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Zaman F, Kamiran F, Shardlow M, Hassan SU, Karim A, Aljohani NR. SATS: simplification aware text summarization of scientific documents. Front Artif Intell 2024; 7:1375419. [PMID: 39049961 PMCID: PMC11266102 DOI: 10.3389/frai.2024.1375419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 06/06/2024] [Indexed: 07/27/2024] Open
Abstract
Simplifying summaries of scholarly publications has been a popular method for conveying scientific discoveries to a broader audience. While text summarization aims to shorten long documents, simplification seeks to reduce the complexity of a document. To accomplish these tasks collectively, there is a need to develop machine learning methods to shorten and simplify longer texts. This study presents a new Simplification Aware Text Summarization model (SATS) based on future n-gram prediction. The proposed SATS model extends ProphetNet, a text summarization model, by enhancing the objective function using a word frequency lexicon for simplification tasks. We have evaluated the performance of SATS on a recently published text summarization and simplification corpus consisting of 5,400 scientific article pairs. Our results in terms of automatic evaluation demonstrate that SATS outperforms state-of-the-art models for simplification, summarization, and joint simplification-summarization across two datasets on ROUGE, SARI, and CSS1 . We also provide human evaluation of summaries generated by the SATS model. We evaluated 100 summaries from eight annotators for grammar, coherence, consistency, fluency, and simplicity. The average human judgment for all evaluated dimensions lies between 4.0 and 4.5 on a scale from 1 to 5 where 1 means low and 5 means high.
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Affiliation(s)
- Farooq Zaman
- Scientometrics Lab, Information Technology University, Lahore, Pakistan
| | - Faisal Kamiran
- Scientometrics Lab, Information Technology University, Lahore, Pakistan
| | - Matthew Shardlow
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom
| | - Saeed-Ul Hassan
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom
| | - Asim Karim
- Department of Computer Science, Syed Babar Ali School of Science and Engineering (SBASSE), Lahore University of Management Sciences, Lahore, Pakistan
| | - Naif Radi Aljohani
- Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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Almutairi R, Alsarraf A, Alkandari D, Ashkanani H, Albazali A. Dissecting Through the Literature: A Review of the Critical Appraisal Process. Cureus 2024; 16:e59658. [PMID: 38836144 PMCID: PMC11148477 DOI: 10.7759/cureus.59658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
Critical appraisal is a crucial step in evidence-based practice, enabling researchers to evaluate the credibility and applicability of research findings. Healthcare professionals are encouraged to cultivate critical appraisal skills to assess the trustworthiness and value of available evidence. This process involves scrutinizing key components of a research publication, understanding the strengths and weaknesses of the study, and assessing its relevance to a specific context. It is essential for researchers to become familiar with the core elements of a research article and utilize key questions and guidelines to rigorously assess a study. This paper aims to provide an overview of the critical appraisal process. By understanding the main points of critical appraisal, researchers can assess the quality, relevance, and reliability of articles, thereby enhancing the validity of their findings and decision-making processes.
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3
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Boguslav MR, Salem NM, White EK, Sullivan KJ, Bada M, Hernandez TL, Leach SM, Hunter LE. Creating an ignorance-base: Exploring known unknowns in the scientific literature. J Biomed Inform 2023; 143:104405. [PMID: 37270143 PMCID: PMC10528083 DOI: 10.1016/j.jbi.2023.104405] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 05/18/2023] [Accepted: 05/21/2023] [Indexed: 06/05/2023]
Abstract
BACKGROUND Scientific discovery progresses by exploring new and uncharted territory. More specifically, it advances by a process of transforming unknown unknowns first into known unknowns, and then into knowns. Over the last few decades, researchers have developed many knowledge bases to capture and connect the knowns, which has enabled topic exploration and contextualization of experimental results. But recognizing the unknowns is also critical for finding the most pertinent questions and their answers. Prior work on known unknowns has sought to understand them, annotate them, and automate their identification. However, no knowledge-bases yet exist to capture these unknowns, and little work has focused on how scientists might use them to trace a given topic or experimental result in search of open questions and new avenues for exploration. We show here that a knowledge base of unknowns can be connected to ontologically grounded biomedical knowledge to accelerate research in the field of prenatal nutrition. RESULTS We present the first ignorance-base, a knowledge-base created by combining classifiers to recognize ignorance statements (statements of missing or incomplete knowledge that imply a goal for knowledge) and biomedical concepts over the prenatal nutrition literature. This knowledge-base places biomedical concepts mentioned in the literature in context with the ignorance statements authors have made about them. Using our system, researchers interested in the topic of vitamin D and prenatal health were able to uncover three new avenues for exploration (immune system, respiratory system, and brain development) by searching for concepts enriched in ignorance statements. These were buried among the many standard enriched concepts. Additionally, we used the ignorance-base to enrich concepts connected to a gene list associated with vitamin D and spontaneous preterm birth and found an emerging topic of study (brain development) in an implied field (neuroscience). The researchers could look to the field of neuroscience for potential answers to the ignorance statements. CONCLUSION Our goal is to help students, researchers, funders, and publishers better understand the state of our collective scientific ignorance (known unknowns) in order to help accelerate research through the continued illumination of and focus on the known unknowns and their respective goals for scientific knowledge.
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Affiliation(s)
- Mayla R Boguslav
- Computational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA.
| | - Nourah M Salem
- Computational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA
| | - Elizabeth K White
- Computational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA; Center for Genes, Environment and Health, National Jewish Health, Jackson Street, Denver, 80206, CO, USA
| | - Katherine J Sullivan
- Computational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA
| | - Michael Bada
- Computational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA
| | - Teri L Hernandez
- College of Nursing, Department of Medicine/Division of Endocrinology, Metabolism, & Diabetes, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA
| | - Sonia M Leach
- Computational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA; Center for Genes, Environment and Health, National Jewish Health, Jackson Street, Denver, 80206, CO, USA
| | - Lawrence E Hunter
- Computational Bioscience Program, University of Colorado, Anschutz Medical Campus, E 17th Avenue, Aurora, 80045, CO, USA
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Wu H, Wang M, Wu J, Francis F, Chang YH, Shavick A, Dong H, Poon MTC, Fitzpatrick N, Levine AP, Slater LT, Handy A, Karwath A, Gkoutos GV, Chelala C, Shah AD, Stewart R, Collier N, Alex B, Whiteley W, Sudlow C, Roberts A, Dobson RJB. A survey on clinical natural language processing in the United Kingdom from 2007 to 2022. NPJ Digit Med 2022; 5:186. [PMID: 36544046 PMCID: PMC9770568 DOI: 10.1038/s41746-022-00730-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019-2022 was 80 times that of 2007-2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.
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Affiliation(s)
- Honghan Wu
- Institute of Health Informatics, University College London, London, UK.
| | - Minhong Wang
- Institute of Health Informatics, University College London, London, UK
| | - Jinge Wu
- Institute of Health Informatics, University College London, London, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Farah Francis
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Yun-Hsuan Chang
- Institute of Health Informatics, University College London, London, UK
| | - Alex Shavick
- Research Department of Pathology, UCL Cancer Institute, University College London, London, UK
| | - Hang Dong
- Usher Institute, University of Edinburgh, Edinburgh, UK
- Department of Computer Science, University of Oxford, Oxford, UK
| | | | | | - Adam P Levine
- Research Department of Pathology, UCL Cancer Institute, University College London, London, UK
| | - Luke T Slater
- Institute of Cancer and Genomics, University of Birmingham, Birmingham, UK
| | - Alex Handy
- Institute of Health Informatics, University College London, London, UK
- University College London Hospitals NHS Trust, London, UK
| | - Andreas Karwath
- Institute of Cancer and Genomics, University of Birmingham, Birmingham, UK
| | - Georgios V Gkoutos
- Institute of Cancer and Genomics, University of Birmingham, Birmingham, UK
| | - Claude Chelala
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Anoop Dinesh Shah
- Institute of Health Informatics, University College London, London, UK
| | - Robert Stewart
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Nigel Collier
- Theoretical and Applied Linguistics, Faculty of Modern & Medieval Languages & Linguistics, University of Cambridge, Cambridge, UK
| | - Beatrice Alex
- Edinburgh Futures Institute, University of Edinburgh, Edinburgh, UK
| | | | - Cathie Sudlow
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Angus Roberts
- Department of Biostatistics & Health Informatics, King's College London, London, UK
| | - Richard J B Dobson
- Institute of Health Informatics, University College London, London, UK
- Department of Biostatistics & Health Informatics, King's College London, London, UK
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Sosa DN, Altman RB. Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference. Brief Bioinform 2022; 23:bbac268. [PMID: 35817308 PMCID: PMC9294417 DOI: 10.1093/bib/bbac268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/25/2022] [Accepted: 06/07/2022] [Indexed: 11/30/2022] Open
Abstract
The cost of drug development continues to rise and may be prohibitive in cases of unmet clinical need, particularly for rare diseases. Artificial intelligence-based methods are promising in their potential to discover new treatment options. The task of drug repurposing hypothesis generation is well-posed as a link prediction problem in a knowledge graph (KG) of interacting of drugs, proteins, genes and disease phenotypes. KGs derived from biomedical literature are semantically rich and up-to-date representations of scientific knowledge. Inference methods on scientific KGs can be confounded by unspecified contexts and contradictions. Extracting context enables incorporation of relevant pharmacokinetic and pharmacodynamic detail, such as tissue specificity of interactions. Contradictions in biomedical KGs may arise when contexts are omitted or due to contradicting research claims. In this review, we describe challenges to creating literature-scale representations of pharmacological knowledge and survey current approaches toward incorporating context and resolving contradictions.
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Affiliation(s)
- Daniel N Sosa
- Department of Biomedical Data Science, Stanford University, 443 Via Ortega, 94305, California, USA
| | - Russ B Altman
- Department of Biological Engineering; Department of Genetics; Department of Biomedical Data Science, Stanford University, 443 Via Ortega, 94305, California, USA
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6
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Multi-class sentiment analysis of urdu text using multilingual BERT. Sci Rep 2022; 12:5436. [PMID: 35361890 PMCID: PMC8971433 DOI: 10.1038/s41598-022-09381-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 03/22/2022] [Indexed: 12/02/2022] Open
Abstract
Sentiment analysis (SA) is an important task because of its vital role in analyzing people’s opinions. However, existing research is solely based on the English language with limited work on low-resource languages. This study introduced a new multi-class Urdu dataset based on user reviews for sentiment analysis. This dataset is gathered from various domains such as food and beverages, movies and plays, software and apps, politics, and sports. Our proposed dataset contains 9312 reviews manually annotated by human experts into three classes: positive, negative and neutral. The main goal of this research study is to create a manually annotated dataset for Urdu sentiment analysis and to set baseline results using rule-based, machine learning (SVM, NB, Adabbost, MLP, LR and RF) and deep learning (CNN-1D, LSTM, Bi-LSTM, GRU and Bi-GRU) techniques. Additionally, we fine-tuned Multilingual BERT(mBERT) for Urdu sentiment analysis. We used four text representations: word n-grams, char n-grams,pre-trained fastText and BERT word embeddings to train our classifiers. We trained these models on two different datasets for evaluation purposes. Finding shows that the proposed mBERT model with BERT pre-trained word embeddings outperformed deep learning, machine learning and rule-based classifiers and achieved an F1 score of 81.49%.
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Saeed A, Nawab RMA, Stevenson M. Investigating the Feasibility of Deep Learning Methods for Urdu Word Sense Disambiguation. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3477578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Word Sense Disambiguation (WSD), the process of automatically identifying the correct meaning of a word used in a given context, is a significant challenge in Natural Language Processing. A range of approaches to the problem has been explored by the research community. The majority of these efforts has focused on a relatively small set of languages, particularly English. Research on WSD for South Asian languages, particularly Urdu, is still in its infancy. In recent years, deep learning methods have proved to be extremely successful for a range of Natural Language Processing tasks. The main aim of this study is to apply, evaluate, and compare a range of deep learning methods approaches to Urdu WSD (both Lexical Sample and All-Words) including Simple Recurrent Neural Networks, Long-Short Term Memory, Gated Recurrent Units, Bidirectional Long-Short Term Memory, and Ensemble Learning. The evaluation was carried out on two benchmark corpora: (1) the ULS-WSD-18 corpus and (2) the UAW-WSD-18 corpus. Results (Accuracy = 63.25% and F1-Measure = 0.49) show that a deep learning approach outperforms previously reported results for the Urdu All-Words WSD task, whereas performance using deep learning approaches (Accuracy = 72.63% and F1-Measure = 0.60) are low in comparison to previously reported for the Urdu Lexical Sample task.
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Affiliation(s)
- Ali Saeed
- Department of Software Engineering, The University of Lahore, and Department of Computer Sciences, COMSATS University Islamabad, Lahore, Punjab, Pakistan
| | - Rao Muhammad Adeel Nawab
- Department of Computer Sciences, COMSATS UniversityIslamabad, Lahore Campus, Lahore, Punjab, Pakistan
| | - Mark Stevenson
- Department of Computer Sciences, University of Sheffield, Western Bank, Sheffield, UK
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Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12052694] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Sentiment analysis (SA) has been an active research subject in the domain of natural language processing due to its important functions in interpreting people’s perspectives and drawing successful opinion-based judgments. On social media, Roman Urdu is one of the most extensively utilized dialects. Sentiment analysis of Roman Urdu is difficult due to its morphological complexities and varied dialects. The purpose of this paper is to evaluate the performance of various word embeddings for Roman Urdu and English dialects using the CNN-LSTM architecture with traditional machine learning classifiers. We introduce a novel deep learning architecture for Roman Urdu and English dialect SA based on two layers: LSTM for long-term dependency preservation and a one-layer CNN model for local feature extraction. To obtain the final classification, the feature maps learned by CNN and LSTM are fed to several machine learning classifiers. Various word embedding models support this concept. Extensive tests on four corpora show that the proposed model performs exceptionally well in Roman Urdu and English text sentiment classification, with an accuracy of 0.904, 0.841, 0.740, and 0.748 against MDPI, RUSA, RUSA-19, and UCL datasets, respectively. The results show that the SVM classifier and the Word2Vec CBOW (Continuous Bag of Words) model are more beneficial options for Roman Urdu sentiment analysis, but that BERT word embedding, two-layer LSTM, and SVM as a classifier function are more suitable options for English language sentiment analysis. The suggested model outperforms existing well-known advanced models on relevant corpora, improving the accuracy by up to 5%.
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9
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Chen J, Geard N, Zobel J, Verspoor K. Automatic consistency assurance for literature-based gene ontology annotation. BMC Bioinformatics 2021; 22:565. [PMID: 34823464 PMCID: PMC8620237 DOI: 10.1186/s12859-021-04479-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 11/15/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Literature-based gene ontology (GO) annotation is a process where expert curators use uniform expressions to describe gene functions reported in research papers, creating computable representations of information about biological systems. Manual assurance of consistency between GO annotations and the associated evidence texts identified by expert curators is reliable but time-consuming, and is infeasible in the context of rapidly growing biological literature. A key challenge is maintaining consistency of existing GO annotations as new studies are published and the GO vocabulary is updated. RESULTS In this work, we introduce a formalisation of biological database annotation inconsistencies, identifying four distinct types of inconsistency. We propose a novel and efficient method using state-of-the-art text mining models to automatically distinguish between consistent GO annotation and the different types of inconsistent GO annotation. We evaluate this method using a synthetic dataset generated by directed manipulation of instances in an existing corpus, BC4GO. We provide detailed error analysis for demonstrating that the method achieves high precision on more confident predictions. CONCLUSIONS Two models built using our method for distinct annotation consistency identification tasks achieved high precision and were robust to updates in the GO vocabulary. Our approach demonstrates clear value for human-in-the-loop curation scenarios.
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Affiliation(s)
- Jiyu Chen
- School of Computing and Information Systems, University of Melbourne, Melbourne, 3010, Australia
| | - Nicholas Geard
- School of Computing and Information Systems, University of Melbourne, Melbourne, 3010, Australia
| | - Justin Zobel
- School of Computing and Information Systems, University of Melbourne, Melbourne, 3010, Australia
| | - Karin Verspoor
- School of Computing and Information Systems, University of Melbourne, Melbourne, 3010, Australia. .,School of Computing Technologies, RMIT University, Melbourne, VIC, 3000, Australia.
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10
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Rashid A, Saleem J, Amin M, Ali SM, Khan AA, Qureshi MB, Ali S, Dancey D, Nawaz R. Investigation of 9000 hours multi-stress aging effects on High-Temperature Vulcanized Silicone Rubber with silica (nano/micro) filler hybrid composite insulator. PLoS One 2021; 16:e0253372. [PMID: 34319996 PMCID: PMC8318273 DOI: 10.1371/journal.pone.0253372] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 06/04/2021] [Indexed: 12/01/2022] Open
Abstract
Degradation in the polymeric insulators is caused due to the environmental stresses. The main aim of this paper is to explore the improved aging characteristics of hybrid samples by adding nano/micro silica in High Temperature Vulcanized Silicone Rubber (HTV-SiR) under long term accelerated aging conditions for 9000 hours. As HTV-SiR is unable to sustain environmental stresses for a long time, thus a long term accelerated aging behavior is an important phenomenon to be considered for field application. The aging characteristics of nano/micro filled HTV-SiR are analyzed by using techniques such as Scanning Electron Microscopy (SEM), Leakage Current (LC), Fourier Transform Infrared Microscopy (FTIR), Hydrophobicity Classification (HC), and breakdown strength for the aging time of 9000 hours. FTIR and leakage currents are measured after every cycle. All the co-filled samples revealed escalated aging characteristics as compared to the neat sample except the SN8 sample (8% nano-silica+20% micro-silica) after 9000 hours of aging. The highest loading of 6% and 8% nano-silica with 20% micro-silica do not contribute to the improved performance when compared with the neat and hybrid samples. However, from the critical experimental analysis, it is deduced that SN2 sample (2% nano-silica+20% micro-silica) is highly resistant to the long term accelerated aging conditions. SN2 has no cracks, lower loss percentages in the important FTIR absorption peaks, higher breakdown strength and superior HC after aging as compared to the unfilled and hybrid samples.
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Affiliation(s)
- Arooj Rashid
- Electrical and Computer Engineering Department, COMSATS University, Abbottabad, KPK, Pakistan
| | - Jawad Saleem
- Electrical and Computer Engineering Department, COMSATS University, Abbottabad, KPK, Pakistan
| | - Muhammad Amin
- Ghulam Ishaq Institute of Engineering Sciences, Swabi, KPK, Pakistan
| | - Sahibzada Muhammad Ali
- Electrical and Computer Engineering Department, COMSATS University, Abbottabad, KPK, Pakistan
| | - Aftab Ahmad Khan
- Electrical and Computer Engineering Department, COMSATS University, Abbottabad, KPK, Pakistan
| | - Muhammad Bilal Qureshi
- Electrical and Computer Engineering Department, COMSATS University, Abbottabad, KPK, Pakistan
| | - Sara Ali
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology, Islamabad, Pakistan
| | - Darren Dancey
- Manchester Metropolitan University, Manchester, United Kingdom
| | - Raheel Nawaz
- Manchester Metropolitan University, Manchester, United Kingdom
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Boguslav MR, Salem NM, White EK, Leach SM, Hunter LE. Identifying and classifying goals for scientific knowledge. BIOINFORMATICS ADVANCES 2021; 1:vbab012. [PMID: 34661112 PMCID: PMC8508177 DOI: 10.1093/bioadv/vbab012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/17/2021] [Indexed: 01/26/2023]
Abstract
MOTIVATION Science progresses by posing good questions, yet work in biomedical text mining has not focused on them much. We propose a novel idea for biomedical natural language processing: identifying and characterizing the questions stated in the biomedical literature. Formally, the task is to identify and characterize statements of ignorance, statements where scientific knowledge is missing or incomplete. The creation of such technology could have many significant impacts, from the training of PhD students to ranking publications and prioritizing funding based on particular questions of interest. The work presented here is intended as the first step towards these goals. RESULTS We present a novel ignorance taxonomy driven by the role statements of ignorance play in research, identifying specific goals for future scientific knowledge. Using this taxonomy and reliable annotation guidelines (inter-annotator agreement above 80%), we created a gold standard ignorance corpus of 60 full-text documents from the prenatal nutrition literature with over 10 000 annotations and used it to train classifiers that achieved over 0.80 F1 scores. AVAILABILITY AND IMPLEMENTATION Corpus and source code freely available for download at https://github.com/UCDenver-ccp/Ignorance-Question-Work. The source code is implemented in Python.
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Affiliation(s)
- Mayla R Boguslav
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA,To whom correspondence should be addressed.
| | - Nourah M Salem
- Health Informatics Program, College of Health Solutions at Arizona State University, Phoenix, AZ 85004, USA
| | - Elizabeth K White
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO 80206, USA
| | - Sonia M Leach
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO 80206, USA
| | - Lawrence E Hunter
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
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12
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Luo M, Cohen AM, Addepalli S, Smalheiser NR. Identifying main finding sentences in clinical case reports. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2020:5855433. [PMID: 32525207 PMCID: PMC7287507 DOI: 10.1093/database/baaa041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 03/10/2020] [Accepted: 05/06/2020] [Indexed: 11/20/2022]
Abstract
Clinical case reports are the ‘eyewitness reports’ of medicine and provide a valuable, unique, albeit noisy and underutilized type of evidence. Generally, a case report has a single main finding that represents the reason for writing up the report in the first place. However, no one has previously created an automatic way of identifying main finding sentences in case reports. We previously created a manual corpus of main finding sentences extracted from the abstracts and full text of clinical case reports. Here, we have utilized the corpus to create a machine learning-based model that automatically predicts which sentence(s) from abstracts state the main finding. The model has been evaluated on a separate manual corpus of clinical case reports and found to have good performance. This is a step toward setting up a retrieval system in which, given one case report, one can find other case reports that report the same or very similar main findings. The code and necessary files to run the main finding model can be downloaded from https://github.com/qi29/main_ finding_recognition, released under the Apache License, Version 2.0.
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Affiliation(s)
- Mengqi Luo
- Department of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL 60612, USA.,School of Information Management, Wuhan University, Wuhan, Hubei 430072, China
| | - Aaron M Cohen
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Sidharth Addepalli
- Department of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL 60612, USA
| | - Neil R Smalheiser
- Department of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL 60612, USA
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Iqbal S, Hassan SU, Aljohani NR, Alelyani S, Nawaz R, Bornmann L. A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies. Scientometrics 2021. [DOI: 10.1007/s11192-021-04055-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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14
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Aljohani NR, Fayoumi A, Hassan SU. An in-text citation classification predictive model for a scholarly search system. Scientometrics 2021. [DOI: 10.1007/s11192-021-03986-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Hassan SU, Shabbir M, Iqbal S, Said A, Kamiran F, Nawaz R, Saif U. Leveraging Deep Learning and SNA approaches for Smart City Policing in the Developing World. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2021. [DOI: 10.1016/j.ijinfomgt.2019.102045] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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16
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Sarwar R, Zia A, Nawaz R, Fayoumi A, Aljohani NR, Hassan SU. Webometrics: evolution of social media presence of universities. Scientometrics 2021. [DOI: 10.1007/s11192-020-03804-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Safder I, Hassan SU, Visvizi A, Noraset T, Nawaz R, Tuarob S. Deep Learning-based Extraction of Algorithmic Metadata in Full-Text Scholarly Documents. Inf Process Manag 2020. [DOI: 10.1016/j.ipm.2020.102269] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Abstract
OBJECTIVE To determine how the representation of women's health has changed in clinical studies over the course of 70 years. DESIGN Observational study of 71 866 research articles published between 1948 and 2018 in The BMJ. MAIN OUTCOME MEASURES The incidence of women-specific health topics over time. General linear, additive and segmented regression models were used to estimate trends. RESULTS Over 70 years, the overall odds that a word in a BMJ research article was 'woman' or 'women' increased by an annual factor of 1.023, but this rate of increase varied by clinical specialty with some showing little or no change. The odds that an article was about some aspect of women-specific health increased much more slowly, by an annual factor of 1.004. The incidence of articles about particular areas of women-specific medicine such as pregnancy did not show a general increase, but rather fluctuated over time. The incidence of articles making any mention of women, gender or sex declined between 1948 and 2005, after which it rose steeply so that by 2018 few papers made no mention of them at all. CONCLUSIONS Over time women have become ever more prominent in BMJ research articles. However, the importance of women-specific health topics has waxed and waned as researchers responded ephemerally to medical advances, public health programmes, and sociolegal changes. The appointment of a woman editor-inchief in 2005 may have had a dramatic effect on whether women were mentioned in research articles.
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Affiliation(s)
- Eva N Hamulyák
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Austin J Brockmeier
- Department of Electrical & Computer Engineering, University of Delaware, Newark, Delaware, USA
| | - Johanna D Killas
- Health Studies Programme, University of Toronto, Toronto, Ontario, Canada
| | - Sophia Ananiadou
- Department of Computer Science, The University of Manchester National Centre for Text Mining, Manchester, UK
- The Alan Turing Institute, London, UK
| | - Saskia Middeldorp
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Armand M Leroi
- Department of Life Sciences, Imperial College London, South Kensington Campus, London, UK
- Data Science Institute, Imperial College London, South Kensington Campus, London, UK
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Mahmood Z, Safder I, Nawab RMA, Bukhari F, Nawaz R, Alfakeeh AS, Aljohani NR, Hassan SU. Deep sentiments in Roman Urdu text using Recurrent Convolutional Neural Network model. Inf Process Manag 2020. [DOI: 10.1016/j.ipm.2020.102233] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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22
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Hassan SU, Saleem A, Soroya SH, Safder I, Iqbal S, Jamil S, Bukhari F, Aljohani NR, Nawaz R. Sentiment analysis of tweets through Altmetrics: A machine learning approach. J Inf Sci 2020. [DOI: 10.1177/0165551520930917] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The purpose of the study is to (a) contribute to annotating an Altmetrics dataset across five disciplines, (b) undertake sentiment analysis using various machine learning and natural language processing–based algorithms, (c) identify the best-performing model and (d) provide a Python library for sentiment analysis of an Altmetrics dataset. First, the researchers gave a set of guidelines to two human annotators familiar with the task of related tweet annotation of scientific literature. They duly labelled the sentiments, achieving an inter-annotator agreement (IAA) of 0.80 (Cohen’s Kappa). Then, the same experiments were run on two versions of the dataset: one with tweets in English and the other with tweets in 23 languages, including English. Using 6388 tweets about 300 papers indexed in Web of Science, the effectiveness of employed machine learning and natural language processing models was measured by comparing with well-known sentiment analysis models, that is, SentiStrength and Sentiment140, as the baseline. It was proved that Support Vector Machine with uni-gram outperformed all the other classifiers and baseline methods employed, with an accuracy of over 85%, followed by Logistic Regression at 83% accuracy and Naïve Bayes at 80%. The precision, recall and F1 scores for Support Vector Machine, Logistic Regression and Naïve Bayes were (0.89, 0.86, 0.86), (0.86, 0.83, 0.80) and (0.85, 0.81, 0.76), respectively.
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Affiliation(s)
| | | | - Saira Hanif Soroya
- Department of Information Management, University of the Punjab, Pakistan
| | | | | | - Saqib Jamil
- Department of Management Sciences, University of Okara, Pakistan
| | - Faisal Bukhari
- Punjab University College for Information Technology (PUCIT), University of the Punjab, Pakistan
| | - Naif Radi Aljohani
- Faculty of Computing and Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia
| | - Raheel Nawaz
- School of Computer Science, Manchester Metropolitan University, UK
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Sarwar R, Porthaveepong T, Rutherford A, Rakthanmanon T, Nutanong S. StyloThai:. ACM T ASIAN LOW-RESO 2020. [DOI: 10.1145/3365832] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Authorship identification helps to identify the true author of a given anonymous document from a set of candidate authors. The applications of this task can be found in several domains, such as law enforcement agencies and information retrieval. These application domains are not limited to a specific language, community, or ethnicity. However, most of the existing solutions are designed for English, and a little attention has been paid to Thai. These existing solutions are not directly applicable to Thai due to the linguistic differences between these two languages. Moreover, the existing solution designed for Thai is unable to (i) handle outliers in the dataset, (ii) scale when the size of the candidate authors set increases, and (iii) perform well when the number of writing samples for each candidate author is low. We identify a stylometric feature space for the Thai authorship identification task. Based on our feature space, we present an authorship identification solution that uses the probabilistic
k
nearest neighbors classifier by transforming each document into a collection of point sets. Specifically, this document transformation allows us to (i) use set distance measures associated with an outlier handling mechanism, (ii) capture stylistic variations within a document, and (iii) produce multiple predictions for a query document. We create a new Thai authorship identification corpus containing 547 documents from 200 authors, which is significantly larger than the corpus used by the existing study (an increase of 32 folds in terms of the number of candidate authors). The experimental results show that our solution can overcome the limitations of the existing solution and outperforms all competitors with an accuracy level of 91.02%. Moreover, we investigate the effectiveness of each stylometric features category with the help of an ablation study. We found that combining all categories of the stylometric features outperforms the other combinations. Finally, we cross compare the feature spaces and classification methods of all solutions. We found that (i) our solution can scale as the number of candidate authors increases, (ii) our method outperforms all the competitors, and (iii) our feature space provides better performance than the feature space used by the existing study.
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Linking Work-Family Conflict (WFC) and Talent Management: Insights from a Developing Country. SUSTAINABILITY 2020. [DOI: 10.3390/su12072861] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Considering the profound societal change taking place in several developing countries, the objective of this paper is to reflect on work-family conflict (WFC) both as a concept and a social phenomenon. Given that WFC is a concept rooted in academic debate focusing on developments in Western, largely individualistic, societies, this paper reconsiders WFC’s value added as applied in a context of a collectivist society in a developing country. The objective of this paper is thus threefold, i.e., (i) to assess WFC’s applicability in a context of a collectivist society in a developing country, where the position and role of women gradually changes; (ii) to develop a culturally adjusted/sensitive scale to measure the scope of WFC in Pakistan, whereby the latter is treated here as a case study; and (iii) to reflect on the possibility of devising a set of good practices that would allow a smooth inclusion of women in the formal workforce, while at the same time mitigating the scope and scale of WFC. The value added of this paper stems from these three objectives.
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Measuring the Scale and Scope of Social Anxiety among Students in Pakistani Higher Education Institutions: An Alternative Social Anxiety Scale. SUSTAINABILITY 2020. [DOI: 10.3390/su12062164] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Social Anxiety Disorder (SAD) is neither just shyness, nor for most victims does it merely involve an inability to speak in public. For most sufferers of this disorder, it could be a pervasive, disabling condition that steals away opportunities for a richer, fuller life. Having an early onset and combining high prevalence rates with serious negative effects on functioning and quality of life, SAD is a public health problem of considerable magnitude. Hence, its assessment using a standardized measure and timely intervention can completely preempt or at least lessen the severity of this psychiatric illness. So far SAD among students in higher education institutions is a less investigated area of study in Pakistan. Students generally avoid reporting difficulties they experience while making interactions with people and quietly try to combat with their fears in social settings. Proper and timely diagnosis and treatment of SAD are required, and for this purpose, the need of the hour is to create a culturally oriented measuring instrument for proper surveillance of the student population in Pakistan. This paper, drawing from a study conducted at Higher Education Institutions (HEI) across Pakistan, addresses this issue by devising an indigenous, comprehensive, well-founded and valid scale of social anxiety in the Urdu language. The use of this scale, both in general and patient care settings, would effectively screen individuals who could be at risk of being victimized by this disorder. This alternative Social Anxiety Scale (SAS) carefully evaluates social behaviors and attitudes while also ensuring that cultural perspectives are considered, which would also encourage clinicians to evaluate SAD in Pakistani population.
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27
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Predicting academic performance of students from VLE big data using deep learning models. COMPUTERS IN HUMAN BEHAVIOR 2020. [DOI: 10.1016/j.chb.2019.106189] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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28
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A Gated Dilated Convolution with Attention Model for Clinical Cloze-Style Reading Comprehension. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17041323. [PMID: 32092861 PMCID: PMC7068278 DOI: 10.3390/ijerph17041323] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 02/14/2020] [Accepted: 02/15/2020] [Indexed: 02/04/2023]
Abstract
The machine comprehension research of clinical medicine has great potential value in practical application, but it has not received sufficient attention and many existing models are very time consuming for the cloze-style machine reading comprehension. In this paper, we study the cloze-style machine reading comprehension in the clinical medical field and propose a Gated Dilated Convolution with Attention (GDCA) model, which consists of a gated dilated convolution module and an attention mechanism. Our model has high parallelism and is capable of capturing long-distance dependencies. On the CliCR data set, our model surpasses the present best model on several metrics and obtains state-of-the-art result, and the training speed is 8 times faster than that of the best model.
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Aljohani NR, Fayoumi A, Hassan SU. Bot prediction on social networks of Twitter in altmetrics using deep graph convolutional networks. Soft comput 2020. [DOI: 10.1007/s00500-020-04689-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Arshad N, Bakar A, Soroya SH, Safder I, Haider S, Hassan SU, Aljohani NR, Alelyani S, Nawaz R. Extracting scientific trends by mining topics from Call for Papers. LIBRARY HI TECH 2019. [DOI: 10.1108/lht-02-2019-0048] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe purpose of this paper is to present a novel approach for mining scientific trends using topics from Call for Papers (CFP). The work contributes a valuable input for researchers, academics, funding institutes and research administration departments by sharing the trends to set directions of research path.Design/methodology/approachThe authors procure an innovative CFP data set to analyse scientific evolution and prestige of conferences that set scientific trends using scientific publications indexed in DBLP. Using the Field of Research code 804 from Australian Research Council, the authors identify 146 conferences (from 2006 to 2015) into different thematic areas by matching the terms extracted from publication titles with the Association for Computing Machinery Computing Classification System. Furthermore, the authors enrich the vocabulary of terms from the WordNet dictionary and Growbag data set. To measure the significance of terms, the authors adopt the following weighting schemas: probabilistic, gram, relative, accumulative and hierarchal.FindingsThe results indicate the rise of “big data analytics” from CFP topics in the last few years. Whereas the topics related to “privacy and security” show an exponential increase, the topics related to “semantic web” show a downfall in recent years. While analysing publication output in DBLP that matches CFP indexed in ERA Core A* to C rank conference, the authors identified that A* and A tier conferences not merely set publication trends, since B or C tier conferences target similar CFP.Originality/valueOverall, the analyses presented in this research are prolific for the scientific community and research administrators to study research trends and better data management of digital libraries pertaining to the scientific literature.
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Lanera C, Berchialla P, Sharma A, Minto C, Gregori D, Baldi I. Screening PubMed abstracts: is class imbalance always a challenge to machine learning? Syst Rev 2019; 8:317. [PMID: 31810495 PMCID: PMC6896747 DOI: 10.1186/s13643-019-1245-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 11/25/2019] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND The growing number of medical literature and textual data in online repositories led to an exponential increase in the workload of researchers involved in citation screening for systematic reviews. This work aims to combine machine learning techniques and data preprocessing for class imbalance to identify the outperforming strategy to screen articles in PubMed for inclusion in systematic reviews. METHODS We trained four binary text classifiers (support vector machines, k-nearest neighbor, random forest, and elastic-net regularized generalized linear models) in combination with four techniques for class imbalance: random undersampling and oversampling with 50:50 and 35:65 positive to negative class ratios and none as a benchmark. We used textual data of 14 systematic reviews as case studies. Difference between cross-validated area under the receiver operating characteristic curve (AUC-ROC) for machine learning techniques with and without preprocessing (delta AUC) was estimated within each systematic review, separately for each classifier. Meta-analytic fixed-effect models were used to pool delta AUCs separately by classifier and strategy. RESULTS Cross-validated AUC-ROC for machine learning techniques (excluding k-nearest neighbor) without preprocessing was prevalently above 90%. Except for k-nearest neighbor, machine learning techniques achieved the best improvement in conjunction with random oversampling 50:50 and random undersampling 35:65. CONCLUSIONS Resampling techniques slightly improved the performance of the investigated machine learning techniques. From a computational perspective, random undersampling 35:65 may be preferred.
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Affiliation(s)
- Corrado Lanera
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, 35131, Padova, Italy
| | - Paola Berchialla
- Department of Clinical and Biological Sciences, University of Torino, Torino, Italy
| | - Abhinav Sharma
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, India
| | - Clara Minto
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, 35131, Padova, Italy
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, 35131, Padova, Italy
| | - Ileana Baldi
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, 35131, Padova, Italy.
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Fouad MM, Mahany A, Aljohani N, Abbasi RA, Hassan SU. ArWordVec: efficient word embedding models for Arabic tweets. Soft comput 2019. [DOI: 10.1007/s00500-019-04153-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Smalheiser NR, Luo M, Addepalli S, Cui X. A manual corpus of annotated main findings of clinical case reports. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2019; 2019:5290151. [PMID: 30657910 PMCID: PMC6335863 DOI: 10.1093/database/bay143] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 12/13/2018] [Indexed: 12/04/2022]
Abstract
Clinical case reports are the `eyewitness reports’ of medicine and provide a valuable, unique, albeit noisy and underutilized type of evidence. Generally a case report has a single main finding that represents the reason for writing up the report in the first place. In the present study, we present the results of manual annotation carried out by two individuals on 500 randomly sampled case reports. This corpus contains main finding sentences extracted from title, abstract and full-text of the same article that can be regarded as semantically related and are often paraphrases. The final reconciled corpus of 416 articles comprises an open resource for further study. This is the first step in establishing text mining models and tools that can identify main finding sentences in an automated fashion, and in measuring quantitatively how similar any two main findings are. We envision that case reports in PubMed may be automatically indexed by main finding, so that users can carry out information queries for specific main findings (rather than general topics)—and given one case report, a user can retrieve those having the most similar main findings. The metric of main finding similarity may also potentially be relevant to the modeling of paraphrasing, summarization and entailment within the biomedical literature.
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Affiliation(s)
- Neil R Smalheiser
- Department of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL, USA
| | - Mengqi Luo
- School of Information Management, Wuhan University, Wuhan, Hubei, China
| | - Sidharth Addepalli
- Department of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL, USA
| | - Xiaokai Cui
- School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA
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Hassan SU, Imran M, Iqbal S, Aljohani NR, Nawaz R. Deep context of citations using machine-learning models in scholarly full-text articles. Scientometrics 2018. [DOI: 10.1007/s11192-018-2944-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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