# Detect and Classify -- Joint Span Detection and Classification for Health Outcomes

arXiv: https://arxiv.org/abs/2104.07789 · HTML: https://mlsanity.com/paper/2104.07789/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [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)
- [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)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1904.02232/) (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)
- [Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration](https://mlsanity.com/paper/2102.04110/) (2021)
- [Pre-trained Language Model for Biomedical Question Answering](https://mlsanity.com/paper/1909.08229/) (2019)
- [Adversarial Training for Aspect-Based Sentiment Analysis with BERT](https://mlsanity.com/paper/2001.11316/) (2020)
- [Explicit Interaction Model towards Text Classification](https://mlsanity.com/paper/1811.09386/) (2018)
- [A Hierarchical Structured Self-Attentive Model for Extractive Document Summarization (HSSAS)](https://mlsanity.com/paper/1805.07799/) (2018)
- [Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks](https://mlsanity.com/paper/1709.10191/) (2017)
- [Advancing PICO Element Detection in Biomedical Text via Deep Neural Networks](https://mlsanity.com/paper/1810.12780/) (2018)

## Built on by (most cited first, 6 of 6)

- [Position-based Prompting for Health Outcome Generation](https://mlsanity.com/paper/2204.03489/) (2022)
- [No perspective, no perception!! Perspective-aware Healthcare Answer Summarization](https://mlsanity.com/paper/2406.08881/) (2024)
- [Continual Graph Convolutional Network for Text Classification](https://mlsanity.com/paper/2304.04152/) (2023)
- [MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries](https://mlsanity.com/paper/2408.12980/) (2024)
- [Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation](https://mlsanity.com/paper/2307.15776/) (2023)
- [Improving Pre-trained Language Model Sensitivity via Mask Specific losses: A case study on Biomedical NER](https://mlsanity.com/paper/2403.18025/) (2024)

## Abstract (arXiv)

A health outcome is a measurement or an observation used to capture and assess the effect of a treatment. Automatic detection of health outcomes from text would undoubtedly speed up access to evidence necessary in healthcare decision making. Prior work on outcome detection has modelled this task as either (a) a sequence labelling task, where the goal is to detect which text spans describe health outcomes, or (b) a classification task, where the goal is to classify a text into a pre-defined set of categories depending on an outcome that is mentioned somewhere in that text. However, this decoupling of span detection and classification is problematic from a modelling perspective and ignores global structural correspondences between sentence-level and word-level information present in a given text. To address this, we propose a method that uses both word-level and sentence-level information to simultaneously perform outcome span detection and outcome type classification. In addition to injecting contextual information to hidden vectors, we use label attention to appropriately weight both word and sentence level information. Experimental results on several benchmark datasets for health outcome detection show that our proposed method consistently outperforms decoupled methods, reporting competitive results.