Literature-Augmented Clinical Outcome Prediction
Aakanksha Naik, Sravanthi Parasa, Sergey Feldman, Lucy Lu Wang, Tom Hope
Abstract
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%.- Anthology ID:
- 2022.findings-naacl.33
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 438–453
- Language:
- URL:
- https://aclanthology.org/2022.findings-naacl.33
- DOI:
- 10.18653/v1/2022.findings-naacl.33
- Cite (ACL):
- Aakanksha Naik, Sravanthi Parasa, Sergey Feldman, Lucy Lu Wang, and Tom Hope. 2022. Literature-Augmented Clinical Outcome Prediction. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 438–453, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Literature-Augmented Clinical Outcome Prediction (Naik et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2022.findings-naacl.33.pdf
- Code
- allenai/beep