Sequence-Walking Decision Tree for Multivariate Healthcare Data

Patients, whether they are currently hospitalized or cured at home via telemedicine, are constantly monitored by stationary/wearable devices that provide accurate, yet an overwhelming amount of temporal data. Due to the heterogeneous nature of such data, reconciling them in order to support decision...

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Bibliographic Details
Published in2024 IEEE 12th International Conference on Healthcare Informatics (ICHI) pp. 21 - 30
Main Authors Sala, Pietro, Zare, Omid
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.06.2024
Subjects
Association rule learning
Codes
Data Mining
Data models
Decision Tree
Decision trees
Event Logs
Medical services
Telemedicine
Temporal Data
Turning
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Summary:Patients, whether they are currently hospitalized or cured at home via telemedicine, are constantly monitored by stationary/wearable devices that provide accurate, yet an overwhelming amount of temporal data. Due to the heterogeneous nature of such data, reconciling them in order to support decisions about the patient in an effective, interpretable, and explainable way is not a trivial task. In this paper, we propose a framework for turning a large collection of heterogeneous intrapatient data into a uniform representation based on event-logs; moreover, on such a representation, we introduce a new tree-based decision model for performing tasks such as classification, early detection, data mining, and cluster-based similarity.
ISSN:2575-2634
DOI:10.1109/ICHI61247.2024.00011