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Nerella S, Bandyopadhyay S, Zhang J, Contreras M, Siegel S, Bumin A, Silva B, Sena J, Shickel B, Bihorac A, Khezeli K, Rashidi P. Transformers and large language models in healthcare: A review. Artif Intell Med 2024; 154:102900. [PMID: 38878555 DOI: 10.1016/j.artmed.2024.102900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 05/28/2024] [Accepted: 05/30/2024] [Indexed: 08/09/2024]
Abstract
With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications. Transformer is a type of deep learning architecture initially developed to solve general-purpose Natural Language Processing (NLP) tasks and has subsequently been adapted in many fields, including healthcare. In this survey paper, we provide an overview of how this architecture has been adopted to analyze various forms of healthcare data, including clinical NLP, medical imaging, structured Electronic Health Records (EHR), social media, bio-physiological signals, biomolecular sequences. Furthermore, which have also include the articles that used the transformer architecture for generating surgical instructions and predicting adverse outcomes after surgeries under the umbrella of critical care. Under diverse settings, these models have been used for clinical diagnosis, report generation, data reconstruction, and drug/protein synthesis. Finally, we also discuss the benefits and limitations of using transformers in healthcare and examine issues such as computational cost, model interpretability, fairness, alignment with human values, ethical implications, and environmental impact.
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Affiliation(s)
- Subhash Nerella
- Department of Biomedical Engineering, University of Florida, Gainesville, United States
| | | | - Jiaqing Zhang
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, United States
| | - Miguel Contreras
- Department of Biomedical Engineering, University of Florida, Gainesville, United States
| | - Scott Siegel
- Department of Biomedical Engineering, University of Florida, Gainesville, United States
| | - Aysegul Bumin
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, United States
| | - Brandon Silva
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, United States
| | - Jessica Sena
- Department Of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Benjamin Shickel
- Department of Medicine, University of Florida, Gainesville, United States
| | - Azra Bihorac
- Department of Medicine, University of Florida, Gainesville, United States
| | - Kia Khezeli
- Department of Biomedical Engineering, University of Florida, Gainesville, United States
| | - Parisa Rashidi
- Department of Biomedical Engineering, University of Florida, Gainesville, United States.
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2
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Wada S, Takeda T, Okada K, Manabe S, Konishi S, Kamohara J, Matsumura Y. Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT. Artif Intell Med 2024; 153:102889. [PMID: 38728811 DOI: 10.1016/j.artmed.2024.102889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 05/03/2024] [Accepted: 05/04/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Pretraining large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing. With the introduction of transformer-based language models, such as bidirectional encoder representations from transformers (BERT), the performance of information extraction from free text has improved significantly in both the general and medical domains. However, it is difficult to train specific BERT models to perform well in domains for which few databases of a high quality and large size are publicly available. OBJECTIVE We hypothesized that this problem could be addressed by oversampling a domain-specific corpus and using it for pretraining with a larger corpus in a balanced manner. In the present study, we verified our hypothesis by developing pretraining models using our method and evaluating their performance. METHODS Our proposed method was based on the simultaneous pretraining of models with knowledge from distinct domains after oversampling. We conducted three experiments in which we generated (1) English biomedical BERT from a small biomedical corpus, (2) Japanese medical BERT from a small medical corpus, and (3) enhanced biomedical BERT pretrained with complete PubMed abstracts in a balanced manner. We then compared their performance with those of conventional models. RESULTS Our English BERT pretrained using both general and small medical domain corpora performed sufficiently well for practical use on the biomedical language understanding evaluation (BLUE) benchmark. Moreover, our proposed method was more effective than the conventional methods for each biomedical corpus of the same corpus size in the general domain. Our Japanese medical BERT outperformed the other BERT models built using a conventional method for almost all the medical tasks. The model demonstrated the same trend as that of the first experiment in English. Further, our enhanced biomedical BERT model, which was not pretrained on clinical notes, achieved superior clinical and biomedical scores on the BLUE benchmark with an increase of 0.3 points in the clinical score and 0.5 points in the biomedical score. These scores were above those of the models trained without our proposed method. CONCLUSIONS Well-balanced pretraining using oversampling instances derived from a corpus appropriate for the target task allowed us to construct a high-performance BERT model.
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Affiliation(s)
- Shoya Wada
- Department of Medical Informatics, Osaka University Graduate School of Medicine, Japan.
| | - Toshihiro Takeda
- Department of Medical Informatics, Osaka University Graduate School of Medicine, Japan
| | - Katsuki Okada
- Department of Medical Informatics, Osaka University Graduate School of Medicine, Japan
| | - Shirou Manabe
- Department of Medical Informatics, Osaka University Graduate School of Medicine, Japan
| | - Shozo Konishi
- Department of Medical Informatics, Osaka University Graduate School of Medicine, Japan
| | | | - Yasushi Matsumura
- Department of Medical Informatics, Osaka University Graduate School of Medicine, Japan
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3
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Jahan I, Laskar MTR, Peng C, Huang JX. A comprehensive evaluation of large Language models on benchmark biomedical text processing tasks. Comput Biol Med 2024; 171:108189. [PMID: 38447502 DOI: 10.1016/j.compbiomed.2024.108189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/14/2024] [Accepted: 02/18/2024] [Indexed: 03/08/2024]
Abstract
Recently, Large Language Models (LLMs) have demonstrated impressive capability to solve a wide range of tasks. However, despite their success across various tasks, no prior work has investigated their capability in the biomedical domain yet. To this end, this paper aims to evaluate the performance of LLMs on benchmark biomedical tasks. For this purpose, a comprehensive evaluation of 4 popular LLMs in 6 diverse biomedical tasks across 26 datasets has been conducted. To the best of our knowledge, this is the first work that conducts an extensive evaluation and comparison of various LLMs in the biomedical domain. Interestingly, we find based on our evaluation that in biomedical datasets that have smaller training sets, zero-shot LLMs even outperform the current state-of-the-art models when they were fine-tuned only on the training set of these datasets. This suggests that pre-training on large text corpora makes LLMs quite specialized even in the biomedical domain. We also find that not a single LLM can outperform other LLMs in all tasks, with the performance of different LLMs may vary depending on the task. While their performance is still quite poor in comparison to the biomedical models that were fine-tuned on large training sets, our findings demonstrate that LLMs have the potential to be a valuable tool for various biomedical tasks that lack large annotated data.
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Affiliation(s)
- Israt Jahan
- Department of Biology, York University, Canada; Information Retrieval and Knowledge Management Research Lab, York University, Canada.
| | - Md Tahmid Rahman Laskar
- School of Information Technology, York University, Canada; Information Retrieval and Knowledge Management Research Lab, York University, Canada; Dialpad Inc., Canada.
| | - Chun Peng
- Department of Biology, York University, Canada.
| | - Jimmy Xiangji Huang
- School of Information Technology, York University, Canada; Information Retrieval and Knowledge Management Research Lab, York University, Canada.
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Luo L, Ning J, Zhao Y, Wang Z, Ding Z, Chen P, Fu W, Han Q, Xu G, Qiu Y, Pan D, Li J, Li H, Feng W, Tu S, Liu Y, Yang Z, Wang J, Sun Y, Lin H. Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks. J Am Med Inform Assoc 2024:ocae037. [PMID: 38422367 DOI: 10.1093/jamia/ocae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/08/2024] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
OBJECTIVE Most existing fine-tuned biomedical large language models (LLMs) focus on enhancing performance in monolingual biomedical question answering and conversation tasks. To investigate the effectiveness of the fine-tuned LLMs on diverse biomedical natural language processing (NLP) tasks in different languages, we present Taiyi, a bilingual fine-tuned LLM for diverse biomedical NLP tasks. MATERIALS AND METHODS We first curated a comprehensive collection of 140 existing biomedical text mining datasets (102 English and 38 Chinese datasets) across over 10 task types. Subsequently, these corpora were converted to the instruction data used to fine-tune the general LLM. During the supervised fine-tuning phase, a 2-stage strategy is proposed to optimize the model performance across various tasks. RESULTS Experimental results on 13 test sets, which include named entity recognition, relation extraction, text classification, and question answering tasks, demonstrate that Taiyi achieves superior performance compared to general LLMs. The case study involving additional biomedical NLP tasks further shows Taiyi's considerable potential for bilingual biomedical multitasking. CONCLUSION Leveraging rich high-quality biomedical corpora and developing effective fine-tuning strategies can significantly improve the performance of LLMs within the biomedical domain. Taiyi shows the bilingual multitasking capability through supervised fine-tuning. However, those tasks such as information extraction that are not generation tasks in nature remain challenging for LLM-based generative approaches, and they still underperform the conventional discriminative approaches using smaller language models.
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Affiliation(s)
- Ling Luo
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jinzhong Ning
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Yingwen Zhao
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Zhijun Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Zeyuan Ding
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Peng Chen
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Weiru Fu
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Qinyu Han
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Guangtao Xu
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Yunzhi Qiu
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Dinghao Pan
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jiru Li
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Hao Li
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Wenduo Feng
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Senbo Tu
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Yuqi Liu
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Zhihao Yang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jian Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Yuanyuan Sun
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Hongfei Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
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5
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Shao L, Chen B, Zhang Z, Zhang Z, Chen X. Artificial intelligence generated content (AIGC) in medicine: A narrative review. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:1672-1711. [PMID: 38303483 DOI: 10.3934/mbe.2024073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Recently, artificial intelligence generated content (AIGC) has been receiving increased attention and is growing exponentially. AIGC is generated based on the intentional information extracted from human-provided instructions by generative artificial intelligence (AI) models. AIGC quickly and automatically generates large amounts of high-quality content. Currently, there is a shortage of medical resources and complex medical procedures in medicine. Due to its characteristics, AIGC can help alleviate these problems. As a result, the application of AIGC in medicine has gained increased attention in recent years. Therefore, this paper provides a comprehensive review on the recent state of studies involving AIGC in medicine. First, we present an overview of AIGC. Furthermore, based on recent studies, the application of AIGC in medicine is reviewed from two aspects: medical image processing and medical text generation. The basic generative AI models, tasks, target organs, datasets and contribution of studies are considered and summarized. Finally, we also discuss the limitations and challenges faced by AIGC and propose possible solutions with relevant studies. We hope this review can help readers understand the potential of AIGC in medicine and obtain some innovative ideas in this field.
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Affiliation(s)
- Liangjing Shao
- Academy for Engineering & Technology, Fudan University, Shanghai 200433, China
- Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Fudan University, Shanghai 200032, China
| | - Benshuang Chen
- Academy for Engineering & Technology, Fudan University, Shanghai 200433, China
- Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Fudan University, Shanghai 200032, China
| | - Ziqun Zhang
- Information office, Fudan University, Shanghai 200032, China
| | - Zhen Zhang
- Baoshan Branch of Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200444, China
| | - Xinrong Chen
- Academy for Engineering & Technology, Fudan University, Shanghai 200433, China
- Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Fudan University, Shanghai 200032, China
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Collins C, Baker S, Brown J, Zheng H, Chan A, Stenius U, Narita M, Korhonen A. Text mining for contexts and relationships in cancer genomics literature. Bioinformatics 2024; 40:btae021. [PMID: 38258418 PMCID: PMC10822582 DOI: 10.1093/bioinformatics/btae021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/27/2023] [Accepted: 01/15/2024] [Indexed: 01/24/2024] Open
Abstract
MOTIVATION Scientific advances build on the findings of existing research. The 2001 publication of the human genome has led to the production of huge volumes of literature exploring the context-specific functions and interactions of genes. Technology is needed to perform large-scale text mining of research papers to extract the reported actions of genes in specific experimental contexts and cell states, such as cancer, thereby facilitating the design of new therapeutic strategies. RESULTS We present a new corpus and Text Mining methodology that can accurately identify and extract the most important details of cancer genomics experiments from biomedical texts. We build a Named Entity Recognition model that accurately extracts relevant experiment details from PubMed abstract text, and a second model that identifies the relationships between them. This system outperforms earlier models and enables the analysis of gene function in diverse and dynamically evolving experimental contexts. AVAILABILITY AND IMPLEMENTATION Code and data are available here: https://github.com/cambridgeltl/functional-genomics-ie.
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Affiliation(s)
- Charlotte Collins
- Language Technology Laboratory, Theoretical and Applied Linguistics, Faculty of Modern and Medieval Languages and Linguistics, University of Cambridge, Cambridge CB3 9DA, United Kingdom
| | - Simon Baker
- Language Technology Laboratory, Theoretical and Applied Linguistics, Faculty of Modern and Medieval Languages and Linguistics, University of Cambridge, Cambridge CB3 9DA, United Kingdom
| | - Jason Brown
- Language Technology Laboratory, Theoretical and Applied Linguistics, Faculty of Modern and Medieval Languages and Linguistics, University of Cambridge, Cambridge CB3 9DA, United Kingdom
| | - Huiyuan Zheng
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Adelyne Chan
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Ulla Stenius
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Masashi Narita
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Anna Korhonen
- Language Technology Laboratory, Theoretical and Applied Linguistics, Faculty of Modern and Medieval Languages and Linguistics, University of Cambridge, Cambridge CB3 9DA, United Kingdom
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7
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Tian S, Jin Q, Yeganova L, Lai PT, Zhu Q, Chen X, Yang Y, Chen Q, Kim W, Comeau DC, Islamaj R, Kapoor A, Gao X, Lu Z. Opportunities and challenges for ChatGPT and large language models in biomedicine and health. Brief Bioinform 2023; 25:bbad493. [PMID: 38168838 PMCID: PMC10762511 DOI: 10.1093/bib/bbad493] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 11/15/2023] [Accepted: 12/06/2023] [Indexed: 01/05/2024] Open
Abstract
ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and health. In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health. Specifically, we explore the areas of biomedical information retrieval, question answering, medical text summarization, information extraction and medical education and investigate whether LLMs possess the transformative power to revolutionize these tasks or whether the distinct complexities of biomedical domain presents unique challenges. Following an extensive literature survey, we find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods. For other applications, the advances have been modest. Overall, LLMs have not yet revolutionized biomedicine, but recent rapid progress indicates that such methods hold great potential to provide valuable means for accelerating discovery and improving health. We also find that the use of LLMs, like ChatGPT, in the fields of biomedicine and health entails various risks and challenges, including fabricated information in its generated responses, as well as legal and privacy concerns associated with sensitive patient data. We believe this survey can provide a comprehensive and timely overview to biomedical researchers and healthcare practitioners on the opportunities and challenges associated with using ChatGPT and other LLMs for transforming biomedicine and health.
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Affiliation(s)
- Shubo Tian
- National Library of Medicine, National Institutes of Health
| | - Qiao Jin
- National Library of Medicine, National Institutes of Health
| | - Lana Yeganova
- National Library of Medicine, National Institutes of Health
| | - Po-Ting Lai
- National Library of Medicine, National Institutes of Health
| | - Qingqing Zhu
- National Library of Medicine, National Institutes of Health
| | - Xiuying Chen
- King Abdullah University of Science and Technology
| | - Yifan Yang
- National Library of Medicine, National Institutes of Health
| | - Qingyu Chen
- National Library of Medicine, National Institutes of Health
| | - Won Kim
- National Library of Medicine, National Institutes of Health
| | | | | | - Aadit Kapoor
- National Library of Medicine, National Institutes of Health
| | - Xin Gao
- King Abdullah University of Science and Technology
| | - Zhiyong Lu
- National Library of Medicine, National Institutes of Health
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8
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Chen Q, Sun H, Liu H, Jiang Y, Ran T, Jin X, Xiao X, Lin Z, Chen H, Niu Z. An extensive benchmark study on biomedical text generation and mining with ChatGPT. Bioinformatics 2023; 39:btad557. [PMID: 37682111 PMCID: PMC10562950 DOI: 10.1093/bioinformatics/btad557] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/09/2023] [Accepted: 09/06/2023] [Indexed: 09/09/2023] Open
Abstract
MOTIVATION In recent years, the development of natural language process (NLP) technologies and deep learning hardware has led to significant improvement in large language models (LLMs). The ChatGPT, the state-of-the-art LLM built on GPT-3.5 and GPT-4, shows excellent capabilities in general language understanding and reasoning. Researchers also tested the GPTs on a variety of NLP-related tasks and benchmarks and got excellent results. With exciting performance on daily chat, researchers began to explore the capacity of ChatGPT on expertise that requires professional education for human and we are interested in the biomedical domain. RESULTS To evaluate the performance of ChatGPT on biomedical-related tasks, this article presents a comprehensive benchmark study on the use of ChatGPT for biomedical corpus, including article abstracts, clinical trials description, biomedical questions, and so on. Typical NLP tasks like named entity recognization, relation extraction, sentence similarity, question and answering, and document classification are included. Overall, ChatGPT got a BLURB score of 58.50 while the state-of-the-art model had a score of 84.30. Through a series of experiments, we demonstrated the effectiveness and versatility of ChatGPT in biomedical text understanding, reasoning and generation, and the limitation of ChatGPT build on GPT-3.5. AVAILABILITY AND IMPLEMENTATION All the datasets are available from BLURB benchmark https://microsoft.github.io/BLURB/index.html. The prompts are described in the article.
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Affiliation(s)
- Qijie Chen
- AIDD, Mindrank AI Ltd, Zhejiang 310000, China
| | - Haotong Sun
- AIDD, Mindrank AI Ltd, Zhejiang 310000, China
| | - Haoyang Liu
- College of Life Sciences, Nankai University, Tianjin 300071, China
- Guangzhou Laboratory, GuangDong 510005, China
| | | | - Ting Ran
- Guangzhou Laboratory, GuangDong 510005, China
| | - Xurui Jin
- AIDD, Mindrank AI Ltd, Zhejiang 310000, China
| | | | - Zhimin Lin
- AIDD, Mindrank AI Ltd, Zhejiang 310000, China
| | | | - Zhangmin Niu
- AIDD, Mindrank AI Ltd, Zhejiang 310000, China
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
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9
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A weighted-link graph neural network for lung cancer knowledge classification. APPL INTELL 2023. [DOI: 10.1007/s10489-022-04437-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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10
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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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11
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Luo R, Sun L, Xia Y, Qin T, Zhang S, Poon H, Liu TY. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief Bioinform 2022; 23:6713511. [PMID: 36156661 DOI: 10.1093/bib/bbac409] [Citation(s) in RCA: 115] [Impact Index Per Article: 57.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/05/2022] [Accepted: 08/23/2022] [Indexed: 12/14/2022] Open
Abstract
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.
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Affiliation(s)
| | - Liai Sun
- Peking University, Beijing, China
| | | | - Tao Qin
- Microsoft Research Asia, Beijing, China
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Hall K, Chang V, Jayne C. A review on Natural Language Processing Models for COVID-19 research. HEALTHCARE ANALYTICS 2022. [PMID: 37520621 PMCID: PMC9295335 DOI: 10.1016/j.health.2022.100078] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
This survey paper reviews Natural Language Processing Models and their use in COVID-19 research in two main areas. Firstly, a range of transformer-based biomedical pretrained language models are evaluated using the BLURB benchmark. Secondly, models used in sentiment analysis surrounding COVID-19 vaccination are evaluated. We filtered literature curated from various repositories such as PubMed and Scopus and reviewed 27 papers. When evaluated using the BLURB benchmark, the novel T-BPLM BioLinkBERT gives groundbreaking results by incorporating document link knowledge and hyperlinking into its pretraining. Sentiment analysis of COVID-19 vaccination through various Twitter API tools has shown the public’s sentiment towards vaccination to be mostly positive. Finally, we outline some limitations and potential solutions to drive the research community to improve the models used for NLP tasks.
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Naseem U, Dunn AG, Khushi M, Kim J. Benchmarking for biomedical natural language processing tasks with a domain specific ALBERT. BMC Bioinformatics 2022; 23:144. [PMID: 35448946 PMCID: PMC9022356 DOI: 10.1186/s12859-022-04688-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/31/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The abundance of biomedical text data coupled with advances in natural language processing (NLP) is resulting in novel biomedical NLP (BioNLP) applications. These NLP applications, or tasks, are reliant on the availability of domain-specific language models (LMs) that are trained on a massive amount of data. Most of the existing domain-specific LMs adopted bidirectional encoder representations from transformers (BERT) architecture which has limitations, and their generalizability is unproven as there is an absence of baseline results among common BioNLP tasks. RESULTS We present 8 variants of BioALBERT, a domain-specific adaptation of a lite bidirectional encoder representations from transformers (ALBERT), trained on biomedical (PubMed and PubMed Central) and clinical (MIMIC-III) corpora and fine-tuned for 6 different tasks across 20 benchmark datasets. Experiments show that a large variant of BioALBERT trained on PubMed outperforms the state-of-the-art on named-entity recognition (+ 11.09% BLURB score improvement), relation extraction (+ 0.80% BLURB score), sentence similarity (+ 1.05% BLURB score), document classification (+ 0.62% F1-score), and question answering (+ 2.83% BLURB score). It represents a new state-of-the-art in 5 out of 6 benchmark BioNLP tasks. CONCLUSIONS The large variant of BioALBERT trained on PubMed achieved a higher BLURB score than previous state-of-the-art models on 5 of the 6 benchmark BioNLP tasks. Depending on the task, 5 different variants of BioALBERT outperformed previous state-of-the-art models on 17 of the 20 benchmark datasets, showing that our model is robust and generalizable in the common BioNLP tasks. We have made BioALBERT freely available which will help the BioNLP community avoid computational cost of training and establish a new set of baselines for future efforts across a broad range of BioNLP tasks.
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Affiliation(s)
- Usman Naseem
- School of Computer Science, The University of Sydney, Sydney, Australia.
| | - Adam G Dunn
- Biomedical Informatics and Digital Health and Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, Australia
| | - Matloob Khushi
- School of Computer Science, The University of Sydney, Sydney, Australia.,School of EAST, University of Suffolk, Ipswich, UK
| | - Jinman Kim
- School of Computer Science, The University of Sydney, Sydney, Australia
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Balabin H, Hoyt CT, Birkenbihl C, Gyori BM, Bachman J, Kodamullil AT, Plöger PG, Hofmann-Apitius M, Domingo-Fernández D. STonKGs: a sophisticated transformer trained on biomedical text and knowledge graphs. Bioinformatics 2022; 38:1648-1656. [PMID: 34986221 PMCID: PMC8896635 DOI: 10.1093/bioinformatics/btac001] [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: 08/18/2021] [Revised: 12/09/2021] [Accepted: 01/03/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models. However, representations based on a single modality are inherently limited. RESULTS To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs (KGs). This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations in a shared embedding space. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against three baseline models trained on either one of the modalities (i.e. text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.084 (i.e. from 0.881 to 0.965). Finally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications. AVAILABILITY AND IMPLEMENTATION We make the source code and the Python package of STonKGs available at GitHub (https://github.com/stonkgs/stonkgs) and PyPI (https://pypi.org/project/stonkgs/). The pre-trained STonKGs models and the task-specific classification models are respectively available at https://huggingface.co/stonkgs/stonkgs-150k and https://zenodo.org/communities/stonkgs. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Charles Tapley Hoyt
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53757 Sankt Augustin, Germany
| | - Benjamin M Gyori
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - John Bachman
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53757 Sankt Augustin, Germany
| | - Paul G Plöger
- Department of Bonn-Rhein-Sieg, University of Applied Sciences, 53757 Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53757 Sankt Augustin, Germany
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15
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Using semantics to scale up evidence-based chemical risk-assessments. PLoS One 2021; 16:e0260712. [PMID: 34910747 PMCID: PMC8673667 DOI: 10.1371/journal.pone.0260712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/15/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The manual processes used for risk assessments are not scaling to the amount of data available. Although automated approaches appear promising, they must be transparent in a public policy setting. OBJECTIVE Our goal is to create an automated approach that moves beyond retrieval to the extraction step of the information synthesis process, where evidence is characterized as supporting, refuting, or neutral with respect to a given outcome. METHODS We combine knowledge resources and natural language processing to resolve coordinated ellipses and thus avoid surface level differences between concepts in an ontology and outcomes in an abstract. As with a systematic review, the search criterion, and inclusion and exclusion criterion are explicit. RESULTS The system scales to 482K abstracts on 27 chemicals. Results for three endpoints that are critical for cancer risk assessments show that refuting evidence (where the outcome decreased) was higher for cell proliferation (45.9%), and general cell changes (37.7%) than for cell death (25.0%). Moreover, cell death was the only end point where supporting claims were the majority (61.3%). If the number of abstracts that measure an outcome was used as a proxy for association there would be a stronger association with cell proliferation than cell death (20/27 chemicals). However, if the amount of supporting evidence was used (where the outcome increased) the conclusion would change for 21/27 chemicals (20 from proliferation to death and 1 from death to proliferation). CONCLUSIONS We provide decision makers with a visual representation of supporting, neutral, and refuting evidence whilst maintaining the reproducibility and transparency needed for public policy. Our findings show that results from the retrieval step where the number of abstracts that measure an outcome are reported can be misleading if not accompanied with results from the extraction step where the directionality of the outcome is established.
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Diori Karidio I, Sanlier SH. Reviewing cancer's biology: an eclectic approach. J Egypt Natl Canc Inst 2021; 33:32. [PMID: 34719756 DOI: 10.1186/s43046-021-00088-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 09/11/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Cancer refers to a group of some of the worldwide most diagnosed and deadliest pathophysiological conditions that conquered researchers' attention for decades and yet begs for more questions for a full comprehension of its complex cellular and molecular pathology. MAIN BODY The disease conditions are commonly characterized by unrestricted cell proliferation and dysfunctional replicative senescence pathways. In fact, the cell cycle operates under the rigorous control of complex signaling pathways involving cyclins and cyclin-dependent kinases assumed to be specific to each phase of the cycle. At each of these checkpoints, the cell is checked essentially for its DNA integrity. Genetic defects observed in these molecules (i.e., cyclins, cyclin-dependent kinases) are common features of cancer cells. Nevertheless, each cancer is different concerning its molecular and cellular etiology. These could range from the genetic defects mechanisms and/or the environmental conditions favoring epigenetically harbored homeostasis driving tumorigenesis alongside with the intratumoral heterogeneity with respect to the model that the tumor follows. CONCLUSIONS This review is not meant to be an exhaustive interpretation of carcinogenesis but to summarize some basic features of the molecular etiology of cancer and the intratumoral heterogeneity models that eventually bolster anticancer drug resistance for a more efficient design of drug targeting the pitfalls of the models.
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Affiliation(s)
- Ibrahim Diori Karidio
- Department of Biochemistry, Faculty of Science, E Block, Ege University, Erzene Mahallesi, Bornova, 35040, Izmir, Turkey.
| | - Senay Hamarat Sanlier
- Department of Biochemistry, Faculty of Science, E Block, Ege University, Erzene Mahallesi, Bornova, 35040, Izmir, Turkey.,ARGEFAR, Faculty of Medicine, Ege University, Bornova, 35040, Izmir, Turkey
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17
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Majewska O, Collins C, Baker S, Björne J, Brown SW, Korhonen A, Palmer M. BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine. J Biomed Semantics 2021; 12:12. [PMID: 34266499 PMCID: PMC8280585 DOI: 10.1186/s13326-021-00247-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 07/01/2021] [Indexed: 11/10/2022] Open
Abstract
Background Recent advances in representation learning have enabled large strides in natural language understanding; However, verbal reasoning remains a challenge for state-of-the-art systems. External sources of structured, expert-curated verb-related knowledge have been shown to boost model performance in different Natural Language Processing (NLP) tasks where accurate handling of verb meaning and behaviour is critical. The costliness and time required for manual lexicon construction has been a major obstacle to porting the benefits of such resources to NLP in specialised domains, such as biomedicine. To address this issue, we combine a neural classification method with expert annotation to create BioVerbNet. This new resource comprises 693 verbs assigned to 22 top-level and 117 fine-grained semantic-syntactic verb classes. We make this resource available complete with semantic roles and VerbNet-style syntactic frames. Results We demonstrate the utility of the new resource in boosting model performance in document- and sentence-level classification in biomedicine. We apply an established retrofitting method to harness the verb class membership knowledge from BioVerbNet and transform a pretrained word embedding space by pulling together verbs belonging to the same semantic-syntactic class. The BioVerbNet knowledge-aware embeddings surpass the non-specialised baseline by a significant margin on both tasks. Conclusion This work introduces the first large, annotated semantic-syntactic classification of biomedical verbs, providing a detailed account of the annotation process, the key differences in verb behaviour between the general and biomedical domain, and the design choices made to accurately capture the meaning and properties of verbs used in biomedical texts. The demonstrated benefits of leveraging BioVerbNet in text classification suggest the resource could help systems better tackle challenging NLP tasks in biomedicine.
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Affiliation(s)
- Olga Majewska
- Language Technology Laboratory, MMLL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.
| | - Charlotte Collins
- Language Technology Laboratory, MMLL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK
| | - Simon Baker
- Language Technology Laboratory, MMLL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK
| | - Jari Björne
- Department of Future Technologies, University of Turku, Vesilinnantie 5, Turku, 20500, Finland
| | - Susan Windisch Brown
- Department of Linguistics, University of Colorado Boulder, 295 UCB, Boulder, 80309-0295, Colorado, USA
| | - Anna Korhonen
- Language Technology Laboratory, MMLL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK
| | - Martha Palmer
- Department of Linguistics, University of Colorado Boulder, 295 UCB, Boulder, 80309-0295, Colorado, USA
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18
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A Comparative Analysis of Active Learning for Biomedical Text Mining. APPLIED SYSTEM INNOVATION 2021. [DOI: 10.3390/asi4010023] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
An enormous amount of clinical free-text information, such as pathology reports, progress reports, clinical notes and discharge summaries have been collected at hospitals and medical care clinics. These data provide an opportunity of developing many useful machine learning applications if the data could be transferred into a learn-able structure with appropriate labels for supervised learning. The annotation of this data has to be performed by qualified clinical experts, hence, limiting the use of this data due to the high cost of annotation. An underutilised technique of machine learning that can label new data called active learning (AL) is a promising candidate to address the high cost of the label the data. AL has been successfully applied to labelling speech recognition and text classification, however, there is a lack of literature investigating its use for clinical purposes. We performed a comparative investigation of various AL techniques using ML and deep learning (DL)-based strategies on three unique biomedical datasets. We investigated random sampling (RS), least confidence (LC), informative diversity and density (IDD), margin and maximum representativeness-diversity (MRD) AL query strategies. Our experiments show that AL has the potential to significantly reducing the cost of manual labelling. Furthermore, pre-labelling performed using AL expediates the labelling process by reducing the time required for labelling.
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Ibrahim MA, Ghani Khan MU, Mehmood F, Asim MN, Mahmood W. GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification. J Biomed Inform 2021; 116:103699. [PMID: 33601013 DOI: 10.1016/j.jbi.2021.103699] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 11/30/2020] [Accepted: 02/02/2021] [Indexed: 01/16/2023]
Abstract
Exponential growth of biomedical literature and clinical data demands more robust yet precise computational methodologies to extract useful insights from biomedical literature and to perform accurate assignment of disease-specific codes. Such approaches can largely enhance the effectiveness of diverse biomedicine and bioinformatics applications. State-of-the-art computational biomedical text classification methodologies either solely leverage discrimintaive features extracted through convolution operations performed by deep convolutional neural network or contextual information extracted by recurrent neural network. However, none of the methodology takes advantage of both convolutional and recurrent neural networks. Further, existing methodologies lack to produce decent performance for the classification of different genre biomedical text such as biomedical literature or clinical notes. We, for the very first time, present a generic deep learning based hybrid multi-label classification methodology namely GHS-NET which can be utilized to accurately classify biomedical text of diverse genre. GHS-NET makes use of convolutional neural network to extract most discriminative features and bi-directional Long Short-Term Memory to acquire contextual information. GHS-NET effectiveness is evaluated for extreme multi-label biomedical literature classification and assignment of ICD-9 codes to clinical notes. For the task of extreme multi-label biomedical literature classification, performance comparison of GHS-Net and state-of-the-art deep learning based methodology reveals that GHS-Net marks the increment of 1%, 6%, and 1% for hallmarks of cancer dataset, 10%, 16%, and 11% for chemical exposure dataset in terms of precision, recall, and F1-score. For the task of clinical notes classification, GHS-Net outperforms previous best deep learning based methodology over Medical Information Mart for Intensive Care dataset (MIMIC-III) by the significant margin of 6%, 8% in terms of recall and F1-score. GHS-NET is available as a web service at1 and potentially can be used to accurately classify multi-variate disease and chemical exposure specific text.
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Affiliation(s)
- Muhammad Ali Ibrahim
- Intelligent Criminology Research Lab, National Center of Artificial Intelligence, Al-Khawarizmi Institute of Computer Science, UET, Lahore, Pakistan; German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany
| | - Muhammad Usman Ghani Khan
- Intelligent Criminology Research Lab, National Center of Artificial Intelligence, Al-Khawarizmi Institute of Computer Science, UET, Lahore, Pakistan; Department of Computer Science, University of Engineering and Technology (UET), Lahore, Pakistan
| | - Faiza Mehmood
- Intelligent Criminology Research Lab, National Center of Artificial Intelligence, Al-Khawarizmi Institute of Computer Science, UET, Lahore, Pakistan
| | - Muhammad Nabeel Asim
- Intelligent Criminology Research Lab, National Center of Artificial Intelligence, Al-Khawarizmi Institute of Computer Science, UET, Lahore, Pakistan; German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
| | - Waqar Mahmood
- Intelligent Criminology Research Lab, National Center of Artificial Intelligence, Al-Khawarizmi Institute of Computer Science, UET, Lahore, Pakistan
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Neves M, Ševa J. An extensive review of tools for manual annotation of documents. Brief Bioinform 2021; 22:146-163. [PMID: 31838514 PMCID: PMC7820865 DOI: 10.1093/bib/bbz130] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Indexed: 12/16/2022] Open
Abstract
MOTIVATION Annotation tools are applied to build training and test corpora, which are essential for the development and evaluation of new natural language processing algorithms. Further, annotation tools are also used to extract new information for a particular use case. However, owing to the high number of existing annotation tools, finding the one that best fits particular needs is a demanding task that requires searching the scientific literature followed by installing and trying various tools. METHODS We searched for annotation tools and selected a subset of them according to five requirements with which they should comply, such as being Web-based or supporting the definition of a schema. We installed the selected tools (when necessary), carried out hands-on experiments and evaluated them using 26 criteria that covered functional and technical aspects. We defined each criterion on three levels of matches and a score for the final evaluation of the tools. RESULTS We evaluated 78 tools and selected the following 15 for a detailed evaluation: BioQRator, brat, Catma, Djangology, ezTag, FLAT, LightTag, MAT, MyMiner, PDFAnno, prodigy, tagtog, TextAE, WAT-SL and WebAnno. Full compliance with our 26 criteria ranged from only 9 up to 20 criteria, which demonstrated that some tools are comprehensive and mature enough to be used on most annotation projects. The highest score of 0.81 was obtained by WebAnno (of a maximum value of 1.0).
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Affiliation(s)
- Mariana Neves
- German Centre for the Protection of Laboratory Animals (BfR), German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Jurica Ševa
- German Centre for the Protection of Laboratory Animals (BfR), German Federal Institute for Risk Assessment (BfR), Berlin, Germany
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21
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DECAB-LSTM: Deep Contextualized Attentional Bidirectional LSTM for cancer hallmark classification. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106486] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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22
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Multi-Task Topic Analysis Framework for Hallmarks of Cancer with Weak Supervision. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10030834] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The hallmarks of cancer represent an essential concept for discovering novel knowledge about cancer and for extracting the complexity of cancer. Due to the lack of topic analysis frameworks optimized specifically for cancer data, the studies on topic modeling in cancer research still have a strong challenge. Recently, deep learning (DL) based approaches were successfully employed to learn semantic and contextual information from scientific documents using word embeddings according to the hallmarks of cancer (HoC). However, those are only applicable to labeled data. There is a comparatively small number of documents that are labeled by experts. In the real world, there is a massive number of unlabeled documents that are available online. In this paper, we present a multi-task topic analysis (MTTA) framework to analyze cancer hallmark-specific topics from documents. The MTTA framework consists of three main subtasks: (1) cancer hallmark learning (CHL)—used to learn cancer hallmarks on existing labeled documents; (2) weak label propagation (WLP)—used to classify a large number of unlabeled documents with the pre-trained model in the CHL task; and (3) topic modeling (ToM)—used to discover topics for each hallmark category. In the CHL task, we employed a convolutional neural network (CNN) with pre-trained word embedding that represents semantic meanings obtained from an unlabeled large corpus. In the ToM task, we employed a latent topic model such as latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (PLSA) model to catch the semantic information learned by the CNN model for topic analysis. To evaluate the MTTA framework, we collected a large number of documents related to lung cancer in a case study. We also conducted a comprehensive performance evaluation for the MTTA framework, comparing it with several approaches.
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Abstract
Medical text categorization is a specific area of text categorization. Classification for medical texts is considered a special case of text classification. Medical text includes medical records and medical literature, both of which are important clinical information resources. However, medical text contains complex medical vocabularies, medical measures, which has problems with high-dimensionality and data sparsity, so text classification in the medical domain is more challenging than those in other general domains. In order to solve these problems, this paper proposes a unified neural network method. In the sentence representation, the convolutional layer extracts features from the sentence and a bidirectional gated recurrent unit (BIGRU) is used to access both the preceding and succeeding sentence features. An attention mechanism is employed to obtain the sentence representation with the important word weights. In the document representation, the method uses the BIGRU to encode the sentences, which is obtained in sentence representation and then decode it through the attention mechanism to get the document representation with important sentence weights. Finally, a category of medical text is obtained through a classifier. Experimental verifications are conducted on four medical text datasets, including two medical record datasets and two medical literature datasets. The results clearly show that our method is effective.
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Du J, Chen Q, Peng Y, Xiang Y, Tao C, Lu Z. ML-Net: multi-label classification of biomedical texts with deep neural networks. J Am Med Inform Assoc 2019; 26:1279-1285. [PMID: 31233120 PMCID: PMC7647240 DOI: 10.1093/jamia/ocz085] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 04/21/2019] [Accepted: 05/08/2019] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE In multi-label text classification, each textual document is assigned 1 or more labels. As an important task that has broad applications in biomedicine, a number of different computational methods have been proposed. Many of these methods, however, have only modest accuracy or efficiency and limited success in practical use. We propose ML-Net, a novel end-to-end deep learning framework, for multi-label classification of biomedical texts. MATERIALS AND METHODS ML-Net combines a label prediction network with an automated label count prediction mechanism to provide an optimal set of labels. This is accomplished by leveraging both the predicted confidence score of each label and the deep contextual information (modeled by ELMo) in the target document. We evaluate ML-Net on 3 independent corpora in 2 text genres: biomedical literature and clinical notes. For evaluation, we use example-based measures, such as precision, recall, and the F measure. We also compare ML-Net with several competitive machine learning and deep learning baseline models. RESULTS Our benchmarking results show that ML-Net compares favorably to state-of-the-art methods in multi-label classification of biomedical text. ML-Net is also shown to be robust when evaluated on different text genres in biomedicine. CONCLUSION ML-Net is able to accuractely represent biomedical document context and dynamically estimate the label count in a more systematic and accurate manner. Unlike traditional machine learning methods, ML-Net does not require human effort for feature engineering and is a highly efficient and scalable approach to tasks with a large set of labels, so there is no need to build individual classifiers for each separate label.
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Affiliation(s)
- Jingcheng Du
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, USA
- The University of Texas School of Biomedical Informatics, Houston, Texas, USA
| | - Qingyu Chen
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Yifan Peng
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Yang Xiang
- The University of Texas School of Biomedical Informatics, Houston, Texas, USA
| | - Cui Tao
- The University of Texas School of Biomedical Informatics, Houston, Texas, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, USA
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Pyysalo S, Baker S, Ali I, Haselwimmer S, Shah T, Young A, Guo Y, Högberg J, Stenius U, Narita M, Korhonen A. LION LBD: a literature-based discovery system for cancer biology. Bioinformatics 2019; 35:1553-1561. [PMID: 30304355 PMCID: PMC6499247 DOI: 10.1093/bioinformatics/bty845] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 09/19/2018] [Accepted: 10/08/2018] [Indexed: 01/01/2023] Open
Abstract
MOTIVATION The overwhelming size and rapid growth of the biomedical literature make it impossible for scientists to read all studies related to their work, potentially leading to missed connections and wasted time and resources. Literature-based discovery (LBD) aims to alleviate these issues by identifying implicit links between disjoint parts of the literature. While LBD has been studied in depth since its introduction three decades ago, there has been limited work making use of recent advances in biomedical text processing methods in LBD. RESULTS We present LION LBD, a literature-based discovery system that enables researchers to navigate published information and supports hypothesis generation and testing. The system is built with a particular focus on the molecular biology of cancer using state-of-the-art machine learning and natural language processing methods, including named entity recognition and grounding to domain ontologies covering a wide range of entity types and a novel approach to detecting references to the hallmarks of cancer in text. LION LBD implements a broad selection of co-occurrence based metrics for analyzing the strength of entity associations, and its design allows real-time search to discover indirect associations between entities in a database of tens of millions of publications while preserving the ability of users to explore each mention in its original context in the literature. Evaluations of the system demonstrate its ability to identify undiscovered links and rank relevant concepts highly among potential connections. AVAILABILITY AND IMPLEMENTATION The LION LBD system is available via a web-based user interface and a programmable API, and all components of the system are made available under open licenses from the project home page http://lbd.lionproject.net. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sampo Pyysalo
- Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK
| | - Simon Baker
- Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK
| | - Imran Ali
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Stefan Haselwimmer
- Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK
| | - Tejas Shah
- Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK
| | - Andrew Young
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Yufan Guo
- Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK
| | - Johan Högberg
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Ulla Stenius
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Masashi Narita
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Anna Korhonen
- Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK
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26
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Nowak-Sliwinska P, Scapozza L, Ruiz i Altaba A. Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer. Biochim Biophys Acta Rev Cancer 2019; 1871:434-454. [PMID: 31034926 PMCID: PMC6528778 DOI: 10.1016/j.bbcan.2019.04.005] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 04/09/2019] [Accepted: 04/15/2019] [Indexed: 02/08/2023]
Abstract
The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and difficult process of drug development. However, the number of existing drugs and diseases, together with the heterogeneity of patients and diseases, notably including cancers, can make repurposing time consuming and inefficient. The key question we address is how to efficiently repurpose an existing drug to treat a given indication. As drug efficacy remains the main bottleneck for overall success, we discuss the need for machine-learning computational methods in combination with specific phenotypic studies along with mechanistic studies, chemical genetics and omics assays to successfully predict disease-drug pairs. Such a pipeline could be particularly important to cancer patients who face heterogeneous, recurrent and metastatic disease and need fast and personalized treatments. Here we focus on drug repurposing for colorectal cancer and describe selected therapeutics already repositioned for its prevention and/or treatment as well as potential candidates. We consider this review as a selective compilation of approaches and methodologies, and argue how, taken together, they could bring drug repurposing to the next level.
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Affiliation(s)
- Patrycja Nowak-Sliwinska
- School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, Geneva, Switzerland; Translational Research Center in Oncohaematology, University of Geneva, Rue Michel Servet 1, 1211 Geneva 4, Switzerland.
| | - Leonardo Scapozza
- School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, Geneva, Switzerland
| | - Ariel Ruiz i Altaba
- Department of Genetic Medicine and Development, Faculty of Medicine, University of Geneva, Rue Michel Servet 1, 1211 Geneva 4, Switzerland
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27
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Baker S, Ali I, Silins I, Pyysalo S, Guo Y, Högberg J, Stenius U, Korhonen A. Cancer Hallmarks Analytics Tool (CHAT): a text mining approach to organize and evaluate scientific literature on cancer. Bioinformatics 2018; 33:3973-3981. [PMID: 29036271 PMCID: PMC5860084 DOI: 10.1093/bioinformatics/btx454] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 07/12/2017] [Indexed: 11/23/2022] Open
Abstract
Motivation To understand the molecular mechanisms involved in cancer development, significant efforts are being invested in cancer research. This has resulted in millions of scientific articles. An efficient and thorough review of the existing literature is crucially important to drive new research. This time-demanding task can be supported by emerging computational approaches based on text mining which offer a great opportunity to organize and retrieve the desired information efficiently from sizable databases. One way to organize existing knowledge on cancer is to utilize the widely accepted framework of the Hallmarks of Cancer. These hallmarks refer to the alterations in cell behaviour that characterize the cancer cell. Results We created an extensive Hallmarks of Cancer taxonomy and developed automatic text mining methodology and a tool (CHAT) capable of retrieving and organizing millions of cancer-related references from PubMed into the taxonomy. The efficiency and accuracy of the tool was evaluated intrinsically as well as extrinsically by case studies. The correlations identified by the tool show that it offers a great potential to organize and correctly classify cancer-related literature. Furthermore, the tool can be useful, for example, in identifying hallmarks associated with extrinsic factors, biomarkers and therapeutics targets. Availability and implementation CHAT can be accessed at: http://chat.lionproject.net. The corpus of hallmark-annotated PubMed abstracts and the software are available at: http://chat.lionproject.net/about Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Simon Baker
- Computer Laboratory.,Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge CB3 9DA, UK
| | - Imran Ali
- Institute of Environmental Medicine, Karolinska Institutet, SE 171 77 Stockholm, Sweden
| | - Ilona Silins
- Institute of Environmental Medicine, Karolinska Institutet, SE 171 77 Stockholm, Sweden
| | - Sampo Pyysalo
- Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge CB3 9DA, UK
| | - Yufan Guo
- Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge CB3 9DA, UK
| | - Johan Högberg
- Institute of Environmental Medicine, Karolinska Institutet, SE 171 77 Stockholm, Sweden
| | - Ulla Stenius
- Institute of Environmental Medicine, Karolinska Institutet, SE 171 77 Stockholm, Sweden
| | - Anna Korhonen
- Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge CB3 9DA, UK
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28
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Baladrón C, Santos-Lozano A, Aguiar JM, Lucia A, Martín-Hernández J. Tool for filtering PubMed search results by sample size. J Am Med Inform Assoc 2018; 25:774-779. [PMID: 29409012 DOI: 10.1093/jamia/ocx155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Accepted: 12/20/2017] [Indexed: 11/13/2022] Open
Abstract
Objective The most used search engine for scientific literature, PubMed, provides tools to filter results by several fields. When searching for reports on clinical trials, sample size can be among the most important factors to consider. However, PubMed does not currently provide any means of filtering search results by sample size. Such a filtering tool would be useful in a variety of situations, including meta-analyses or state-of-the-art analyses to support experimental therapies. In this work, a tool was developed to filter articles identified by PubMed based on their reported sample sizes. Materials and Methods A search engine was designed to send queries to PubMed, retrieve results, and compute estimates of reported sample sizes using a combination of syntactical and machine learning methods. The sample size search tool is publicly available for download at http://ihealth.uemc.es. Its accuracy was assessed against a manually annotated database of 750 random clinical trials returned by PubMed. Results Validation tests show that the sample size search tool is able to accurately (1) estimate sample size for 70% of abstracts and (2) classify 85% of abstracts into sample size quartiles. Conclusions The proposed tool was validated as useful for advanced PubMed searches of clinical trials when the user is interested in identifying trials of a given sample size.
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Affiliation(s)
- Carlos Baladrón
- i+HeALTH Research Group, Miguel de Cervantes European University, Higher Polytechnic School, Department of Technical Teachings, Valladolid, Spain
| | - Alejandro Santos-Lozano
- i+HeALTH Research Group, Miguel de Cervantes European University, Faculty of Health Sciences, Department of Health Sciences, Valladolid, Spain
| | - Javier M Aguiar
- Data Engineering Research Group, Universidad de Valladolid, Higher Technical School of Telecommunications Engineering, TSyCeIT Department, Valladolid, Spain
| | - Alejandro Lucia
- Research Institute of Hospital 12 de Octubre and European University, Madrid, Spain
| | - Juan Martín-Hernández
- i+HeALTH Research Group, Miguel de Cervantes European University, Faculty of Health Sciences, Department of Health Sciences, Valladolid, Spain
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29
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Van Ostade X, Dom M, Tjalma W, Van Raemdonck G. Candidate biomarkers in the cervical vaginal fluid for the (self-)diagnosis of cervical precancer. Arch Gynecol Obstet 2017; 297:295-311. [PMID: 29143101 PMCID: PMC5778162 DOI: 10.1007/s00404-017-4587-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 11/06/2017] [Indexed: 11/29/2022]
Abstract
Purpose Despite improvement in vaccines against human papilloma virus (HPV), the causative agent of cervical cancer, screening women for cervical precancer will remain indispensable in the coming 30–40 years. A simple test that could be performed at home or at a doctor’s practice and that informs the woman whether she is at risk would significantly help make a broader group of patients who aware that they need medical treatment. Cervical vaginal fluid (CVF) is a body fluid that is very well suited for such a test. Methods Narrative review of cervical (pre)cancer candidate biomarkers from cervicovaginal fluid, is based on a detailed review of the literature. We will also discuss the possibilities that these biomarkers create for the development of a self-test or point-of-care test for cervical (pre)cancer. Results Several DNA, DNA methylation, miRNA, and protein biomarkers were identified in the cervical vaginal fluid; however, not all of these biomarkers are suited for development of a simple diagnostic assay. Conclusions Proteins, especially alpha-actinin-4, are most suited for development of a simple assay for cervical (pre)cancer. Accuracy of the test could further be improved by combination of several proteins or by combination with a new type of biomarker, e.g., originating from the cervicovaginal microbiome or metabolome.
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Affiliation(s)
- Xaveer Van Ostade
- Laboratory of Protein Science, Proteomics and Epigenetic Signaling (PPES), University of Antwerp, Wilrijk, Belgium. .,Centre for Proteomics (CfP), University of Antwerp, Wilrijk, Belgium.
| | - Martin Dom
- Laboratory of Protein Science, Proteomics and Epigenetic Signaling (PPES), University of Antwerp, Wilrijk, Belgium.,Centre for Proteomics (CfP), University of Antwerp, Wilrijk, Belgium
| | - Wiebren Tjalma
- Gynecological Oncology Unit, Department of Obstetrics and Gynecology, Multidisciplinary Breast Clinic, Antwerp University Hospital, University of Antwerp, Edegem, Belgium
| | - Geert Van Raemdonck
- Laboratory of Protein Science, Proteomics and Epigenetic Signaling (PPES), University of Antwerp, Wilrijk, Belgium.,Centre for Proteomics (CfP), University of Antwerp, Wilrijk, Belgium
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30
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Yan S, Wong KC. Elucidating high-dimensional cancer hallmark annotation via enriched ontology. J Biomed Inform 2017; 73:84-94. [PMID: 28723579 DOI: 10.1016/j.jbi.2017.07.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Revised: 05/23/2017] [Accepted: 07/14/2017] [Indexed: 10/19/2022]
Abstract
MOTIVATION Cancer hallmark annotation is a promising technique that could discover novel knowledge about cancer from the biomedical literature. The automated annotation of cancer hallmarks could reveal relevant cancer transformation processes in the literature or extract the articles that correspond to the cancer hallmark of interest. It acts as a complementary approach that can retrieve knowledge from massive text information, advancing numerous focused studies in cancer research. Nonetheless, the high-dimensional nature of cancer hallmark annotation imposes a unique challenge. RESULTS To address the curse of dimensionality, we compared multiple cancer hallmark annotation methods on 1580 PubMed abstracts. Based on the insights, a novel approach, UDT-RF, which makes use of ontological features is proposed. It expands the feature space via the Medical Subject Headings (MeSH) ontology graph and utilizes novel feature selections for elucidating the high-dimensional cancer hallmark annotation space. To demonstrate its effectiveness, state-of-the-art methods are compared and evaluated by a multitude of performance metrics, revealing the full performance spectrum on the full set of cancer hallmarks. Several case studies are conducted, demonstrating how the proposed approach could reveal novel insights into cancers. AVAILABILITY https://github.com/cskyan/chmannot.
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Affiliation(s)
- Shankai Yan
- Department of Computer Science, City University of Hong Kong, Hong Kong Special Administrative Region
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Hong Kong Special Administrative Region.
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31
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Larsson K, Baker S, Silins I, Guo Y, Stenius U, Korhonen A, Berglund M. Text mining for improved exposure assessment. PLoS One 2017; 12:e0173132. [PMID: 28257498 PMCID: PMC5336247 DOI: 10.1371/journal.pone.0173132] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 02/15/2017] [Indexed: 01/24/2023] Open
Abstract
Chemical exposure assessments are based on information collected via different methods, such as biomonitoring, personal monitoring, environmental monitoring and questionnaires. The vast amount of chemical-specific exposure information available from web-based databases, such as PubMed, is undoubtedly a great asset to the scientific community. However, manual retrieval of relevant published information is an extremely time consuming task and overviewing the data is nearly impossible. Here, we present the development of an automatic classifier for chemical exposure information. First, nearly 3700 abstracts were manually annotated by an expert in exposure sciences according to a taxonomy exclusively created for exposure information. Natural Language Processing (NLP) techniques were used to extract semantic and syntactic features relevant to chemical exposure text. Using these features, we trained a supervised machine learning algorithm to automatically classify PubMed abstracts according to the exposure taxonomy. The resulting classifier demonstrates good performance in the intrinsic evaluation. We also show that the classifier improves information retrieval of chemical exposure data compared to keyword-based PubMed searches. Case studies demonstrate that the classifier can be used to assist researchers by facilitating information retrieval and classification, enabling data gap recognition and overviewing available scientific literature using chemical-specific publication profiles. Finally, we identify challenges to be addressed in future development of the system.
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Affiliation(s)
- Kristin Larsson
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Simon Baker
- Computer Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Ilona Silins
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Yufan Guo
- Computer Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Ulla Stenius
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Anna Korhonen
- Computer Laboratory, University of Cambridge, Cambridge, United Kingdom
- Language Technology Lab, DTAL, University of Cambridge, Cambridge, United Kingdom
| | - Marika Berglund
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
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