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Comorbidity progression analysis: patient stratification and comorbidity prediction using temporal comorbidity network

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Abstract

Objective

The study aims to identify distinct population-specific comorbidity progression patterns, timely detect potential comorbidities, and gain better understanding of the progression of comorbid conditions among patients.

Methods

This work presents a comorbidity progression analysis framework that utilizes temporal comorbidity networks (TCN) for patient stratification and comorbidity prediction. We propose a TCN construction approach that utilizes longitudinal, temporal diagnosis data of patients to construct their TCN. Subsequently, we employ the TCN for patient stratification by conducting preliminary analysis, and typical prescription analysis to uncover potential comorbidity progression patterns in different patient groups. Finally, we propose an innovative comorbidity prediction method by utilizing the distance-matched temporal comorbidity network (TCN-DM). This method identifies similar patients with disease prevalence and disease transition patterns and combines their diagnosis information with that of the current patient to predict potential comorbidity at the patient’s next visit.

Results

This study validated the capability of the framework using a real-world dataset MIMIC-III, with heart failure (HF) as interested disease to investigate comorbidity progression in HF patients. With TCN, this study can identify four significant distinctive HF subgroups, revealing the progression of comorbidities in patients. Furthermore, compared to other methods, TCN-DM demonstrated better predictive performance with F1-Score values ranging from 0.454 to 0.612, showcasing its superiority.

Conclusions

This study can identify comorbidity patterns for individuals and population, and offer promising prediction for future comorbidity developments in patients.

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Data availibility

The dataset can be obtained from the website of MIMIC-III clinical database (https://physionet.org/content/mimiciii/1.4/).

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Funding

This research is supported by the National Natural Science Foundation of China (Grant No. 71771034), the Liaoning Province Applied Basic Research Program Project (Grant No. 2023JH2/101300208), and the Dalian High Level Talents Innovation Support Plan (Grant No. 2021RD01).

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Authors and Affiliations

  1. Institute of Systems Engineering, Dalian University of Technology, Dalian, Liaoning, China

    Ye Liang & Chonghui Guo

  2. College of Business Administration, Huaqiao University, Quanzhou, Fujian, China

    Hailin Li

  3. Research Center for Applied Statistics and Big Data, Huaqiao University, Xiamen, Fujian, China

    Hailin Li

Authors
  1. Ye Liang
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  2. Chonghui Guo
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Contributions

Ye Liang: Conceptualization, Methodology, Software, Visualization, Writing—Original Draft. Chonghui Guo: Validation, Supervision, Writing—Review & Editing. Hailin Li: Writing—Review & Editing.

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Correspondence to Chonghui Guo.

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Liang, Y., Guo, C. & Li, H. Comorbidity progression analysis: patient stratification and comorbidity prediction using temporal comorbidity network. Health Inf Sci Syst 12, 48 (2024). https://doi.org/10.1007/s13755-024-00307-5

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