# Explaining an increase in predicted risk for clinical alerts

arXiv: https://arxiv.org/abs/1907.04911 · HTML: https://mlsanity.com/paper/1907.04911/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement](https://mlsanity.com/paper/2301.03028/) (2023)
- [MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset](https://mlsanity.com/paper/2102.06761/) (2021)
- [Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series](https://mlsanity.com/paper/2109.07602/) (2021)
- [Counterfactual Explanations and Predictive Models to Enhance Clinical Decision-Making in Schizophrenia using Digital Phenotyping](https://mlsanity.com/paper/2306.03980/) (2023)
- [SSET: Swapping-Sliding Explanation for Time Series Classifiers in Affect Detection](https://mlsanity.com/paper/2410.12996/) (2024)

## Abstract (arXiv)

Much work aims to explain a model's prediction on a static input. We consider explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step. When the estimated risk increases, the goal of the explanation is to attribute the increase to a few relevant inputs from the past. While our formal setup and techniques are general, we carry out an in-depth case study in a clinical setting. The goal here is to alert a clinician when a patient's risk of deterioration rises. The clinician then has to decide whether to intervene and adjust the treatment. Given a potentially long sequence of new events since she last saw the patient, a concise explanation helps her to quickly triage the alert. We develop methods to lift static attribution techniques to the dynamical setting, where we identify and address challenges specific to dynamics. We then experimentally assess the utility of different explanations of clinical alerts through expert evaluation.