MINING TEMPORAL PATTERNS FROM SEQUENTIAL HEALTHCARE DATA

MINING TEMPORAL PATTERNS FROM SEQUENTIAL HEALTHCARE DATA
Author(s):
Prashant Bharti, Vijay saboo, Mayank kumar, S.Satheesh kumar
ISSN:
2349-6002
Cite This Article:
MINING TEMPORAL PATTERNS FROM SEQUENTIAL HEALTHCARE DATA, International Journal of Innovative Research in Technology(www.ijirt.org) ,ISSN: 2349-6002 ,Volume 5 ,Issue 11 ,Page(s):528-530 ,April 2019 ,Available :IJIRT147940_PAPER.pdf
Keywords:
Electronic health records, Left Ventricular Assist Device, temporal data
Abstract
Mining Temporal patterns from sequential healthcare data is a simple algorithmic prediction project that helps individuals to identify the disease in advance. Electronic health records (EHRs) can be represented as sequences of time-stamped events. These are used to predict the cause and the nature of disease keeping in mind the previous records. Therefore, temporal patterns, such as transitions between clinical events over time, can be extracted using temporal mining. A methodological approach is used to extract patterns of transitions between adverse events after Left Ventricular Assist Device (LVAD) implant in patients with advanced heart failure. The project includes various features that could enable prevent diseases as well as keep a person updated and healthy.
Article Details
Unique Paper ID: 147940 Publication Volume & Issue: Volume 5, Issue 11 Page(s): 528 - 530
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International Journal of Innovative Research in Technology