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publications
Prediction of Hypotension Events with Physiologic Vital Sign Signatures in The Intensive Care Unit
Published in Critical Care, 2020
The study developed a machine learning model using vital signs data from ICU patients to predict the risk of hypotension events, achieving high accuracy with alerts generated up to 1 hour before the episode, indicating potential real-life utility in improving patient care.
Recommended citation: Yoon, J. H.*, Jeanselme, V.*, Dubrawski, A., Hravnak, M., Pinsky, M. R., Clermont, G. (2020). Prediction of Hypotension Events with Physiologic Vital Sign Signatures in The Intensive Care Unit. In Critical Care, 24(1), 1-9. https://ccforum.biomedcentral.com/articles/10.1186/s13054-020-03379-3
Deep Parametric Time-to-Event Regression with Time-Varying Covariates
Published in AAAI Spring Symposium on Survival Analysis, 2021
This paper introduces a parametric approach using Recurrent Neural Networks (RNNs) to model censored time-to-event outcomes with time-varying covariates, demonstrating its competitive performance in predicting ICU stay durations and short-term life expectancy compared to traditional time-to-event regression models on MIMIC III.
Recommended citation: Nagpal, C.*, Jeanselme, V.*, Dubrawski, A. (2021, May). Deep parametric time-to-event regression with time-varying covariates. In Survival Prediction-Algorithms, Challenges and Applications (pp. 184-193). PMLR. https://proceedings.mlr.press/v146/nagpal21a.html
Sex differences in post cardiac arrest discharge locations
Published in Resuscitation Plus, 2021
This study investigated sex-based differences in discharge location after cardiac arrest resuscitation and found that female sex was weakly associated with an unfavorable discharge location, suggesting a potential disparity.
Recommended citation: Jeanselme, V., De-Arteaga, M., Elmer, J., Perman, S. M., Dubrawski, A. (2021). Sex differences in post cardiac arrest discharge locations. In Resuscitation plus, 8, 100185. https://www.sciencedirect.com/science/article/pii/S2666520421001107
Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering
Published in Conference on Health, Inference, and Learning (CHIL), 2022
This research presents a novel clustering algorithm that uses pairwise constraints to enhance clustering performance and kernel learning without the common practice of transforming constraints into continuous domains, leading to improved generalization and scalability for large datasets.
Recommended citation: Jeanselme, V., Tom, B., Barrett, J. (2022, April). Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering. In Conference on Health, Inference, and Learning (pp. 92-102). PMLR. https://proceedings.mlr.press/v174/jeanselme22a/jeanselme22a.pdf
Constrained clustering and multiple kernel learning without pairwise constraint relaxation
Published in Springer - Advances in Data Analysis and Classification, 2022
This research presents a novel clustering algorithm that uses pairwise constraints to enhance clustering performance and kernel learning without the common practice of transforming constraints into continuous domains, leading to improved generalization and scalability for large datasets.
Recommended citation: Boecking, B., Jeanselme, V., Dubrawski, A (2022). Constrained clustering and multiple kernel learning without pairwise constraint relaxation. In Advances in Data Analysis and Classification, 1-16. https://doi.org/10.1007/s11634-022-00507-5
Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness
Published in Machine Learning for Health (ML4H), 2022
This work investigates how biases are not only present in the data we observe but also it was is missing from the data.
Recommended citation: Jeanselme, V., De-Arteaga, M., Zhang, Z., Barrett, J., Tom, B, (2022, November). Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness. In Machine Learning for Health (pp. 12-34). PMLR. https://proceedings.mlr.press/v193/jeanselme22a/jeanselme22a.pdf
DeepJoint: Robust Survival Modelling Under Clinical Presence Shift
Published in NeurIPS Workshop TS4H, 2022
This work investigates the impact of patient-healthcare system interactions on medical data and introduces a multi-task recurrent neural network to address potential performance issues caused by changes in this interaction.
Recommended citation: Jeanselme, V., Martin, G., Peek, N., Sperrin, M., Tom, B., Barrett, J. (2022). DeepJoint: Robust Survival Modelling Under Clinical Presence Shift . In NeurIPS 2022 Workshop on Learning from Time Series for Health. https://arxiv.org/abs/2205.13481
Neural Fine-Gray
Published in Conference on Health, Inference, and Learning (CHIL), 2023
The paper introduces a novel approach using constrained monotonic neural networks to address the challenge of competing risks in survival analysis, which is often overlooked by machine learning methods.
Recommended citation: Jeanselme, V., Yoon, C. H., Tom, B., Barrett, J. (2023, June). Neural Fine-Gray: Monotonic neural networks for competing risks. In Conference on Health, Inference, and Learning (pp. 379-392). PMLR. https://arxiv.org/abs/2305.06703
talks
Recurrent Deep Survival Machines: Deep Survival Regression with Time-Varying Covariates
Published:
Neural Survival Clustering: Non parametric mixture of neural networks for survival clustering
Published:
Using observation processes to predict survival: A deep learning approach to joint modelling
Published:
Predictive modelling under clinical presence
Published:
Neural Fine-Gray: Monotonic neural networks for competing risks
Published:
Predictive Modelling Under Clinical Presence
Published:
workings
Leveraging Expert Consistency to Improve Algorithmic Decision Support
Under review at Management Science
Machine learning (ML) is increasingly being used to support high-stakes decisions, a trend owed in part to its promise of superior predictive power relative to human assessment. However, there is frequently a gap between decision objectives and what is captured in the observed outcomes used as labels to train ML models. As a result, machine learning models may fail to capture important dimensions of decision criteria, hampering their utility for decision support. In this work, we explore the use of historical expert decisions as a rich – yet imperfect – source of information that is commonly available in organizational information systems, and show that it can be leveraged to bridge the gap between decision objectives and algorithm objectives. We consider the problem of estimating expert consistency indirectly when each case in the data is assessed by a single expert, and propose influence function-based methodology as a solution to this problem. We then incorporate the estimated expert consistency into a predictive model through a training-time label amalgamation approach. This approach allows ML models to learn from experts when there is inferred expert consistency, and from observed labels otherwise. We also propose alternative ways of leveraging inferred consistency via hybrid and deferral models. In our empirical evaluation, focused on the context of child maltreatment hotline screenings, we show that (1) there are high-risk cases whose risk is considered by the experts but not wholly captured in the target labels used to train a deployed model, and (2) the proposed approach significantly improves precision for these cases.
Recommended citation: De-Arteaga, M., Jeanselme, V., Dubrawski, A., Chouldechova, A. Leveraging Expert Consistency to Improve Algorithmic Decision Support. https://arxiv.org/abs/2101.09648
Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness
Under review at Management Science
Machine learning risks reinforcing biases present in data, and, as we argue in this work, in what is absent from data. In healthcare, biases have marked medical history, leading to unequal care affecting marginalised groups. Patterns in missing data often reflect these group discrepancies, but the algorithmic fairness implications of group-specific missingness are not well understood. Despite its potential impact, imputation is often an overlooked preprocessing step, with attention placed on the reduction of reconstruction error and overall performance, ignoring how imputation can affect groups differently. Our work studies how imputation choices affect reconstruction errors across groups and algorithmic fairness properties of downstream predictions. First, we provide a structured view of the relationship between clinical presence mechanisms and group-specific missingness patterns. Then, we theoretically demonstrate that the optimal choice between two common imputation strategies is under-determined, both in terms of group-specific imputation quality and of the gap in quality across groups. Particularly, the use of group-specific imputation strategies may counter-intuitively reduce data quality for marginalised group. We complement these theoretical results with simulations and real-world empirical evidence showing that imputation choices influence group-specific data quality and downstream algorithmic fairness, and that no imputation strategy consistently reduces group disparities in reconstruction error or predictions. Importantly, our results show that current practices may be detrimental to health equity as similarly performing imputation strategies at the population level can affect marginalised groups differently. Finally, we propose recommendations for mitigating inequities that may stem from an overlooked step of the machine learning pipeline.
Recommended citation: Jeanselme, V., De-Arteaga, M., Zhang, Z., Barrett, J., Tom, B. Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness. https://arxiv.org/abs/2208.06648