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OBJECTIVE: To study the suitability of costsensitive ordinal artificial intelligence-machine learning (AIML) strategies in the prognosis of SARS-CoV-2 pneumonia severity. MATERIALS & METHODS: Observational, retrospective, longitudinal, cohort study in 4 hospitals in Spain. Information regarding demographic and clinical status was supplemented by socioeconomic data and air pollution exposures. We proposed AI-ML algorithms for ordinal classification via ordinal decomposition and for cost-sensitive learning via resampling techniques. For performance-based model selection, we defined a custom score including per-class sensitivities and asymmetric misprognosis costs. 260 distinct AI-ML models were evaluated via 10 repetitions of 5×5 nested cross-validation with hyperparameter tuning. Model selection was followed by the calibration of predicted probabilities. Final overall performance was compared against five well-established clinical severity scores and against a 'standard' (non-cost sensitive, non-ordinal) AI-ML baseline. In our best model, we also evaluated its explainability with respect to each of the input variables. RESULTS: The study enrolled n = 1548 patients: 712 experienced low, 238 medium, and 598 high clinical severity. d = 131 variables were collected, becoming d ' = 148 features after categorical encoding. Model selection resulted in our best-performing AI-ML pipeline having: a) no imputation of missing data, b) no feature selection (i.e. using the full set of d ' features), c) 'Ordered Partitions' ordinal decomposition, d) cost-based reimbalance, and e) a Histogram-based Gradient Boosting classifier. This best model (calibrated) obtained a median accuracy of 68.1% [67.3%, 68.8%] (95% confidence interval), a balanced accuracy of 57.0% [55.6%, 57.9%], and an overall area under the curve (AUC) 0.802 [0.795, 0.808]. In our dataset, it outperformed all five clinical severity scores and the 'standard' AI-ML baseline. DISCUSSION & CONCLUSION: We conducted an exhaustive exploration of AI-ML methods designed for both ordinal and cost-sensitive classification, motivated by a real-world application domain (clinical severity prognosis) in which these topics arise naturally. Our model with the best classification performance exploited successfully the ordering information of ground truth classes, coping with imbalance and asymmetric costs. However, these ordinal and cost-sensitive aspects are seldom explored in the literature.
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In this paper the Analytic Network Process (ANP) is applied to select the best location for the construction of a municipal solid waste (MSW) plant in the Metropolitan area of Valencia (Spain). Selection of the appropriate MSW facility location can be viewed as a complex multicriteria decision-making problem that requires an extensive evaluation process of the potential MSW plant locations and other factors as diverse as economic, technical, legal, social or environmental issues. The decision-making process includes the identification of six candidate MSW plant sites and 21 criteria grouped into clusters for the construction of a network. Two technicians of the Metropolitan Waste Disposal Agency acted as decision makers (DMs). The influences between the elements of the network were identified and analyzed using the ANP multicriteria decision method. Two different ANP models were used: one hierarchy model (that considers AHP as a particular case of ANP) and another network-based model. The results obtained in each model were compared and analyzed. The strengths and weaknesses of ANP as a multicriteria decision analysis tool are also described in the paper. The main findings of this research have proved that ANP is a useful tool to help technicians to make their decision process traceable and reliable. Moreover, this approach helps DMs undertake a sound reflection of the siting problem.