TY - JOUR
T1 - Mining post-surgical care processes in breast cancer patients
AU - Chiudinelli, Lorenzo
AU - Dagliati, Arianna
AU - Tibollo, Valentina
AU - Albasini, Sara
AU - Geifman, Nophar
AU - Peek, Niels
AU - Holmes, John H.
AU - Corsi, Fabio
AU - Bellazzi, Riccardo
AU - Sacchi, Lucia
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - 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.
AB - 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.
KW - Breast cancer
KW - Electronic Health Records
KW - Latent Dirichlet Allocation
KW - Process Mining
KW - Temporal Data Analytics
KW - Temporal Electronic Phenotyping
KW - Topic Modelling
UR - http://www.scopus.com/inward/record.url?scp=85083809140&partnerID=8YFLogxK
U2 - 10.1016/j.artmed.2020.101855
DO - 10.1016/j.artmed.2020.101855
M3 - Article
C2 - 32505422
AN - SCOPUS:85083809140
SN - 0933-3657
VL - 105
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
M1 - 101855
ER -