Evaluating the Coverage of three Controlled Health Vocabularies with Focus on Findings, Signs & Symptoms

Proceedings of the Workshop CHAT 2011: Creation, Harmonization and Application of Terminology Resources. Editors: Tatiana Gornostay and Andrejs Vasiļjevs. NEALT Proceedings Series, Vol. 12 (2011), 27-31. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/16956

Identification of Entity References in Hospital Discharge Letters

Proceedings of the 16th Nordic Conference of Computational Linguistics NODALIDA-2007. Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit. University of Tartu, Tartu, 2007. ISBN 978-9985-4-0513-0 (online) ISBN 978-9985-4-0514-7 (CD-ROM) pp. 329-332

Negative vaccine voices in Swedish social media

Vaccinations are one of the most significant interventions to public health, but vaccine hesitancy creates concerns for a portion of the population in many countries, including Sweden. Since discussions on vaccine hesitancy are often taken on social networking sites, data from Swedish social media are used to study and quantify the sentiment among the discussants on the vaccination-or-not topic during phases of the COVID-19 pandemic. Out of all the posts analyzed a majority showed a stronger negative sentiment, prevailing throughout the whole of the examined period, with some spikes or jumps due to the occurrence of certain vaccine-related events distinguishable in the results. Sentiment analysis can be a valuable tool to track public opinions regarding the use, efficacy, safety, and importance of vaccination

Sense-Tagging at the Cycle-Level Using GLDB

This report describes a large-scale attempt to identify automatically the appropriate sense for content words taken from Swedish open-source texts. Sense-tagging, 'the process of assigning the appropriate sense from some kind of lexicon to the (content) words in a text', is a difficult and demanding task in Natural Language Processing and researchers have been engaged in finding a suitable solution to the problem for a very long time. The usefulness of automatically assigning each word in unrestricted text with its most likely sense is necessary for a great spectrum of applications. The sense-tagger described here has been tested both on a random sample of content words, as well as on a large population of a single ambiguous entry. In the first case, the achieved precision was 84,21 %, and in the second 82,75% respectively. Evaluation was made against manually sense-annotated texts

Métodos complementarios para la anonimización de datos en el dominio clínico

En la era de las notas clínicas en formato electrónico, las disponibilidad de datos personales para investigación, planificación y estadísticas sobre salud y seguimiento de enfermedades son algunas de las áreas en las cuales la protección de la información de los pacientes se ha convertido en un importante asunto. El objetivo de este estudio es adaptar y aplicar métodos para la anonimización de documentos, en particular en el dominio clínico. El principal reto y objetivo de esta investigación es mantener importantes conceptos en los documentos en una manera estandar y neutral que significa la encriptación sin violar la integridad de los datos personales y sin sacrificar la calidad y el significado previsto por los autores.In the era of the Electronic Health Record (EHR) the release of individual data for research, public health planning, health care statistics, monitoring of diagnostic tests, automated data collection for health care registries and tracking disease outbreaks are some of the areas in which the protection of Personal Health Information (PHI) has become an important concern. The purpose of this study is to adapt and apply synergetic methods to document de-identification, particularly clinical, or other sources of sensitive data. The main challenge and goal of this research is to retain important concepts and PHI in the documents in a standardized and neutral manner as means of encryption without violating the integrity of the PHI and without sacrificing the quality and intended meaning of the authors

Extraction and Classification of Acoustic Features from Italian Speaking Children with Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are a group of complex developmental conditions whose effects and severity show high intraindividual variability. However, one of the main symptoms shared along the spectrum is social interaction impairments that can be explored through acoustic analysis of speech production. In this paper, we compare 14 Italian-speaking children with ASD and 14 typically developing peers. Accordingly, we extracted and selected the acoustic features related to prosody, quality of voice, loudness, and spectral distribution using the parameter set eGeMAPS provided by the openSMILE feature extraction toolkit. We implemented four supervised machine learning methods to evaluate the extraction performances. Our findings show that Decision Trees (DTs) and Support Vector Machines (SVMs) are the best-performing methods. The overall DT models reach a 100% recall on all the trials, meaning they correctly recognise autistic features. However, half of its models overfit, while SVMs are more consistent. One of the results of the work is the creation of a speech pipeline to extract Italian speech biomarkers typical of ASD by comparing our results with studies based on other languages. A better understanding of this topic can support clinicians in diagnosing the disorder

Digital Linguistic Biomarkers: Beyond Paper and Pencil Tests

noneRecent research has demonstrated that automatically obtainable and analysed quantitative linguistic data, extractable from a person’s verbal productions, can be useful for identifying and classifying individuals with cognitive impairments, at an early stage. Subtle language deficits can be employed as “digital linguistic biomarkers”, namely objective, quantifiable behavioral data which can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Classical pen-and-paper neuropsychological tests are costly and time consuming to process, imposing limitations since manually captured features and results can be prone to human error and bias. This Research Topic aims at bringing together research on digital linguistic biomarkers from different cognitive science subfields. We welcome original research or systematic reviews on the use of Natural Language Processing (NLP) methods and tools for e.g. clinical diagnosis, evaluation of disease severity, and prognosis.openGloria Gagliardi; Dimitrios Kokkinakis; Jon Andoni DunabeitiaGloria Gagliardi; Dimitrios Kokkinakis; Jon Andoni Dunabeiti

Covid-19 vaccine hesitancy : A mixed methods investigation of matters of life and death

In this article, hesitancy towards COVID-19 vaccinations is investigated as a phenomenon touching upon existential questions. We argue that it encompasses ideas of illness and health, and also of dying and fear of suffering. Building on a specific strand within anti-vaccination studies, we conjecture that vaccine hesitancy is, to some extent, reasonable, and that this scepticism should be studied with compassion. Through a mixed methods approach, vaccine hesitancy, as it is being expressed in a Swedish digital open forum, is investigated and understood as, on the one hand, a perceived need of protecting one’s body from techno-scientific experiments, and thus the risk of becoming a victim of medicine itself. On the other hand, the community members express what we call a tacit belief in modern medicine by demonstrating their own “expert” pandemic knowledge. The analysis also shows how the COVID-19 pandemic triggers memories of another pandemic, namely the swine flu in 2009–2010, and what we term a medical crisis that occurred then, due to a vaccine that caused a rare but severe side effect in Sweden and elsewhere

The Prevalence of mRNA Related Discussions During the Post-COVID-19 Era

Vaccinations are one of the most significant interventions to public health, but vaccine hesitancy and skepticism are raising serious concerns for a portion of the population in many countries, including Sweden. In this study, we use Swedish social media data and structural topic modeling to automatically identify mRNA-vaccine related discussion themes and gain deeper insights into how people\u27s refusal or acceptance of the mRNA technology affects vaccine uptake. Our point of departure is a scientific study published in February 2022, which seems to once again sparked further suspicion and concern and highlight the necessity to focus on issues about the nature and trustworthiness in vaccine safety. Structural topic modelling is a statistical method that facilitates the study of topic prevalence, temporal topic evolution, and topic correlation automatically. Using such a method, our research goal is to identify the current understanding of the mechanisms on how the public perceives the mRNA vaccine in the light of new experimental findings

Lexical Parameters, Based on Corpus Analysis of English and Swedish Cancer Data, of Relevance for NLG

Proceedings of the 16th Nordic Conference of Computational Linguistics NODALIDA-2007. Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit. University of Tartu, Tartu, 2007. ISBN 978-9985-4-0513-0 (online) ISBN 978-9985-4-0514-7 (CD-ROM) pp. 333-336