NDLI: Exploring Patient Risk Groups with Incomplete Knowledge

Content Provider	IEEE Xplore Digital Library
Author	Xiang Wang Fei Wang Jun Wang Buyue Qian Jianying Hu
Copyright Year	2013
Description	Author affiliation: IBM T.J. Watson Res. Center, Yorktown Heights, NY, USA (Xiang Wang; Fei Wang; Jun Wang; Buyue Qian; Jianying Hu)
Abstract	Patient risk stratification, which aims to stratify a patient cohort into a set of homogeneous groups according to some risk evaluation criteria, is an important task in modern medical informatics. Good risk stratification is the key to good personalized care plan design and delivery. The typical procedure for risk stratification is to first identify a set of risk-relevant medical features (also called risk factors), and then construct a predictive model to estimate the risk scores for individual patients. However, due to the heterogeneity of patients' clinical conditions, the risk factors and their importance vary across different patient groups. Therefore a better approach is to first segment the patient cohort into a set of homogeneous groups with consistent clinical conditions, namely risk groups, and then develop group-specific risk prediction models. In this paper, we propose RISGAL (RISk Group Analysis), a novel semi-supervised learning framework for patient risk group exploration. Our method segments a patient similarity graph into a set of risk groups such that some risk groups are in alignment with (incomplete) prior knowledge from the domain experts while the remaining groups reveal new knowledge from the data. Our method is validated on public benchmark datasets as well as a real electronic medical record database to identify risk groups from a set of potential Congestive Heart Failure (CHF) patients.
Sponsorship	Toshiba
Starting Page	1223
Ending Page	1228
File Size	209719
Page Count	6
File Format	PDF
ISBN	9780769551081
ISSN	15504786
DOI	10.1109/ICDM.2013.129
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Diseases Accuracy Clustering algorithms Heart Benchmark testing Medical diagnostic imaging Semisupervised learning Semi-Supervised Learning Patient Risk Stratification Risk Group Analysis Electronic Medical Records
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Informativity-based graph: Exploring mutual kNN and labeled vertices for semi-supervised learning

A hybrid selection method of helpful unlabeled data applicable for semi-supervised learning algorithms

Semi-supervised Collaborative Clustering with Partial Background Knowledge

Simply recycled selection and incrementally reinforced selection methods applicable for semi-supervised learning algorithms

Semi-supervised learning methods for network intrusion detection

Semi-Supervised Patient Similarity Clustering Algorithm Based on Electronic Medical Records

Active Semi-supervised Spectral Clustering

Fast semi-supervised classification based on bisecting clustering

Label-based semi-supervised fuzzy co-clustering for document categoraization

Exploring Patient Risk Groups with Incomplete Knowledge

Similar Documents

Informativity-based graph: Exploring mutual kNN and labeled vertices for semi-supervised learning

A hybrid selection method of helpful unlabeled data applicable for semi-supervised learning algorithms

Semi-supervised Collaborative Clustering with Partial Background Knowledge

Simply recycled selection and incrementally reinforced selection methods applicable for semi-supervised learning algorithms

Semi-supervised learning methods for network intrusion detection

Semi-Supervised Patient Similarity Clustering Algorithm Based on Electronic Medical Records

Active Semi-supervised Spectral Clustering

Fast semi-supervised classification based on bisecting clustering

Label-based semi-supervised fuzzy co-clustering for document categoraization

Exploring Patient Risk Groups with Incomplete Knowledge