Mining post-surgical care processes in breast cancer patients

• Published in: Artificial intelligence in medicine Vol. 105; p. 101855
• Main Authors: Chiudinelli, Lorenzo, Dagliati, Arianna, Tibollo, Valentina, Albasini, Sara, Geifman, Nophar, Peek, Niels, Holmes, John H., Corsi, Fabio, Bellazzi, Riccardo, Sacchi, Lucia
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2020
• Subjects: Breast cancer; Electronic Health Records; Latent Dirichlet Allocation; Process Mining; Temporal Data Analytics; Temporal Electronic Phenotyping; Topic Modelling; Latent Dirichlet Allocation; Temporal Data Analytics; Temporal Electronic Phenotyping; Topic Modelling; Process Mining; Breast cancer; Electronic Health Records
• ISSN: 0933-3657; 1873-2860; 1873-2860
• DOI: 10.1016/j.artmed.2020.101855

•A data analysis pipeline to extract frequent patterns in breast cancer patients using administrative data from EHR.•A Topic Modeling step allows synthesizing the ICD9-CM codes of the procedures carried out during hospitalizations.•Frequent patterns of care are extracted through a careflow mining algorithm.•The results reveal interesting temporal phenotypes, which are different in terms of clinical outcome.•The resulting careflows reflect the clinical practice guidelines enacted at the considered Breast Unit. 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.