Mining post-surgical care processes in breast cancer patients

Lorenzo Chiudinelli, Arianna Dagliati, Valentina Tibollo, Sara Albasini, Nophar Geifman, Niels Peek, John H. Holmes, Fabio Corsi, Riccardo Bellazzi, Lucia Sacchi

Research output: Contribution to journalArticlepeer-review

Abstract

In this work we describe the application of a careflow mining algorithm to detect the most frequent patterns of care in a cohort of 3000 breast cancer patients. The applied method relies on longitudinal data extracted from electronic health records, recorded from the first surgical procedure after a breast cancer diagnosis. Careflows are mined from events data recorded for administrative purposes, including procedures from ICD9 – CM billing codes and chemotherapy treatments. Events data have been pre-processed with Topic Modelling to create composite events based on concurrent procedures. The results of the careflow mining algorithm allow the discovery of electronic temporal phenotypes across the studied population. These phenotypes are further characterized on the basis of clinical traits and tumour histopathology, as well as in terms of relapses, metastasis occurrence and 5-year survival rates. Results are highly significant from a clinical perspective, since phenotypes describe well characterized pathology classes, and the careflows are well matched with existing clinical guidelines. The analysis thus facilitates deriving real-world evidence that can inform clinicians as well as hospital decision makers.

Original languageEnglish
Article number101855
JournalArtificial Intelligence in Medicine
Volume105
DOIs
Publication statusPublished - May 2020

Keywords

  • Breast cancer
  • Electronic Health Records
  • Latent Dirichlet Allocation
  • Process Mining
  • Temporal Data Analytics
  • Temporal Electronic Phenotyping
  • Topic Modelling

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence

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