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. 2020 Jun 17;8(6):e16372. doi: 10.2196/16372

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©Syed Hasib Akhter Faruqui, Adel Alaeddini, Mike C Chang, Sara Shirinkam, Carlos Jaramillo, Peyman NajafiRad, Jing Wang, Mary Jo Pugh. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 17.06.2020.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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(a) Unsummarized probabilistic graphical model of the emergence of MCCs. (b) The summarized probabilistic graphical model using the EAGL summarization algorithm at a 20% summarization rate. Each node is a binary (0,1) variable representing the status (presence or absence) of a chronic condition in a particular year, that is, TBIY1 denotes the status of TBI at year-1 (base year), and BaPaY5 denotes the status of BaPa in year-5. BaPa, back pain. In the figure, BaPa: back pain; TBI: traumatic brain injury, PTSD: posttraumatic stress disorder and SuAb: substance abuse, MCC: multiple chronic condition, EAGL: eigen analysis of graph Laplacian.