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

By Mengying Sun, Fengyi Tang, Jinfeng Yi, Fei Wang, Jiayu Zhou. 2018

67 citations recorded by the source corpus.

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 a 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.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Huan Zhang
2. Pin-Yu Chen
3. Hongge Chen
4. Jinfeng Yi
5. Deliang Fan
6. Adnan Siraj Rakin
7. Zhezhi He
8. Dong Su

In Radiology, Nuclear Medicine and Imaging

1. Jiayu Zhou
2. Fei Wang
3. Deliang Fan
4. Anita M. Preininger

Citations in the corpus

• Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack (2018, background): “In addition to image classification, attacks to other DNN-powered tasks have also been actively investigated, such as visual question answering [43, 1], image captioning [10], semantic segmentation [31, 1] and etc [12, 8, 39].”
• Is Robustness the Cost of Accuracy? - A Comprehensive Study on the Robustness of 18 Deep Image Classification Models (2018, background): “A common approach to evaluate the robustness of DNNs is via adversarial attacks [3,4,5,6,7,8,9,10,11], where imperceptible adversarial examples are crafted to mislead DNNs.”
• AI in Health: State of the Art, Challenges, and Future Directions (2019, background): “[125] showed that slight modifications of lab values in a patient’s EHR can completely alter the mortality prediction made by what is otherwise a well-trained predictor.”
• MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records (2019, background): “Predictive modeling of clinical risk, such as mortality [36, 38], hospital readmission [6, 34], onset of chronic disease [8], condition exacerbation [22], etc.”
• Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey (2019, methodology): “C&W-based [130] medical features Y LSTM L1 MSP [24] word embedding Y OpenNMT-py [64] L2+gradient regularization TS, MT”