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Information Extraction for Biomedical Literature Using Artificial Intelligence: A Comparative Study

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International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023) (AI2SD 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 904))

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Abstract

Growing biomedical literature necessitates efficient and effective knowledge extraction methodologies. Automated information extraction (IE) techniques offer promising solutions. This article presents a comprehensive comparative study that evaluates the latest research publications focusing on extracting information such as drug interactions and diseases from Biomedical Literature. The study analyses the selected studies, compares their findings, and discusses IE techniques’ attributes. This investigation examines a comprehensive range of IE methods, including deep learning, machine learning, rule-based methods, and hybrid approaches. The performance of specific methods is evaluated using discerning metrics such as F1-score, precision, recall, and accuracy. Evidence suggests that deep learning methods achieved significant improvements in accuracy, while hybrid approaches demonstrated flexibility and robustness. Also, domain-specific models and pre-trained language models are emphasized to enhance contextual understanding. Despite progress, challenges persist, including the handling of complex sentences, data availability, and generalization. In the context of the rapidly evolving biomedical field, IE methods play an increasingly critical role in the development of medical knowledge and patient care. This study highlights the critical need for continued research to develop and refine IE techniques for their broader application.

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Abbreviations

In :

this paper, the following abbreviations are used:

IE:

Information Extraction

NLP:

Natural Language Processing

ML:

Machine Learning

DL:

Deep Learning

SVM:

Support Vector Machine

CNN:

Convolutional Neural Network

McDepCNN:

Multichannel Dependency-based Convolutional Neural Network

LSTM:

Long Short-Term Memory

BiLSTM:

Bidirectional long-short term memory

DS-LSTM:

Deep-contextualized Stacked Bi-LSTM

RNN:

Recursive Neural Networks

BERT:

Bidirectional Encoder Representations from Transformers

BioBERT:

Biomedical BERT

R-BERT:

Relationship-BERT

ADVBERT:

Adversarial BERT

CRF:

Conditional Random Field

NER:

Named entity recognition

TF-IDF:

Term Frequency-Inverse Document Frequency

SRL:

Semantic Role Labelling

SDP:

Shortest dependency path

GRGT:

Grammatical Relationship Graph for Triplets

DSTK:

Distributed Smoothed Tree Kernel

ROUGE:

Recall-Oriented Understudy for Gisting Evaluation

MaxEnt:

Maximum Entropy

DDI:

Drug-Drug Interaction

PPI:

Protein-Protein Interaction

PLMs:

Pre-trained language models

MeSH:

Medical Subject Headings

CORD-19:

COVID-19 Open Research Dataset

IoT:

Internet of Things

EMFs:

Electromagnetic Fields

EESE:

EMF Exposure Source Extraction

DTI:

Drug Target Interaction

CPI:

Compound-Protein Interaction

EMF-Portal:

EMF-Scientific Literature Portal

SpiNet:

Domain-specific FrameNet

PICO:

Population, Intervention, Comparator, Outcome

RE:

Regular Expressions

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Author information

Authors and Affiliations

  1. LARI Laboratory, Ibn Tofail University, Kenitra, Morocco

    Bouchaib Benkassioui & Moulay Youssef Hadi

  2. 2IACS Laboratory, ENSET, University Hassan II of Casablanca, Mohammedia, Morocco

    Sara Retal

  3. SmartiLab, Moroccan School of Engineering Sciences (EMSI), Rabat, Morocco

    Sara Retal

  4. National Center for Scientific and Technical Research (CNRST), Rabat, Morocco

    Nassim Kharmoum

  5. IPSS Research Team, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco

    Nassim Kharmoum

  6. E2SN Research Team, ENSAM Rabat, Mohammed V University in Rabat, Rabat, Morocco

    Wajih Rhalem

  7. Moroccan Society of Digital Health, Rabat, Morocco

    Nassim Kharmoum & Wajih Rhalem

Authors
  1. Bouchaib Benkassioui
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  2. Sara Retal
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  3. Nassim Kharmoum
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  4. Moulay Youssef Hadi
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  5. Wajih Rhalem
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Corresponding author

Correspondence to Bouchaib Benkassioui .

Editor information

Editors and Affiliations

  1. Computer Sciences Department, Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

  2. Systems Research Institute, Polish Academy of Science, Warsaw, Poland

    Janusz Kacprzyk

  3. Department of Automatics and Applied Software at the Faculty of Engineering, University “Aurel Vlaicu” Arad and Romanian Academy of Scientists, Arad, Romania

    Valentina Emilia Balas

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Benkassioui, B., Retal, S., Kharmoum, N., Hadi, M.Y., Rhalem, W. (2024). Information Extraction for Biomedical Literature Using Artificial Intelligence: A Comparative Study. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 904. Springer, Cham. https://doi.org/10.1007/978-3-031-52388-5_6

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