1,138 research outputs found
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data
The ability to perform accurate prognosis of patients is crucial for
proactive clinical decision making, informed resource management and
personalised care. Existing outcome prediction models suffer from a low recall
of infrequent positive outcomes. We present a highly-scalable and robust
machine learning framework to automatically predict adversity represented by
mortality and ICU admission from time-series vital signs and laboratory results
obtained within the first 24 hours of hospital admission. The stacked platform
comprises two components: a) an unsupervised LSTM Autoencoder that learns an
optimal representation of the time-series, using it to differentiate the less
frequent patterns which conclude with an adverse event from the majority
patterns that do not, and b) a gradient boosting model, which relies on the
constructed representation to refine prediction, incorporating static features
of demographics, admission details and clinical summaries. The model is used to
assess a patient's risk of adversity over time and provides visual
justifications of its prediction based on the patient's static features and
dynamic signals. Results of three case studies for predicting mortality and ICU
admission show that the model outperforms all existing outcome prediction
models, achieving PR-AUC of 0.891 (95 CI: 0.878 - 0.969) in predicting
mortality in ICU and general ward settings and 0.908 (95 CI: 0.870-0.935) in
predicting ICU admission.Comment: 14 page
Explainable artificial intelligence model to predict acute critical illness from electronic health records
We developed an explainable artificial intelligence (AI) early warning score
(xAI-EWS) system for early detection of acute critical illness. While
maintaining a high predictive performance, our system explains to the clinician
on which relevant electronic health records (EHRs) data the prediction is
grounded. Acute critical illness is often preceded by deterioration of
routinely measured clinical parameters, e.g., blood pressure and heart rate.
Early clinical prediction is typically based on manually calculated screening
metrics that simply weigh these parameters, such as Early Warning Scores (EWS).
The predictive performance of EWSs yields a tradeoff between sensitivity and
specificity that can lead to negative outcomes for the patient. Previous work
on EHR-trained AI systems offers promising results with high levels of
predictive performance in relation to the early, real-time prediction of acute
critical illness. However, without insight into the complex decisions by such
system, clinical translation is hindered. In this letter, we present our
xAI-EWS system, which potentiates clinical translation by accompanying a
prediction with information on the EHR data explaining it
- …