3 research outputs found
Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities
Recent advancements in AI applications to healthcare have shown incredible
promise in surpassing human performance in diagnosis and disease prognosis.
With the increasing complexity of AI models, however, concerns regarding their
opacity, potential biases, and the need for interpretability. To ensure trust
and reliability in AI systems, especially in clinical risk prediction models,
explainability becomes crucial. Explainability is usually referred to as an AI
system's ability to provide a robust interpretation of its decision-making
logic or the decisions themselves to human stakeholders. In clinical risk
prediction, other aspects of explainability like fairness, bias, trust, and
transparency also represent important concepts beyond just interpretability. In
this review, we address the relationship between these concepts as they are
often used together or interchangeably. This review also discusses recent
progress in developing explainable models for clinical risk prediction,
highlighting the importance of quantitative and clinical evaluation and
validation across multiple common modalities in clinical practice. It
emphasizes the need for external validation and the combination of diverse
interpretability methods to enhance trust and fairness. Adopting rigorous
testing, such as using synthetic datasets with known generative factors, can
further improve the reliability of explainability methods. Open access and
code-sharing resources are essential for transparency and reproducibility,
enabling the growth and trustworthiness of explainable research. While
challenges exist, an end-to-end approach to explainability in clinical risk
prediction, incorporating stakeholders from clinicians to developers, is
essential for success
DySurv: Dynamic Deep Learning Model for Survival Prediction in the ICU
Survival analysis helps approximate underlying distributions of
time-to-events which in the case of critical care like in the ICU can be a
powerful tool for dynamic mortality risk prediction. Extending beyond the
classical Cox model, deep learning techniques have been leveraged over the last
years relaxing the many constraints of their counterparts from statistical
methods. In this work, we propose a novel conditional variational
autoencoder-based method called DySurv which uses a combination of static and
time-series measurements from patient electronic health records in estimating
risk of death dynamically in the ICU. DySurv has been tested on standard
benchmarks where it outperforms most existing methods including other deep
learning methods and we evaluate it on a real-world patient database from
MIMIC-IV. The predictive capacity of DySurv is consistent and the survival
estimates remain disentangled across different datasets supporting the idea
that dynamic deep learning models based on conditional variational inference in
multi-task cases can be robust models for survival analysis
XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic Prediction of Mortality in the ICU for Heart Attack Patients
Heart attack remain one of the greatest contributors to mortality in the
United States and globally. Patients admitted to the intensive care unit (ICU)
with diagnosed heart attack (myocardial infarction or MI) are at higher risk of
death. In this study, we use two retrospective cohorts extracted from the eICU
and MIMIC-IV databases, to develop a novel pseudo-dynamic machine learning
framework for mortality prediction in the ICU with interpretability and
clinical risk analysis. The method provides accurate prediction for ICU
patients up to 24 hours before the event and provide time-resolved
interpretability results. The performance of the framework relying on extreme
gradient boosting was evaluated on a held-out test set from eICU, and
externally validated on the MIMIC-IV cohort using the most important features
identified by time-resolved Shapley values achieving AUCs of 91.0 (balanced
accuracy of 82.3) for 6-hour prediction of mortality respectively. We show that
our framework successfully leverages time-series physiological measurements by
translating them into stacked static prediction problems to be robustly
predictive through time in the ICU stay and can offer clinical insight from
time-resolved interpretabilit