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dr inż. Aleksandra Nabożny
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- asystent w Katedra Inżynierii Oprogramowania
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wszystkich: 6
Katalog Publikacji
Rok 2022
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Improving medical experts’ efficiency of misinformation detection: an exploratory study
PublikacjaFighting medical disinformation in the era of the pandemic is an increasingly important problem. Today, automatic systems for assessing the credibility of medical information do not offer sufficient precision, so human supervision and the involvement of medical expert annotators are required. Our work aims to optimize the utilization of medical experts’ time. We also equip them with tools for semi-automatic initial verification...
Rok 2021
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Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning
PublikacjaMethods Results Discussion References Abbreviations Copyright Abstract Background: The spread of false medical information on the web is rapidly accelerating. Establishing the credibility of web-based medical information has become a pressing necessity. Machine learning offers a solution that, when properly deployed, can be an effective tool in fighting medical misinformation on the web. Objective: The aim of this study is to...
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Focus on Misinformation: Improving Medical Experts’ Efficiency of Misinformation Detection
PublikacjaFighting medical disinformation in the era of the global pandemic is an increasingly important problem. As of today, automatic systems for assessing the credibility of medical information do not offer sufficient precision to be used without human supervision, and the involvement of medical expert annotators is required. Thus, our work aims to optimize the utilization of medical experts’ time. We use the dataset of sentences taken...
Rok 2019
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Enriching the Context: Methods of Improving the Non-contextual Assessment of Sentence Credibility
PublikacjaThis paper presents several methods of automatic context enrichment of sentences that need to be evaluated, tagged or fact-checked by human judges. We have created a corpus of medical Web articles. Sentences from this corpus have been fact-checked by medical experts in two modes: contextually (reading the entire article and evaluating sentence by sentence) and without context (evaluating sentences from all articles in random order)....
Rok 2018
Rok 2015
wyświetlono 1106 razy