# Literature-Augmented Clinical Outcome Prediction

arXiv: https://arxiv.org/abs/2111.08374 · HTML: https://mlsanity.com/paper/2111.08374/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 45 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [MS2: Multi-Document Summarization of Medical Studies](https://mlsanity.com/paper/2104.06486/) (2021)
- [Inferring Which Medical Treatments Work from Reports of Clinical Trials](https://mlsanity.com/paper/1904.01606/) (2019)
- [UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus](https://mlsanity.com/paper/2010.10391/) (2020)
- [Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration](https://mlsanity.com/paper/2102.04110/) (2021)
- [Evidence Inference 2.0: More Data, Better Models](https://mlsanity.com/paper/2005.04177/) (2020)
- [Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python](https://mlsanity.com/paper/2106.07799/) (2021)
- [Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation](https://mlsanity.com/paper/1912.11975/) (2019)
- [Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning](https://mlsanity.com/paper/1811.12276/) (2018)
- [Towards unstructured mortality prediction with free-text clinical notes](https://mlsanity.com/paper/1911.08437/) (2019)
- [Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature](https://mlsanity.com/paper/2106.13375/) (2021)
- [An Analysis of Attention over Clinical Notes for Predictive Tasks](https://mlsanity.com/paper/1904.03244/) (2019)

## Built on by (most cited first, 20 of 20)

- [Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain](https://mlsanity.com/paper/2307.03042/) (2023)
- [A Computational Inflection for Scientific Discovery](https://mlsanity.com/paper/2205.02007/) (2022)
- [Retrieval augmentation of large language models for lay language generation](https://mlsanity.com/paper/2211.03818/) (2022)
- [RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records](https://mlsanity.com/paper/2403.00815/) (2024)
- [PMC-Patients: A Large-scale Dataset of Patient Summaries and Relations for Benchmarking Retrieval-based Clinical Decision Support Systems](https://mlsanity.com/paper/2202.13876/) (2022)
- [Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding](https://mlsanity.com/paper/2305.14232/) (2023)
- [Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications](https://mlsanity.com/paper/2501.02460/) (2025)
- [General-Purpose Retrieval-Enhanced Medical Prediction Model Using Near-Infinite History](https://mlsanity.com/paper/2310.20204/) (2023)
- [Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs](https://mlsanity.com/paper/2305.03642/) (2023)
- [Explainable AI for Clinical Outcome Prediction: A Survey of Clinician Perceptions and Preferences](https://mlsanity.com/paper/2502.20478/) (2025)
- [MRAG: Benchmarking Retrieval-Augmented Generation for Bio-medicine](https://mlsanity.com/paper/2601.16503/) (2026)
- [BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction](https://mlsanity.com/paper/2302.06860/) (2023)
- [Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks for Patient-Level Representation Learning](https://mlsanity.com/paper/2305.09756/) (2023)
- [Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs](https://mlsanity.com/paper/2505.23299/) (2025)
- [CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models](https://mlsanity.com/paper/2509.26136/) (2025)
- [Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domain](https://mlsanity.com/paper/2602.03368/) (2026)
- [Automatic Extraction of Disease Risk Factors from Medical Publications](https://mlsanity.com/paper/2407.07373/) (2024)
- [CKD-EHR:Clinical Knowledge Distillation for Electronic Health Records](https://mlsanity.com/paper/2506.15118/) (2025)
- [Integrating Text and Time-Series into (Large) Language Models to Predict Medical Outcomes](https://mlsanity.com/paper/2509.13696/) (2025)
- [Enhancing Extubation Failure Prediction with LLM-Derived Features from Respiratory Therapy Clinical Notes](https://mlsanity.com/paper/2609.17532/) (2026)

## Abstract (arXiv)

We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models. Based on each individual patient's clinical notes, we train language models (LMs) to find relevant papers and fuse them with information from notes to predict outcomes such as in-hospital mortality. We develop methods to retrieve literature based on noisy, information-dense patient notes, and to augment existing outcome prediction models with retrieved papers in a manner that maximizes predictive accuracy. Our approach boosts predictive performance on three important clinical tasks in comparison to strong recent LM baselines, increasing F1 by up to 5 points and precision@Top-K by a large margin of over 25%.