mlsanityData to 7 Oct 2026

Clinical Decision Transformer: Intended Treatment Recommendation through Goal Prompting

arXiv 2302.00612 ↗First posted 1 Feb 2023cs.AIcited by 8

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Abstract (arXiv)

With recent achievements in tasks requiring context awareness, foundation models have been adopted to treat large-scale data from electronic health record (EHR) systems. However, previous clinical recommender systems based on foundation models have a limited purpose of imitating clinicians' behavior and do not directly consider a problem of missing values. In this paper, we propose Clinical Decision Transformer (CDT), a recommender system that generates a sequence of medications to reach a desired range of clinical states given as goal prompts. For this, we conducted goal-conditioned sequencing, which generated a subsequence of treatment history with prepended future goal state, and trained the CDT to model sequential medications required to reach that goal state. For contextual embedding over intra-admission and inter-admissions, we adopted a GPT-based architecture with an admission-wise attention mask and column embedding. In an experiment, we extracted a diabetes dataset from an EHR system, which contained treatment histories of 4788 patients. We observed that the CDT achieved the intended treatment effect according to goal prompt ranges (e.g., NormalA1c, LowerA1c, and HigherA1c), contrary to the case with behavior cloning. To the best of our knowledge, this is the first study to explore clinical recommendations from the perspective of goal prompting. See https://clinical-decision-transformer.github.io for code and additional information.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 22 of its 32 references that are in the map. First posted on arXiv 1 Feb 2023.

Builds on

22 of 32 references.

References · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  5. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  6. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,528
  7. 2022A Generalist Agentcited by 802
  8. 2021Revisiting Deep Learning Models for Tabular Datacited by 725
  9. 2021Offline Reinforcement Learning as One Big Sequence Modeling Problemcited by 636
  10. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 548
  11. 2020Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlationcited by 420
  12. 2020TabTransformer: Tabular Data Modeling Using Contextual Embeddingscited by 398
10 more
  1. 2020Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease predictioncited by 368
  2. 2020Query-Key Normalization for Transformerscited by 266
  3. 2019BEHRT: Transformer for Electronic Health Recordscited by 258
  4. 2022Multi-Game Decision Transformerscited by 212
  5. 2015Simultaneous Deep Transfer Across Domains and Taskscited by 205
  6. 2020Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representationscited by 154
  7. 2019Pre-training of Graph Augmented Transformers for Medication Recommendationcited by 124
  8. 2022Causal Transformer for Estimating Counterfactual Outcomescited by 121
  9. 2021Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmarkcited by 4
  10. 2021Transfer Learning in Electronic Health Records through Clinical Concept Embeddingcited by 1

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Cited by year

Citing papers per year: 2023: 5; 2024: 3.

8 papers.

  1. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  2. 2023Prompt Engineering for Healthcare: Methodologies and Applicationscited by 52
  3. 2023Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Eventscited by 46
  4. 2023The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRscited by 29
  5. 2023Event-Based Contrastive Learning for Medical Time Seriescited by 13
  6. 2023A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learningcited by 8
  7. 2024Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing Systemcited by 2
  8. 2024Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language Modelscited by 1

Similar, not cited

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  1. 2024Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment
  2. 2025medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support
  3. 2025Retrieval-Augmented Framework for LLM-Based Clinical Decision Support
  4. 2023An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing
  5. 2025MediRec: Enhancing Chinese Medication Recommendation with Explainable Clinical Reasoning
  6. 2026CPGPrompt: Translating Clinical Guidelines into LLM-Executable Decision Support
  7. 2025CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records
  8. 2025CDR-Agent: Intelligent Selection and Execution of Clinical Decision Rules Using Large Language Model Agents