# A Biomedical Entity Extraction Pipeline for Oncology Health Records in Portuguese

arXiv: https://arxiv.org/abs/2304.08999 · HTML: https://mlsanity.com/paper/2304.08999/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning](https://mlsanity.com/paper/1801.09851/) (2018)
- [Natural Language Generation for Electronic Health Records](https://mlsanity.com/paper/1806.01353/) (2018)
- [CollaboNet: collaboration of deep neural networks for biomedical named entity recognition](https://mlsanity.com/paper/1809.07950/) (2018)
- [A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation](https://mlsanity.com/paper/1910.14076/) (2019)
- [Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition](https://mlsanity.com/paper/1706.09569/) (2017)
- [Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility](https://mlsanity.com/paper/1806.02814/) (2018)
- [Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion](https://mlsanity.com/paper/1804.04225/) (2018)
- [Terminologies augmented recurrent neural network model for clinical named entity recognition](https://mlsanity.com/paper/1904.11473/) (2019)

## Abstract (arXiv)

Textual health records of cancer patients are usually protracted and highly unstructured, making it very time-consuming for health professionals to get a complete overview of the patient's therapeutic course. As such limitations can lead to suboptimal and/or inefficient treatment procedures, healthcare providers would greatly benefit from a system that effectively summarizes the information of those records. With the advent of deep neural models, this objective has been partially attained for English clinical texts, however, the research community still lacks an effective solution for languages with limited resources. In this paper, we present the approach we developed to extract procedures, drugs, and diseases from oncology health records written in European Portuguese. This project was conducted in collaboration with the Portuguese Institute for Oncology which, besides holding over 10 years of duly protected medical records, also provided oncologist expertise throughout the development of the project. Since there is no annotated corpus for biomedical entity extraction in Portuguese, we also present the strategy we followed in annotating the corpus for the development of the models. The final models, which combined a neural architecture with entity linking, achieved F1 scores of 88.6, 95.0, and 55.8 per cent in the mention extraction of procedures, drugs, and diseases, respectively.