An Association Rule Analysis Framework for Complex Physiological and Genetic Data

Ahmed

2013

Sensors

383

180

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

Section: Data Mining Approachmentioning

confidence: 99%

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

Ahmed

2013

Sensors

383

180

“…In (Combi and Sabaini, 2013), the authors present temporal rule extraction for physiological data and address the problem of visually analysing this kind of data. (He et al, 2012) propose a novel multivariate association rule mining based on change detection for complex data set including numerical data streams. The authors in (Muflikhah et al, 2013) introduce an approach to generate the rules automatically from the linguistic data of coronary heart disease using subtractive clustering and fuzzy inference in order to determine the diagnosis of disease.…”

Section: Introductionmentioning

confidence: 99%

Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data

Ahmed

Proceedings of the International Conference on Health Informatics

2015

This paper presents an approach to automatically mine rules in time series data representing physiological parameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined for each physiological time series data. The generated rules based on the prototypical patterns are then described in a textual representation which captures trends in each physiological parameter and their relation to the other physiological data. In this paper, a method for measuring similarity of rule sets is introduced in order to validate the uniqueness of rule sets. This method is evaluated on physiological records from clinical classes in the MIMIC online database such as angina, sepsis, respiratory failure, etc.. The results show that the rule mining technique is able to acquire a distinctive model for each clinical condition, and represent the generated rules in a human understandable textual representation.

Data-Driven Rule Mining and Representation of Temporal Patterns in Physiological Sensor Data

IEEE J. Biomed. Health Inform.

2015

Mining and representation of qualitative patterns is a growing field in sensor data analytics. This paper leverages from rule mining techniques to extract and represent temporal relation of prototypical patterns in clinical data streams. The approach is fully data-driven, where the temporal rules are mined from physiological time series such as heart rate, respiration rate, and blood pressure. To validate the rules, a novel similarity method is introduced, that compares the similarity between rule sets. An additional aspect of the proposed approach has been to utilize natural language generation techniques to represent the temporal relations between patterns. In this study, the sensor data in the MIMIC online database was used for evaluation, in which the mined temporal rules as they relate to various clinical conditions (respiratory failure, angina, sepsis, …) were made explicit as a textual representation. Furthermore, it was shown that the extracted rule set for any particular clinical condition was distinct from other clinical conditions.