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comoR: a software for disease comorbidity risk assessment.

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This study introduces comoR, an R software aiding physicians in diagnosing disease comorbidities. It uses patient data to predict comorbidity risks and causal relationships, improving patient survival probability estimates.

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Area of Science:

  • Computational biology
  • Medical informatics
  • Bioinformatics

Background:

  • Diagnosing comorbidities (coexisting diseases) is challenging due to physician specialization.
  • A dedicated software tool could significantly aid healthcare professionals in comorbidity diagnosis.

Purpose of the Study:

  • To develop an R software, comoR, for estimating disease comorbidity associations.
  • To provide a tool that aids in identifying comorbidity risks and predicting causal relationships between diseases.

Main Methods:

  • Developed comoR, an R software package.
  • Integrated causal inference packages (e.g., pcalg, qtlnet) for predicting disease relationships.
  • Incorporated network regression and survival analysis tools (e.g., Net-Cox, rbsurv) for survival probability prediction.

Main Results:

  • comoR computes novel estimators for disease comorbidity associations.
  • The software identifies comorbidity risks based on initial diagnosis, genetic, and clinical data.
  • Predicts causal relationships between diseases and enhances survival probability estimations.

Conclusions:

  • comoR offers flexible diagnostic applications for predicting disease comorbidities.
  • The software can be readily integrated into high-throughput and clinical data analysis pipelines.