Article

Disease progression modeling from historical clinical databases

Authors:

Ronald K. Pearson,

Robert J. Kingan,

Alan HochbergAuthors Info & Claims

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

Pages 788 - 793

https://doi.org/10.1145/1081870.1081974

Published: 21 August 2005 Publication History

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Abstract

This paper considers the problem of modeling disease progression from historical clinical databases, with the ultimate objective of stratifying patients into groups with clearly distinguishable prognoses or suitability for different treatment strategies. To meet this objective, we describe a procedure that first fits clinical variables measured over time to a disease progression model. The resulting parameter estimates are then used as the basis for a stepwise clustering procedure to stratify patients into groups with distinct survival characteristics. As a practical illustration, we apply this procedure to survival prediction, using a liver transplant database from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).

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Zhou ZYu HWang G(2023)Ensemble Learning with Time Accumulative Effect for Early Diagnosis of Alzheimer’s DiseaseArtificial Intelligence10.1007/978-981-99-9119-8_13(136-146)Online publication date: 22-Jul-2023
https://dl.acm.org/doi/10.1007/978-981-99-9119-8_13
Ismail AShehab AEl-Henawy I(2018)Healthcare Analysis in Smart Big Data Analytics: Reviews, Challenges and RecommendationsSecurity in Smart Cities: Models, Applications, and Challenges10.1007/978-3-030-01560-2_2(27-45)Online publication date: 5-Nov-2018
https://doi.org/10.1007/978-3-030-01560-2_2
Nie LZhang LMeng LSong XChang XLi X(2017)Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer’s DiseaseIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2016.252096428:7(1508-1519)Online publication date: Jul-2017
https://doi.org/10.1109/TNNLS.2016.2520964
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Index Terms

Disease progression modeling from historical clinical databases
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
    2. Health informatics
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis

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Published In

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

August 2005

844 pages

ISBN:159593135X

DOI:10.1145/1081870

General Chair:
Robert Grossman
University of Illinois at Chicago & Open Data Partners, USA
,
Program Chairs:
Roberto Bayardo
IBM Almaden Research, USA
,
Kristin Bennett
RPI, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2005

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KDD05

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KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2005

Illinois, Chicago, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

View all

Zhou ZYu HWang G(2023)Ensemble Learning with Time Accumulative Effect for Early Diagnosis of Alzheimer’s DiseaseArtificial Intelligence10.1007/978-981-99-9119-8_13(136-146)Online publication date: 22-Jul-2023
https://dl.acm.org/doi/10.1007/978-981-99-9119-8_13
Ismail AShehab AEl-Henawy I(2018)Healthcare Analysis in Smart Big Data Analytics: Reviews, Challenges and RecommendationsSecurity in Smart Cities: Models, Applications, and Challenges10.1007/978-3-030-01560-2_2(27-45)Online publication date: 5-Nov-2018
https://doi.org/10.1007/978-3-030-01560-2_2
Nie LZhang LMeng LSong XChang XLi X(2017)Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer’s DiseaseIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2016.252096428:7(1508-1519)Online publication date: Jul-2017
https://doi.org/10.1109/TNNLS.2016.2520964
Lee CLuo ZNgiam KZhang MZheng KChen GOoi BYip W(2017)Big Healthcare Data Analytics: Challenges and ApplicationsHandbook of Large-Scale Distributed Computing in Smart Healthcare10.1007/978-3-319-58280-1_2(11-41)Online publication date: 8-Aug-2017
https://doi.org/10.1007/978-3-319-58280-1_2
Xie QWang SZhu JZhang X(2016)Modeling and predicting AD progression by regression analysis of sequential clinical dataNeurocomputing10.1016/j.neucom.2015.07.145195:C(50-55)Online publication date: 26-Jun-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.07.145
Nie LZhang LYang YWang MHong RChua TZhou XSmeaton ATian QBulterman DShen HMayer-Patel KYan S(2015)Beyond DoctorsProceedings of the 23rd ACM international conference on Multimedia10.1145/2733373.2806217(591-600)Online publication date: 13-Oct-2015
https://dl.acm.org/doi/10.1145/2733373.2806217
Zhou JLiu JNarayan VYe JYang QAgarwal DPei J(2012)Modeling disease progression via fused sparse group lassoProceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2339530.2339702(1095-1103)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2339530.2339702

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