Causal relationship extraction from biomedical text using deep neural models: A comprehensive survey

Akkasi, Abbas; Moens, Marie‐Francine

doi:10.1016/j.jbi.2021.103820

Cited by 37 publications

(20 citation statements)

References 36 publications

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“…Our finding further shows the potential of machine learning in the precise diagnosis of SPPT and SNPT patients. Machine learning is becoming ubiquitous for analyzing multi-dimensional big data, and has been widely applied to many biological/medical fields, including diagnostic biomarker identification [ 52 ], therapeutic targets detection [ 53 ], disease progression prediction [ 54 ], and causal relationship between phenotype and genotype [ 55 ]. In our study, three machine learning methods are used to screen out potential biomarkers for precise classification of various types of TB from multidimensional data.…”

Section: Discussionmentioning

confidence: 99%

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis

Wang

et al. 2022

BMC Infect Dis

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Background Tuberculosis (TB) had been the leading lethal infectious disease worldwide for a long time (2014–2019) until the COVID-19 global pandemic, and it is still one of the top 10 death causes worldwide. One important reason why there are so many TB patients and death cases in the world is because of the difficulties in precise diagnosis of TB using common detection methods, especially for some smear-negative pulmonary tuberculosis (SNPT) cases. The rapid development of metabolome and machine learning offers a great opportunity for precision diagnosis of TB. However, the metabolite biomarkers for the precision diagnosis of smear-positive and smear-negative pulmonary tuberculosis (SPPT/SNPT) remain to be uncovered. In this study, we combined metabolomics and clinical indicators with machine learning to screen out newly diagnostic biomarkers for the precise identification of SPPT and SNPT patients. Methods Untargeted plasma metabolomic profiling was performed for 27 SPPT patients, 37 SNPT patients and controls. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was then conducted to screen differential metabolites among the three groups. Metabolite enriched pathways, random forest (RF), support vector machines (SVM) and multilayer perceptron neural network (MLP) were performed using Metaboanalyst 5.0, “caret” R package, “e1071” R package and “Tensorflow” Python package, respectively. Results Metabolomic analysis revealed significant enrichment of fatty acid and amino acid metabolites in the plasma of SPPT and SNPT patients, where SPPT samples showed a more serious dysfunction in fatty acid and amino acid metabolisms. Further RF analysis revealed four optimized diagnostic biomarker combinations including ten features (two lipid/lipid-like molecules and seven organic acids/derivatives, and one clinical indicator) for the identification of SPPT, SNPT patients and controls with high accuracy (83–93%), which were further verified by SVM and MLP. Among them, MLP displayed the best classification performance on simultaneously precise identification of the three groups (94.74%), suggesting the advantage of MLP over RF/SVM to some extent. Conclusions Our findings reveal plasma metabolomic characteristics of SPPT and SNPT patients, provide some novel promising diagnostic markers for precision diagnosis of various types of TB, and show the potential of machine learning in screening out biomarkers from big data.

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Section: Discussionmentioning

confidence: 99%

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis

Wang

et al. 2022

BMC Infect Dis

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“…Their study provides in-depth details on the fundamentals of IE, while comparing the strengths and weaknesses of each technique. Akkasi and Moens [24] published a survey of state-of-the-art models for extracting causal relationships from textual data using DL and deep neural network (DNN) approaches. Their study covers not only state-of-the-art methods, but also the applications, datasets used, and remaining challenges in the area.…”

Section: Literature Reviewmentioning

confidence: 99%

Systematic Literature Review of Information Extraction From Textual Data: Recent Methods, Applications, Trends, and Challenges

et al. 2023

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Information extraction (IE) is a challenging task, particularly when dealing with highly heterogeneous data. State-of-the-art data mining technologies struggle to process information from textual data. Therefore, various IE techniques have been developed to enable the use of IE for textual data. However, each technique differs from one another because it is designed for different data types and has different target information to be extracted. This study investigated and described the most contemporary methods for extracting information from textual data, emphasizing their benefits and shortcomings. To provide a holistic view of the domain, this comprehensive systematic literature review employed a systematic mapping process to summarize studies published in the last six years (from 2017 to 2022). It covers fundamental concepts, recent approaches, applications, and trends, in addition to challenges and future research prospects in this domain area. Based on an analysis of 161 selected studies, we found that the state-of-the-art models employ deep learning to extract information from textual data. Finally, this study aimed to guide novice and experienced researchers in future research and serve as a foundation for this research area.

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“…Research topics include automatic information extraction from clinical reports, discharge summaries or life science articles, e.g., in the form of entity recognition for diseases, proteins, drug and gene names (Habibi et al, 2017;Giorgi and Bader, 2018;Lee et al, 2019, i.a.). A subsequent task to entity recognition is relation extraction which covers clinical relations (Uzuner et al, 2011;Wang and Fan, 2014;Sahu et al, 2016;Lin et al, 2019;Akkasi and Moens, 2021) or biomedical relations/interactions (e.g., drug-drug-interactions) between entities (Lamurias et al, 2019;Sousa et al, 2021, i.a.). While scientific resources contain high quality information, studies might not be fully representative regarding population groups or gender (Weber et al, 2021), which leads to blind spots in the literature -the general population can barely be captured in such studies.…”

Section: Related Workmentioning

confidence: 99%

Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)

Wührl¹,

Klinger²

2022

Preprint

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For a long time, text mining and information extraction for the medical domain has focused on scientific text generated by researchers. However, their direct access to individual patient experiences or patient-doctor interactions is sometimes limited. Information provided on social media, e.g., by patients and their relatives, complements the knowledge available in scientific text. It reflects the patient's journey and their subjective perspective on the process of developing symptoms, being diagnosed and offered a treatment, being cured or learning to live with a medical condition. The value of this type of data is therefore twofold: Firstly, it offers direct access to people's perspectives. Secondly, it might cover information that is not available elsewhere, including self-treatment or self-diagnoses. Named entity recognition and relation extraction are methods to structure information that is available in unstructured text. However, existing medical social media corpora focused on a comparably small set of entities and relations and were focused on particular domains, rather than putting the patient into the center of analyses. With this paper we contribute a corpus with a rich set of annotation layers following the motivation to uncover and model patients' journeys and experiences in more detail. We label 14 entity classes (incl. environmental factors, diagnostics, biochemical processes, patients' quality-of-life descriptions, pathogens, medical conditions, and treatments) and 20 relation classes (e.g., prevents, influences, interactions, causes) most of which have not been considered before for social media data. The publicly available dataset consists of 2,100 tweets with ≈6,000 entity and ≈3,000 relation annotations. In a corpus analysis we find that over 80 % of documents contain relevant entities. Over 50 % of tweets express relations which we consider essential for uncovering patients' narratives about their journeys.

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Causal relationship extraction from biomedical text using deep neural models: A comprehensive survey

Cited by 37 publications

References 36 publications

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis

Systematic Literature Review of Information Extraction From Textual Data: Recent Methods, Applications, Trends, and Challenges

Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)

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