Data Clustering
Early Prediction of Patient Mortality Based on Routine Laboratory Tests and Predictive Models in Critically Ill Patients
We propose a method for quantitative analysis of predictive power of laboratory tests and early detection of mortality risk by usage of predictive models and feature selection techniques. Our method allows automatic feature selection, model selection, and evaluation of predictive models. Experimental evaluation was conducted on patients with renal failure admitted to ICUs (medical intensive care, surgical intensive care, cardiac, and cardiac surgery recovery units) at Boston’s Beth Israel Deaconess Medical Center. Data are extracted from Multi parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) database. We built and evaluated different single (e.g. Logistic regression) and ensemble (e.g. Random Forest) learning methods. Results revealed high predictive accuracy (area under the precision-recall curve (AUPRC) values >86%) from day four, with acceptable results on the second (>81%) and third day (>85%). Random forests seem to provide the best predictive accuracy. Feature selection techniques Gini and ReliefF scored best in most cases. Lactate, white blood cells, sodium, anion gap, chloride, bicarbonate, creatinine, urea nitrogen, potassium, glucose, INR, hemoglobin, phosphate, total bilirubin, and base excess were most predictive for hospital mortality. Ensemble learning methods are able to predict hospital mortality with high accuracy, based on laboratory tests and provide ranking in predictive priority.
Part of the book: Data Mining