A systematic review of the clinical application of data-driven population segmentation analysis
Background Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups with similar healthcare characteristics. It is a promising patient-centric analysis that enables effective integrated healthcare int...
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| Published in | BMC medical research methodology Vol. 18; no. 1; pp. 121 - 12 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
03.11.2018
BioMed Central Ltd BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2288 1471-2288 |
| DOI | 10.1186/s12874-018-0584-9 |
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| Abstract | Background
Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups with similar healthcare characteristics. It is a promising patient-centric analysis that enables effective integrated healthcare interventions specific for each segment. Although widely applied, there is no systematic review on the clinical application of data-driven population segmentation analysis.
Methods
We carried out a systematic literature search using PubMed, Embase and Web of Science following PRISMA criteria. We included English peer-reviewed articles that applied data-driven population segmentation analysis on empirical health data. We summarized the clinical settings in which segmentation analysis was applied, compared and contrasted strengths, limitations, and practical considerations of different segmentation methods, and assessed the segmentation outcome of all included studies. The studies were assessed by two independent reviewers.
Results
We retrieved 14,514 articles and included 216 articles. Data-driven population segmentation analysis was widely used in different clinical contexts. 163 studies examined the general population while 53 focused on specific population with certain diseases or conditions, including psychological, oncological, respiratory, cardiovascular, and gastrointestinal conditions. Variables used for segmentation in the studies are heterogeneous. Most studies (
n
= 170) utilized secondary data in community settings (
n
= 185). The most common segmentation method was latent class/profile/transition/growth analysis (
n
= 96) followed by K-means cluster analysis (
n
= 60) and hierarchical analysis (
n
= 50), each having its advantages, disadvantages, and practical considerations. We also identified key criteria to evaluate a segmentation framework: internal validity, external validity, identifiability/interpretability, substantiality, stability, actionability/accessibility, and parsimony.
Conclusions
Data-driven population segmentation has been widely applied and holds great potential in managing population health. The evaluations of segmentation outcome require the interplay of data analytics and subject matter expertise. The optimal framework for segmentation requires further research. |
|---|---|
| AbstractList | Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups with similar healthcare characteristics. It is a promising patient-centric analysis that enables effective integrated healthcare interventions specific for each segment. Although widely applied, there is no systematic review on the clinical application of data-driven population segmentation analysis.BACKGROUNDData-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups with similar healthcare characteristics. It is a promising patient-centric analysis that enables effective integrated healthcare interventions specific for each segment. Although widely applied, there is no systematic review on the clinical application of data-driven population segmentation analysis.We carried out a systematic literature search using PubMed, Embase and Web of Science following PRISMA criteria. We included English peer-reviewed articles that applied data-driven population segmentation analysis on empirical health data. We summarized the clinical settings in which segmentation analysis was applied, compared and contrasted strengths, limitations, and practical considerations of different segmentation methods, and assessed the segmentation outcome of all included studies. The studies were assessed by two independent reviewers.METHODSWe carried out a systematic literature search using PubMed, Embase and Web of Science following PRISMA criteria. We included English peer-reviewed articles that applied data-driven population segmentation analysis on empirical health data. We summarized the clinical settings in which segmentation analysis was applied, compared and contrasted strengths, limitations, and practical considerations of different segmentation methods, and assessed the segmentation outcome of all included studies. The studies were assessed by two independent reviewers.We retrieved 14,514 articles and included 216 articles. Data-driven population segmentation analysis was widely used in different clinical contexts. 163 studies examined the general population while 53 focused on specific population with certain diseases or conditions, including psychological, oncological, respiratory, cardiovascular, and gastrointestinal conditions. Variables used for segmentation in the studies are heterogeneous. Most studies (n = 170) utilized secondary data in community settings (n = 185). The most common segmentation method was latent class/profile/transition/growth analysis (n = 96) followed by K-means cluster analysis (n = 60) and hierarchical analysis (n = 50), each having its advantages, disadvantages, and practical considerations. We also identified key criteria to evaluate a segmentation framework: internal validity, external validity, identifiability/interpretability, substantiality, stability, actionability/accessibility, and parsimony.RESULTSWe retrieved 14,514 articles and included 216 articles. Data-driven population segmentation analysis was widely used in different clinical contexts. 163 studies examined the general population while 53 focused on specific population with certain diseases or conditions, including psychological, oncological, respiratory, cardiovascular, and gastrointestinal conditions. Variables used for segmentation in the studies are heterogeneous. Most studies (n = 170) utilized secondary data in community settings (n = 185). The most common segmentation method was latent class/profile/transition/growth analysis (n = 96) followed by K-means cluster analysis (n = 60) and hierarchical analysis (n = 50), each having its advantages, disadvantages, and practical considerations. We also identified key criteria to evaluate a segmentation framework: internal validity, external validity, identifiability/interpretability, substantiality, stability, actionability/accessibility, and parsimony.Data-driven population segmentation has been widely applied and holds great potential in managing population health. The evaluations of segmentation outcome require the interplay of data analytics and subject matter expertise. The optimal framework for segmentation requires further research.CONCLUSIONSData-driven population segmentation has been widely applied and holds great potential in managing population health. The evaluations of segmentation outcome require the interplay of data analytics and subject matter expertise. The optimal framework for segmentation requires further research. Background Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups with similar healthcare characteristics. It is a promising patient-centric analysis that enables effective integrated healthcare interventions specific for each segment. Although widely applied, there is no systematic review on the clinical application of data-driven population segmentation analysis. Methods We carried out a systematic literature search using PubMed, Embase and Web of Science following PRISMA criteria. We included English peer-reviewed articles that applied data-driven population segmentation analysis on empirical health data. We summarized the clinical settings in which segmentation analysis was applied, compared and contrasted strengths, limitations, and practical considerations of different segmentation methods, and assessed the segmentation outcome of all included studies. The studies were assessed by two independent reviewers. Results We retrieved 14,514 articles and included 216 articles. Data-driven population segmentation analysis was widely used in different clinical contexts. 163 studies examined the general population while 53 focused on specific population with certain diseases or conditions, including psychological, oncological, respiratory, cardiovascular, and gastrointestinal conditions. Variables used for segmentation in the studies are heterogeneous. Most studies (n = 170) utilized secondary data in community settings (n = 185). The most common segmentation method was latent class/profile/transition/growth analysis (n = 96) followed by K-means cluster analysis (n = 60) and hierarchical analysis (n = 50), each having its advantages, disadvantages, and practical considerations. We also identified key criteria to evaluate a segmentation framework: internal validity, external validity, identifiability/interpretability, substantiality, stability, actionability/accessibility, and parsimony. Conclusions Data-driven population segmentation has been widely applied and holds great potential in managing population health. The evaluations of segmentation outcome require the interplay of data analytics and subject matter expertise. The optimal framework for segmentation requires further research. Abstract Background Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups with similar healthcare characteristics. It is a promising patient-centric analysis that enables effective integrated healthcare interventions specific for each segment. Although widely applied, there is no systematic review on the clinical application of data-driven population segmentation analysis. Methods We carried out a systematic literature search using PubMed, Embase and Web of Science following PRISMA criteria. We included English peer-reviewed articles that applied data-driven population segmentation analysis on empirical health data. We summarized the clinical settings in which segmentation analysis was applied, compared and contrasted strengths, limitations, and practical considerations of different segmentation methods, and assessed the segmentation outcome of all included studies. The studies were assessed by two independent reviewers. Results We retrieved 14,514 articles and included 216 articles. Data-driven population segmentation analysis was widely used in different clinical contexts. 163 studies examined the general population while 53 focused on specific population with certain diseases or conditions, including psychological, oncological, respiratory, cardiovascular, and gastrointestinal conditions. Variables used for segmentation in the studies are heterogeneous. Most studies (n = 170) utilized secondary data in community settings (n = 185). The most common segmentation method was latent class/profile/transition/growth analysis (n = 96) followed by K-means cluster analysis (n = 60) and hierarchical analysis (n = 50), each having its advantages, disadvantages, and practical considerations. We also identified key criteria to evaluate a segmentation framework: internal validity, external validity, identifiability/interpretability, substantiality, stability, actionability/accessibility, and parsimony. Conclusions Data-driven population segmentation has been widely applied and holds great potential in managing population health. The evaluations of segmentation outcome require the interplay of data analytics and subject matter expertise. The optimal framework for segmentation requires further research. Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups with similar healthcare characteristics. It is a promising patient-centric analysis that enables effective integrated healthcare interventions specific for each segment. Although widely applied, there is no systematic review on the clinical application of data-driven population segmentation analysis. We carried out a systematic literature search using PubMed, Embase and Web of Science following PRISMA criteria. We included English peer-reviewed articles that applied data-driven population segmentation analysis on empirical health data. We summarized the clinical settings in which segmentation analysis was applied, compared and contrasted strengths, limitations, and practical considerations of different segmentation methods, and assessed the segmentation outcome of all included studies. The studies were assessed by two independent reviewers. We retrieved 14,514 articles and included 216 articles. Data-driven population segmentation analysis was widely used in different clinical contexts. 163 studies examined the general population while 53 focused on specific population with certain diseases or conditions, including psychological, oncological, respiratory, cardiovascular, and gastrointestinal conditions. Variables used for segmentation in the studies are heterogeneous. Most studies (n = 170) utilized secondary data in community settings (n = 185). The most common segmentation method was latent class/profile/transition/growth analysis (n = 96) followed by K-means cluster analysis (n = 60) and hierarchical analysis (n = 50), each having its advantages, disadvantages, and practical considerations. We also identified key criteria to evaluate a segmentation framework: internal validity, external validity, identifiability/interpretability, substantiality, stability, actionability/accessibility, and parsimony. Data-driven population segmentation has been widely applied and holds great potential in managing population health. The evaluations of segmentation outcome require the interplay of data analytics and subject matter expertise. The optimal framework for segmentation requires further research. Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups with similar healthcare characteristics. It is a promising patient-centric analysis that enables effective integrated healthcare interventions specific for each segment. Although widely applied, there is no systematic review on the clinical application of data-driven population segmentation analysis. We carried out a systematic literature search using PubMed, Embase and Web of Science following PRISMA criteria. We included English peer-reviewed articles that applied data-driven population segmentation analysis on empirical health data. We summarized the clinical settings in which segmentation analysis was applied, compared and contrasted strengths, limitations, and practical considerations of different segmentation methods, and assessed the segmentation outcome of all included studies. The studies were assessed by two independent reviewers. We retrieved 14,514 articles and included 216 articles. Data-driven population segmentation analysis was widely used in different clinical contexts. 163 studies examined the general population while 53 focused on specific population with certain diseases or conditions, including psychological, oncological, respiratory, cardiovascular, and gastrointestinal conditions. Variables used for segmentation in the studies are heterogeneous. Most studies (n = 170) utilized secondary data in community settings (n = 185). The most common segmentation method was latent class/profile/transition/growth analysis (n = 96) followed by K-means cluster analysis (n = 60) and hierarchical analysis (n = 50), each having its advantages, disadvantages, and practical considerations. We also identified key criteria to evaluate a segmentation framework: internal validity, external validity, identifiability/interpretability, substantiality, stability, actionability/accessibility, and parsimony. Data-driven population segmentation has been widely applied and holds great potential in managing population health. The evaluations of segmentation outcome require the interplay of data analytics and subject matter expertise. The optimal framework for segmentation requires further research. Background Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups with similar healthcare characteristics. It is a promising patient-centric analysis that enables effective integrated healthcare interventions specific for each segment. Although widely applied, there is no systematic review on the clinical application of data-driven population segmentation analysis. Methods We carried out a systematic literature search using PubMed, Embase and Web of Science following PRISMA criteria. We included English peer-reviewed articles that applied data-driven population segmentation analysis on empirical health data. We summarized the clinical settings in which segmentation analysis was applied, compared and contrasted strengths, limitations, and practical considerations of different segmentation methods, and assessed the segmentation outcome of all included studies. The studies were assessed by two independent reviewers. Results We retrieved 14,514 articles and included 216 articles. Data-driven population segmentation analysis was widely used in different clinical contexts. 163 studies examined the general population while 53 focused on specific population with certain diseases or conditions, including psychological, oncological, respiratory, cardiovascular, and gastrointestinal conditions. Variables used for segmentation in the studies are heterogeneous. Most studies ( n = 170) utilized secondary data in community settings ( n = 185). The most common segmentation method was latent class/profile/transition/growth analysis ( n = 96) followed by K-means cluster analysis ( n = 60) and hierarchical analysis ( n = 50), each having its advantages, disadvantages, and practical considerations. We also identified key criteria to evaluate a segmentation framework: internal validity, external validity, identifiability/interpretability, substantiality, stability, actionability/accessibility, and parsimony. Conclusions Data-driven population segmentation has been widely applied and holds great potential in managing population health. The evaluations of segmentation outcome require the interplay of data analytics and subject matter expertise. The optimal framework for segmentation requires further research. Background Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups with similar healthcare characteristics. It is a promising patient-centric analysis that enables effective integrated healthcare interventions specific for each segment. Although widely applied, there is no systematic review on the clinical application of data-driven population segmentation analysis. Methods We carried out a systematic literature search using PubMed, Embase and Web of Science following PRISMA criteria. We included English peer-reviewed articles that applied data-driven population segmentation analysis on empirical health data. We summarized the clinical settings in which segmentation analysis was applied, compared and contrasted strengths, limitations, and practical considerations of different segmentation methods, and assessed the segmentation outcome of all included studies. The studies were assessed by two independent reviewers. Results We retrieved 14,514 articles and included 216 articles. Data-driven population segmentation analysis was widely used in different clinical contexts. 163 studies examined the general population while 53 focused on specific population with certain diseases or conditions, including psychological, oncological, respiratory, cardiovascular, and gastrointestinal conditions. Variables used for segmentation in the studies are heterogeneous. Most studies (n = 170) utilized secondary data in community settings (n = 185). The most common segmentation method was latent class/profile/transition/growth analysis (n = 96) followed by K-means cluster analysis (n = 60) and hierarchical analysis (n = 50), each having its advantages, disadvantages, and practical considerations. We also identified key criteria to evaluate a segmentation framework: internal validity, external validity, identifiability/interpretability, substantiality, stability, actionability/accessibility, and parsimony. Conclusions Data-driven population segmentation has been widely applied and holds great potential in managing population health. The evaluations of segmentation outcome require the interplay of data analytics and subject matter expertise. The optimal framework for segmentation requires further research. Keywords: Systematic review, Population segmentation, Data analytics, Population health, Public health, Health policy, Health services research |
| ArticleNumber | 121 |
| Audience | Academic |
| Author | Tan, Chuen Seng Kwan, Yu Heng Thumboo, Julian Low, Lian Leng Yan, Shi |
| Author_xml | – sequence: 1 givenname: Shi surname: Yan fullname: Yan, Shi organization: Duke-NUS Medical School – sequence: 2 givenname: Yu Heng surname: Kwan fullname: Kwan, Yu Heng organization: Program in Health Services and Systems Research, Duke-NUS Medical School – sequence: 3 givenname: Chuen Seng surname: Tan fullname: Tan, Chuen Seng organization: Saw Swee Hock School of Public Health, National University of Singapore – sequence: 4 givenname: Julian surname: Thumboo fullname: Thumboo, Julian organization: Rheumatology and Immunology, Singapore General Hospital – sequence: 5 givenname: Lian Leng orcidid: 0000-0003-4228-2862 surname: Low fullname: Low, Lian Leng email: low.lian.leng@singhealth.com.sg organization: Family Medicine and Continuing Care, Singapore General Hospital |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30390641$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1093/aje/kwq458 10.1093/ajcn/76.1.245 10.1016/j.datak.2007.01.002 10.1007/s11121-011-0255-0 10.1079/PHN200098 10.5334/ijic.67 10.47102/annals-acadmedsg.V46N7p287 10.1186/s12963-016-0115-z 10.1016/S0376-8716(06)80012-8 10.1371/journal.pone.0195243 10.20982/tqmp.11.1.p008 10.1080/10705510701575396 10.1007/s12160-008-9074-3 10.1016/j.lindif.2017.11.001 10.1111/j.1468-0394.2007.00428.x 10.1142/9789812832153_0010 10.1111/j.1468-0009.2007.00483.x 10.1016/j.jad.2014.03.024 10.1079/BJN20051456 10.1016/j.ypmed.2014.03.023 10.1093/jpepsy/jst084 10.1016/j.jpsychires.2016.08.018 10.1093/ije/24.2.313 10.1016/j.ypmed.2003.09.011 10.1097/01.NNR.0000280654.50642.1a 10.1093/eurpub/ckp057 10.1111/j.1753-4887.2004.tb00040.x 10.1007/0-387-25465-X_9 10.5993/AJHB.36.6.4 10.1016/j.amepre.2015.05.024 10.1093/ajcn/80.3.759 10.1111/j.1532-5415.2007.01078.x 10.1016/S0212-6567(14)70061-7 10.1016/j.amepre.2004.12.006 10.1164/rccm.201301-0156OC 10.1080/03610927408827101 10.1016/j.jadohealth.2013.03.007 10.1016/j.ypmed.2014.07.007 10.1093/aje/154.12.1143 10.1145/331499.331504 10.1038/ejcn.2008.40 10.1007/s12603-009-0213-8 10.1016/j.socscimed.2014.05.012 10.1183/09031936.00120810 10.6339/JDS.201104_09(2).0009 10.1007/BF03403944 10.1183/09031936.00174408 10.1007/s11121-009-0140-2 10.1016/j.ahj.2005.05.010 10.1093/geront/gnw037 10.1016/j.ijporl.2011.10.019 10.1007/978-3-642-12541-6_9 10.1016/j.jada.2006.05.012 10.1007/s12160-014-9589-8 10.1136/jech.55.1.29 10.1038/sj.ejcn.1602129 10.1001/jama.2012.154302 10.1001/archpsyc.61.2.192 10.1007/s00520-005-0899-z 10.1080/10705510701575602 10.5993/AJHB.38.4.2 10.1016/j.ypmed.2011.02.020 10.1093/ajcn/77.6.1417 10.1002/9780470977811 10.1001/archgenpsychiatry.2011.1574 10.6339/JDS.2005.03(1).192 10.1017/S0007114508014128 10.3758/BF03196342 10.1016/j.ypmed.2015.09.027 10.1186/1479-5868-10-34 10.7812/TPP/14-005 10.1016/j.aap.2012.10.011 10.11118/actaun201361041215 10.1007/s11136-007-9272-7 10.1377/hlthaff.2015.1311 10.1017/S1368980014000111 10.1136/gut.52.11.1616 10.1186/1471-2458-11-692 10.1093/aje/kwp393 |
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| Keywords | Health policy Systematic review Data analytics Population segmentation Public health Population health Health services research |
| Language | English |
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| References | Trent D. Buskirk (584_CR90) 2018; 11 MA Adams (584_CR27) 2015; 49 A Gjelsvik (584_CR39) 2014; 38 José J. Mira-Solves (584_CR13) 2014; 46 Stephanie T. Lanza (584_CR57) 2007; 14 V. Siroux (584_CR93) 2011; 38 MC Nelson (584_CR51) 2005; 28 PK Keel (584_CR19) 2004; 61 Nicholas W. Stine (584_CR3) 2013; 309 P Verger (584_CR20) 2009; 19 584_CR77 E Guthrie (584_CR71) 2003; 52 Karen L. Nylund (584_CR58) 2007; 14 584_CR78 JOANNE LYNN (584_CR4) 2007; 85 E van der Laan MR (584_CR7) 2014; 113 Z Erlich (584_CR56) 2003; 43 J A Pryer (584_CR33) 2001; 55 584_CR1 584_CR2 P. Terry (584_CR65) 2001; 154 T. Calinski (584_CR86) 1974; 3 Gregory J Norman (584_CR38) 2003; 37 A. K. Jain (584_CR61) 1999; 31 584_CR6 Veronika Jadczaková (584_CR48) 2013; 61 584_CR9 Hedwig Hofstetter (584_CR60) 2014; 67 Patrick Peretti-Watel (584_CR70) 2006; 82 R Gelbard (584_CR80) 2007; 63 584_CR82 584_CR81 H Finch (584_CR84) 2005; 3 584_CR85 A Boudier (584_CR94) 2013; 188 584_CR88 584_CR46 John R. Beard (584_CR5) 2016; 56 B Griffin (584_CR52) 2014; 48 584_CR89 584_CR37 SR Horn (584_CR91) 2016; 83 MN Laska (584_CR21) 2009; 10 Marc A Adams (584_CR24) 2013; 10 E Wirfält (584_CR29) 2009; 63 Lian Leng Low (584_CR66) 2018; 13 Anne M. Fitzpatrick (584_CR68) 2011; 127 K Freeman (584_CR50) 2012; 76 Yoshimi Fukuoka (584_CR35) 2007; 16 Artyom Sedrakyan (584_CR72) 2006; 151 D Engeset (584_CR73) 2005; 59 Janas M Harrington (584_CR31) 2014; 17 SI Vuik (584_CR8) 2016; 35 PK Newby (584_CR63) 2004; 62 RM Bittmann (584_CR79) 2007; 24 584_CR11 JD Penrod (584_CR41) 2007; 55 584_CR55 584_CR10 MA Adams (584_CR25) 2012; 36 M Héroux (584_CR36) 2012; 13 584_CR54 J Reedy (584_CR18) 2010; 171 KL Tucker (584_CR83) 2002; 76 584_CR12 J Boone-Heinonen (584_CR22) 2008; 36 584_CR49 S Croezen (584_CR45) 2009; 13 BG Simons-Morton (584_CR17) 2013; 51 Áine P. Hearty (584_CR43) 2008; 101 PK Newby (584_CR53) 2003; 77 RH Pietrzak (584_CR16) 2014; 162 J Magidson (584_CR76) 2002; 20 BC Love (584_CR42) 2002; 9 Christina Bamia (584_CR34) 2005; 94 584_CR62 D Walsh (584_CR67) 2006; 14 PK Newby (584_CR30) 2004; 80 M Weatherall (584_CR74) 2009; 34 Kristoffer S. Berlin (584_CR75) 2013; 39 PATHRICIA P C W HUIJBREGTS (584_CR47) 1995; 24 JA Pryer (584_CR64) 2001; 4 Teri G. Lindgren (584_CR69) 2008; 57 584_CR15 RL Bailey (584_CR32) 2006; 106 584_CR59 584_CR14 MA Adams (584_CR26) 2011; 52 Kimberly A. Miller (584_CR40) 2015; 81 RJ Iannotti (584_CR23) 2013; 53 J Kang (584_CR92) 2014; 64 G Lo Siou (584_CR28) 2011; 173 BS Ledere (584_CR44) 2009; 100 O Yim (584_CR87) 2015; 11 |
| References_xml | – volume: 173 start-page: 956 year: 2011 ident: 584_CR28 publication-title: Am J Epidemiol doi: 10.1093/aje/kwq458 – volume: 76 start-page: 245 year: 2002 ident: 584_CR83 publication-title: Am J Clin Nutr doi: 10.1093/ajcn/76.1.245 – volume: 63 start-page: 155 year: 2007 ident: 584_CR80 publication-title: Data Knowl Eng doi: 10.1016/j.datak.2007.01.002 – volume: 13 start-page: 183 year: 2012 ident: 584_CR36 publication-title: Prev Sci doi: 10.1007/s11121-011-0255-0 – volume: 4 start-page: 787 year: 2001 ident: 584_CR64 publication-title: Public Health Nutr doi: 10.1079/PHN200098 – ident: 584_CR1 doi: 10.5334/ijic.67 – ident: 584_CR11 doi: 10.47102/annals-acadmedsg.V46N7p287 – ident: 584_CR6 doi: 10.1186/s12963-016-0115-z – volume: 11 start-page: 1 issue: 1 year: 2018 ident: 584_CR90 publication-title: Survey Practice – volume: 82 start-page: S71 year: 2006 ident: 584_CR70 publication-title: Drug and Alcohol Dependence doi: 10.1016/S0376-8716(06)80012-8 – volume: 13 start-page: e0195243 issue: 4 year: 2018 ident: 584_CR66 publication-title: PLOS ONE doi: 10.1371/journal.pone.0195243 – volume: 11 start-page: 8 year: 2015 ident: 584_CR87 publication-title: Quant Methods Psychol doi: 10.20982/tqmp.11.1.p008 – volume: 14 start-page: 535 issue: 4 year: 2007 ident: 584_CR58 publication-title: Structural Equation Modeling: A Multidisciplinary Journal doi: 10.1080/10705510701575396 – volume: 36 start-page: 217 year: 2008 ident: 584_CR22 publication-title: Ann Behav Med doi: 10.1007/s12160-008-9074-3 – ident: 584_CR77 doi: 10.1016/j.lindif.2017.11.001 – volume: 24 start-page: 171 year: 2007 ident: 584_CR79 publication-title: Expert Syst doi: 10.1111/j.1468-0394.2007.00428.x – ident: 584_CR46 doi: 10.1142/9789812832153_0010 – volume: 85 start-page: 185 issue: 2 year: 2007 ident: 584_CR4 publication-title: The Milbank Quarterly doi: 10.1111/j.1468-0009.2007.00483.x – volume: 162 start-page: 102 year: 2014 ident: 584_CR16 publication-title: J Affect Disord doi: 10.1016/j.jad.2014.03.024 – volume: 94 start-page: 100 issue: 01 year: 2005 ident: 584_CR34 publication-title: British Journal of Nutrition doi: 10.1079/BJN20051456 – volume: 64 start-page: 121 year: 2014 ident: 584_CR92 publication-title: Prev Med (Baltim) doi: 10.1016/j.ypmed.2014.03.023 – volume: 39 start-page: 174 issue: 2 year: 2013 ident: 584_CR75 publication-title: Journal of Pediatric Psychology doi: 10.1093/jpepsy/jst084 – volume: 83 start-page: 151 year: 2016 ident: 584_CR91 publication-title: J Psychiatr Res doi: 10.1016/j.jpsychires.2016.08.018 – volume: 24 start-page: 313 issue: 2 year: 1995 ident: 584_CR47 publication-title: International Journal of Epidemiology doi: 10.1093/ije/24.2.313 – volume: 37 start-page: 635 issue: 6 year: 2003 ident: 584_CR38 publication-title: Preventive Medicine doi: 10.1016/j.ypmed.2003.09.011 – volume: 57 start-page: 14 issue: 1 year: 2008 ident: 584_CR69 publication-title: Nursing Research doi: 10.1097/01.NNR.0000280654.50642.1a – ident: 584_CR9 – volume: 19 start-page: 618 year: 2009 ident: 584_CR20 publication-title: Eur J Pub Health doi: 10.1093/eurpub/ckp057 – volume: 62 start-page: 177 year: 2004 ident: 584_CR63 publication-title: Nutr Rev doi: 10.1111/j.1753-4887.2004.tb00040.x – volume: 127 start-page: 382-389.e13 issue: 2 year: 2011 ident: 584_CR68 publication-title: Journal of Allergy and Clinical Immunology – ident: 584_CR89 doi: 10.1007/0-387-25465-X_9 – volume: 36 start-page: 757 year: 2012 ident: 584_CR25 publication-title: Am J Health Behav doi: 10.5993/AJHB.36.6.4 – volume: 49 start-page: 878 year: 2015 ident: 584_CR27 publication-title: Am J Prev Med doi: 10.1016/j.amepre.2015.05.024 – ident: 584_CR2 – volume: 80 start-page: 759 year: 2004 ident: 584_CR30 publication-title: Am J Clin Nutr doi: 10.1093/ajcn/80.3.759 – volume: 55 start-page: 407 year: 2007 ident: 584_CR41 publication-title: J Am Geriatr Soc doi: 10.1111/j.1532-5415.2007.01078.x – volume: 46 start-page: 16 year: 2014 ident: 584_CR13 publication-title: Atención Primaria doi: 10.1016/S0212-6567(14)70061-7 – volume: 28 start-page: 259 year: 2005 ident: 584_CR51 publication-title: Am J Prev Med doi: 10.1016/j.amepre.2004.12.006 – ident: 584_CR81 – volume: 188 start-page: 550 year: 2013 ident: 584_CR94 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201301-0156OC – ident: 584_CR85 – ident: 584_CR78 – volume: 3 start-page: 1 issue: 1 year: 1974 ident: 584_CR86 publication-title: Communications in Statistics - Theory and Methods doi: 10.1080/03610927408827101 – volume: 53 start-page: 280 year: 2013 ident: 584_CR23 publication-title: J Adolesc Health doi: 10.1016/j.jadohealth.2013.03.007 – volume: 67 start-page: 141 year: 2014 ident: 584_CR60 publication-title: Preventive Medicine doi: 10.1016/j.ypmed.2014.07.007 – volume: 154 start-page: 1143 issue: 12 year: 2001 ident: 584_CR65 publication-title: American Journal of Epidemiology doi: 10.1093/aje/154.12.1143 – volume: 31 start-page: 264 issue: 3 year: 1999 ident: 584_CR61 publication-title: ACM Computing Surveys doi: 10.1145/331499.331504 – volume: 63 start-page: 707 year: 2009 ident: 584_CR29 publication-title: Eur J Clin Nutr doi: 10.1038/ejcn.2008.40 – volume: 13 start-page: 776 year: 2009 ident: 584_CR45 publication-title: J Nutr Heal Aging doi: 10.1007/s12603-009-0213-8 – volume: 113 start-page: 68 year: 2014 ident: 584_CR7 publication-title: Soc Sci Med doi: 10.1016/j.socscimed.2014.05.012 – volume: 38 start-page: 310 issue: 2 year: 2011 ident: 584_CR93 publication-title: European Respiratory Journal doi: 10.1183/09031936.00120810 – ident: 584_CR55 doi: 10.6339/JDS.201104_09(2).0009 – volume: 20 start-page: 37 year: 2002 ident: 584_CR76 publication-title: Can J Mark Res – volume: 100 start-page: 263 year: 2009 ident: 584_CR44 publication-title: Can J Public Heal doi: 10.1007/BF03403944 – volume: 34 start-page: 812 year: 2009 ident: 584_CR74 publication-title: Eur Respir J doi: 10.1183/09031936.00174408 – volume: 10 start-page: 376 year: 2009 ident: 584_CR21 publication-title: Prev Sci doi: 10.1007/s11121-009-0140-2 – volume: 151 start-page: 720 issue: 3 year: 2006 ident: 584_CR72 publication-title: American Heart Journal doi: 10.1016/j.ahj.2005.05.010 – volume: 56 start-page: S163 issue: Suppl 2 year: 2016 ident: 584_CR5 publication-title: The Gerontologist doi: 10.1093/geront/gnw037 – volume: 76 start-page: 122 year: 2012 ident: 584_CR50 publication-title: Int J Pediatr Otorhinolaryngol doi: 10.1016/j.ijporl.2011.10.019 – ident: 584_CR54 doi: 10.1007/978-3-642-12541-6_9 – volume: 43 start-page: 100 year: 2003 ident: 584_CR56 publication-title: J Comput Inf Syst – volume: 106 start-page: 1194 year: 2006 ident: 584_CR32 publication-title: J Am Diet Assoc doi: 10.1016/j.jada.2006.05.012 – volume: 48 start-page: 205 year: 2014 ident: 584_CR52 publication-title: Ann Behav Med doi: 10.1007/s12160-014-9589-8 – volume: 55 start-page: 29 issue: 1 year: 2001 ident: 584_CR33 publication-title: Journal of Epidemiology & Community Health doi: 10.1136/jech.55.1.29 – volume: 59 start-page: 675 year: 2005 ident: 584_CR73 publication-title: Eur J Clin Nutr doi: 10.1038/sj.ejcn.1602129 – volume: 309 start-page: 449 issue: 5 year: 2013 ident: 584_CR3 publication-title: JAMA doi: 10.1001/jama.2012.154302 – volume: 61 start-page: 192 year: 2004 ident: 584_CR19 publication-title: Arch Gen Psychiatry doi: 10.1001/archpsyc.61.2.192 – volume: 14 start-page: 831 year: 2006 ident: 584_CR67 publication-title: Support Care Cancer doi: 10.1007/s00520-005-0899-z – ident: 584_CR88 – volume: 14 start-page: 671 issue: 4 year: 2007 ident: 584_CR57 publication-title: Structural Equation Modeling: A Multidisciplinary Journal doi: 10.1080/10705510701575602 – volume: 38 start-page: 492 year: 2014 ident: 584_CR39 publication-title: Am J Health Behav doi: 10.5993/AJHB.38.4.2 – volume: 52 start-page: 326 year: 2011 ident: 584_CR26 publication-title: Prev Med (Baltim) doi: 10.1016/j.ypmed.2011.02.020 – volume: 77 start-page: 1417 year: 2003 ident: 584_CR53 publication-title: Am J Clin Nutr doi: 10.1093/ajcn/77.6.1417 – ident: 584_CR82 doi: 10.1002/9780470977811 – ident: 584_CR37 doi: 10.1001/archgenpsychiatry.2011.1574 – volume: 3 start-page: 85 year: 2005 ident: 584_CR84 publication-title: J Data Sci doi: 10.6339/JDS.2005.03(1).192 – volume: 101 start-page: 590 issue: 04 year: 2008 ident: 584_CR43 publication-title: British Journal of Nutrition doi: 10.1017/S0007114508014128 – volume: 9 start-page: 829 year: 2002 ident: 584_CR42 publication-title: Psychon Bull Rev doi: 10.3758/BF03196342 – volume: 81 start-page: 303 year: 2015 ident: 584_CR40 publication-title: Preventive Medicine doi: 10.1016/j.ypmed.2015.09.027 – ident: 584_CR14 – volume: 10 start-page: 34 issue: 1 year: 2013 ident: 584_CR24 publication-title: International Journal of Behavioral Nutrition and Physical Activity doi: 10.1186/1479-5868-10-34 – ident: 584_CR12 doi: 10.7812/TPP/14-005 – volume: 51 start-page: 27 year: 2013 ident: 584_CR17 publication-title: Accid Anal Prev doi: 10.1016/j.aap.2012.10.011 – ident: 584_CR59 – ident: 584_CR10 – volume: 61 start-page: 1215 issue: 4 year: 2013 ident: 584_CR48 publication-title: Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis doi: 10.11118/actaun201361041215 – volume: 16 start-page: 1655 issue: 10 year: 2007 ident: 584_CR35 publication-title: Quality of Life Research doi: 10.1007/s11136-007-9272-7 – volume: 35 start-page: 769 year: 2016 ident: 584_CR8 publication-title: Health Aff doi: 10.1377/hlthaff.2015.1311 – volume: 17 start-page: 2674 issue: 12 year: 2014 ident: 584_CR31 publication-title: Public Health Nutrition doi: 10.1017/S1368980014000111 – volume: 52 start-page: 1616 issue: 11 year: 2003 ident: 584_CR71 publication-title: Gut doi: 10.1136/gut.52.11.1616 – ident: 584_CR15 doi: 10.1186/1471-2458-11-692 – ident: 584_CR49 – volume: 171 start-page: 479 year: 2010 ident: 584_CR18 publication-title: Am J Epidemiol doi: 10.1093/aje/kwp393 – ident: 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Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively... Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups... Background Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively... Abstract Background Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively... |
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| SubjectTerms | Asthma Chronic illnesses Classification Cluster analysis Clustering Data Analysis Data analytics Data mining Delivery of Health Care - methods Delivery of Health Care - statistics & numerical data Electronic health records Evidence-based medicine Health care policy Health policy Health Sciences Health services Humans Medical Record Linkage - methods Medicine Medicine & Public Health Methods Older people Patient satisfaction Population health Population Health - statistics & numerical data Population Health Management Population segmentation Public health Reproducibility of Results Research Article Statistical Theory and Methods statistics and modelling Statistics for Life Sciences Systematic review Theory of Medicine/Bioethics Variables |
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| Title | A systematic review of the clinical application of data-driven population segmentation analysis |
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