Key result
Biclustering identifies interpretable patterns of post-surgical risk, survival, and hospitalization needs in oncology patients.
Why the study?
Personalizing surgical care requires understanding individual risks, but discovering discriminative patterns of post-surgical risk in oncology remains hampered by high-dimensional heterogeneous data and lack of interpretability.
Answers are grounded in this paper and cite their sources.
Biclustering is an effective computational method for identifying actionable and interpretable patterns of post-surgical risk in oncology patients.
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.
No takes yet. Share an insight, caveat, or question.
Alexandre et al. (2021) studied Oncology. Biclustering was evaluated on Post-surgical complications, survival, and hospitalization needs. Biclustering successfully identified interpretable and significant patterns of post-surgical risk, survival, and hospitalization needs in oncology patients.
Synapse has enriched 10 related papers. Consider them for comparative context: