Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018

Artemova, Svetlana; von Schenck, Ursula; Fa, Rui; Stoessel, Daniel; Nowparast Rostami, Hadiseh; Madiot, Pierre-Ephrem; Januel, Jean-Marie; Pagonis, Daniel; Landelle, Caroline; Gallouche, Meghann; Cancé, Christophe; Olive, Frederic; Moreau-Gaudry, Alexandre; Prieur, Sigurd; Bosson, Jean-Luc

doi:10.1136/bmjopen-2022-070929

Cited by 3 publications

References 29 publications

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Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist

Yao,

Jenkins,

Nguyen

et al. 2024

Semin Reprod Med

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Although in vitro fertilization (IVF) has become an extremely effective treatment option for infertility, there is significant underutilization of IVF by patients who could benefit from such treatment. In order for patients to choose to consider IVF treatment when appropriate, it is critical for them to be provided with an accurate, understandable IVF prognosis. Machine learning (ML) can meet the challenge of personalized prognostication based on data available prior to treatment. The development, validation, and deployment of ML prognostic models and related patient counseling report delivery require specialized human and platform expertise. This review article takes a pragmatic approach to review relevant reports of IVF prognostic models and draws from extensive experience meeting patients' and providers' needs with the development of data and model pipelines to implement validated ML models at scale, at the point-of-care. Requirements of using ML-based IVF prognostics at point-of-care will be considered alongside clinical ML implementation factors critical for success. Finally, we discuss health, social, and economic objectives that may be achieved by leveraging combined human expertise and ML prognostics to expand fertility care access and advance health and social good.

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Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist

Yao,

Jenkins,

Nguyen

et al. 2024

Semin Reprod Med

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Early prediction of in-hospital mortality utilizing multivariate predictive modelling of electronic medical records and socio-determinants of health of the first day of hospitalization

Stoessel,

Fa,

Artemova

et al. 2023

BMC Med Inform Decis Mak

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Background In France an average of 4% of hospitalized patients die during their hospital stay. To aid medical decision making and the attribution of resources, within a few days of admission the identification of patients at high risk of dying in hospital is essential. Methods We used de-identified routine patient data available in the first 2 days of hospitalization in a French University Hospital (between 2016 and 2018) to build models predicting in-hospital mortality (at ≥ 2 and ≤ 30 days after admission). We tested nine different machine learning algorithms with repeated 10-fold cross-validation. Models were trained with 283 variables including age, sex, socio-determinants of health, laboratory test results, procedures (Classification of Medical Acts), medications (Anatomical Therapeutic Chemical code), hospital department/unit and home address (urban, rural etc.). The models were evaluated using various performance metrics. The dataset contained 123,729 admissions, of which the outcome for 3542 was all-cause in-hospital mortality and 120,187 admissions (no death reported within 30 days) were controls. Results The support vector machine, logistic regression and Xgboost algorithms demonstrated high discrimination with a balanced accuracy of 0.81 (95%CI 0.80–0.82), 0.82 (95%CI 0.80–0.83) and 0.83 (95%CI 0.80–0.83) and AUC of 0.90 (95%CI 0.88–0.91), 0.90 (95%CI 0.89–0.91) and 0.90 (95%CI 0.89–0.91) respectively. The most predictive variables for in-hospital mortality in all three models were older age (greater risk), and admission with a confirmed appointment (reduced risk). Conclusion We propose three highly discriminating machine-learning models that could improve clinical and organizational decision making for adult patients at hospital admission.

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A SHAP Value-Based Variable Selection Method Improves the Prediction of Prolonged Hospital Length of Stay (Preprint)

Fa,

Stoessel,

Artemova

et al. 2024

Preprint

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BACKGROUND A prolonged length of hospitalization drains both human and material hospital resources as well having a deleterious psychological effect on the patient. Some patients are at greater risk of a prolonged hospital stay than others and it is important to identify them in the first days after admission so as to implement appropriate care as soon as possible and program staff and bed occupancy needs. OBJECTIVE The objective of this study is to optimize the prediction of prolonged length of hospital stay (LOS) by refining the selection of variables using an interpretable machine-learning algorithm. METHODS Deidentified patient administrative and clinical data from various sources are stored in our University Hospital’s Clinical Data Warehouse, which contains data from 134,840 adult patients with 273,693 hospitalizations between 2016 and 2018. We conducted a two-stage predictive modeling experiment. Initially, we utilized conventional clinical variables and composite variables (by aggregating appropriate conventional variables to form new variables) in several machine-learning algorithms to select the best-performing model. Next, we employed the SHAP method to identify the most important predictive variables and used these to further improve the predictive model. RESULTS XGBoost with an undersampling method outperformed other methods with an AUC-ROC of 0.802 (95% CI: 0.801-0.803) and an F2 score of 0.533 (95% CI: 0.533-0.534). The predictive performance was equivalent if we selected half the number of variables based on the SHAP-value with an AUC-ROC of 0.804 (95%: CI: 0.803-0.805) and F2 score of 0.536 (95%: CI: 0.535-0.536). This consistency held for significant variable reduction with SHAP values of more than 70% from 523 to 150. CONCLUSIONS SHAP-value-based variable selection allowed a reduction in the number of variables for equivalent predictive performance, making optimum prediction of prolonged LOS easier to implement in routine clinical practice by prioritizing the predictive factors.

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Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018

Cited by 3 publications

References 29 publications

Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist

Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist

Early prediction of in-hospital mortality utilizing multivariate predictive modelling of electronic medical records and socio-determinants of health of the first day of hospitalization

A SHAP Value-Based Variable Selection Method Improves the Prediction of Prolonged Hospital Length of Stay (Preprint)

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