Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach

Dudko, Alexander; Endrjukaite, Tatiana; Kiyoki, Yasushi

doi:10.1145/3151759.3151784

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Regarding information collection, concept and relation extraction (50/54, 56%), time-series analysis (6/54, 11%), encoding (22/54, 41%), temporal abstraction (6/54, 11%), and topic extraction (5/54, 9%) were proposed as solutions. Medical concepts are extracted from textual data either using publicly available solutions (eg, cTAKES [ 164 ] in the study by Goff and Loehfelm [ 94 ]) or tools developed by the authors (eg, [ 113 , 114 , 157 ]). The retrieved list of concepts can be used for simpler tasks, such as problem list generation [ 88 ], or some records present systems that take a step further extracting the context [ 115 ], syntactic structure [ 94 ], or approximate semantic structure of a sentence [ 116 ] as well.…”

Section: Resultsmentioning

confidence: 99%

Patient Information Summarization in Clinical Settings: Scoping Review

Keszthelyi,

Gaudet-Blavignac,

Bjelogrlic

et al. 2023

JMIR Med Inform

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Background Information overflow, a common problem in the present clinical environment, can be mitigated by summarizing clinical data. Although there are several solutions for clinical summarization, there is a lack of a complete overview of the research relevant to this field. Objective This study aims to identify state-of-the-art solutions for clinical summarization, to analyze their capabilities, and to identify their properties. Methods A scoping review of articles published between 2005 and 2022 was conducted. With a clinical focus, PubMed and Web of Science were queried to find an initial set of reports, later extended by articles found through a chain of citations. The included reports were analyzed to answer the questions of where, what, and how medical information is summarized; whether summarization conserves temporality, uncertainty, and medical pertinence; and how the propositions are evaluated and deployed. To answer how information is summarized, methods were compared through a new framework “collect—synthesize—communicate” referring to information gathering from data, its synthesis, and communication to the end user. Results Overall, 128 articles were included, representing various medical fields. Exclusively structured data were used as input in 46.1% (59/128) of papers, text in 41.4% (53/128) of articles, and both in 10.2% (13/128) of papers. Using the proposed framework, 42.2% (54/128) of the records contributed to information collection, 27.3% (35/128) contributed to information synthesis, and 46.1% (59/128) presented solutions for summary communication. Numerous summarization approaches have been presented, including extractive (n=13) and abstractive summarization (n=19); topic modeling (n=5); summary specification (n=11); concept and relation extraction (n=30); visual design considerations (n=59); and complete pipelines (n=7) using information extraction, synthesis, and communication. Graphical displays (n=53), short texts (n=41), static reports (n=7), and problem-oriented views (n=7) were the most common types in terms of summary communication. Although temporality and uncertainty information were usually not conserved in most studies (74/128, 57.8% and 113/128, 88.3%, respectively), some studies presented solutions to treat this information. Overall, 115 (89.8%) articles showed results of an evaluation, and methods included evaluations with human participants (median 15, IQR 24 participants): measurements in experiments with human participants (n=31), real situations (n=8), and usability studies (n=28). Methods without human involvement included intrinsic evaluation (n=24), performance on a proxy (n=10), or domain-specific tasks (n=11). Overall, 11 (8.6%) reports described a system deployed in clinical settings. Conclusions The scientific literature contains many propositions for summarizing patient information but reports very few comparisons of these proposals. This work proposes to compare these algorithms through how they conserve essential aspects of clinical information and through the “collect—synthesize—communicate” framework. We found that current propositions usually address these 3 steps only partially. Moreover, they conserve and use temporality, uncertainty, and pertinent medical aspects to varying extents, and solutions are often preliminary.

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Section: Resultsmentioning

confidence: 99%

Patient Information Summarization in Clinical Settings: Scoping Review

Keszthelyi,

Gaudet-Blavignac,

Bjelogrlic

et al. 2023

JMIR Med Inform

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Natural Language Processing Knowledge Network Approach for Interactive Highlighting and Summary

Dudko

Endrjukaite

Kiyoki

2019

Lecture Notes in Networks and Systems

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A systematic review of automatic text summarization for biomedical literature and EHRs

Wang

et al. 2021

Journal of the American Medical Informatics Association

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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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Medical documents processing for summary generation and keywords highlighting based on natural language processing and ontology graph descriptor approach

Cited by 3 publications

References 7 publications

Patient Information Summarization in Clinical Settings: Scoping Review

Patient Information Summarization in Clinical Settings: Scoping Review

Natural Language Processing Knowledge Network Approach for Interactive Highlighting and Summary

A systematic review of automatic text summarization for biomedical literature and EHRs

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