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Background Cutaneous malignant melanoma (CMM) ranks among the ten most frequent malignancies, clinicopathological staging being of key importance to predict prognosis. Artificial intelligence (AI) has been recently applied to develop prognostically reliable staging systems for CMM. This study aims to provide a useful machine learning based tool to predict the overall CMM short-term survival. Methods CMM records as collected at the Veneto Cancer Registry (RTV) and at the Veneto regional health service were considered. A univariate Cox regression validated the strength and direction of each independent variable with overall mortality. A range of machine learning models (Logistic Regression classifier, Support-Vector Machine, Random Forest, Gradient Boosting, and k-Nearest Neighbors) and a Deep Neural Network were then trained to predict the 3-years mortality probability. Five-fold cross-validation and Grid Search were performed to test the best data preprocessing procedures, features selection, and to optimize models hyperparameters. A final evaluation was carried out on a separate test set in terms of balanced accuracy, precision, recall and F1 score. The best model was deployed as online tool. Results The univariate analysis confirmed the significant prognostic value of TNM staging. Adjunctive clinicopathological variables not included in the AJCC 8th melanoma staging system, i.e., sex, tumor site, histotype, growth phase, and age, were significantly linked to overall survival. Among the models, the Neural Network and the Random Forest models featured the best prognostic performance, achieving a balanced accuracy of 91% and 88%, respectively. According to the Gini importance score, age, T and M stages, mitotic count, and ulceration appeared to be the variables with the greatest impact on survival prediction. Conclusions Using data from patients with CMM, we developed an AI algorithm with high staging reliability, on top of which a web tool was implemented (unipd.link/melanomaprediction). Being essentially based on routinely recorded clinicopathological variables, it can already be implemented with minimal effort and further tested in the current clinical practice, an essential phase for validating the model’s accuracy beyond the original research context.

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ObjectivesThe aim of this study was to examine the odds of readmission and mortality after discharge against medical advice (DAMA) in the Veneto region of Northeast Italy, drawing on data from the regional archives of emergency department records and hospital discharge records.DesignA retrospective cohort study.SettingHospital discharges, Veneto region, Italy.ParticipantsAll patients discharged after being admitted to a public or accredited private hospital between January 2016 and 31 January 2021 in the Veneto region were considered. A total of 3 574 124 index discharges were examined for inclusion in the analysis.Primary and secondary outcome measuresReadmission and overall mortality at 30 days after the index discharge against admission.ResultsIn our cohort, 7.6‰ of patients left hospital against their doctor’s advice (n=19 272). These DAMA patients were more likely to be younger (mean age: 45.5 vs 55.0), foreign (22.1% vs 9.1%). The adjusted odds of readmission after DAMA was 2.76 (CI 95% 2.62–2.90) at 30 days (9.5% DAMA vs 4.6% not-DAMA), and the highest readmission rate was recorded in the first 24 hours after the index discharge. Mortality was higher for DAMA patients after adjusting for patient-level and hospital-level characteristics (with adjusted ORs of 1.40 for in-hospital mortality and 1.48 for overall mortality).ConclusionsThe present study shows that DAMA patients are more likely to die and to need hospital readmission than patients discharged by their doctors. DAMA patients should be more committed to a proactive and diligent postdischarge care.

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