A Machine Learning-based Method for Question Type Classification in Biomedical Question Answering

Sarrouti, Mourad; Alaoui, Said Ouatik El

doi:10.3414/me16-01-0116

Cited by 28 publications

(4 citation statements)

References 31 publications

(48 reference statements)

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“…In healthcare it comes as an attempt to overcome the shortcoming in providing the required informational need through the legacy clinical Frequently Asked Questions (FAQs) portals established by almost every healthcare institution like the CDC. 9 To solve this problem several researchers from the natural language and machine learning fields developed attempts to provide automated techniques for generate clinical synthetic information [15,16]. Several notable attempts in this direction brought extended attention to the Q&A and GenAI field such as the development IBM Watson DeepQA [17], the availability several Q&A open benchmarks and datasets [18] (e.g.…”

Section: IImentioning

confidence: 99%

Empowering Transformers for Evidence-Based Medicine

Mohammed,

Fiaidhi,

Shaik

2023

Preprint

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Breaking the barrier for practicing evidence-based medicine rely on effective methods for rapidly identifying relevant evidences from the body of biomedical literature. An important challenge confronted by the medical practitioners is the long time needed to browse, filter, summarize and compile information from different medical resources. Deep learning can help in solving this based on the automatic question answering (Q&A) and transformers. However, Q&A and transformers technologies are not trained to answer clinical queries that can be used for evidence-based practice nor it can respond to structured clinical questioning protocol like PICO (Patient/Problem, Intervention, Comparison and Outcome). This article describes the use of deep learning techniques for Q&A that is based on transformer models like BERT and GPT to answer PICO clinical questions that can be used for evidence-based practice extracted from sound medical research resources like PubMed. We are reporting acceptable clinical answers that are supported by findings from PubMed. Our transformer methods are reaching an acceptable state of the art performance based on two staged bootstrapping process involving filtering relevant articles followed by identifying articles that support the requested outcome expressed by the PICO question. Moreover, we are also reporting experimentations to empower our bootstrapping techniques with patch attentions to the most important keywords in the clinical case and the PICO questions. Our bootstrapped patched with attention is showing relevancy of the evidences collected based on an entropy metrics.

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Section: IImentioning

confidence: 99%

Empowering Transformers for Evidence-Based Medicine

Mohammed,

Fiaidhi,

Shaik

2023

Preprint

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“…The machine learning techniques are superior to manual techniques discussed by authors (Sarrouti & El Alaoui, 2017). They described machine learning techniques give a reasonably easy way to classify questions as compared to manual techniques.…”

Section: Related Workmentioning

confidence: 99%

A comparative analysis on question classification task based on deep learning approaches

Zulqarnain

Alsaedi

Ghazali

et al. 2021

PeerJ Computer Science

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Question classification is one of the essential tasks for automatic question answering implementation in natural language processing (NLP). Recently, there have been several text-mining issues such as text classification, document categorization, web mining, sentiment analysis, and spam filtering that have been successfully achieved by deep learning approaches. In this study, we illustrated and investigated our work on certain deep learning approaches for question classification tasks in an extremely inflected Turkish language. In this study, we trained and tested the deep learning architectures on the questions dataset in Turkish. In addition to this, we used three main deep learning approaches (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN)) and we also applied two different deep learning combinations of CNN-GRU and CNN-LSTM architectures. Furthermore, we applied the Word2vec technique with both skip-gram and CBOW methods for word embedding with various vector sizes on a large corpus composed of user questions. By comparing analysis, we conducted an experiment on deep learning architectures based on test and 10-cross fold validation accuracy. Experiment results were obtained to illustrate the effectiveness of various Word2vec techniques that have a considerable impact on the accuracy rate using different deep learning approaches. We attained an accuracy of 93.7% by using these techniques on the question dataset.

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“…However, with the large and increasing volume of textual data in the biomedical domain makes it difficult to absorb all relevant information (Sarrouti and Alaoui, 2017a). Since time and quality are of the essence in finding answers to biomedical questions, developing and improving question answering systems are desirable.…”

Section: Introductionmentioning

confidence: 99%

“…Finding accurate answers to biomedical questions written in natural language from the biomedical literature is the key to creating high-quality systematic reviews that support the practice of evidence-based medicine (Kropf et al, 2017;Wang et al, 2017;Sarrouti and Lachkar, 2017) and improve the quality of patient care (Sarrouti and Alaoui, 2017b). However, with the large and increasing volume of textual data in the biomedical domain makes it difficult to absorb all relevant information (Sarrouti and Alaoui, 2017a). Since time and quality are of the essence in finding an-swers to biomedical questions, developing and improving question answering systems are desirable.…”

Section: Introductionmentioning

confidence: 99%

A Biomedical Question Answering System in BioASQ 2017

Sarrouti¹,

Alaoui²

2017

BioNLP 2017

Self Cite

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Question answering, the identification of short accurate answers to users questions, is a longstanding challenge widely studied over the last decades in the opendomain. However, it still requires further efforts in the biomedical domain. In this paper, we describe our participation in phase B of task 5b in the 2017 BioASQ challenge using our biomedical question answering system. Our system, dealing with four types of questions (i.e., yes/no, factoid, list, and summary), is based on (1) a dictionary-based approach for generating the exact answers of yes/no questions, (2) UMLS metathesaurus and term frequency metric for extracting the exact answers of factoid and list questions, and (3) the BM25 model and UMLS concepts for retrieving the ideal answers (i.e., paragraph-sized summaries). Preliminary results show that our system achieves good and competitive results in both exact and ideal answers extraction tasks as compared with the participating systems.

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A Machine Learning-based Method for Question Type Classification in Biomedical Question Answering

Abstract: The proposed method can automatically classify BioASQ questions into one of the four categories: yes/no, factoid, list, and summary. Furthermore, the results demonstrated that our method produced the best classification performance compared to four baseline systems.

Cited by 28 publications

References 31 publications

Empowering Transformers for Evidence-Based Medicine

Empowering Transformers for Evidence-Based Medicine

A comparative analysis on question classification task based on deep learning approaches

A Biomedical Question Answering System in BioASQ 2017

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