Mining biomedical time series by combining structural analysis and temporal abstractions.

R. Bellazzi, P. Magni, C. Larizza, G. De Nicolao, A. Riva, M. Stefanelli

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

This paper describes the combination of Structural Time Series analysis and Temporal Abstractions for the interpretation of data coming from home monitoring of diabetic patients. Blood Glucose data are analyzed by a novel Bayesian technique for time series analysis. The results obtained are post-processed using Temporal Abstractions in order to extract knowledge that can be exploited "at the point of use" from physicians. The proposed data analysis procedure can be viewed as a Knowledge Discovery in Data Base process that is applied to time-varying data. The work here described is part of a Web-based telemedicine system for the management of Insulin Dependent Diabetes Mellitus patients, called T-IDDM.

Original languageEnglish
Pages (from-to)160-164
Number of pages5
JournalProceedings / AMIA ... Annual Symposium. AMIA Symposium
Publication statusPublished - 1998

Fingerprint

Type 1 Diabetes Mellitus
Telemedicine
Physiologic Monitoring
Blood Glucose
Databases
Physicians

Cite this

Mining biomedical time series by combining structural analysis and temporal abstractions. / Bellazzi, R.; Magni, P.; Larizza, C.; De Nicolao, G.; Riva, A.; Stefanelli, M.

In: Proceedings / AMIA ... Annual Symposium. AMIA Symposium, 1998, p. 160-164.

Research output: Contribution to journalArticle

@article{6c781c25d13647d9ad4612e38e999a0b,
title = "Mining biomedical time series by combining structural analysis and temporal abstractions.",
abstract = "This paper describes the combination of Structural Time Series analysis and Temporal Abstractions for the interpretation of data coming from home monitoring of diabetic patients. Blood Glucose data are analyzed by a novel Bayesian technique for time series analysis. The results obtained are post-processed using Temporal Abstractions in order to extract knowledge that can be exploited {"}at the point of use{"} from physicians. The proposed data analysis procedure can be viewed as a Knowledge Discovery in Data Base process that is applied to time-varying data. The work here described is part of a Web-based telemedicine system for the management of Insulin Dependent Diabetes Mellitus patients, called T-IDDM.",
author = "R. Bellazzi and P. Magni and C. Larizza and {De Nicolao}, G. and A. Riva and M. Stefanelli",
year = "1998",
language = "English",
pages = "160--164",
journal = "Proceedings / AMIA . Annual Symposium. AMIA Symposium",
issn = "1531-605X",
publisher = "Hanley & Belfus",

}

TY - JOUR

T1 - Mining biomedical time series by combining structural analysis and temporal abstractions.

AU - Bellazzi, R.

AU - Magni, P.

AU - Larizza, C.

AU - De Nicolao, G.

AU - Riva, A.

AU - Stefanelli, M.

PY - 1998

Y1 - 1998

N2 - This paper describes the combination of Structural Time Series analysis and Temporal Abstractions for the interpretation of data coming from home monitoring of diabetic patients. Blood Glucose data are analyzed by a novel Bayesian technique for time series analysis. The results obtained are post-processed using Temporal Abstractions in order to extract knowledge that can be exploited "at the point of use" from physicians. The proposed data analysis procedure can be viewed as a Knowledge Discovery in Data Base process that is applied to time-varying data. The work here described is part of a Web-based telemedicine system for the management of Insulin Dependent Diabetes Mellitus patients, called T-IDDM.

AB - This paper describes the combination of Structural Time Series analysis and Temporal Abstractions for the interpretation of data coming from home monitoring of diabetic patients. Blood Glucose data are analyzed by a novel Bayesian technique for time series analysis. The results obtained are post-processed using Temporal Abstractions in order to extract knowledge that can be exploited "at the point of use" from physicians. The proposed data analysis procedure can be viewed as a Knowledge Discovery in Data Base process that is applied to time-varying data. The work here described is part of a Web-based telemedicine system for the management of Insulin Dependent Diabetes Mellitus patients, called T-IDDM.

UR - http://www.scopus.com/inward/record.url?scp=0032254515&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032254515&partnerID=8YFLogxK

M3 - Article

SP - 160

EP - 164

JO - Proceedings / AMIA . Annual Symposium. AMIA Symposium

JF - Proceedings / AMIA . Annual Symposium. AMIA Symposium

SN - 1531-605X

ER -