PMID- 30001371
OWN - NLM
STAT- MEDLINE
DCOM- 20181231
LR  - 20181231
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 13
IP  - 7
DP  - 2018
TI  - Mining patterns of comorbidity evolution in patients with multiple chronic 
      conditions using unsupervised multi-level temporal Bayesian network.
PG  - e0199768
LID - 10.1371/journal.pone.0199768 [doi]
LID - e0199768
AB  - Over the past few decades, the rise of multiple chronic conditions has become a 
      major concern for clinicians. However, it is still not known precisely how 
      multiple chronic conditions emerge among patients. We propose an unsupervised 
      multi-level temporal Bayesian network to provide a compact representation of the 
      relationship among emergence of multiple chronic conditions and patient level 
      risk factors over time. To improve the efficiency of the learning process, we use 
      an extension of maximum weight spanning tree algorithm and greedy search 
      algorithm to study the structure of the proposed network in three stages, 
      starting with learning the inter-relationship of comorbidities within each year, 
      followed by learning the intra-relationship of comorbidity emergence between 
      consecutive years, and finally learning the hierarchical relationship of 
      comorbidities and patient level risk factors. We also use a longest path 
      algorithm to identify the most likely sequence of comorbidities emerging from 
      and/or leading to specific chronic conditions. Using a de-identified dataset of 
      more than 250,000 patients receiving care from the U.S. Department of Veterans 
      Affairs for a period of five years, we compare the performance of the proposed 
      unsupervised Bayesian network in comparison with those of Bayesian networks 
      developed based on supervised and semi-supervised learning approaches, as well as 
      multivariate probit regression, multinomial logistic regression, and latent 
      regression Markov mixture clustering focusing on traumatic brain injury (TBI), 
      post-traumatic stress disorder (PTSD), depression (Depr), substance abuse (SuAb), 
      and back pain (BaPa). Our findings show that the unsupervised approach has 
      noticeably accurate predictive performance that is comparable to the best 
      performing semi-supervised and the second-best performing supervised approaches. 
      These findings also revealed that the unsupervised approach has improved 
      performance over multivariate probit regression, multinomial logistic regression, 
      and latent regression Markov mixture clustering.
FAU - Faruqui, Syed Hasib Akhter
AU  - Faruqui SHA
AD  - Department of Mechanical Engineering, The University of Texas at San Antonio, San 
      Antonio, TX, United States of America.
FAU - Alaeddini, Adel
AU  - Alaeddini A
AUID- ORCID: 0000-0003-4451-3150
AD  - Department of Mechanical Engineering, The University of Texas at San Antonio, San 
      Antonio, TX, United States of America.
FAU - Jaramillo, Carlos A
AU  - Jaramillo CA
AD  - South Texas Veterans Health Care System, San Antonio, TX, United States of 
      America.
FAU - Potter, Jennifer S
AU  - Potter JS
AD  - Department of Psychiatry, University of Texas Health Science Center at San 
      Antonio, San Antonio, TX, United States of America.
FAU - Pugh, Mary Jo
AU  - Pugh MJ
AD  - VA Salt Lake City Health Care System, Salt Lake City, UT, United States of 
      America.
LA  - eng
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20180712
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Adolescent
MH  - Back Pain/*epidemiology
MH  - Bayes Theorem
MH  - Brain Injuries, Traumatic/*epidemiology
MH  - Comorbidity
MH  - Depression/*epidemiology
MH  - Female
MH  - Humans
MH  - Male
MH  - Middle Aged
MH  - Stress Disorders, Post-Traumatic/*epidemiology
MH  - *Unsupervised Machine Learning
MH  - Veterans/statistics & numerical data
PMC - PMC6042705
COIS- The authors have declared that no competing interests exist.
EDAT- 2018/07/13 06:00
MHDA- 2019/01/01 06:00
PMCR- 2018/07/12
CRDT- 2018/07/13 06:00
PHST- 2018/01/25 00:00 [received]
PHST- 2018/06/13 00:00 [accepted]
PHST- 2018/07/13 06:00 [entrez]
PHST- 2018/07/13 06:00 [pubmed]
PHST- 2019/01/01 06:00 [medline]
PHST- 2018/07/12 00:00 [pmc-release]
AID - PONE-D-18-02716 [pii]
AID - 10.1371/journal.pone.0199768 [doi]
PST - epublish
SO  - PLoS One. 2018 Jul 12;13(7):e0199768. doi: 10.1371/journal.pone.0199768. 
      eCollection 2018.