TY  - JOUR
A1  - Shi, Xi
A1  - Nikolic, Gorana
A1  - Pottelbergh, Gijs van
A1  - Akker, Marjan van den
A1  - Vos, Rein
A1  - De Moor, Bart
T1  - Development of multimorbidity over time: an analysis of Belgium primary care data using Markov chains and weighted association rule mining
T2  - The journals of Gerontology Series A
N2  - Background: The prevalence of multimorbidity is increasing in recent years, and patients with multimorbidity often have a decrease in quality of life and require more health care. The aim of this study was to explore the evolution of multimorbidity taking the sequence of diseases into consideration.
Methods: We used a Belgian database collected by extracting coded parameters and more than 100 chronic conditions from the Electronic Health Records of general practitioners to study patients older than 40 years with multiple diagnoses between 1991 and 2015 (N = 65 939). We applied Markov chains to estimate the probability of developing another condition in the next state after a diagnosis. The results of Weighted Association Rule Mining (WARM) allow us to show strong associations among multiple conditions.
Results: About 66.9% of the selected patients had multimorbidity. Conditions with high prevalence, such as hypertension and depressive disorder, were likely to occur after the diagnosis of most conditions. Patterns in several disease groups were apparent based on the results of both Markov chain and WARM, such as musculoskeletal diseases and psychological diseases. Psychological diseases were frequently followed by irritable bowel syndrome.
Conclusions: Our study used Markov chains and WARM for the first time to provide a comprehensive view of the relations among 103 chronic conditions, taking sequential chronology into consideration. Some strong associations among specific conditions were detected and the results were consistent with current knowledge in literature, meaning the approaches were valid to be used on larger data sets, such as National Health care Systems or private insurers.
KW  - Chronic conditions
KW  - Chronology of disease
KW  - Machine learning
Y1  - 2020
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/63306
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-633068
SN  - 1758-535X
N1  - This work was supported by KU Leuven: Research Fund (projects C16/15/059, C32/16/013, C24/18/022), Industrial Research Fund (Fellowship 13-0260), and several Leuven Research and Development bilateral industrial projects, Flemish Government Agencies: Research Foundation - Flanders (FWO) (EOS Project no 30468160 [SeLMA], SBO project I013218N, PhD grants [SB/1SA1319N, SB/1S93918, SB/151622]), EWI (PhD and postdoc grants Flanders AI Impulse Program), Flanders Agency Innovation & Entrepreneurship (VLAIO) (City of Things [COT.2018.018], PhD grants: Baekeland [HBC.20192204] and Innovation mandate [HBC.2019.2209], Industrial Projects [HBC.2018.0405]), European Commission (EU H2020-SC1-2016-2017 grant agreement No. 727721: MIDAS), and the European Research Council (ERC) (advanced grant No. 885682 [B.D.M.]).
VL  - 76
IS  - 7
SP  - 1234
EP  - 1241
PB  - Oxford Univ. Press
CY  - Oxford [u.a.]
ER  -