# Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models

arXiv: https://arxiv.org/abs/1802.04822 · HTML: https://mlsanity.com/paper/1802.04822/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2018.

## Built on by (most cited first, 15 of 15)

- [Evaluating the Robustness of Neural Language Models to Input Perturbations](https://mlsanity.com/paper/2108.12237/) (2021)
- [Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View](https://mlsanity.com/paper/2112.02625/) (2021)
- [Surfacing Biases in Large Language Models using Contrastive Input Decoding](https://mlsanity.com/paper/2305.07378/) (2023)
- [Residue-Based Natural Language Adversarial Attack Detection](https://mlsanity.com/paper/2204.10192/) (2022)
- [Improving the robustness and accuracy of biomedical language models through adversarial training](https://mlsanity.com/paper/2111.08529/) (2021)
- [Rethinking Textual Adversarial Defense for Pre-trained Language Models](https://mlsanity.com/paper/2208.10251/) (2022)
- [Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks](https://mlsanity.com/paper/2106.07925/) (2021)
- [Stress Test Evaluation of Biomedical Word Embeddings](https://mlsanity.com/paper/2107.11652/) (2021)
- [Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy](https://mlsanity.com/paper/2306.02488/) (2023)
- [SoK: Security and Privacy Risks of Healthcare AI](https://mlsanity.com/paper/2409.07415/) (2024)
- [MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare](https://mlsanity.com/paper/2112.06063/) (2021)
- [Sentiment Perception Adversarial Attacks on Neural Machine Translation Systems](https://mlsanity.com/paper/2305.01437/) (2023)
- [Interpretable Predictive Models for Healthcare via Rational Logistic Regression](https://mlsanity.com/paper/2411.03224/) (2024)
- [Attacks against Ranking Algorithms with Text Embeddings: a Case Study on Recruitment Algorithms](https://mlsanity.com/paper/2108.05490/) (2021)
- [Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices](https://mlsanity.com/paper/2506.15028/) (2025)

## Abstract (arXiv)

The surging availability of electronic medical records (EHR) leads to increased research interests in medical predictive modeling. Recently many deep learning based predicted models are also developed for EHR data and demonstrated impressive performance. However, a series of recent studies showed that these deep models are not safe: they suffer from certain vulnerabilities. In short, a well-trained deep network can be extremely sensitive to inputs with negligible changes. These inputs are referred to as adversarial examples. In the context of medical informatics, such attacks could alter the result of a high performance deep predictive model by slightly perturbing a patient's medical records. Such instability not only reflects the weakness of deep architectures, more importantly, it offers guide on detecting susceptible parts on the inputs. In this paper, we propose an efficient and effective framework that learns a time-preferential minimum attack targeting the LSTM model with EHR inputs, and we leverage this attack strategy to screen medical records of patients and identify susceptible events and measurements. The efficient screening procedure can assist decision makers to pay extra attentions to the locations that can cause severe consequence if not measured correctly. We conduct extensive empirical studies on a real-world urgent care cohort and demonstrate the effectiveness of the proposed screening approach.