4.6 Article

Mining Pre-Surgical Patterns Able to Discriminate Post-Surgical Outcomes in the Oncological Domain

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2021.3064786

关键词

Surgery; Physiology; Cancer; Oncology; Input variables; Bioinformatics; Tumors; Biclustering; discriminative pattern mining; oncology; post-surgical complications; surgical risk

资金

  1. Fundacao para a Ciencia e a Tecnologia (FCT) [UIDB/50022/2020, DSAIPA/DS/0042/2018, DSAIPA/DS/0044/2018]
  2. Associate Laboratory for Green Chemistry (LAQV) - FCT/MCTES funds [UIDB/50006/2020, UIDP/50006/2020]
  3. INESC-ID pluriannual [UIDB/50021/2020]
  4. [CEECIND/01399/2017]
  5. Fundação para a Ciência e a Tecnologia [DSAIPA/DS/0042/2018] Funding Source: FCT

向作者/读者索取更多资源

Understanding the individualized risks of surgical procedures is crucial in personalized preparatory, intervention, and post-care protocols, especially in oncology. Biclustering presents a unique approach to identifying discriminative patterns of post-surgical risk, offering comprehensive and actionable insights for healthcare professionals.
Understanding the individualized risks of undertaking surgical procedures is essential to personalize preparatory, intervention and post-care protocols for minimizing post-surgical complications. This knowledge is key in oncology given the nature of interventions, the fragile profile of patients with comorbidities and cytotoxic drug exposure, and the possible cancer recurrence. Despite its relevance, the discovery of discriminative patterns of post-surgical risk is hampered by major challenges: i) the unique physiological and demographic profile of individuals, as well as their differentiated post-surgical care; ii) the high-dimensionality and heterogeneous nature of available biomedical data, combining non-identically distributed risk factors, clinical and molecular variables; iii) the need to generalize tumors have significant histopathological differences and individuals undertake unique surgical procedures; iv) the need to focus on non-trivial patterns of post-surgical risk, while guaranteeing their statistical significance and discriminative power; and v) the lack of interpretability and actionability of current approaches. Biclustering, the discovery of groups of individuals correlated on subsets of variables, has unique properties of interest, being positioned to satisfy the aforementioned challenges. In this context, this work proposes a structured view on why, when and how to apply biclustering to mine discriminative patterns of post-surgical risk with guarantees of usability, a subject remaining unexplored up to date. These patterns offer a comprehensive view on how the patient profile, cancer histopathology and entailed surgical procedures determine: i) post-surgical complications, ii) survival, and iii) hospitalization needs. The gathered results confirm the role of biclustering in comprehensively finding interpretable, actionable and statistically significant patterns of post-surgical risk. The found patterns are already assisting healthcare professionals at IPO-Porto to establish specialized pre-habilitation protocols and bedside care.

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