Deep representation learning for individualized treatment effect estimation using electronic health records

Research article (Journal of Biomedical Informatics, 2019) · cited 28× · AI/ML
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Deep representation learning for individualized treatment effect estimation using electronic health records

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Deep representation learning for individualized treatment effect estimation using electronic health records is a scholarly article[1].

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  • Deep representation learning for individualized treatment effect estimation using electronic health records's instance of is recorded as scholarly article[2].

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APA 4ort.xyz Knowledge Graph. (2026). Deep representation learning for individualized treatment effect estimation using electronic health records. Retrieved May 24, 2026, from https://4ort.xyz/entity/deep-representation-learning-for-individualized-treatment-effect-estimation-using-electronic-health-records
MLA “Deep representation learning for individualized treatment effect estimation using electronic health records.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/deep-representation-learning-for-individualized-treatment-effect-estimation-using-electronic-health-records.
BibTeX @misc{4ortxyz_deep-representation-learning-for-individualized-treatment-effect-estimation-using-electronic-health-records_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Deep representation learning for individualized treatment effect estimation using electronic health records}}, year = {2026}, url = {https://4ort.xyz/entity/deep-representation-learning-for-individualized-treatment-effect-estimation-using-electronic-health-records}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Deep representation learning for individualized treatment effect estimation using electronic health records — https://4ort.xyz/entity/deep-representation-learning-for-individualized-treatment-effect-estimation-using-electronic-health-records (retrieved 2026-05-24)

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