Incremental Learning of Medical Data for Multi-Step Patient Health Classification

In this chapter we presented major challenges derived out of next generation mobile health surveillance. For the emergency detection task we highlighted semi-automated classification as ongoing research in this field. We showed how novel index-based classifiers build the core for multivariate multi-step classification in health surveillance. By supporting anytime learning and anytime classification the presented Bayes tree technique can handle huge amounts of data, which makes it a consistent solution for the described medical scenario. Moreover, as we laid out in this chapter, the Bayes tree fulfills all requirements which are crucial for classifying medical patient data in a scalable health surveillance.

Future challenges include extending the existing framework and evaluating the Bayes tree classifier based on sensor measurements in a broad health surveillance project. This project will include extensions of textile sensors, body sensors and preprocessing techniques as well as the integration and merging of sensor data in electronic health record systems. Emergency detection on multiple levels will show the benefits of multi-step classification and further enhance the scalability of emergency detection systems.

Authors: Kranen P., Müller E., Assent I., Krieger R., Seidl T.
Published in: Plant C., Böhm C. (eds.): Database Technology for Life Sciences and Medicine, World Scientific Publishing
Publisher: World Scientific - Singapore
Sprache: EN
Jahr: 2010
Seiten: 321-344
ISBN: 978-981-4307-70-3
ISSN: 1793-3692
URL:World Scientific Publishing
Typ: Buchbeiträge
Forschungsgebiet: Data Analysis and Knowledge Extraction