Natural Language Terms Research Articles

Automatic inference of ICD-10 codes from German ophthalmologic physicians’ letters using natural language processing

Physicians’ letters are the optimal source of diagnoses for registries. However, most registries demand for diagnosis codes such as ICD-10. We herein describe an algorithm that infers ICD-10 codes from German ophthalmologic physicians’ letters. We assess the method in three German eye hospitals. Our algorithm is based on the nearest-neighbor method as well as on a large thesaurus for ICD-10 codes. This thesaurus was embedded into a Word2Vec space created from anonymized physicians’ reports of the first hospital. For evaluation, each of the three hospitals sent all diagnoses taken from 100 letters. The inferred ICD-10 codes were evaluated for correctness by the senders. A total of 3332 natural language terms had been sent in (812 hospital one, 1473 hospital two, 1047 hospital three). A total of 526 non-diagnoses were excluded upfront. 2806 ICD-10 codes were inferred (771 hospital one, 1226 hospital two, 809 hospital three). In the first hospital, 98% were fully correct and 99% correct at the level of the superordinate disease concept. The percentages in hospital two were 69% and 86%. The respective numbers for hospital three were 69% and 91%. Our simple method is capable of inferring ICD-10 codes for German natural language diagnoses, especially when the embedding space has been built with physicians’ letters from the same hospital. The method may yield sufficient accuracy for many tasks in the multi-centric setting and can easily be adapted to other languages/specialities.

Anatomical location and number of injection sites of intravesical OnabotulinumtoxinA for females with refractory idiopathic overactive bladder: Ascoping review.

The negative impact on quality of life and the economic-related burden to the patient and the health care system associated with idiopathic overactive bladder (iOAB) is well-documented. Intradetrusor OnabotulinumtoxinA (BTN/A) injections are a well-used treatment modality for the management of overactive detrusor refractory to medical management, with well-documented efficacy and safety profiles. There is currently no best practice guideline for the administration of BTN/A for this procedure and historically the trigone of the bladder has been excluded from the injection paradigm given the risk of vesicoureteral reflux (VUR). A scoping review methodology was employed to assess available literature to evaluate current techniques used. There is emerging literature that the inclusion of the trigone may increase the efficacy of the procedure, while maintaining a similar adverse effect profile. Similar results could also be obtained by decreasing the number of injection sites. A scoping review was completed with systematic methodology using the Preferred Systematic Reviews and Meta Analyses extension for Scoping Review checklist. The search strategy looked to evaluate BTN/A and number of injection sites and the inclusion of the trigone in female patients with iOAB. Studies with male or neurogenic bladder only were excluded. Mixed studies were included. A specialist research librarian was engaged, with supervision from a functional urologist using a combination of MeSH and natural language terms. Two investigators independently reviewed the titles and abstracts. Twelve articles were included and were published between 2005 and 2021. There was no evidence of VUR in any of the results. All but one study reported similar if not improved efficacy of trigone-inclusion. Lower number of injection sites had similar efficacy profiles to higher numbers of intradetrusor injections. Further high-quality randomized control trials of trigone inclusion and reduction of injection sites are required. It is hoped that with further exploration of intraoperative methods for BTN/A injections, the development of universally accepted guidelines may optimize management and experiences for patients with iOAB.

РИМСКОЕ ПРАВО И ЮРИСПРУДЕНЦИЯ У ИСТОКОВ ПРАВОВЕДЕНИЯ ЦИФРОВОЙ ЭПОХИ (ПОНЯТИЙНО-ТЕРМИНОЛОГИЧЕСКИЙ АСПЕКТ)

The article considers methodological features of Roman law and jurisprudence within the context of their concepto-terminological foundations. The study argues the naturalized character of Roman legal imago mundi, which became the ontological base of legal dogmatics. Taking from this Roman legal constructs were thought as functio-nominative entities derived from the natural language (terms for the legal events and processes), and not as abstract theoretical structures (concepts). The research proves the way of Roman legal cognition to be organized in the logic of “an object — a nomination — a construct”. Affecting objectively the organization of continental legal thought, this method remains in demand in jurisprudence of digital age. The author shows that basic constructs of legal dogma predetermined the impossibility of representation of many phenomena and processes of modern high-tech world, especially digital property. The study concludes that methodological issues are most significant for the legal science in digital age.

APPLICATION OF FUZZY LOGIC IN COMPUTER SYSTEMS OF MEDICAL DIAGNOSIS

The features of medical diagnostics that affect the quality of decision-making in decision support systems are considered. The purpose of this article is to briefly introduce various soft computing methodologies and present various applications in medicine. Formalized expert information on the given sets of diagnoses and their symptoms. A method has been developed for generating a diagnostic inference during therapeutic examinations based on fuzzy logic. The goal is to demonstrate the possibilities of applying soft computing to problems related to medicine. A transition is made from fuzzy values to certain physical parameters that can serve as commands to the actuator. The result of fuzzy inference, of course, will be fuzzy, but for the executive device it means absolutely nothing, therefore, to eliminate fuzziness, special mathematical methods can be applied to obtain the exact values transmitted by a number of diseases directly to the performer in medical diagnostics. Fuzzy logic and fuzzy set theory is a branch of mathematics that generalizes classical logic and set theory. The fuzzy system copes with such a task very quickly, despite the fact that instead of complex differential equations, the entire process of movement is described in terms of natural language: “norm”, “average”, “high” - as if the operator controls the system. Keywords: medical diagnostics, fuzzy logic, decision support system, fuzzy systems.

Deep learning and knowledge graph for image/video captioning: A review of datasets, evaluation metrics, and methods

AbstractGenerating an image/video caption has always been a fundamental problem of Artificial Intelligence, which is usually performed using the potential of Deep Learning Methods, Computer Vision, Knowledge Graphs, and Natural Language Processing (NLP). The significant task of image/video captioning is to describe visual content in terms of natural language. Due to a semantic gap, this presents a massive problem in understanding and explaining images or videos syntactically and semantically. The current systems need somewhere to fill the gap between low‐level and high‐level features while mapping. Therefore, to tackle this problem, there is a need to describe the latest research and methods to overcome difficulties and to propose effective solutions. This work thoroughly analyses and investigates the most related methods (deep learning and knowledge graph‐based approaches), benchmark datasets, and evaluation metrics with their benefits and limitations. Here we have also reviewed the state‐of‐the‐art methods related to image/video captioning and their applications in the current scenario. Finally, we provide thorough information on existing research with comparisons of results on benchmark datasets. We have also mentioned the existing challenges and future direction of research.

Measurements from "per" without complex dimensions

To what extent is the compositional structure of quantity terms in natural language aligned with the structure of the quantity calculus commonly used in scientific practice, a calculus that critically relies on mathematical operations like division and the computation of quotients? In pioneering work, Coppock (2021) addresses this general question through a case study on the English preposition "per", as in "0.9 grams per milliliter". Coppock proposes that "per" expresses the operation of quantity division, an operation that forms quantities like 0.9g/mL by using ratios of measurements from different dimensions. Here we show that this “division theory” of "per" makes the wrong prediction with respect to statements about measures of density and concentration. We argue that these types of expressions call for an “anaphoric theory” ofper. On this analysis, anaphora allows for the composition to invoke multiple measurements in basic dimensions, creating the appearance of reference to complex quantities like 0.9g/mL, even though no such quantities are actually composed nor denoted in the formal semantics.

SAMPURNA- A Multi-Functional Medical Assistant Application Framework

Abstract: In recent times, the need for the advancement of medical facilities has been majorly realised and a lot of efforts have been made in the same direction. Healthcare is vital to leading a healthy and prosperous life. However, there are still certain arenas where there is a need for improvement so that people can benefit the most. The COVID-19 pandemic made everyone realise that there is nothing more important than their own health and family. During the peak times when India was trembling with the COVID waves, in spite of the ceaseless efforts from the doctors, there were still many people who remained unattended and eventually perished because of the lack of awareness and aid facilities. Had there been a system through which they could have gotten the aid on time, they wouldn’t have died a helpless death. Here we bring a system which has the capability of solving such issues for one and all. This Multi-Functional Medical Assistant Framework, as the name suggests, would provide the patient or the person in need with medical aid. The Chatbot will provide the exact medical composition that would suite their metabolism in an orchestrated and expedited manner. In this paper we have provided the design, the internal functioning, and the implementation of our application. General Terms Natural Processing Language Pattern Matching Artificial Intelligence Medicine. Keywords: Chatbots, Health, Bots, Natural Language, Covid-19

A Rapid Review of Environmental Health Gaps in Antimicrobial Resistance and Water-Related Research from 1990-2020.

Antimicrobial resistance (AMR) is a pervasive global health threat linked to human antimicrobial misuse and abuse, food production, and broader environmental contamination. While global agencies promote a multi-sectoral One Health system approach to equitably combat human, animal, and environmental health AMR risks, it is widely acknowledged that the human and animal sectors dominate discussions. Given this disproportionate focus, identification of critical research gaps is needed to develop stewardship plans that equitably address One Health AMR threats. This review used natural language processing and term frequency algorithms to classify 12,638 records from 1990–2020 thematically in order to highlight sectoral prioritization and gaps. It also specifically assessed water-related gaps as water is recognized as both a primary environmental dissemination pathway and key means of intervention. Drawing from systemic health and integrated water management lenses, this review found that themes related to plant, wildlife, and environmental-related AMR threats—in particular, the role that environmental (ambient) waters play in AMR development, transmission, and spread—are under-prioritized as compared to human and food animal health concerns regardless of geographic region or income level. Further prioritization of these themes is needed to strengthen the environmental dimension of One Health AMR responses and systemically protect global health.

Artificial Intelligence-Assisted Editorial Tools: Case Studies

MODERATOR: Jonathan Schultz Director, Journal Operations American Heart Association Cantonsville, Maryland SPEAKERS: Robyn Mugridge Publishing Partnerships Manager Frontiers United Kingdom Hannah Hutt Product Manager Frontiers Lausanne, Switzerland Jennifer M Chapman Senior Managing Editor American Society of Civil Engineers Reston, Virginia Daniel Evanko Director of Journal Operations and Systems American Association for Cancer Research Philadelphia, Pennsylvania REPORTER: Ellen F Lazarus Freelance technical and medical editor Lazarus Editing and Consulting, LLC Washington, DC Journal publishers are increasingly turning to artificial intelligence (AI) technologies to address the challenges and complexities of scientific editing and publishing. The types of challenges addressed are limited only by the imagination and ingenuity of the AI system designers and end-users. In this session, Robyn Mugridge and Hannah Hutt (Publishing Partnerships Manager and Product Manager at Frontiers), Jennifer Chapman (Senior Managing Editor at the American Society of Civil Engineers [ASCE]), and Daniel Evanko (Director of Journal Operations and Systems at the American Association for Cancer Research [AACR]) presented 3 cases studies involving specific journal publication challenges and the proprietary and public AI-assisted editorial tools used to solve them. Jonathan Schultz, Editor-In-Chief of Science Editor and Director of Journal Operations at the American Heart Association, started the session with a nontechnical introduction to the terms AI, machine learning, data/text mining, and natural language processing. (This session was generally limited to the application of the tools and did not cover technical aspects of their development and implementation.) Case 1: Addressing Shared Challenges in Journal Publication by Developing an AI-Assisted Editorial […]

MODELLING SEMANTIC KERNEL OF WEB RESOURCE

The article presents an attempt to describe mathematically the effect of the semantic kernel of a web resource on the Internet. In accordance with the theory of marketing, the product that we want to sell on the network is characterized by the following basic properties: price, time and place. In other words, a potential buyer wants to receive a given product in the right place at a given time. To satisfy this need, it is necessary to use the classic component of marketing, product promotion. However, this component is now becoming a fully virtual instrument. This tool functions in a hypertext, video and image environment. Therefore, the user analyzes the meaning of these elements in order to get the desired product. The results of web projects carried out in this area indicate the emergence of a new phenomenon, which reflects the main meaning of virtual promotion – this is the semantic core. The core is a short annotation of the main properties of the product, its location and time of appearance. Therefore, the purpose of this article is both a presentation of a new object of research and a mathematical description. It is assumed that the semantic core is formed on the basis of natural language terms. In other words, the semantic core is a set of keywords that are grouped by meaning. We propose to use data mining approaches for clustering to group terms. The classic clustering method at the moment is k-means. The article presents a model of the semantic core based on this method. This method and its distance function are considered as the second stage of web content processing. At the first stage, web content is converted into a semantic web. However, the k-means technique has significant drawbacks when modeling the semantic core. Therefore, in the development of this idea, the work shows an alternative way to modeling the kernel. As an alternative approach, the construction of clusters based on the concept of maximum flow is considered. This approach has the significant advantage that the type of links in the semantic network overlaps with the type of distance function in this method. As a result, on a real web project, the effect of the connection between the semantic core model and the level of new users of the web resource was demonstrated over the past five years.
 Keywords: semantic kernel, keyword, k-means, max flow.

The Impact of Postoperative Complications on Long-Term Oncological Outcomes Following Curative Resection of Colorectal Cancer (Stage I-III): A Systematic Review and Meta-Analysis.

Background and objectiveThe incidence of post-surgical complications (PSCs) after curative resection of non-metastatic colorectal cancer (CRC) is very widespread and evident. Some studies suggest that PSCs often predict poor long-term oncological outcomes. However, the available data on the topic is often controversial. The aim of this systematic review and meta-analysis was to study the effect of postoperative complications (POCs) on long-term oncological outcomes following curative resection of non-metastatic (stage I-III) CRC.MethodsA comprehensive search of MEDLINE® and Excerpta Medica dataBASE (EMBASE) databases was performed via the Ovid platform, by using controlled vocabulary as well as natural language terms for POCs, outcomes, and CRC. Two authors independently screened the studies and extracted data. Conflicts were resolved by discussion among authors and also independently with the help of a third author. Meta-analysis was performed using a random-effects model (REM) to calculate pooled estimates for overall survival (OS), disease-free survival (DFS), and overall recurrence.ResultsOverall, 3,836 studies were initially screened, and 16 studies involving 37,192 patients were ultimately selected for final inclusion in the analysis. Meta-analysis of these studies showed that PSCs following non-metastatic CRC surgery predicted worse OS rates [hazard ratio (HR): 1.36; 95% CI: 1.15-1.61; p<0.00001], DFS (HR: 1.41; 95% CI: 1.11-1.80; p<0.00001), and overall recurrence (HR: 1.19; 95% CI: 1.04-1.37; p=0.01).ConclusionBased on our findings, PSCs predict poor OS rates, DFS, and overall recurrence following curative resection of non-metastatic CRC.

Does Legal Epistemology Rest on a Mistake? On Fetishism, Two-Tier System Design, and Conscientious Fact-Finding

Legal epistemology seems to be exploding. More and more philosophers seem to be taking an interest in the theory of evidence law, and to bring along with them to legal theory the freshest news from the abstract study of epistemology . This is understandable, of course: The law in general, and evidence law in particular, seems to be employing the same natural-language terms epistemologists are (or are at least supposed to be) interested in (“knew or should have known”, “reasonable doubt”, “evidence”, “presumption” (of innocence), and so on) . In this paper we argue that a large part of this project is based on a mistake, roughly analogous to the mistake involved in thinking of studies of intelligence as relevant to the understanding of military intelligence. With qualifications shortly to emerge, epistemology is not, we think, intrinsically and directly relevant to normative evidence law theory, at least, that is, as long as the relevant perspective is that of designing the evidence law regime. When it comes to the conscientious fact-finder, things may be – we’re not sure about this – importantly different.

Entrelazando la compasión y la competencia cultural en hospitalización: Una revisión de experiencias

El mundo globalizado actual expresa importantes desafíos para el cuidado de enfermería, de manera que se reconozca y alivie el sufrimiento teniendo en cuenta la cultura del sujeto de cuidado. Objetivo: Describir los hallazgos de investigaciones sobre experiencias de enfermeras de hospitalización con respecto a la compasión y el cuidado a personas culturalmente diversas. Método. Revisión sistematizada. Las búsquedas fueron realizadas en: Web of Science, CINAHL, OVID Nursing, Evidence Based Medicine Reviews, Medline (PubMed), PsycARTICLES, Biblioteca Virtual en Salud, CUIDEN Plus, TripDatabase, Epistemonikos y el Descubridor de la Universidad Nacional de Colombia. Se ejecutaron 83 búsquedas con términos MeSH, DeCS y lenguaje natural, incluyendo artículos de investigación o revisión con abordajes cualitativos, publicados en español, inglés o portugués. Resultados: Se incluyeron 42 estudios, con predominio de diseños fenomenológicos, etnográficos y de teoría fundamentada. Estos reflejan el vínculo entre compasión y el cuidado a personas culturalmente diversas, desarrollado a través de tres temas y 14 subtemas. Conclusión. Las experiencias de enfermeras y pacientes son diversas, pero es visible la conexión entre compasión y competencia cultural en el cuidado de enfermería en el ámbito clínico. Esta sistematización muestra necesidades de formación y entrenamiento, refleja tensiones entre cultura personal, profesional e institucional.

Nursing Diagnoses for Coronavirus Disease, COVID-19: Identification by Taxonomic Triangulation.

To identify the nursing care problems related to the clinical process of disease by COVID-19. The study applied the taxonomic triangulation technique on a clinical management guide to coronavirus disease, COVID-19, from the World Health Organization. The technique is divided into the phases: extraction of knowledge in natural language about assessment, planning and intervention, translation into standard language NOC and NIC, linking to NANDA-I diagnoses, triangulation looking for diagnostic matches in the three sets, and, finally, validation by a panel of experts from a hospital and a university. The extraction identified 159 terms in natural language that were translated into 173 variables: 34 NOC for assessment, 19 NOC for planning, and 120 NIC for intervention. The relationships to NANDA-I diagnoses recorded 2,182 links and the triangulation returned 109 diagnoses, 54 of them for a critical situation. The panel of experts unanimously validated the 29 diagnoses with the highest number of links. Coronavirus disease, COVID-19, involves a complex situation with multiple associated care problems that can be identified using the taxonomic triangulation technique. The links between taxonomies and the taxonomic triangulation technique are an important tool for generating knowledge. The results of this study may guide the diagnosis and treatment of coronavirus disease, COVID-19, as well as similar processes that occur with acute respiratory distress syndrome.

Hyperintensional Reasoning Based on Natural Language Knowledge Base

The success of automated reasoning techniques over large natural-language texts heavily relies on a fine-grained analysis of natural language assumptions. While there is a common agreement that the analysis should be hyperintensional, most of the automatic reasoning systems are still based on an intensional logic, at the best. In this paper, we introduce the system of reasoning based on a fine-grained, hyperintensional analysis. To this end we apply Tichy’s Transparent Intensional Logic (TIL) with its procedural semantics. TIL is a higher-order, hyperintensional logic of partial functions, in particular apt for a fine-grained natural-language analysis. Within TIL we recognise three kinds of context, namely extensional, intensional and hyperintensional, in which a particular natural-language term, or rather its meaning, can occur. Having defined the three kinds of context and implemented an algorithm of context recognition, we are in a position to develop and implement an extensional logic of hyperintensions with the inference machine that should neither over-infer nor under-infer.

Matemática, linguagem e tradução

O discurso matemático não é possível sem o vasto uso de uma língua natural. Seus símbolos referem-se, acima de tudo, a termos da língua natural. Seus textos são uma combinação de símbolos, língua natural, diagramas, e assim por diante. Ler esses textos de forma coerente significa estar envolvido na atividade da tradução. A matemática aplicada, assim como a física, muda constantemente de uma linguagem (e cultura) para outra e, portanto, é melhor compreendida no âmbito dos Estudos da Tradução.

Inferring Knowledge from Textual Data by Natural Deduction

In this paper, we introduce the system for inferring implicit computable knowledge from textual data by natural deduction. Our background system is Transparent Intensional Logic (TIL) with its procedural semantics that assigns abstract procedures known as TIL constructions to terms of natural language as their context-invariant meanings. The input data for our method are produced by the so-called Normal Translation Algorithm (NTA). The algorithm processes natural-language texts and produces TIL constructions. In this way we have obtained a large corpus of TIL meaning procedures. These procedures are furthermore processed by our algorithms for type checking and context recognition so that the rules of natural deduction for inferring computable knowledge can be afterwards applied.

Inferring Knowledge from Textual Data by Natural Deduction

In this paper, we introduce the system for inferring implicit computable knowledge from textual data by natural deduction. Our background system is Transparent Intensional Logic (TIL) with its procedural semantics that assigns abstract procedures known as TIL constructions to terms of natural language as their context-invariant meanings . The input data for our method are produced by the so-called Normal Translation Algorithm (NTA). The algorithm processes natural-language texts and produces TIL constructions. In this way we have obtained a large corpus of TIL meaning procedures. These procedures are furthermore processed by our algorithms for type checking and context recognition so that the rules of natural deduction for inferring computable knowledge can be afterwards applied.

An extended solution to recommend fuzzy MDX queries for decision makers by collaborative filtering profile

Education decision-makers need to analyse the success rate of the baccalaureate according to different criteria. In this paper, we propose to extend our previous solution where we have defined only three terms in natural language. Our extended solution consists modelling a data warehouse according to the Fasel model. It allows the recommendation of MDX queries which contain these terms in natural language according to the decision-maker's profile. We present an overview of the work related to our subject and we present the fuzzy data model together with the meta tables and the modelling of the membership functions. Finally, we present our results. Furthermore, we have carried out a comparative study between the use of SQL and MDX requests and we have concluded that the use of MDX made the system more efficient.

СПОСОБ ФОРМИРОВАНИЯ ДЕСКРИПТИВНОЙ МОДЕЛИ ПРОЦЕССА ОПЕРАТИВНОГО ВОССТАНОВЛЕНИЯ ИЗДЕЛИЙ ВВТ ГРУППИРОВКИ ПВО НА ОСНОВЕ ОНТОЛОГИЙ

The operative restoration process of weapons and military equipment of the air defense group is considered from the point of view of its model representation, intended for the automation of the duty personnel activities of the situational management of repair and maintenance center. In this paper, the reduction in the time required to generate operational information is associated with the improvement of automation tools for information support of the process under consideration by switching to formalized models that belong to the descriptive class. Ontologies are used as a mathematical tool for creating such models. Despite the use of ontologies for model representation of various subject areas, there is no way to construct a descriptive model of the presented process using the mathematical apparatus of ontologies. We propose a two-level structure of the descriptive model of this process in the form of a set of subject and applied ontologies. We consider the procedures that ensure the formation of a unified ontology of the process under consideration. The peculiarity of using the descriptive model is to allow the personnel of the duty shift of the situational management center to use natural language terms to form the requests for obtaining the necessary operational information. This helps to increase the information content of requests from the personnel of the duty shift of the situational management center. The use of a descriptive model in automation tools is aimed at reducing the time of the operative restoration of weapons and military equipment of the air defense group by reducing the time of formation of operational information necessary for the implementation of the activities of the personnel of the duty shift of the situational management center.