# Explainable artificial intelligence model to predict acute critical illness from electronic health records

arXiv: https://arxiv.org/abs/1912.01266 · HTML: https://mlsanity.com/paper/1912.01266/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 30 of 42)

- [Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond](https://mlsanity.com/paper/2102.01998/) (2021)
- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [Explainable AI for clinical and remote health applications: a survey on tabular and time series data](https://mlsanity.com/paper/2209.06528/) (2022)
- [WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Values](https://mlsanity.com/paper/2211.06507/) (2022)
- [A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?](https://mlsanity.com/paper/2304.04780/) (2023)
- [Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research](https://mlsanity.com/paper/2211.15343/) (2022)
- [The role of causality in explainable artificial intelligence](https://mlsanity.com/paper/2309.09901/) (2023)
- [Remote patient monitoring using artificial intelligence: Current state, applications, and challenges](https://mlsanity.com/paper/2301.10009/) (2023)
- [Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria](https://mlsanity.com/paper/2211.01738/) (2022)
- [MAP: Evaluation and Multi-Agent Enhancement of Large Language Models for Inpatient Pathways](https://mlsanity.com/paper/2503.13205/) (2025)
- [Offline reinforcement learning with uncertainty for treatment strategies in sepsis](https://mlsanity.com/paper/2107.04491/) (2021)
- [Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review](https://mlsanity.com/paper/2112.12705/) (2021)
- [Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records](https://mlsanity.com/paper/2201.06779/) (2022)
- [Coalitional strategies for efficient individual prediction explanation](https://mlsanity.com/paper/2104.00765/) (2021)
- [An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models](https://mlsanity.com/paper/2204.11351/) (2022)
- [Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoff](https://mlsanity.com/paper/2107.05693/) (2021)
- [MLOps Spanning Whole Machine Learning Life Cycle: A Survey](https://mlsanity.com/paper/2304.07296/) (2023)
- [Modelling Patient Trajectories Using Multimodal Information](https://mlsanity.com/paper/2209.04224/) (2022)
- [Using explainability to design physics-aware CNNs for solving subsurface inverse problems](https://mlsanity.com/paper/2211.08651/) (2022)
- [A Brief Review of Explainable Artificial Intelligence in Healthcare](https://mlsanity.com/paper/2304.01543/) (2023)
- [Interpreting Deep Neural Networks with the Package innsight](https://mlsanity.com/paper/2306.10822/) (2023)
- [Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities](https://mlsanity.com/paper/2308.08407/) (2023)
- [DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation](https://mlsanity.com/paper/2503.22257/) (2025)
- [The Consequences of the Framing of Machine Learning Risk Prediction Models: Evaluation of Sepsis in General Wards](https://mlsanity.com/paper/2101.10790/) (2021)
- [Simple black-box universal adversarial attacks on medical image classification based on deep neural networks](https://mlsanity.com/paper/2108.04979/) (2021)
- [Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making](https://mlsanity.com/paper/2206.04050/) (2022)
- [NPRL: Nightly Profile Representation Learning for Early Sepsis Onset Prediction in ICU Trauma Patients](https://mlsanity.com/paper/2304.12737/) (2023)
- [Evaluation of Popular XAI Applied to Clinical Prediction Models: Can They be Trusted?](https://mlsanity.com/paper/2306.11985/) (2023)
- [Applications of Explainable artificial intelligence in Earth system science](https://mlsanity.com/paper/2406.11882/) (2024)
- [Explainable AI For Early Detection Of Sepsis](https://mlsanity.com/paper/2511.06492/) (2025)

## Abstract (arXiv)

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.