Proceedings of the 18th BioNLP Workshop and Shared Task

Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii (Editors)


Anthology ID:
W19-50
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/W19-50
DOI:
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PDF:
https://aclanthology.org/W19-50.pdf

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Proceedings of the 18th BioNLP Workshop and Shared Task
Dina Demner-Fushman | Kevin Bretonnel Cohen | Sophia Ananiadou | Junichi Tsujii

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Learning from the Experience of Doctors : Automated Diagnosis of Appendicitis Based on Clinical Notes
Steven Kester Yuwono | Hwee Tou Ng | Kee Yuan Ngiam

The objective of this work is to develop an automated diagnosis system that is able to predict the probability of appendicitis given a free-text emergency department (ED) note and additional structured information (e.g., lab test results). Our clinical corpus consists of about 180,000 ED notes based on ten years of patient visits to the Accident and Emergency (A&E) Department of the National University Hospital (NUH), Singapore. We propose a novel neural network approach that learns to diagnose acute appendicitis based on doctors’ free-text ED notes without any feature engineering. On a test set of 2,000 ED notes with equal number of appendicitis (positive) and non-appendicitis (negative) diagnosis and in which all the negative ED notes only consist of abdominal-related diagnosis, our model is able to achieve a promising F_0.5-score of 0.895 while ED doctors achieve F_0.5-score of 0.900. Visualization shows that our model is able to learn important features, signs, and symptoms of patients from unstructured free-text ED notes, which will help doctors to make better diagnosis.

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A Paraphrase Generation System for EHR Question AnsweringEHR Question Answering
Sarvesh Soni | Kirk Roberts

This paper proposes a dataset and method for automatically generating paraphrases for clinical questions relating to patient-specific information in electronic health records (EHRs). Crowdsourcing is used to collect 10,578 unique questions across 946 semantically distinct paraphrase clusters. This corpus is then used with a deep learning-based question paraphrasing method utilizing variational autoencoder and LSTM encoder / decoder. The ultimate use of such a method is to improve the performance of automatic question answering methods for EHRs.

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Transfer Learning in Biomedical Natural Language Processing : An Evaluation of BERT and ELMo on Ten Benchmarking DatasetsBERT and ELMo on Ten Benchmarking Datasets
Yifan Peng | Shankai Yan | Zhiyong Lu

Inspired by the success of the General Language Understanding Evaluation benchmark, we introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to facilitate research in the development of pre-training language representations in the biomedicine domain. The benchmark consists of five tasks with ten datasets that cover both biomedical and clinical texts with different dataset sizes and difficulties. We also evaluate several baselines based on BERT and ELMo and find that the BERT model pre-trained on PubMed abstracts and MIMIC-III clinical notes achieves the best results. We make the datasets, pre-trained models, and codes publicly available at https://github.com/ ncbi-nlp / BLUE_Benchmark.

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Combining Structured and Free-text Electronic Medical Record Data for Real-time Clinical Decision Support
Emilia Apostolova | Tony Wang | Tim Tschampel | Ioannis Koutroulis | Tom Velez

The goal of this work is to utilize Electronic Medical Record (EMR) data for real-time Clinical Decision Support (CDS). We present a deep learning approach to combining in real time available diagnosis codes (ICD codes) and free-text notes : Patient Context Vectors. Patient Context Vectors are created by averaging ICD code embeddings, and by predicting the same from free-text notes via a Convolutional Neural Network. The Patient Context Vectors were then simply appended to available structured data (vital signs and lab results) to build prediction models for a specific condition. Experiments on predicting ARDS, a rare and complex condition, demonstrate the utility of Patient Context Vectors as a means of summarizing the patient history and overall condition, and improve significantly the prediction model results.

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Deep Contextualized Biomedical Abbreviation Expansion
Qiao Jin | Jinling Liu | Xinghua Lu

Automatic identification and expansion of ambiguous abbreviations are essential for biomedical natural language processing applications, such as information retrieval and question answering systems. In this paper, we present DEep Contextualized Biomedical Abbreviation Expansion (DECBAE) model. DECBAE automatically collects substantial and relatively clean annotated contexts for 950 ambiguous abbreviations from PubMed abstracts using a simple heuristic. Then it utilizes BioELMo to extract the contextualized features of words, and feed those features to abbreviation-specific bidirectional LSTMs, where the hidden states of the ambiguous abbreviations are used to assign the exact definitions. Our DECBAE model outperforms other baselines by large margins, achieving average accuracy of 0.961 and macro-F1 of 0.917 on the dataset. It also surpasses human performance for expanding a sample abbreviation, and remains robust in imbalanced, low-resources and clinical settings.

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RNN Embeddings for Identifying Difficult to Understand Medical WordsRNN Embeddings for Identifying Difficult to Understand Medical Words
Hanna Pylieva | Artem Chernodub | Natalia Grabar | Thierry Hamon

Patients and their families often require a better understanding of medical information provided by doctors. We currently address this issue by improving the identification of difficult to understand medical words. We introduce novel embeddings received from RNN-FrnnMUTE (French RNN Medical Understandability Text Embeddings) which allow to reach up to 87.0 F1 score in identification of difficult words. We also note that adding pre-trained FastText word embeddings to the feature set substantially improves the performance of the model which classifies words according to their difficulty. We study the generalizability of different models through three cross-validation scenarios which allow testing classifiers in real-world conditions : understanding of medical words by new users, and classification of new unseen words by the automatic models. The RNN-FrnnMUTE embeddings and the categorization code are being made available for the research.

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A distantly supervised dataset for automated data extraction from diagnostic studies
Christopher Norman | Mariska Leeflang | René Spijker | Evangelos Kanoulas | Aurélie Névéol

Systematic reviews are important in evidence based medicine, but are expensive to produce. Automating or semi-automating the data extraction of index test, target condition, and reference standard from articles has the potential to decrease the cost of conducting systematic reviews of diagnostic test accuracy, but relevant training data is not available. We create a distantly supervised dataset of approximately 90,000 sentences, and let two experts manually annotate a small subset of around 1,000 sentences for evaluation. We evaluate the performance of BioBERT and logistic regression for ranking the sentences, and compare the performance for distant and direct supervision. Our results suggest that distant supervision can work as well as, or better than direct supervision on this problem, and that distantly trained models can perform as well as, or better than human annotators.

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A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics
Aditya Joshi | Sarvnaz Karimi | Ross Sparks | Cecile Paris | C Raina MacIntyre

Distributed representations of text can be used as features when training a statistical classifier. These representations may be created as a composition of word vectors or as context-based sentence vectors. We compare the two kinds of representations (word versus context) for three classification problems : influenza infection classification, drug usage classification and personal health mention classification. For statistical classifiers trained for each of these problems, context-based representations based on ELMo, Universal Sentence Encoder, Neural-Net Language Model and FLAIR are better than Word2Vec, GloVe and the two adapted using the MESH ontology. There is an improvement of 2-4 % in the accuracy when these context-based representations are used instead of word-based representations.

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Annotating Temporal Information in Clinical Notes for Timeline Reconstruction : Towards the Definition of Calendar Expressions
Natalia Viani | Hegler Tissot | Ariane Bernardino | Sumithra Velupillai

To automatically analyse complex trajectory information enclosed in clinical text (e.g. timing of symptoms, duration of treatment), it is important to understand the related temporal aspects, anchoring each event on an absolute point in time. In the clinical domain, few temporally annotated corpora are currently available. Moreover, underlying annotation schemas-which mainly rely on the TimeML standard-are not necessarily easily applicable for applications such as patient timeline reconstruction. In this work, we investigated how temporal information is documented in clinical text by annotating a corpus of medical reports with time expressions (TIMEXes), based on TimeML. The developed corpus is available to the NLP community. Starting from our annotations, we analysed the suitability of the TimeML TIMEX schema for capturing timeline information, identifying challenges and possible solutions. As a result, we propose a novel annotation schema that could be useful for timeline reconstruction : CALendar EXpression (CALEX).

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Enhancing PIO Element Detection in Medical Text Using Contextualized EmbeddingPIO Element Detection in Medical Text Using Contextualized Embedding
Hichem Mezaoui | Isuru Gunasekara | Aleksandr Gontcharov

In this paper, we presented an improved methodology to extract PIO elements, from abstracts of medical papers, that reduces ambiguity. The proposed technique was used to build a dataset of PIO elements that we call PICONET. We further proposed a model of PIO elements classification using state of the art BERT embedding. In addition, we investigated a contextualized embedding, BioBERT, trained on medical corpora. It has been found that using the BioBERT embedding improved the classification accuracy, outperforming the BERT-based model. This result reinforces the idea of the importance of embedding contextualization in subsequent classification tasks in this specific context. Furthermore, to enhance the accuracy of the model, we have investigated an ensemble method based on the LGBM algorithm. We trained the LGBM model, with the above models as base learners, to learn a linear combination of the predicted probabilities for the 3 classes with the TF-IDF score and the QIEF that optimizes the classification. The results indicate that these text features were good features to consider in order to boost the deeply contextualized classification model. We compared the performance of the classifier when using the features with one of the base learners and the case where we combine the base learners along with the features. We obtained the highest score in terms of AUC when we combine the base learners. The present work resulted in the creation of a PIO element dataset, PICONET, and a classification tool. These constitute and important component of our system of automatic mining of medical abstracts. We intend to extend the dataset to full medical articles.

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Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives?
Zhaodong Yan | Serena Jeblee | Graeme Hirst

We present two models for combining word and character embeddings for cause-of-death classification of verbal autopsy reports using the text of the narratives. We find that for smaller datasets (500 to 1000 records), adding character information to the model improves classification, making character-based CNNs a promising method for automated verbal autopsy coding.

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Is artificial data useful for biomedical Natural Language Processing algorithms?
Zixu Wang | Julia Ive | Sumithra Velupillai | Lucia Specia

A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility. This problem can be addressed by generating medical data artificially. Most previous studies have focused on the generation of short clinical text, and evaluation of the data utility has been limited. We propose a generic methodology to guide the generation of clinical text with key phrases. We use the artificial data as additional training data in two key biomedical NLP tasks : text classification and temporal relation extraction. We show that artificially generated training data used in conjunction with real training data can lead to performance boosts for data-greedy neural network algorithms. We also demonstrate the usefulness of the generated data for NLP setups where it fully replaces real training data.

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ChiMed : A Chinese Medical Corpus for Question AnsweringChiMed: A Chinese Medical Corpus for Question Answering
Yuanhe Tian | Weicheng Ma | Fei Xia | Yan Song

Question answering (QA) is a challenging task in natural language processing (NLP), especially when it is applied to specific domains. While models trained in the general domain can be adapted to a new target domain, their performance often degrades significantly due to domain mismatch. Alternatively, one can require a large amount of domain-specific QA data, but such data are rare, especially for the medical domain. In this study, we first collect a large-scale Chinese medical QA corpus called ChiMed ; second we annotate a small fraction of the corpus to check the quality of the answers ; third, we extract two datasets from the corpus and use them for the relevancy prediction task and the adoption prediction task. Several benchmark models are applied to the datasets, producing good results for both tasks.

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Extracting relations between outcomes and significance levels in Randomized Controlled Trials (RCTs) publicationsRCTs) publications
Anna Koroleva | Patrick Paroubek

Randomized controlled trials assess the effects of an experimental intervention by comparing it to a control intervention with regard to some variables-trial outcomes. Statistical hypothesis testing is used to test if the experimental intervention is superior to the control. Statistical significance is typically reported for the measured outcomes and is an important characteristic of the results. We propose a machine learning approach to automatically extract reported outcomes, significance levels and the relation between them. We annotated a corpus of 663 sentences with 2,552 outcome-significance level relations (1,372 positive and 1,180 negative relations). We compared several classifiers, using a manually crafted feature set, and a number of deep learning models. The best performance (F-measure of 94 %) was shown by the BioBERT fine-tuned model.

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Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question AnsweringMEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering
Asma Ben Abacha | Chaitanya Shivade | Dina Demner-Fushman

This paper presents the MEDIQA 2019 shared task organized at the ACL-BioNLP workshop. The shared task is motivated by a need to develop relevant methods, techniques and gold standards for inference and entailment in the medical domain, and their application to improve domain specific information retrieval and question answering systems. MEDIQA 2019 includes three tasks : Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain. 72 teams participated in the challenge, achieving an accuracy of 98 % in the NLI task, 74.9 % in the RQE task, and 78.3 % in the QA task. In this paper, we describe the tasks, the datasets, and the participants’ approaches and results. We hope that this shared task will attract further research efforts in textual inference, question entailment, and question answering in the medical domain.

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Surf at MEDIQA 2019 : Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language ModelMEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model
Jiin Nam | Seunghyun Yoon | Kyomin Jung

While deep learning techniques have shown promising results in many natural language processing (NLP) tasks, it has not been widely applied to the clinical domain. The lack of large datasets and the pervasive use of domain-specific language (i.e. abbreviations and acronyms) in the clinical domain causes slower progress in NLP tasks than that of the general NLP tasks. To fill this gap, we employ word / subword-level based models that adopt large-scale data-driven methods such as pre-trained language models and transfer learning in analyzing text for the clinical domain. Empirical results demonstrate the superiority of the proposed methods by achieving 90.6 % accuracy in medical domain natural language inference task. Furthermore, we inspect the independent strengths of the proposed approaches in quantitative and qualitative manners. This analysis will help researchers to select necessary components in building models for the medical domain.

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WTMED at MEDIQA 2019 : A Hybrid Approach to Biomedical Natural Language InferenceWTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference
Zhaofeng Wu | Yan Song | Sicong Huang | Yuanhe Tian | Fei Xia

Natural language inference (NLI) is challenging, especially when it is applied to technical domains such as biomedical settings. In this paper, we propose a hybrid approach to biomedical NLI where different types of information are exploited for this task. Our base model includes a pre-trained text encoder as the core component, and a syntax encoder and a feature encoder to capture syntactic and domain-specific information. Then we combine the output of different base models to form more powerful ensemble models. Finally, we design two conflict resolution strategies when the test data contain multiple (premise, hypothesis) pairs with the same premise. We train our models on the MedNLI dataset, yielding the best performance on the test set of the MEDIQA 2019 Task 1.

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KU_ai at MEDIQA 2019 : Domain-specific Pre-training and Transfer Learning for Medical NLIKU_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI
Cemil Cengiz | Ulaş Sert | Deniz Yuret

In this paper, we describe our system and results submitted for the Natural Language Inference (NLI) track of the MEDIQA 2019 Shared Task. As KU_ai team, we used BERT as our baseline model and pre-processed the MedNLI dataset to mitigate the negative impact of de-identification artifacts. Moreover, we investigated different pre-training and transfer learning approaches to improve the performance. We show that pre-training the language model on rich biomedical corpora has a significant effect in teaching the model domain-specific language. In addition, training the model on large NLI datasets such as MultiNLI and SNLI helps in learning task-specific reasoning. Finally, we ensembled our highest-performing models, and achieved 84.7 % accuracy on the unseen test dataset and ranked 10th out of 17 teams in the official results.

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Dr. Quad at MEDIQA 2019 : Towards Textual Inference and Question Entailment using contextualized representationsDr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations
Vinayshekhar Bannihatti Kumar | Ashwin Srinivasan | Aditi Chaudhary | James Route | Teruko Mitamura | Eric Nyberg

This paper presents the submissions by TeamDr. Quad to the ACL-BioNLP 2019 shared task on Textual Inference and Question Entailment in the Medical Domain. Our system is based on the prior work Liu et al. (2019) which uses a multi-task objective function for textual entailment. In this work, we explore different strategies for generalizing state-of-the-art language understanding models to the specialized medical domain. Our results on the shared task demonstrate that incorporating domain knowledge through data augmentation is a powerful strategy for addressing challenges posed specialized domains such as medicine.

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Sieg at MEDIQA 2019 : Multi-task Neural Ensemble for Biomedical Inference and EntailmentMEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment
Sai Abishek Bhaskar | Rashi Rungta | James Route | Eric Nyberg | Teruko Mitamura

This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain. Recognizing textual inference relations and question similarity can address the issue of answering new consumer health questions by mapping them to Frequently Asked Questions on reputed websites like the NIH. We show that leveraging information from parallel tasks across domains along with medical knowledge integration allows our model to learn better biomedical feature representations. Our final models for the NLI and RQE tasks achieve the 4th and 2nd rank on the shared-task leaderboard respectively.

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MSIT_SRIB at MEDIQA 2019 : Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.MSIT_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.
Sahil Chopra | Ankita Gupta | Anupama Kaushik

In this paper, we present Biomedical Multi-Task Deep Neural Network (Bio-MTDNN) on the NLI task of MediQA 2019 challenge. Bio-MTDNN utilizes transfer learning based paradigm where not only the source and target domains are different but also the source and target tasks are varied, although related. Further, Bio-MTDNN integrates knowledge from external sources such as clinical databases (UMLS) enhancing its performance on the clinical domain. Our proposed method outperformed the official baseline and other prior models (such as ESIM and Infersent on dev set) by a considerable margin as evident from our experimental results.

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IITP at MEDIQA 2019 : Systems Report for Natural Language Inference, Question Entailment and Question AnsweringIITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering
Dibyanayan Bandyopadhyay | Baban Gain | Tanik Saikh | Asif Ekbal

This paper presents the experiments accomplished as a part of our participation in the MEDIQA challenge, an (Abacha et al., 2019) shared task. We participated in all the three tasks defined in this particular shared task. The tasks are viz. i. Natural Language Inference (NLI) ii. Recognizing Question Entailment(RQE) and their application in medical Question Answering (QA). We submitted runs using multiple deep learning based systems (runs) for each of these three tasks. We submitted five system results in each of the NLI and RQE tasks, and four system results for the QA task. The systems yield encouraging results in all the three tasks. The highest performance obtained in NLI, RQE and QA tasks are 81.8 %, 53.2 %, and 71.7 %, respectively.

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LasigeBioTM at MEDIQA 2019 : Biomedical Question Answering using Bidirectional Transformers and Named Entity RecognitionLasigeBioTM at MEDIQA 2019: Biomedical Question Answering using Bidirectional Transformers and Named Entity Recognition
Andre Lamurias | Francisco M Couto

Biomedical Question Answering (QA) aims at providing automated answers to user questions, regarding a variety of biomedical topics. For example, these questions may ask for related to diseases, drugs, symptoms, or medical procedures. Automated biomedical QA systems could improve the retrieval of information necessary to answer these questions. The MEDIQA challenge consisted of three tasks concerning various aspects of biomedical QA. This challenge aimed at advancing approaches to Natural Language Inference (NLI) and Recognizing Question Entailment (RQE), which would then result in enhanced approaches to biomedical QA. Our approach explored a common Transformer-based architecture that could be applied to each task. This approach shared the same pre-trained weights, but which were then fine-tuned for each task using the provided training data. Furthermore, we augmented the training data with external datasets and enriched the question and answer texts using MER, a named entity recognition tool. Our approach obtained high levels of accuracy, in particular on the NLI task, which classified pairs of text according to their relation. For the QA task, we obtained higher Spearman’s rank correlation values using the entities recognized by MER.