| 1. |
- Kokkinakis, Dimitrios, 1965, et al.
(författare)
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Cluster-Based BERTopic Modeling on Swedish COVID-19 Vaccine Posts
- 2024
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Ingår i: The 34th Medical Informatics Europe Conference. - Amsterdam • Washington, DC : IOS Press. - 0926-9630 .- 1879-8365.
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Konferensbidrag (refereegranskat)abstract
- This paper explores the prevalent themes across multiple threads on the popular Swedish discussion forum Flashback. Among its diverse array of topics, the forum actively engages users in addressing and debating questions pertaining to COVID-19 vaccines and vaccination. Through distinguishing between positive and negative perspectives within posts across 14 relevant thread discussions, we employ BERTopic, a modular topic modeling framework, which utilizes pre-trained language models and applies clustering techniques to identify prevailing topics. This enables us to conduct a nuanced exploration of overarching themes, offering valuable insights into the multifaceted nature of the discussions regarding COVID-19 vaccines and vaccination in Sweden.
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| 2. |
- Kokkinakis, Dimitrios, 1965, et al.
(författare)
-
The Prevalence of mRNA Related Discussions during the Post-COVID-19 Era
- 2023
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Ingår i: Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023. - : IOS Press. - 0926-9630 .- 1879-8365. - 9781643683881 ; 302, s. 798-802
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Konferensbidrag (refereegranskat)abstract
- 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's 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.
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| 3. |
- Kokkinakis, Dimitrios, 1965
(författare)
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What is the Coverage of SNOMED CT® on Scientific Medical Corpora?
- 2011
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Ingår i: Studies in Health Technology and Informatics / XXIII International Conference of the European Federation for Medical Informatics. - 0926-9630. ; 169
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Konferensbidrag (refereegranskat)abstract
- This paper reports on the results of a large scale mapping of SNOMED CT on scientific medical corpora. The aim is to automatically access the validity, reliability and coverage of the Swedish SNOMED-CT translation, the largest, most extensive available resource of medical terminology. The method described here is based on the generation of predominantly safe harbor term variants which together with simple linguistic processing and the already available SNOMED term content are mapped to large corpora. The results show that term variations are very frequent and this may have implication on technological applications (such as indexing and information retrieval, decision support systems, text mining) using SNOMED CT. Naïve approaches to terminology mapping and indexing would critically affect the performance, success and results of such applications. SNOMED CT appears not well-suited for automatically capturing the enormous variety of concepts in scientific corpora (only 6,3% of all SNOMED terms could be directly matched to the corpus) unless extensive variant forms are generated and fuzzy and partial matching techniques are applied with the risk of allowing the recognition of a large number of false positives and spurious results.
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| 4. |
- Lundholm Fors, Kristina, 1977, et al.
(författare)
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Automated Syntactic Analysis of Language Abilities in Persons with Mild and Subjective Cognitive Impairment
- 2018
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Ingår i: Building continents of knowledge in oceans of data : the future of co-created eHealth: proceedings of MIE2018, 24-26 April 2018, Gothenburg, Sweden / edited by Adrien Ugon, Daniel Karlsson, Gunnar O. Klein and Anne Moen.. - Amsterdam : IOS Press. - 0926-9630 .- 1879-8365. - 9781614998518
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Konferensbidrag (refereegranskat)abstract
- In this work we analyze the syntactic complexity of transcribed picture descriptions using a variety of automated syntactic features, and investigate the features’ predictive power in classifying narratives from people with subjective and mild cognitive impairment and healthy controls. Our results indicate that while there are no statistically significant differences, syntactic features can still be moderately successful at distinguishing the participant groups when used in a machine learning framework.
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