Application of natural language processing algorithms for extracting information from news articles in event-based surveillance

Ng, Victoria; Rees, Erin E.; Niu, Jingcheng; Zaghlool, Abdelhamid

doi:10.14745/ccdr.v46i06a06

Cited by 15 publications

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“…Also, alerts could be triggered if a number of cases of a given disease surpass predefined thresholds. Other applications of IE from social media through NLP techniques include: the classification of articles and determination of the geographical location of vulnerable populations -both according to threats of particular diseases-, the use of dates to order the appearance of diseases, the use of the number of cases to forecast disease progression [7], and situational awareness and identification of at-risk populations or conditions as an opportunity to enhance public health [8]. In order to develop supervised machine learning algorithms and also to test the developed algorithms, annotated corpora are needed.…”

Section: Introductionmentioning

confidence: 99%

“…The annotated corpus, contains on one side articles' titles and on the other hand complete articles, allowing researchers to select the part of the article that best suits their needs, 2. We published the annotation criteria and guidelines used to annotate the corpus [10], 7 which can help as a guide to other researchers interested in generating their own corpus, 3. We report the techniques developed for automatically extracting information from the articles, and the conclusions of their use on article titles and complete texts.…”

Section: Introductionmentioning

confidence: 99%

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Digital surveillance in Latin American diseases outbreaks: information extraction from a novel Spanish corpus

et al. 2022

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Background In order to detect threats to public health and to be well-prepared for endemic and pandemic illness outbreaks, countries usually rely on event-based surveillance (EBS) and indicator-based surveillance systems. Event-based surveillance systems are key components of early warning systems and focus on fast capturing of data to detect threat signals through channels other than traditional surveillance. In this study, we develop Natural Language Processing tools that can be used within EBS systems. In particular, we focus on information extraction techniques that enable digital surveillance to monitor Internet data and social media. Results We created an annotated Spanish corpus from ProMED-mail health reports regarding disease outbreaks in Latin America. The corpus has been used to train algorithms for two information extraction tasks: named entity recognition and relation extraction. The algorithms, based on deep learning and rules, have been applied to recognize diseases, hosts, and geographical locations where a disease is occurring, among other entities and relations. In addition, an in-depth analysis of micro-average F1 metrics shows the suitability of our approaches for both tasks. Conclusions The annotated corpus and algorithms presented could leverage the development of automated tools for extracting information from news and health reports written in Spanish. Moreover, this framework could be useful within EBS systems to support the early detection of Latin American disease outbreaks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital surveillance in Latin American diseases outbreaks: information extraction from a novel Spanish corpus

et al. 2022

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“…Online news data contains critical information about emerging health threats such as what happened, where it happened, when, and to whom it happened [11].…”

Section: Introductionmentioning

confidence: 99%

Token-Level Multilingual Epidemic Dataset for Event Extraction

Mutuvi

Boroş

Doucet

et al. 2021

Linking Theory and Practice of Digital Libraries

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In this paper, we present a dataset and a baseline evaluation for multilingual epidemic event extraction. We experiment with a multilingual news dataset which we annotate at the token level, a common tagging scheme utilized in event extraction systems. We approach the task of extracting epidemic events by first detecting the relevant documents from a large collection of news reports. Then, event extraction (disease names and locations) is performed on the detected relevant documents. Preliminary experiments with the entire dataset and with ground-truth relevant documents showed promising results, while also establishing a stronger baseline for epidemiological event extraction.

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“…In recent decades, models based on artificial intelligence techniques have performed impressive predictions in different knowledge fields. For example, in exact sciences (Sauceda et al, 2021;Hajibabaei and Kim, 2021;Cerioti et al, 2021;Bahlke et al, 2020;Chmiela et al, 2020;Saar et al, 2021;Artrith and Urban, 2016;Ch'ng et al, 2017;Shallue and Vanderburg, 2018;Sadowski et al, 2016), in social sciences (Ng et al, 2020;Lattner and Grachten, 2019), in technology (Bae et al, 2021;Cunneen et al, 2019;Feldt et al, 2018;Huang et al, 2014), and health sciences (Bashyam et al, 2020;Zhou et al, 2019;Themistocleous et al, 2021;Lagree et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

Bokhimi

2021

Front. Chem. Eng.

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We describe the use of artificial intelligence techniques in heterogeneous catalysis. This description is intended to give readers some clues for the use of these techniques in their research or industrial processes related to hydrodesulfurization. Since the description corresponds to supervised learning, first of all, we give a brief introduction to this type of learning, emphasizing the variables X and Y that define it. For each description, there is a particular emphasis on highlighting these variables. This emphasis will help define them when one works on a new application. The descriptions that we present relate to the construction of learning machines that infer adsorption energies, surface areas, adsorption isotherms of nanoporous materials, novel catalysts, and the sulfur content after hydrodesulfurization. These learning machines can predict adsorption energies with mean absolute errors of 0.15 eV for a diverse chemical space. They predict more precise surface areas of porous materials than the BET technique and can calculate their isotherms much faster than the Monte Carlo method. These machines can also predict new catalysts by learning from the catalytic behavior of materials generated through atomic substitutions. When the machines learn from the variables associated with a hydrodesulfurization process, they can predict the sulfur content in the final product.

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Application of natural language processing algorithms for extracting information from news articles in event-based surveillance

Cited by 15 publications

References 0 publications

Digital surveillance in Latin American diseases outbreaks: information extraction from a novel Spanish corpus

Digital surveillance in Latin American diseases outbreaks: information extraction from a novel Spanish corpus

Token-Level Multilingual Epidemic Dataset for Event Extraction

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

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