{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T06:44:40Z","timestamp":1769582680093,"version":"3.49.0"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. U1636207"],"award-info":[{"award-number":["No. U1636207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. U1936213"],"award-info":[{"award-number":["No. U1936213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 32101206"],"award-info":[{"award-number":["No. 32101206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 12161080"],"award-info":[{"award-number":["No. 12161080"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012543","name":"Shanghai Science and Technology Development Fund","doi-asserted-by":"crossref","award":["No. 19511121204"],"award-info":[{"award-number":["No. 19511121204"]}],"id":[{"id":"10.13039\/100012543","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100012543","name":"Shanghai Science and Technology Development Fund","doi-asserted-by":"crossref","award":["No. 19DZ1200802"],"award-info":[{"award-number":["No. 19DZ1200802"]}],"id":[{"id":"10.13039\/100012543","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Clinical Research Plan of Shanghai Hospital Development Center","award":["SHDC12019124"],"award-info":[{"award-number":["SHDC12019124"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>People live a long time in pre-diabetes\/early diabetes without a formal diagnosis or management. Heterogeneity of progression coupled with deficiencies in electronic health records related to incomplete data, discrete events, and irregular event intervals make identification of pre-diabetes and critical points of diabetes progression challenging.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We utilized longitudinal electronic health records of 9298 patients with type 2 diabetes or prediabetes from 2005 to 2016 from a large regional healthcare delivery network in China. We optimized a generative Markov-Bayesian-based model to generate 5000 synthetic illness trajectories. The synthetic data were manually reviewed by endocrinologists.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We build an optimized generative progression model for type 2 diabetes using anchor information to reduce the number of parameters learning in the third layer of the model from <jats:inline-formula><jats:alternatives><jats:tex-math>$$O\\left(N\\times W\\right)$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mi>O<\/mml:mi>\n                      <mml:mfenced>\n                        <mml:mi>N<\/mml:mi>\n                        <mml:mo>\u00d7<\/mml:mo>\n                        <mml:mi>W<\/mml:mi>\n                      <\/mml:mfenced>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> to <jats:inline-formula><jats:alternatives><jats:tex-math>$$O\\left((N-C)\\times W\\right)$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mi>O<\/mml:mi>\n                      <mml:mfenced>\n                        <mml:mo>(<\/mml:mo>\n                        <mml:mi>N<\/mml:mi>\n                        <mml:mo>-<\/mml:mo>\n                        <mml:mi>C<\/mml:mi>\n                        <mml:mo>)<\/mml:mo>\n                        <mml:mo>\u00d7<\/mml:mo>\n                        <mml:mi>W<\/mml:mi>\n                      <\/mml:mfenced>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, where <jats:inline-formula><jats:alternatives><jats:tex-math>$$N$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mi>N<\/mml:mi>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> is the number of clinical findings, <jats:inline-formula><jats:alternatives><jats:tex-math>$$W$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mi>W<\/mml:mi>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> is the number of complications,  <jats:inline-formula><jats:alternatives><jats:tex-math>$$C$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mi>C<\/mml:mi>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> is the number of anchors. Based on this model, we infer the relationships between progression stages, the onset of complication categories, and the associated diagnoses during the whole progression of type 2 diabetes using electronic health records.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Discussion<\/jats:title>\n                <jats:p>Our findings indicate that 55.3% of single complications and 31.8% of complication patterns could be predicted early and managed appropriately to potentially delay (as it is a progressive disease) or prevented (by lifestyle modifications that keep patient from developing\/triggering diabetes in the first place).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The full type 2 diabetes patient trajectories generated by the chronic disease progression model can counter a lack of real-world evidence of desired longitudinal timeframe while facilitating population health management.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-01915-5","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T13:06:22Z","timestamp":1656680782000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Using an optimized generative model to infer the progression of complications in type 2 diabetes patients"],"prefix":"10.1186","volume":"22","author":[{"given":"Xiaoxia","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suhua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanming","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqing","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhikun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph M.","family":"Plasek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David W.","family":"Bates","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunlei","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,1]]},"reference":[{"key":"1915_CR1","unstructured":"Prediabetes\u2014your chance to prevent type II diabetes. 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Informed and written consent was obtained through a regional 17-hospital-based regional healthcare delivery network by each participant according to the Declaration of Helsinki prior to beginning data collection.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"JP reports receiving personal fees from Summary Medical Inc and DispatchHealth and equity from Summary Medical Inc outside the submitted work. DB reports receiving grants and personal fees from EarlySense, personal fees from CDI Negev, equity from Valera Health, equity from CLEW Medical, equity from MDClone, personal fees and equity from AESOP, personal fees and equity from FeelBetter, and grants from IBM Watson Health, outside the submitted work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"174"}}