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Hark C. The power of graphs in medicine: Introducing BioGraphSum for effective text summarization. Heliyon 2024; 10:e31813. [PMID: 38845961 PMCID: PMC11154598 DOI: 10.1016/j.heliyon.2024.e31813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 05/22/2024] [Accepted: 05/22/2024] [Indexed: 06/09/2024] Open
Abstract
In biomedicine, the expansive scientific literature combined with the frequent use of abbreviations, acronyms, and symbols presents considerable challenges for text processing and summarization. The Unified Medical Language System (UMLS) has been a go-to for extracting concepts and determining correlations in these studies; hence, the BioGraphSum model introduced in this study aims to reduce this UMLS dependence. Through adoption of an innovative perspective, sentences within a piece of text are graphically conceptualized as nodes, enabling the concept of "Malatya centrality" to be leveraged. This approach focuses on pinpointing influential nodes on a graph and, by analogy, the most pertinent sentences within the text for summarization. In order to evaluate the performance of the BioGraphSum approach, a corpus was curated that consisted of 450 contemporary scientific research articles available on the PubMed database, aligned with proven research methodology. The BioGraphSum model was subjected to rigorous testing against this corpus in order to demonstrate its capabilities. Preliminary results, especially in the precision-based and f-score-based ROUGE-(1-2), ROUGE-L, and ROUGE-SU metrics reported significant improvements when compared to other existing models considered state-of-the-art in text summarization.
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Affiliation(s)
- Cengiz Hark
- İnönü University, Department of Computer Engineering, 44000, Malatya, Turkey
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Abstract
In recent years, the evolution of technology has led to an increase in text data obtained from many sources. In the biomedical domain, text information has also evidenced this accelerated growth, and automatic text summarization systems play an essential role in optimizing physicians’ time resources and identifying relevant information. In this paper, we present a systematic review in recent research of text summarization for biomedical textual data, focusing mainly on the methods employed, type of input data text, areas of application, and evaluation metrics used to assess systems. The survey was limited to the period between 1st January 2014 and 15th March 2022. The data collected was obtained from WoS, IEEE, and ACM digital libraries, while the search strategies were developed with the help of experts in NLP techniques and previous systematic reviews. The four phases of a systematic review by PRISMA methodology were conducted, and five summarization factors were determined to assess the studies included: Input, Purpose, Output, Method, and Evaluation metric. Results showed that 3.5% of 801 studies met the inclusion criteria. Moreover, Single-document, Biomedical Literature, Generic, and Extractive summarization proved to be the most common approaches employed, while techniques based on Machine Learning were performed in 16 studies and Rouge (Recall-Oriented Understudy for Gisting Evaluation) was reported as the evaluation metric in 26 studies. This review found that in recent years, more transformer-based methodologies for summarization purposes have been implemented compared to a previous survey. Additionally, there are still some challenges in text summarization in different domains, especially in the biomedical field in terms of demand for further research.
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Increasing Women’s Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19138100. [PMID: 35805761 PMCID: PMC9265758 DOI: 10.3390/ijerph19138100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 06/28/2022] [Accepted: 06/29/2022] [Indexed: 01/09/2023]
Abstract
Despite the availability of online educational resources about human papillomavirus (HPV), many women around the world may be prevented from obtaining the necessary knowledge about HPV. One way to mitigate the lack of HPV knowledge is the use of auto-generated text summarization tools. This study compares the level of HPV knowledge between women who read an auto-generated summary of HPV made using the BERT deep learning model and women who read a long-form text of HPV. We randomly assigned 386 women to two conditions: half read an auto-generated summary text about HPV (n = 193) and half read an original text about HPV (n = 193). We administrated measures of HPV knowledge that consisted of 29 questions. As a result, women who read the original text were more likely to correctly answer two questions on the general HPV knowledge subscale than women who read the summarized text. For the HPV testing knowledge subscale, there was a statistically significant difference in favor of women who read the original text for only one question. The final subscale, HPV vaccination knowledge questions, did not significantly differ across groups. Using BERT for text summarization has shown promising effectiveness in increasing women’s knowledge and awareness about HPV while saving their time.
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Van Meenen J, Leysen H, Chen H, Baccarne R, Walter D, Martin B, Maudsley S. Making Biomedical Sciences publications more accessible for machines. MEDICINE, HEALTH CARE, AND PHILOSOPHY 2022; 25:179-190. [PMID: 35039972 DOI: 10.1007/s11019-022-10069-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/08/2022] [Indexed: 06/14/2023]
Abstract
With the rapidly expanding catalogue of scientific publications, especially within the Biomedical Sciences field, it is becoming increasingly difficult for researchers to search for, read or even interpret emerging scientific findings. PubMed, just one of the current biomedical data repositories, comprises over 33 million citations for biomedical research, and over 2500 publications are added each day. To further strengthen the impact biomedical research, we suggest that there should be more synergy between publications and machines. By bringing machines into the realm of research and publication, we can greatly augment the assessment, investigation and cataloging of the biomedical literary corpus. The effective application of machine-based manuscript assessment and interpretation is now crucial, and potentially stands as the most effective way for researchers to comprehend and process the tsunami of biomedical data and literature. Many biomedical manuscripts are currently published online in poorly searchable document types, with figures and data presented in formats that are partially inaccessible to machine-based approaches. The structure and format of biomedical manuscripts should be adapted to facilitate machine-assisted interrogation of this important literary corpus. In this context, it is important to embrace the concept that biomedical scientists should also write manuscripts that can be read by machines. It is likely that an enhanced human-machine synergy in reading biomedical publications will greatly enhance biomedical data retrieval and reveal novel insights into complex datasets.
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Affiliation(s)
- Joris Van Meenen
- Receptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium
- Antwerp Research Group for Ocular Science, Department of Translational Neurosciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium
| | - Hanne Leysen
- Receptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium
| | - Hongyu Chen
- Weill Cornell Medical College, New York, NY, USA
| | - Rudi Baccarne
- Anet Library Automation, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium
| | - Deborah Walter
- Receptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium
| | - Bronwen Martin
- Faculty of Pharmaceutical, Veterinary and Biomedical Sciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium
| | - Stuart Maudsley
- Receptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium.
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Du Y, Zhao Y, Yan J, Li Q. UGDAS: Unsupervised Graph-Network based Denoiser for Abstractive Summarization in biomedical domain. Methods 2022; 203:160-166. [DOI: 10.1016/j.ymeth.2022.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/14/2021] [Accepted: 03/20/2022] [Indexed: 10/18/2022] Open
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Jing X. The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis. JMIR Med Inform 2021; 9:e20675. [PMID: 34236337 PMCID: PMC8433943 DOI: 10.2196/20675] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 11/25/2020] [Accepted: 07/02/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND The Unified Medical Language System (UMLS) has been a critical tool in biomedical and health informatics, and the year 2021 marks its 30th anniversary. The UMLS brings together many broadly used vocabularies and standards in the biomedical field to facilitate interoperability among different computer systems and applications. OBJECTIVE Despite its longevity, there is no comprehensive publication analysis of the use of the UMLS. Thus, this review and analysis is conducted to provide an overview of the UMLS and its use in English-language peer-reviewed publications, with the objective of providing a comprehensive understanding of how the UMLS has been used in English-language peer-reviewed publications over the last 30 years. METHODS PubMed, ACM Digital Library, and the Nursing & Allied Health Database were used to search for studies. The primary search strategy was as follows: UMLS was used as a Medical Subject Headings term or a keyword or appeared in the title or abstract. Only English-language publications were considered. The publications were screened first, then coded and categorized iteratively, following the grounded theory. The review process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS A total of 943 publications were included in the final analysis. Moreover, 32 publications were categorized into 2 categories; hence the total number of publications before duplicates are removed is 975. After analysis and categorization of the publications, UMLS was found to be used in the following emerging themes or areas (the number of publications and their respective percentages are given in parentheses): natural language processing (230/975, 23.6%), information retrieval (125/975, 12.8%), terminology study (90/975, 9.2%), ontology and modeling (80/975, 8.2%), medical subdomains (76/975, 7.8%), other language studies (53/975, 5.4%), artificial intelligence tools and applications (46/975, 4.7%), patient care (35/975, 3.6%), data mining and knowledge discovery (25/975, 2.6%), medical education (20/975, 2.1%), degree-related theses (13/975, 1.3%), digital library (5/975, 0.5%), and the UMLS itself (150/975, 15.4%), as well as the UMLS for other purposes (27/975, 2.8%). CONCLUSIONS The UMLS has been used successfully in patient care, medical education, digital libraries, and software development, as originally planned, as well as in degree-related theses, the building of artificial intelligence tools, data mining and knowledge discovery, foundational work in methodology, and middle layers that may lead to advanced products. Natural language processing, the UMLS itself, and information retrieval are the 3 most common themes that emerged among the included publications. The results, although largely related to academia, demonstrate that UMLS achieves its intended uses successfully, in addition to achieving uses broadly beyond its original intentions.
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Affiliation(s)
- Xia Jing
- Department of Public Health Sciences, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, SC, United States
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Wang M, Wang M, Yu F, Yang Y, Walker J, Mostafa J. A systematic review of automatic text summarization for biomedical literature and EHRs. J Am Med Inform Assoc 2021; 28:2287-2297. [PMID: 34338801 DOI: 10.1093/jamia/ocab143] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 06/21/2021] [Accepted: 06/24/2021] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVE Biomedical text summarization helps biomedical information seekers avoid information overload by reducing the length of a document while preserving the contents' essence. Our systematic review investigates the most recent biomedical text summarization researches on biomedical literature and electronic health records by analyzing their techniques, areas of application, and evaluation methods. We identify gaps and propose potential directions for future research. MATERIALS AND METHODS This review followed the PRISMA methodology and replicated the approaches adopted by the previous systematic review published on the same topic. We searched 4 databases (PubMed, ACM Digital Library, Scopus, and Web of Science) from January 1, 2013 to April 8, 2021. Two reviewers independently screened title, abstract, and full-text for all retrieved articles. The conflicts were resolved by the third reviewer. The data extraction of the included articles was in 5 dimensions: input, purpose, output, method, and evaluation. RESULTS Fifty-eight out of 7235 retrieved articles met the inclusion criteria. Thirty-nine systems used single-document biomedical research literature as their input, 17 systems were explicitly designed for clinical support, 47 systems generated extractive summaries, and 53 systems adopted hybrid methods combining computational linguistics, machine learning, and statistical approaches. As for the assessment, 51 studies conducted an intrinsic evaluation using predefined metrics. DISCUSSION AND CONCLUSION This study found that current biomedical text summarization systems have achieved good performance using hybrid methods. Studies on electronic health records summarization have been increasing compared to a previous survey. However, the majority of the works still focus on summarizing literature.
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Affiliation(s)
- Mengqian Wang
- Carolina Health Informatics Program, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Manhua Wang
- iSchool, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Fei Yu
- iSchool, University of North Carolina, Chapel Hill, North Carolina, USA.,Health Sciences Library, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Yue Yang
- Carolina Health Informatics Program, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Jennifer Walker
- Health Sciences Library, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Javed Mostafa
- Carolina Health Informatics Program, University of North Carolina, Chapel Hill, North Carolina, USA.,iSchool, University of North Carolina, Chapel Hill, North Carolina, USA.,Biomedical Research Imaging Center, the School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
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Davoodijam E, Ghadiri N, Lotfi Shahreza M, Rinaldi F. MultiGBS: A multi-layer graph approach to biomedical summarization. J Biomed Inform 2021; 116:103706. [PMID: 33610879 DOI: 10.1016/j.jbi.2021.103706] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 01/21/2021] [Accepted: 02/03/2021] [Indexed: 11/26/2022]
Abstract
Automatic text summarization methods generate a shorter version of the input text to assist the reader in gaining a quick yet informative gist. Existing text summarization methods generally focus on a single aspect of text when selecting sentences, causing the potential loss of essential information. In this study, we propose a domain-specific method that models a document as a multi-layer graph to enable multiple features of the text to be processed at the same time. The features we used in this paper are word similarity, semantic similarity, and co-reference similarity, which are modelled as three different layers. The unsupervised method selects sentences from the multi-layer graph based on the MultiRank algorithm and the number of concepts. The proposed MultiGBS algorithm employs UMLS and extracts the concepts and relationships using different tools such as SemRep, MetaMap, and OGER. Extensive evaluation by ROUGE and BERTScore shows increased F-measure values.
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Affiliation(s)
- Ensieh Davoodijam
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.
| | - Nasser Ghadiri
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.
| | | | - Fabio Rinaldi
- Swiss AI Lab IDSIA/Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, CH-6962 Lugano, Switzerland.
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Afzal M, Alam F, Malik KM, Malik GM. Clinical Context-Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation. J Med Internet Res 2020; 22:e19810. [PMID: 33095174 PMCID: PMC7647812 DOI: 10.2196/19810] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 09/24/2020] [Indexed: 01/09/2023] Open
Abstract
Background Automatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions. Deep neural and recurrent networks outperform traditional machine-learning techniques in areas of natural language processing and computer vision; however, they are yet to be explored in the ATS domain, particularly for medical text summarization. Objective Traditional approaches in ATS for biomedical text suffer from fundamental issues such as an inability to capture clinical context, quality of evidence, and purpose-driven selection of passages for the summary. We aimed to circumvent these limitations through achieving precise, succinct, and coherent information extraction from credible published biomedical resources, and to construct a simplified summary containing the most informative content that can offer a review particular to clinical needs. Methods In our proposed approach, we introduce a novel framework, termed Biomed-Summarizer, that provides quality-aware Patient/Problem, Intervention, Comparison, and Outcome (PICO)-based intelligent and context-enabled summarization of biomedical text. Biomed-Summarizer integrates the prognosis quality recognition model with a clinical context–aware model to locate text sequences in the body of a biomedical article for use in the final summary. First, we developed a deep neural network binary classifier for quality recognition to acquire scientifically sound studies and filter out others. Second, we developed a bidirectional long-short term memory recurrent neural network as a clinical context–aware classifier, which was trained on semantically enriched features generated using a word-embedding tokenizer for identification of meaningful sentences representing PICO text sequences. Third, we calculated the similarity between query and PICO text sequences using Jaccard similarity with semantic enrichments, where the semantic enrichments are obtained using medical ontologies. Last, we generated a representative summary from the high-scoring PICO sequences aggregated by study type, publication credibility, and freshness score. Results Evaluation of the prognosis quality recognition model using a large dataset of biomedical literature related to intracranial aneurysm showed an accuracy of 95.41% (2562/2686) in terms of recognizing quality articles. The clinical context–aware multiclass classifier outperformed the traditional machine-learning algorithms, including support vector machine, gradient boosted tree, linear regression, K-nearest neighbor, and naïve Bayes, by achieving 93% (16127/17341) accuracy for classifying five categories: aim, population, intervention, results, and outcome. The semantic similarity algorithm achieved a significant Pearson correlation coefficient of 0.61 (0-1 scale) on a well-known BIOSSES dataset (with 100 pair sentences) after semantic enrichment, representing an improvement of 8.9% over baseline Jaccard similarity. Finally, we found a highly positive correlation among the evaluations performed by three domain experts concerning different metrics, suggesting that the automated summarization is satisfactory. Conclusions By employing the proposed method Biomed-Summarizer, high accuracy in ATS was achieved, enabling seamless curation of research evidence from the biomedical literature to use for clinical decision-making.
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Affiliation(s)
- Muhammad Afzal
- Department of Software, Sejong University, Seoul, Republic of Korea.,Department of Computer Science & Engineering, School of Engineering and Computer Science, Oakland University, Rochester, MI, United States
| | - Fakhare Alam
- Department of Computer Science & Engineering, School of Engineering and Computer Science, Oakland University, Rochester, MI, United States
| | - Khalid Mahmood Malik
- Department of Computer Science & Engineering, School of Engineering and Computer Science, Oakland University, Rochester, MI, United States
| | - Ghaus M Malik
- Department of Neurosurgery, Henry Ford Hospital, Detroit, MI, United States
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Summarization of biomedical articles using domain-specific word embeddings and graph ranking. J Biomed Inform 2020; 107:103452. [DOI: 10.1016/j.jbi.2020.103452] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 05/06/2020] [Accepted: 05/09/2020] [Indexed: 12/21/2022]
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Du Y, Li Q, Wang L, He Y. Biomedical-domain pre-trained language model for extractive summarization. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105964] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Moradi M, Dorffner G, Samwald M. Deep contextualized embeddings for quantifying the informative content in biomedical text summarization. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 184:105117. [PMID: 31627150 DOI: 10.1016/j.cmpb.2019.105117] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 09/19/2019] [Accepted: 10/03/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Capturing the context of text is a challenging task in biomedical text summarization. The objective of this research is to show how contextualized embeddings produced by a deep bidirectional language model can be utilized to quantify the informative content of sentences in biomedical text summarization. METHODS We propose a novel summarization method that utilizes contextualized embeddings generated by the Bidirectional Encoder Representations from Transformers (BERT) model, a deep learning model that recently demonstrated state-of-the-art results in several natural language processing tasks. We combine different versions of BERT with a clustering method to identify the most relevant and informative sentences of input documents. Using the ROUGE toolkit, we evaluate the summarizer against several methods previously described in literature. RESULTS The summarizer obtains state-of-the-art results and significantly improves the performance of biomedical text summarization in comparison to a set of domain-specific and domain-independent methods. The largest language model not specifically pretrained on biomedical text outperformed other models. However, among language models of the same size, the one further pretrained on biomedical text obtained best results. CONCLUSIONS We demonstrate that a hybrid system combining a deep bidirectional language model and a clustering method yields state-of-the-art results without requiring labor-intensive creation of annotated features or knowledge bases or computationally demanding domain-specific pretraining. This study provides a starting point towards investigating deep contextualized language models for biomedical text summarization.
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Affiliation(s)
- Milad Moradi
- Institute for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
| | - Georg Dorffner
- Institute for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Matthias Samwald
- Institute for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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Hasan M, Carcone AI, Naar S, Eggly S, Alexander GL, Hartlieb KEB, Kotov A. Identifying Effective Motivational Interviewing Communication Sequences Using Automated Pattern Analysis. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2019; 3:86-106. [PMID: 31602420 DOI: 10.1007/s41666-018-0037-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Motivational Interviewing (MI) is an evidence-based strategy for communicating with patients about behavior change. Although there is strong empirical evidence linking "MI-consistent" counselor behaviors and patient motivational statements (i.e., "change talk"), the specific counselor communication behaviors effective for eliciting patient change talk vary by treatment context and, thus, are a subject of ongoing research. An integral part of this research is the sequential analysis of pre-coded MI transcripts. In this paper, we evaluate the empirical effectiveness of the Hidden Markov Model, a probabilistic generative model for sequence data, for modeling sequences of behavior codes and closed frequent pattern mining, a method to identify frequently occurring sequential patterns of behavior codes in MI communication sequences to inform MI practice. We conducted experiments with 1,360 communication sequences from 37 transcribed audio recordings of weight loss counseling sessions with African-American adolescents with obesity and their caregivers. Transcripts had been previously annotated with patient-counselor behavior codes using a specialized codebook. Empirical results indicate that Hidden Markov Model and closed frequent pattern mining techniques can identify counselor communication strategies that are effective at eliciting patients' motivational statements to guide clinical practice.
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Affiliation(s)
- Mehedi Hasan
- Department of Computer Science, College of Engineering, Wayne State University, Detroit, MI 48202
| | - April Idalski Carcone
- Division of Behavioral Sciences, Department of Family Medicine and Public Health Sciences, Wayne State University School of Medicine, Detroit, MI 48202
| | - Sylvie Naar
- Director, Center for Translational Behavioral Research, Department of Behavioral Sciences and Social Medicine, Florida State University, FL 32306
| | - Susan Eggly
- Department of Oncology, Wayne State University/Karmanos Cancer Institute, Detroit, MI 48201
| | - Gwen L Alexander
- Department of Public Health Sciences, Henry Ford Health System, Detroit, MI 48202
| | - Kathryn E Brogan Hartlieb
- Department of Humanities, Health and Society, Wertheim College of Medicine, Florida International University, Miami, FL 33199
| | - Alexander Kotov
- Department of Computer Science, College of Engineering, Wayne State University, Detroit, MI 48202
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Moradi M. CIBS: A biomedical text summarizer using topic-based sentence clustering. J Biomed Inform 2018; 88:53-61. [DOI: 10.1016/j.jbi.2018.11.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Revised: 09/26/2018] [Accepted: 11/12/2018] [Indexed: 12/21/2022]
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Nasr Azadani M, Ghadiri N, Davoodijam E. Graph-based biomedical text summarization: An itemset mining and sentence clustering approach. J Biomed Inform 2018; 84:42-58. [DOI: 10.1016/j.jbi.2018.06.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 04/21/2018] [Accepted: 06/10/2018] [Indexed: 10/28/2022]
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Moradi M, Ghadiri N. Different approaches for identifying important concepts in probabilistic biomedical text summarization. Artif Intell Med 2018; 84:101-116. [DOI: 10.1016/j.artmed.2017.11.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 08/25/2017] [Accepted: 11/28/2017] [Indexed: 10/18/2022]
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