Analysis of the Formation of Scientific Communities in the Journal Research and Education in Nursing (2010 - 2020) and its Disciplinary Influence: an Approach from Bibliometric Analysis, Network Analysis, and Natural Language Processing / Análisis de la formación de comunidades científicas en la revista Investigación y Educación en Enfermería(2010-2020) y su influencia disciplinar: Un enfoque desde el análisis bibliométrico, análisis de redes y procesamiento de lenguaje natural / Análise da formação de comunidades científicas na revista Nursing Research and Education (2010-2020) e sua influência disciplinar: Uma abordagem a partir da análise bibliométrica, análise de redes e processamento de linguagem natural

Objectives. This work sought to identify the academic communities that have shown interest and participation in the Journal Research and Education in Nursing and analyze the scientific impact generated by said journal. Methods. A bibliometric analysis was carried out, as well as social network analysis and techniques of natural language processing to conduct the research. The data was gathered and analyzed during a specific study period, covering from 2010 - 2020, for articles published in the journal, and 2010 - 2022, for articles that cited the journal within Scopus. These methods permitted performing an exhaustive evaluation of the journal's influence and reach in diverse academic and geographic contexts. Results. During the analysis, it was noted that the journal Research and Education in Nursing has had significant influence in academic and scientific communities, both nationally and internationally. Collaboration networks were detected among diverse institutions and countries, which indicates active interaction in the field of nursing research. In addition, trends and emerging patterns were identified in this field, providing a more complete view of the discipline's evolution. Conclusion. Based on the results obtained, it is concluded that the journal Research and Education in Nursing has played un fundamental role in disseminating knowledge and promoting research in nursing. The combination of Bibliometric metrics, social network analysis, and natural language processing permitted utmost comprehension of its impact in the scientific and academic community globally.

Objetivos. Identificar las comunidades académicas que han mostrado interés y participación en la revista Investigación y Educación en Enfermería y analizar el impacto científico generado por esta publicación. Métodos. Se realizó un análisis bibliométrico, así como análisis de redes sociales y técnicas de procesamiento de lenguaje natural para llevar a cabo la investigación. Los datos se recopilaron y analizaron durante un período de estudio específico, abarcando los años 2010-2020, para los artículos publicados en la revista, y 2010-2022, para los artículos que citaron la revista dentro de Scopus. Estos métodos permitieron realizar una evaluación exhaustiva de la influencia y alcance de la revista en diversos contextos académicos y geográficos. Resultados. Durante el análisis, se observó que la revista Investigación y Educación en Enfermería ha ejercido una influencia significativa en las comunidades académicas y científicas, tanto a nivel nacional como internacional. Se detectaron redes de colaboración entre diversas instituciones y países, lo que indica una interacción activa en el ámbito de la investigación en enfermería. Además, se identificaron tendencias y patrones emergentes en este campo, proporcionando una visión más completa de la evolución de la disciplina. Conclusión. Basándose en los resultados obtenidos, se concluye que la revista Investigación y Educación en Enfermería ha desempeñado un papel fundamental en la difusión del conocimiento y la promoción de la investigación en enfermería. La combinación de métricas bibliométricas, análisis de redes sociales y procesamiento de lenguaje natural permitió una comprensión más completa de su impacto en la comunidad científica y académica a nivel global.

Objetivos. Identificar as comunidades acadêmicas que demonstraram interesse e participação na revista Nursing Research and Education e analisar o impacto científico gerado por esta publicação colombiana. Métodos. Foi realizada análise bibliométrica, análise de redes sociais e técnicas de processamento de linguagem natural para a realização da pesquisa. Os dados foram coletados e analisados durante um período específico de estudo, abrangendo os anos 2010-2020, para artigos publicados na revista, e 2010-2022, para artigos que citaram a revista dentro do Scopus. Esses métodos permitiram uma avaliação abrangente da influência e do alcance da revista em diversos contextos acadêmicos e geográficos. Resultados. Durante a análise, observou-se que a revista Nursing Research and Education tem exercido influência significativa nas comunidades acadêmica e científica, tanto nacional quanto internacionalmente. Foram detectadas redes de colaboração entre diversas instituições e países, o que indica interação ativa no campo da pesquisa em enfermagem. Além disso, foram identificadas tendências e padrões emergentes neste campo, proporcionando uma visão mais completa da evolução da disciplina. Conclusão. Com base nos resultados obtidos, conclui-se que a revista Nursing Research and Education tem desempenhado um papel fundamental na divulgação do conhecimento e na promoção da investigação em enfermagem. A combinação de métricas bibliométricas, análise de redes sociais e processamento de linguagem natural permitiu uma compreensão mais completa do seu impacto na comunidade científica e académica global.

Percepción de usuarios de Facebook sobre la vacuna contra el VPH en publicaciones del Ministerio de Salud de Perú a partir de procesamiento de lenguaje natural / Facebook Users' Perception of the HP Vaccine in Health Ministry Publications Based on Natural Language Processing

El objetivo de este estudio fue describir las percepciones de los usuarios de Facebook que realizaron comentarios, en las publicaciones realizadas desde la cuenta oficial del Ministerio de Salud de Perú (MINSA), referentes a la campaña de vacunación contra el VPH. Se analizaron 2748 comentarios en Python con procesamiento de lenguaje natural. Con este proceso se obtuvieron palabras claves que luego fueron interpretadas de manera manual. Se encontraron mayoritariamente cuatro tipos de discursos dentro de ellos: a) apoyo a la publicación sobre la vacuna contra el VPH; b) rechazo a la vacuna contra el VPH; c) Vacuna contra el VPH en niños; d) Dudas sobre la vacuna contra el VPH. En su mayoría, los usuarios que expresaron una postura de rechazo de esta vacuna se respaldaban de links a noticias donde se presentaba un evento supuestamente atribuido a la vacunación o inmunización pero que carecía de una fuente de información confiable y/o verificable(AU)

The objective of this study was to describe the perceptions of Facebook users who commented on posts made by the official account of the Ministry of Health of Peru (MINSA) regarding the HPV vaccination campaign. We analyzed 2748 comments in Python with natural language processing. With this process we obtained keywords that were then interpreted manually. We found mostly four types of discourse, within them: a) support for the publications of the HPV vaccine; b) refusal of the HPV vaccine; c) HPV vaccine in children; d) doubts about the HPV vaccine. For the most part, users who expressed a position against this vaccine relied on links to online news stories that presented an event supposedly attributed to vaccination or immunization but lacked a reliable and/or verifiable source of information(AU)

Pictobana: Una herramienta para la comunicación con niños autistas / Pictobana: A tool for Communication with Autistic Children

Introducción: Los avances actuales en el campo de las TICs han permitido un importante impulso en el desarrollo de sistemas que traducen texto plano en español en pictogramas. Sin embargo, las soluciones actuales no pueden ser comprendidas por una persona con dificultades del lenguaje en Cuba, debido a que algunas terminologías no están presentes en el lenguaje cotidiano. Objetivo: Desarrollar el modelo Pictobana para el análisis semántico de un Pictotraductor que integre la semántica del lenguaje cubano. Métodos: El modelo fue desarrollado aplicando técnicas de procesamiento del lenguaje natural. Se realiza un análisis lingüístico con el objetivo de proporcionar las mejores representaciones posibles de los textos en pictogramas. Resultados: El modelo es implementado en una aplicación web que proporciona una herramienta que ayuda a promover las competencias y habilidades de comunicación a personas con dificultades del habla en Cuba y a sus familiares. Conclusiones: Las pruebas realizadas mediante experimentos y criterio de expertos, demuestran que el analizador desarrollado, aumenta la ajustabilidad de los pictogramas al contexto y a la semántica, aminorando la incoherencia y la ambigüedad semántica del futuro sistema(AU)

Introduction: Current advances in the field of ICTs have allowed an important boost in the development of systems that allow translating plain text in Spanish into pictograms. However, the current solutions cannot be understood by a person with language difficulties in Cuba because some terminologies are not present in everyday language. Objective: To develop the Pictobana model for the semantic analysis of a Pictotranslator that integrates the semantics of the Cuban language. Methods: The model was developed by applying natural language processing techniques. A linguistic analysis was carried out with the aim of providing the best possible representations of the texts in pictograms. Results: The model is implemented in a web application that provides a tool that helps promote communication skills and abilities for people with speech difficulties and their families in Cuba. Conclusions: The tests carried out through experiments and expert criteria show that the developed analyzer increases the adjustability of the pictograms to the context and the semantics, reducing the incoherence and semantic ambiguity of the future system(AU)

Chat GPT: origen, evolución, retos e impactos en la educación / Chat GPT: Origen, Evolution, Challenges and Impacts on Education

Introducción: Los avances de unas tecnologías y la obsolescencia de otras marchan a una velocidad inimaginable, especialmente en este siglo xxi. En los últimos meses de 2022 y primeros meses de 2023 muchas incógnitas y controversias en diferentes campos han surgido en torno a los Chat GPS, una innovación que presenta desafíos nunca pensados para la sociedad actual, así como nuevos retos que impactarán de manera directa en la formación y/o desempeño de profesores, estudiantes, profesionales de la salud, juristas, políticos, informáticos, bibliotecarios, científicos y cualquier ciudadano. Objetivo: Identificar algunas características del chat GPT y su posible impacto en el educación. Posicionamiento de los autores: Se leen en las noticias y reportajes valoraciones de especialistas; se han realizado encuentros virtuales y exposiciones; y están disponibles diversos artículos y videos sobre este tema, algunos llegan a ser elaborados con el propio asistente. Por la novedad del tema, la reciente incorporación como herramienta para el desarrollo profesional, así como por el interés mostrado en los últimos días por la comunidad de profesores de las ciencias médicas cubanas, y considerando que esta herramienta es resultado del desarrollo de la inteligencia artificial, cabe preguntarse: ¿en qué consiste? y ¿cuáles son sus perspectivas? Conclusiones: Resulta oportuno acercarse al tema desde las posibilidades y los retos que abre a la educación y el aprendizaje, en particular a la docencia médica(AU)

Introduction: The advances of some technologies and the obsolescence of others are marching at an unimaginable speed, especially in this twenty-first century. In the last months of 2022 and first months of 2023, many questions and controversies in different fields have arisen with respect to Chat GPT, an innovation that presents challenges never thought of before for today's society, as well as new challenges that will have a direct impact on the training and/or performance of professors, students, health professionals, law practitioners, politicians, computer scientists, librarians, scientists and any citizen. Objective: To identify some technological characteristics of Chat GPT. Positioning of the authors: In news and reports, assessments by specialists are read; virtual meetings and presentations have been held; and several articles and videos on this topic are available, some of them even elaborated by the assistant itself. Due to the novelty of the subject, its recent assimilation as a tool for professional development, as well as the interest shown in recent days by the community of professors of Cuban medical sciences and considering that this tool is the result of the development of artificial intelligence, it is worth wondering what it consists in and what its prospects are. Conclusions: It is appropriate to approach the subject with a focus on the possibilities and challenges that it opens to education and learning (AU)

An antibacterial peptides recognition method based on BERT and Text-CNN / 生物工程学报

Antimicrobial peptides (AMPs) are small molecule peptides that are widely found in living organisms with broad-spectrum antibacterial activity and immunomodulatory effect. Due to slower emergence of resistance, excellent clinical potential and wide range of application, AMP is a strong alternative to conventional antibiotics. AMP recognition is a significant direction in the field of AMP research. The high cost, low efficiency and long period shortcomings of the wet experiment methods prevent it from meeting the need for the large-scale AMP recognition. Therefore, computer-aided identification methods are important supplements to AMP recognition approaches, and one of the key issues is how to improve the accuracy. Protein sequences could be approximated as a language composed of amino acids. Consequently, rich features may be extracted using natural language processing (NLP) techniques. In this paper, we combine the pre-trained model BERT and the fine-tuned structure Text-CNN in the field of NLP to model protein languages, develop an open-source available antimicrobial peptide recognition tool and conduct a comparison with other five published tools. The experimental results show that the optimization of the two-phase training approach brings an overall improvement in accuracy, sensitivity, specificity, and Matthew correlation coefficient, offering a novel approach for further research on AMP recognition.

Automatic labeling and extraction of terms in natural language processing in acupuncture clinical literature / 中国针灸

The paper analyzes the specificity of term recognition in acupuncture clinical literature and compares the advantages and disadvantages of three named entity recognition (NER) methods adopted in the field of traditional Chinese medicine. It is believed that the bi-directional long short-term memory networks-conditional random fields (Bi LSTM-CRF) may communicate the context information and complete NER by using less feature rules. This model is suitable for term recognition in acupuncture clinical literature. Based on this model, it is proposed that the process of term recognition in acupuncture clinical literature should include 4 aspects, i.e. literature pretreatment, sequence labeling, model training and effect evaluation, which provides an approach to the terminological structurization in acupuncture clinical literature.

Caracterización de un corpus extraído de historias clínicas electrónicas de maternas a través de técnicas de procesamiento de lenguaje natural / Characterization of a corpus extracted from maternal electronic health records through natural language processing techniques

Este artículo tuvo como propósito caracterizar el texto libre disponible en una historia clínica electrónica de una institución orientada a la atención de pacientes en embarazo. La historia clínica electrónica, más que ser un repositorio de datos, se ha convertido en un sistema de soporte a la toma de decisiones clínicas. Sin embargo, debido al alto volumen de información y a que parte de la información clave de las historias clínicas electrónicas está en forma de texto libre, utilizar todo el potencial que ofrece la información de la historia clínica electrónica para mejorar la toma de decisiones clínicas requiere el apoyo de métodos de minería de texto y procesamiento de lenguaje natural. Particularmente, en el área de Ginecología y Obstetricia, la implementación de métodos del procesamiento de lenguaje natural podría ayudar a agilizar la identificación de factores asociados al riesgo materno. A pesar de esto, en la literatura no se registran trabajos que integren técnicas de procesamiento de lenguaje natural en las historias clínicas electrónicas asociadas al seguimiento materno en idioma español. En este trabajo se obtuvieron 659 789 tokens mediante los métodos de minería de texto, un diccionario con palabras únicas dado por 7 334 tokens y se estudiaron los n-grams más frecuentes. Se generó una caracterización con una arquitectura de red neuronal CBOW (continuos bag of words) para la incrustación de palabras. Utilizando algoritmos de clustering se obtuvo evidencia que indica que palabras cercanas en el espacio de incrustación de 300 dimensiones pueden llegar a representar asociaciones referentes a tipos de pacientes, o agrupar palabras similares, incluyendo palabras escritas con errores ortográficos. El corpus generado y los resultados encontrados sientan las bases para trabajos futuros en la detección de entidades (síntomas, signos, diagnósticos, tratamientos), la corrección de errores ortográficos y las relaciones semánticas entre palabras para generar resúmenes de historias clínicas o asistir el seguimiento de las maternas mediante la revisión automatizada de la historia clínica electrónica(AU)

The purpose of this article was to characterize the free text available in an electronic health record of an institution, directed at the care of patients in pregnancy. More than being a data repository, the electronic health record (HCE) has become a clinical decision support system (CDSS). However, due to the high volume of information, as some of the key information in EHR is in free text form, using the full potential that EHR information offers to improve clinical decision-making requires the support of methods of text mining and natural language processing (PLN). Particularly in the area of gynecology and obstetrics, the implementation of PLN methods could help speed up the identification of factors associated with maternal risk. Despite this, in the literature there are no papers that integrate PLN techniques in EHR associated with maternal follow-up in Spanish. Taking into account this knowledge gap, in this work a corpus was generated and characterized from the EHRs of a gynecology and obstetrics service characterized by treating high-risk maternal patients. PLN and text mining methods were implemented on the data, obtaining 659 789 tokens and a dictionary with unique words given by 7 334 tokens. The characterization of the data was developed from the identification of the most frequent words and n-grams and a vector representation of embedding words in a 300-dimensional space was performed using a CBOW (Continuous Bag of Words) neural network architecture. The embedding of words allowed to verify by means of Clustering algorithms, that the words associated to the same group can come to represent associations referring to types of patients, or group similar words, including words written with spelling errors. The corpus generated and the results found lay the foundations for future work in the detection of entities (symptoms, signs, diagnoses, treatments), correction of spelling errors and semantic relationships between words to generate summaries of medical records or assist the follow-up of mothers through the automated review of the electronic health record(AU)

Artificial intelligence based Chinese clinical trials eligibility criteria classification / 生物医学工程学杂志

Subject recruitment is a key component that affects the progress and results of clinical trials, and generally conducted with eligibility criteria (includes inclusion criteria and exclusion criteria). The semantic category analysis of eligibility criteria can help optimizing clinical trials design and building automated patient recruitment system. This study explored the automatic semantic categories classification of Chinese eligibility criteria based on artificial intelligence by academic shared task. We totally collected 38 341 annotated eligibility criteria sentences and predefined 44 semantic categories. A total of 75 teams participated in competition, with 27 teams having submitted system outputs. Based on the results, we found out that most teams adopted mixed models. The mainstream resolution was applying pre-trained language models capable of providing rich semantic representation, which were combined with neural network models and used to fine-tune the models with reference to classifier tasks, and finally improved classification performance could be obtained by ensemble modeling. The best-performing system achieved a macro

Obtención automática de palabras clave en textos clínicos: una aplicación de procesamiento del lenguaje natural a datos masivos de sospecha diagnóstica en Chile / Automatic keyword retrieval from clinical texts: an application of natural language processing to massive data of Chilean suspected diagnosis

Background: Free-text imposes a challenge in health data analysis since the lack of structure makes the extraction and integration of information difficult, particularly in the case of massive data. An appropriate machine-interpretation of electronic health records in Chile can unleash knowledge contained in large volumes of clinical texts, expanding clinical management and national research capabilities. Aim: To illustrate the use of a weighted frequency algorithm to find keywords. This finding was carried out in the diagnostic suspicion field of the Chilean specialty consultation waiting list, for diseases not covered by the Chilean Explicit Health Guarantees plan. Material and Methods: The waiting lists for a first specialty consultation for the period 2008-2018 were obtained from 17 out of 29 Chilean health services, and total of 2,592,925 diagnostic suspicions were identified. A natural language processing technique called Term Frequency-Inverse Document Frequency was used for the retrieval of diagnostic suspicion keywords. Results: For each specialty, four key words with the highest weighted frequency were determined. Word clouds showing words weighted by their importance were created to obtain a visual representation. These are available at cimt.uchile.cl/lechile/. Conclusions: The algorithm allowed to summarize unstructured clinical free-text data, improving its usefulness and accessibility.

Health Information Technology Trends in Social Media: Using Twitter Data / 대한의료정보학회지

OBJECTIVES: This study analyzed the health technology trends and sentiments of users using Twitter data in an attempt to examine the public's opinions and identify their needs. METHODS: Twitter data related to health technology, from January 2010 to October 2016, were collected. An ontology related to health technology was developed. Frequently occurring keywords were analyzed and visualized with the word cloud technique. The keywords were then reclassified and analyzed using the developed ontology and sentiment dictionary. Python and the R program were used for crawling, natural language processing, and sentiment analysis. RESULTS: In the developed ontology, the keywords are divided into ‘health technology‘ and ‘health information‘. Under health technology, there are are six subcategories, namely, health technology, wearable technology, biotechnology, mobile health, medical technology, and telemedicine. Under health information, there are four subcategories, namely, health information, privacy, clinical informatics, and consumer health informatics. The number of tweets about health technology has consistently increased since 2010; the number of posts in 2014 was double that in 2010, which was about 150 thousand posts. Posts about mHealth accounted for the majority, and the dominant words were ‘care‘, ‘new‘, ‘mental‘, and ‘fitness‘. Sentiment analysis by subcategory showed that most of the posts in nearly all subcategories had a positive tone with a positive score. CONCLUSIONS: Interests in mHealth have risen recently, and consequently, posts about mHealth were the most frequent. Examining social media users' responses to new health technology can be a useful method to understand the trends in rapidly evolving fields.

Improving spaCy dependency annotation and PoS tagging web service using independent NER services

Dependency parsing is often used as a component in many text analysis pipelines. However, performance, especially in specialized domains, suffers from the presence of complex terminology. Our hypothesis is that including named entity annotations can improve the speed and quality of dependency parses. As part of BLAH5, we built a web service delivering improved dependency parses by taking into account named entity annotations obtained by third party services. Our evaluation shows improved results and better speed.

Towards cross-platform interoperability for machine-assisted text annotation

In this paper, we investigate cross-platform interoperability for natural language processing (NLP) and, in particular, annotation of textual resources, with an eye toward identifying the design elements of annotation models and processes that are particularly problematic for, or amenable to, enabling seamless communication across different platforms. The study is conducted in the context of a specific annotation methodology, namely machine-assisted interactive annotation (also known as human-in-the-loop annotation). This methodology requires the ability to freely combine resources from different document repositories, access a wide array of NLP tools that automatically annotate corpora for various linguistic phenomena, and use a sophisticated annotation editor that enables interactive manual annotation coupled with on-the-fly machine learning. We consider three independently developed platforms, each of which utilizes a different model for representing annotations over text, and each of which performs a different role in the process.

OryzaGP: rice gene and protein dataset for named-entity recognition

Text mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific papers. However, few thorough studies on text mining and application development, for plant molecular biology data, have been performed, especially for rice, resulting in a lack of datasets available to solve named-entity recognition tasks for this species. Since there are rare benchmarks available for rice, we faced various difficulties in exploiting advanced machine learning methods for accurate analysis of the rice literature. To evaluate several approaches to automatically extract information from gene/protein entities, we built a new dataset for rice as a benchmark. This dataset is composed of a set of titles and abstracts, extracted from scientific papers focusing on the rice species, and is downloaded from PubMed. During the 5th Biomedical Linked Annotation Hackathon, a portion of the dataset was uploaded to PubAnnotation for sharing. Our ultimate goal is to offer a shared task of rice gene/protein name recognition through the BioNLP Open Shared Tasks framework using the dataset, to facilitate an open comparison and evaluation of different approaches to the task.

Resources for assigning MeSH IDs to Japanese medical terms

Medical Subject Headings (MeSH), a medical thesaurus created by the National Library of Medicine (NLM), is a useful resource for natural language processing (NLP). In this article, the current status of the Japanese version of Medical Subject Headings (MeSH) is reviewed. Online investigation found that Japanese-English dictionaries, which assign MeSH information to applicable terms, but use them for NLP, were found to be difficult to access, due to license restrictions. Here, we investigate an open-source Japanese-English glossary as an alternative method for assigning MeSH IDs to Japanese terms, to obtain preliminary data for NLP proof-of-concept.

PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts

Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text. In the domain of medical texts, for chemical entity recognition (CER), techniques based on hand-crafted rules and graph-based models can provide adequate performance. In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. In this work, we adapt an existing neural NER system, NeuroNER, to the particular domain of Spanish clinical case texts, and extend the neural network to be able to take into account additional features apart from the plain text. NeuroNER can be considered a competitive baseline system for Spanish drug and CER promoted by the Spanish national plan for the advancement of language technologies (Plan TL).

Machine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients / 대한의료정보학회지

OBJECTIVES: Triage is a process to accurately assess and classify symptoms to identify and provide rapid treatment to patients. The Korean Triage and Acuity Scale (KTAS) is used as a triage instrument in all emergency centers. The aim of this study was to train and compare machine learning models to predict KTAS levels. METHODS: This was a cross-sectional study using data from a single emergency department of a tertiary university hospital. Information collected during triage was used in the analysis. Logistic regression, random forest, and XGBoost were used to predict the KTAS level. RESULTS: The models with the highest area under the receiver operating characteristic curve (AUROC) were the random forest and XGBoost models trained on the entire dataset (AUROC = 0.922, 95% confidence interval 0.917–0.925 and AUROC = 0.922, 95% confidence interval 0.918–0.925, respectively). The AUROC of the models trained on the clinical data was higher than that of models trained on text data only, but the models trained on all variables had the highest AUROC among similar machine learning models. CONCLUSIONS: Machine learning can robustly predict the KTAS level at triage, which may have many possibilities for use, and the addition of text data improves the predictive performance compared to that achieved by using structured data alone.

Temporal Segmentation for Capturing Snapshots of Patient Histories in Korean Clinical Narrative / 대한의료정보학회지

OBJECTIVES: Clinical discharge summaries provide valuable information about patients' clinical history, which is helpful for the realization of intelligent healthcare applications. The documents tend to take the form of separate segments based on temporal or topical information. If a patient's clinical history can be seen as a consecutive sequence of clinical events, then each temporal segment can be seen as a snapshot, providing a certain clinical context at a specific moment. This study aimed to demonstrate a temporal segmentation method of Korean clinical narratives for identifying textual snapshots of patient history as a proof-of-a-concept. METHODS: Our method uses pattern-based segmentation to approximate human recognition of the temporal or topical shifts in clinical documents. We utilized rheumatic patients' discharge summaries and transformed them into sequences of constituent chunks. We built 97 single pattern functions to denote whether a certain chunk has attributes that indicate that it can be a segment boundary. We manually defined the relationships between the pattern functions to resolve multiple pattern matchings and to make a final decision. RESULTS: The algorithm segmented 30 discharge summaries and processed 1,849 decision points. Three human judges were asked whether they agreed with the algorithm's prediction, and the agreement percentage on the judges' majority opinion was 89.61%. CONCLUSIONS: Although this method is based on manually constructed rules, our findings demonstrate that the proposed algorithm can achieve fairly good segmentation results, and it may be the basis for methodological improvement in the future.

Sentiment Analysis of the Quotations of Intensive Care Unit Survivors in Qualitative Studies / 중환자간호학회지

PURPOSE: As the intensive care unit (ICU) survival rate increases, interest in the lives of ICU survivors has also been increasing. The purpose of this study was to identify the sentiment of ICU survivors.METHOD: The author analyzed the quotations from previous qualitative studies related to ICU survivors; a total of 1,074 sentences comprising 429 quotations from 25 relevant studies were analyzed. A word cloud created in the R program was utilized to identify the most frequent adjectives used, and sentiment and emotional scores were calculated using the Artificial Intelligence (AI) program.RESULTS: The 10 adjectives that appeared the most in the quotations were ‘difficult’, ‘different’, ‘normal’, ‘able’, ‘hard’, ‘bad’, ‘ill’, ‘better’, ‘weak’, and ‘afraid’, in order of decreasing occurrence. The mean sentiment score was negative (-.31±.23), and the three emotions with the highest score were ‘sadness’(.52±.13), ‘joy’(.35±.22), and ‘fear’(.30±.25).CONCLUSION: The natural language processing of AI used in this study is a relatively new method. As such, it is necessary to refine the methodology through repeated research in various nursing fields. In addition, further studies on nursing interventions that improve the coherency of ICU memory of survivors and familial support for the ICU survivors are needed.

GNI Corpus Version 1.0: Annotated Full-Text Corpus of Genomics & Informatics to Support Biomedical Information Extraction

Genomics & Informatics (NLM title abbreviation: Genomics Inform) is the official journal of the Korea Genome Organization. Text corpus for this journal annotated with various levels of linguistic information would be a valuable resource as the process of information extraction requires syntactic, semantic, and higher levels of natural language processing. In this study, we publish our new corpus called GNI Corpus version 1.0, extracted and annotated from full texts of Genomics & Informatics, with NLTK (Natural Language ToolKit)-based text mining script. The preliminary version of the corpus could be used as a training and testing set of a system that serves a variety of functions for future biomedical text mining.