Predicting Mohs surgery complexity by applying machine learning to patient demographics and tumor characteristics

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The models were created using the training dataset for a 10‐fold cross‐validation. We used a gradient boosting decision tree (GBDT) for classification models, constructed using XGBoost (version 1.2). 25 XGBoost is an optimized distributed gradient boosting library (open source) and the most common method for structured problems in Kaggle competitions. XGBoost shares qualities with other decision tree ensemble algorithms, such as the ability to handle a mixture of numerical and categorical data features. Moreover, XGBoost is a highly suitable tool for these models because it learns trees in a sequential order such that each additional tree is a gradient step with respect to a loss function, and has an extra randomization parameter used to decorrelate the individual trees. 26 (Additional information regarding model design can be found in the Supporting Information.) We selected the area under the receiver operating characteristic curve (AUROC) for the models' evaluation metric. ROC graphs are an extremely useful tool for evaluating classifiers; the AUROC is equivalent to the probability that the classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance. 27 To meet the reporting standard of ML predictive models in biomedical research, we reported accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). 28 All machine learning models were implemented using Python (The Python Software Foundation, Fredericksburg, VA, USA) (version 3.7)."}}], "targets": [{"id": "target-1", "claim_id": "claim-1", "operation": null, "expected_value": 0.79, "unit": "auroc", "tolerance": null, "state": "complete"}], "gaps": ["operations"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["differential_expression", "gwas", "machine_learning", "meta_analysis", "molecular_docking", "network_analysis", "pca", "statistical_regression", "survival_analysis"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "method", "blocked_on": "method:diagnostic_evaluation_roc", "axes": {"method": {"axis": "method", "tier": "R4", "blocker": "method:diagnostic_evaluation_roc", "detail": {"outcome": "determined", "family": "diagnostic_evaluation", "specific_method": "roc", "required_operations": ["diagnostic_evaluation_roc"], "missing_operations": ["diagnostic_evaluation_roc"], "basis": "The DSL has no operation for diagnostic_evaluation_roc. 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