Treatment effect prediction with adversarial deep learning using electronic health records
Open accessAbstract
Background: Treatment effect prediction (TEP) plays an important role in disease management by ensuring that the expected clinical outcomes are obtained after performing specialized and sophisticated treatments on patients given their personalized clinical status. In recent years, the wide adoption of electronic health records (EHRs) has provided a comprehensive data source for intelligent clinical applications including the TEP investigated in this study. Method: We examined the problem of using a large volume of heterogeneous EHR data to predict treatment effects and developed an adversarial deep treatment effect prediction model to address the problem. Our model employed two auto-encoders for learning the representative and discriminative features of both patient characteristics and treatments from EHR data. The discriminative power of the learned features was further enhanced by decoding the correlational information between the patient characteristics and subsequent treatments by means of a generated adversarial learning strategy. Thereafter, a logistic regression layer was appended on the top of the resulting feature representation layer for TEP. Result: The proposed model was evaluated on two real clinical datasets collected from the cardiology department of a Chinese hospital. In particular, on acute coronary syndrome (ACS) dataset, the proposed adversarial deep treatment effect prediction (ADTEP) (0.662) exhibited 1.4, 2.2, and 6.3% performance gains in terms of the area under the ROC curve (AUC) over deep treatment effect prediction (DTEP) (0.653), logistic regression (LR) (0.648), and support vector machine (SVM) (0.621), respectively. As for heart failure (HF) case study, the proposed ADTEP also outperformed all benchmarks. The experimental results demonstrated that our proposed model achieved competitive performance compared to state-of-the-art models in tackling the TEP problem. Conclusion: In this work, we propose a novel model to address the TEP problem by utilizing a large volume of observational data from EHR. With adversarial learning strategy, our proposed model can further explore the correlational information between patient statuses and treatments to extract more robust and discriminative representation of patient samples from their EHR data. Such representation finally benefits the model on TEP. The experimental results of two case studies demonstrate the superiority of our proposed method compared to state-of-the-art methods.
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Chu et al. (2020) proposed an adversarial deep treatment effect prediction (ADTEP) model to predict treatment effects using heterogeneous EHR data.
“Similarly, Chu et al. (2020) proposed an adversarial deep treatment effect prediction (ADTEP) model to predict treatment effects using heterogeneous EHR data [29].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [29]
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“Similarly, Chu et al. (2020) proposed an adversarial deep treatment effect prediction (ADTEP) model to predict treatment effects using heterogeneous EHR data [29].”
Background
- Authors
- Chu, J., Dong, W., Wang, J., He, K., & Huang, Z.
- Affiliations
- Zhejiang University (CN)
- Chinese PLA General Hospital (CN)
- Corresponding author
- He, Kunlun
- Huang, Zhengxing
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2020-12
- Volume
- 20
- Issue
- S4
- Pages
- 139-139
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC7735418
- OpenAlex
- W3110815301
- Cited by
- 24
- References
- 52
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 7 · 2023: 7 · 2022: 3 · 2021: 1 · 2020: 1
- FWCI
- 1.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61672450)
- National Key Research and Development Program of China (2018YFC2001204)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- treatment response prediction, electronic health records, adversarial learning, autoencoder, acute coronary syndrome, heart failure, personalized treatment, clinical decision support, observational data
- MeSH terms
- Deep Learning, Heart Failure, Humans, Logistic Models, Acute Coronary Syndrome, Electronic Health Records
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