# Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations

arXiv: https://arxiv.org/abs/2109.02254 · HTML: https://mlsanity.com/paper/2109.02254/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Multi-Sentence Argument Linking](https://mlsanity.com/paper/1911.03766/) (2019)
- [A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature](https://mlsanity.com/paper/1806.04185/) (2018)
- [Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks](https://mlsanity.com/paper/2004.13826/) (2020)
- [Text Level Graph Neural Network for Text Classification](https://mlsanity.com/paper/1910.02356/) (2019)
- [Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction](https://mlsanity.com/paper/1906.08646/) (2019)
- [Multi-Grained Named Entity Recognition](https://mlsanity.com/paper/1906.08449/) (2019)
- [A Span Selection Model for Semantic Role Labeling](https://mlsanity.com/paper/1810.02245/) (2018)
- [HAMNER: Headword Amplified Multi-span Distantly Supervised Method for Domain Specific Named Entity Recognition](https://mlsanity.com/paper/1912.01731/) (2019)
- [A Study on Agreement in PICO Span Annotations](https://mlsanity.com/paper/1904.09557/) (2019)
- [A shared latent space matrix factorisation method for recommending new trial evidence for systematic review updates](https://mlsanity.com/paper/1709.06758/) (2017)

## Built on by (most cited first, 2 of 2)

- [MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries](https://mlsanity.com/paper/2408.12980/) (2024)
- [AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs](https://mlsanity.com/paper/2409.09704/) (2024)

## Abstract (arXiv)

The rapid growth in published clinical trials makes it difficult to maintain up-to-date systematic reviews, which requires finding all relevant trials. This leads to policy and practice decisions based on out-of-date, incomplete, and biased subsets of available clinical evidence. Extracting and then normalising Population, Intervention, Comparator, and Outcome (PICO) information from clinical trial articles may be an effective way to automatically assign trials to systematic reviews and avoid searching and screening - the two most time-consuming systematic review processes. We propose and test a novel approach to PICO span detection. The major difference between our proposed method and previous approaches comes from detecting spans without needing annotated span data and using only crowdsourced sentence-level annotations. Experiments on two datasets show that PICO span detection results achieve much higher results for recall when compared to fully supervised methods with PICO sentence detection at least as good as human annotations. By removing the reliance on expert annotations for span detection, this work could be used in human-machine pipeline for turning low-quality crowdsourced, and sentence-level PICO annotations into structured information that can be used to quickly assign trials to relevant systematic reviews.