Using an optimized generative model to infer the progression of complications in type 2 diabetes patients
Background 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. Methods 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. Results 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 %$O\left(N\times W\right)%$ to %$O\left((N-C)\times W\right)%$, where %$N%$ is the number of clinical findings, %$W%$ is the number of complications, %$C%$ 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. Discussion 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). Conclusions 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..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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| Enthalten in: |
BMC medical informatics and decision making - 22(2022), 1 vom: 01. Juli |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Xiaoxia [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
Computer simulation |
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| Anmerkungen: |
© The Author(s) 2022 |
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| doi: |
10.1186/s12911-022-01915-5 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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SPR050824236 |
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| 520 | |a Background 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. Methods 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. Results 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 %$O\left(N\times W\right)%$ to %$O\left((N-C)\times W\right)%$, where %$N%$ is the number of clinical findings, %$W%$ is the number of complications, %$C%$ 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. Discussion 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). Conclusions 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. | ||
| 650 | 4 | |a Computer simulation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Disease progression model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Diabetes mellitus, type 2 |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Probabilistic generative model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Electronic health records |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Lin, Yifei |4 aut | |
| 700 | 1 | |a Xiong, Yun |4 aut | |
| 700 | 1 | |a Zhang, Suhua |4 aut | |
| 700 | 1 | |a He, Yanming |4 aut | |
| 700 | 1 | |a He, Yuqing |4 aut | |
| 700 | 1 | |a Zhang, Zhikun |4 aut | |
| 700 | 1 | |a Plasek, Joseph M. |4 aut | |
| 700 | 1 | |a Zhou, Li |4 aut | |
| 700 | 1 | |a Bates, David W. |4 aut | |
| 700 | 1 | |a Tang, Chunlei |4 aut | |
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