On Learning and Visualizing Practice-based Clinical Pathways for Chronic Kidney Disease. Academic Article uri icon

Overview

abstract

  • Chronic Kidney Disease (CKD) is a costly and complex disease affecting 20 million US adults. Recent studies suggest that care delivery changes may improve clinical outcomes and quality of patient experience while reducing costs. This study analyzes the treatment data of 8,553 CKD patients to learn practice-based clinical pathways. Patients' visit history is modeled as sequences of visits containing information on visit type, date, procedures and diagnoses. We use hierarchical clustering based on longest common subsequence (LCS) distance to discover six patient subgroups, with each subgroup differing in the distribution of demographics and health conditions. Transitions of visits with high probabilities are elicited from each patient subgroup to learn common clinical pathways and treatment durations. Insights from this study can potentially result in new evidence to support patient-centered treatment approaches, empower CKD patients to better manage their disease and its complications, and provide a review guide for clinicians.

publication date

  • November 14, 2014

Research

keywords

  • Critical Pathways
  • Data Mining
  • Electronic Health Records
  • Renal Insufficiency, Chronic

Identity

PubMed Central ID

  • PMC4419909

Scopus Document Identifier

  • 84964314556

Digital Object Identifier (DOI)

  • 10.1109/RBME.2010.2083647

PubMed ID

  • 25954471

Additional Document Info

volume

  • 2014