Mining post-surgical care processes in breast cancer patients
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Breast cancer,Process Mining,Topic Modelling,Latent Dirichlet Allocation,Temporal Data Analytics,Temporal Electronic Phenotyping,Electronic Health Records
论文评审过程：Received 6 September 2019, Revised 1 April 2020, Accepted 2 April 2020, Available online 15 April 2020, Version of Record 28 April 2020.
论文官网地址：https://doi.org/10.1016/j.artmed.2020.101855
