Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions

Alaeddini, Adel; Faruqui, Syed Hasib Akhter; Pugh, Mary Jo

doi:10.3414/me16-01-0135

Cited by 20 publications

(11 citation statements)

References 53 publications

(60 reference statements)

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“…After removing duplicates and non-original articles, 2,926 studies were identified, of which 109 were retained for full-text screening. A total of 73 articles were excluded following full-text review or qualitative synthesis, 34 …”

Section: Resultsmentioning

confidence: 99%

“…There is significant diversity in the definition of “patterns” in multimorbidity research, with some studies employing a temporal component 25 , 107 , 111 , 117 , 118 and others using the term almost synonymously with patient clusters. 63 Methods used to discern accumulation were highly heterogeneous and showed novel applications of advanced statistical techniques. A common feature of the five articles was the use of diagrams to demonstrate temporal relationships.…”

Section: Discussionmentioning

confidence: 99%

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The incidence of multimorbidity and patterns in accumulation of chronic conditions: A systematic review

Kudesia

Salimarouny

Stanley

et al. 2021

Journal of Multimorbidity and Comorbidity

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Multimorbidity, the presence of 1+ chronic condition in an individual, remains one of the greatest challenges to health on a global scale. Although the prevalence of multimorbidity has been well-established, its incidence is not fully understood. This systematic review determined the incidence of multimorbidity across the lifespan; the order in which chronic conditions accumulate to result in multimorbidity; and cataloged methods used to determine and report accumulation of chronic conditions resulting in multimorbidity. Studies were identified by searching MEDLINE, Embase, CINAHL, and Cochrane electronic databases. Two independent reviewers evaluated studies for inclusion and performed quality assessments. Of 36 included studies, there was high heterogeneity in study design and operational definitions of multimorbidity. Studies reporting incidence (n = 32) reported a median incidence rate of 30.7 per 1,000 person-years (IQR 39.5 per 1,000 person-years) and a median cumulative incidence of 2.8% (IQR 28.7%). Incidence was notably higher for persons with older age and 1+ chronic conditions at baseline. Studies reporting patterns in accumulation of chronic conditions (n = 5) reported hypertensive and heart diseases, and diabetes, as among the common starting conditions resulting in later multimorbidity. Methods used to discern patterns were highly heterogenous, ranging from the use of latent growth trajectories to divisive cluster analyses, and presentation using alluvial plots to cluster trajectories. Studies reporting the incidence of multimorbidity and patterns in accumulation of chronic conditions vary greatly in study designs and definitions used. To allow for more accurate estimations and comparison, studies must be transparent and consistent in operational definitions of multimorbidity applied.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The incidence of multimorbidity and patterns in accumulation of chronic conditions: A systematic review

Kudesia

Salimarouny

Stanley

et al. 2021

Journal of Multimorbidity and Comorbidity

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“…Likewise, the second block of rows in Table 4 illustrates the prediction accuracy of year-3 to year 5 comorbidities, given year-1 and year-2 comorbidities. Except for the LRMCL method, which can only incorporate the comorbidity information of the immediate preceding year [ 12 ] (See first block of rows in Table 4 ), for all other competing methods, we collect the information of AUC performance for various years of given comorbidities, namely year 1 to year 4. As shown in Table 4 , the MTBNs provides the best performance across all competing methods, followed by the probit and logistic regression and finally LRMCL.…”

Section: Resultsmentioning

confidence: 99%

“…Pugh et al [ 11 ] showed that these comorbidity phenotypes are associated with measures of community reintegration, with individuals in the PCT and Mental Health groups being significantly more likely to report difficulty in the transition from military to civilian life, lower levels of social support, and higher rates of unemployment. Alaeddini, et al [ 12 ] developed a Latent Regression Markov Mixture Clustering (LRMCL) algorithm to identify major transitions of four MCC that include hypertension (HTN), depression, PTSD, and back pain in a cohort of 601,805 Iraq and Afghanistan war Veterans (IAVs). The LRMCL algorithm was also able to predict the exact status of comorbidities about 48% of the time.…”

Section: Introductionmentioning

confidence: 99%

“…We also use a Longest Path Algorithm (LPA) to identify the most probable sequence of comorbidities emerging from and/or leading to specific chronic conditions. Moreover, we demonstrate the performance of the proposed unsupervised MTBN in comparison with the semi-supervised and supervised MTBNs, as well three baseline methods in the literature, including multivariate probit regression [ 20 ], multinomial logistic regression [ 21 ], and Latent Regression Markov Mixture Clustering (LRMCL) [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network

et al. 2018

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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.

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Process mining and data mining applications in the domain of chronic diseases: A systematic review

Chen,

Abtahi,

Carrero

et al. 2023

Artificial Intelligence in Medicine

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Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions

Cited by 20 publications

References 53 publications

The incidence of multimorbidity and patterns in accumulation of chronic conditions: A systematic review

The incidence of multimorbidity and patterns in accumulation of chronic conditions: A systematic review

Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network

Process mining and data mining applications in the domain of chronic diseases: A systematic review

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