Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter‐parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty‐six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age‐specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow‐up. These data show that PPA is a robust, quantitative and explainable ML‐based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.

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Early diagnosis is crucial for individuals who are susceptible to tooth-supporting tissue diseases (e.g., periodontitis) that may lead to tooth loss, so as to prevent systemic implications and maintain quality of life. The aim of this study was to propose a personalized explainable machine learning algorithm, solely based on non-invasive predictors that can easily be collected in a clinic, to identify subjects at risk of developing periodontal diseases. To this end, the individual data and periodontal health of 532 subjects was assessed. A machine learning pipeline combining a feature selection step, multilayer perceptron, and SHapley Additive exPlanations (SHAP) explainability, was used to build the algorithm. The prediction scores for healthy periodontium and periodontitis gave final F1-scores of 0.74 and 0.68, respectively, while gingival inflammation was harder to predict (F1-score of 0.32). Age, body mass index, smoking habits, systemic pathologies, diet, alcohol, educational level, and hormonal status were found to be the most contributive variables for periodontal health prediction. The algorithm clearly shows different risk profiles before and after 35 years of age and suggests transition ages in the predisposition to developing gingival inflammation or periodontitis. This innovative approach to systemic periodontal disease risk profiles, combining both ML and up-to-date explainability algorithms, paves the way for new periodontal health prediction strategies.

Background The ongoing COVID-19 pandemic has highlighted the potential of digital health solutions to adapt the organization of care in a crisis context. Objective Our aim was to describe the relationship between the MyRISK score, derived from self-reported data collected by a chatbot before the preanesthetic consultation, and the occurrence of postoperative complications. Methods This was a single-center prospective observational study that included 401 patients. The 16 items composing the MyRISK score were selected using the Delphi method. An algorithm was used to stratify patients with low (green), intermediate (orange), and high (red) risk. The primary end point concerned postoperative complications occurring in the first 6 months after surgery (composite criterion), collected by telephone and by consulting the electronic medical database. A logistic regression analysis was carried out to identify the explanatory variables associated with the complications. A machine learning model was trained to predict the MyRISK score using a larger data set of 1823 patients classified as green or red to reclassify individuals classified as orange as either modified green or modified red. User satisfaction and usability were assessed. Results Of the 389 patients analyzed for the primary end point, 16 (4.1%) experienced a postoperative complication. A red score was independently associated with postoperative complications (odds ratio 5.9, 95% CI 1.5-22.3; P=.009). A modified red score was strongly correlated with postoperative complications (odds ratio 21.8, 95% CI 2.8-171.5; P=.003) and predicted postoperative complications with high sensitivity (94%) and high negative predictive value (99%) but with low specificity (49%) and very low positive predictive value (7%; area under the receiver operating characteristic curve=0.71). Patient satisfaction numeric rating scale and system usability scale median scores were 8.0 (IQR 7.0-9.0) out of 10 and 90.0 (IQR 82.5-95.0) out of 100, respectively. Conclusions The MyRISK digital perioperative risk score established before the preanesthetic consultation was independently associated with the occurrence of postoperative complications. Its negative predictive strength was increased using a machine learning model to reclassify patients identified as being at intermediate risk. This reliable numerical categorization could be used to objectively refer patients with low risk to teleconsultation.

BACKGROUND The pandemic highlighted the potential of digital health solutions to adapt the organization of care in a crisis context. OBJECTIVE Our aim was to demonstrate the prognostic value of the perioperative ‘MyRISK score’ derived from data collected on a digital conversational agent (chatbot) before the preanesthetic consultation (PAC). METHODS Single-center, prospective, observational study. The 16 items composing the MyRISK score were selected by Delphi method. An algorithm was used to stratify low (‘green’), intermediate (‘orange’) and high (‘red’) risk patients. Postoperative complications occurring in the first 6 months (composite criterion) were numerically collected and verified by phone and consultation of the electronic medical database. A logistic regression was carried out to identify their explanatory variables. A machine learning model was trained to predict the MyRISK score using a dataset of 1823 ‘green’ and ‘red’ patients to re-classify ‘orange’ individuals. User satisfaction and usability were assessed. RESULTS Four hundered and one patients were included. Sixteen of the 389 patients (4.1%) analyzed for the primary endpoint experienced a postoperative complication. An ASA score ≥ 3 and a ‘red’ score were independent predictors of postoperative complications (Odds Ratios of 5.8 [CI95%: 1.7 - 20.2; p=0.006] and 5.9 [CI95%: 1.5 - 22.3; p=0.009] respectively). Once ‘orange’ patients re-classified according to the prediction of the trained model, a ‘red’ score was identified as a strong predictor of postoperative complications with an Odds Ratio of 21.8 [CI95%: 2.8 - 171.5; p=0.003]. Patient satisfaction Numeric Rating Scale and System Usability Scale were 8 [7-9]/10 and 90 [82.5-95]/100 respectively. CONCLUSIONS We demonstrate the good prognostic predictive value of the MyRISK digital perioperative risk score established before the PAC. Its predictive strength was increase using a machine learning model reclassifying intermediate-risk patient. This numerical categorization could be used to guide patients between teleconsultation and face-to-face PAC, or to provide a perioperative personalized care pathway for high-risk patients.

With the extensive use of Machine Learning (ML) in the biomedical field, there was an increasing need for Explainable Artificial Intelligence (XAI) to improve transparency and reveal complex hidden relationships between variables for medical practitioners, while meeting regulatory requirements. Feature Selection (FS) is widely used as a part of a biomedical ML pipeline to significantly reduce the number of variables while preserving as much information as possible. However, the choice of FS methods affects the entire pipeline including the final prediction explanations, whereas very few works investigate the relationship between FS and model explanations. Through a systematic workflow performed on 145 datasets and an illustration on medical data, the present work demonstrated the promising complementarity of two metrics based on explanations (using ranking and influence changes) in addition to accuracy and retention rate to select the most appropriate FS/ML models. Measuring how much explanations differ with/without FS are particularly promising for FS methods recommendation. While reliefF generally performs the best on average, the optimal choice may vary for each dataset. Positioning FS methods in a tridimensional space, integrating explanations-based metrics, accuracy and retention rate, would allow the user to choose the priorities to be given on each of the dimensions. In biomedical applica-

This paper proposes two original approaches to the processing of fuzzy queries in a relational database context. The general idea is to use views, either materialized or not. In the first case, materialized views are used to store the satisfaction degrees related to user-defined fuzzy predicates, instead of calculating them at runtime by means of user functions embedded in the query (which induces an important overhead). In the second case, abstract views are used to efficiently access the tuples that belong to the α-cut of the query result, by means of a derived Boolean selection condition.