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Unsupervised Machine Learning to Identify Patient Clusters and Tailor Perioperative Care in Colorectal Surgery.

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Machine learning identified patient clusters in colorectal surgery recovery, linking demographics and compliance to outcomes. This enables tailored enhanced recovery after surgery (ERAS) protocols for improved patient care.

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Area of Science:

  • Colorectal Surgery
  • Machine Learning
  • Enhanced Recovery After Surgery (ERAS)

Background:

  • Enhanced recovery after surgery (ERAS) protocols aim to optimize patient outcomes.
  • Patient variability in demographics and compliance can impact ERAS effectiveness.
  • Data-driven approaches are needed to personalize ERAS strategies.

Purpose of the Study:

  • To apply machine learning (ML) techniques to identify patient subgroups.
  • To define clusters based on demographics, compliance, and outcomes in colorectal ERAS patients.
  • To improve data-driven, predictive decision-making for personalized ERAS.

Main Methods:

  • Unsupervised K-means clustering algorithm used to identify patient subgroups without pre-defined labels.
  • Analysis included demographic, perioperative compliance, and clinical outcome variables.
  • Cluster transitions were traced from demographics through compliance to outcomes.

Main Results:

  • Three demographic risk clusters (low, intermediate, high) were identified among 1381 patients.
  • Two compliance clusters (high, low) and two outcome clusters (good, poor) were found.
  • Low-risk patients with high compliance showed favorable outcomes; high-risk patients had low compliance and poorer recovery.

Conclusions:

  • ML-based clustering effectively identified distinct patient subgroups in colorectal ERAS.
  • Cluster analysis revealed associations between demographics, compliance, and recovery outcomes.
  • This approach facilitates the development of tailored ERAS protocols to enhance patient compliance and outcomes.