Public Health and Epidemiology Informatics

Thiébaut, Rodolphe; Thiessard, Frantz

doi:10.15265/iy-2017-036

Cited by 10 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The surveillance of epidemics or infectious disease outbreaks using web-based search data (also known as digital surveillance) has been a recurring topic in the literature in recent years [50]. Several researchers have been interested in monitoring vector-borne diseases using web-based and social media data [51].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Machine Learning Approaches for Predicting Potential Lyme Disease Cases and Incidence Rates in United States Using Twitter

Boligarla

Laison

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Lyme disease is one of the most commonly reported infectious diseases in the United States (US), accounting for more than 90% of all vector-borne diseases in North America. Objective: In this paper, self-reported tweets on Twitter were analyzed in order to predict potential Lyme disease cases and accurately assess incidence rates in the US. Methods: The study was done in three stages: (1) Approximately 1.3 million tweets were collected and and pre-processed to extract the most relevant Lyme disease tweets with geolocations. The tweets were manually labelled as relevant or irrelevant to Lyme disease using a set of precise keywords, yielding a curated labelled dataset of 77500 tweets. (2) This labelled data set was used to train, validate, and test various combinations of NLP word embedding methods and prominent ML classification models, such as TF-IDF and logistic regression, Word2vec and XGboost, and BERTweet, among others, to identify potential Lyme disease tweets. (3) Lastly, the presence of spatio-temporal patterns in the US over a 10-year period were studied. Results: Preliminary results showed that BERTweet outperformed all tested NLP classifiers for identifying Lyme disease tweets, achieving the highest classification accuracy and F1-score of 90%. There was also a consistent pattern indicating that the West and Northeast regions of the US had a higher tweet rate over time. Conclusions: We focused on the less-studied problem of using Twitter data as a surveillance tool for Lyme disease in the US. Several crucial findings have emerged from the study. First, there is a fairly strong correlation between classified tweet counts and Lyme disease counts, with both following similar trends. Second, in 2015 and early 2016, the social media network like Twitter was essential in raising popular awareness of Lyme disease. Third, counties with a high incidence rate were not necessarily related with a high tweet rate, and vice versa. Fourth, BERTweet can be used as a reliable NLP classifier for detecting relevant Lyme disease tweets.

show abstract

Section: Introductionmentioning

confidence: 99%

Leveraging Machine Learning Approaches for Predicting Potential Lyme Disease Cases and Incidence Rates in United States Using Twitter

Boligarla

Laison

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The aim of this survey is to provide a view on the main opportunities, challenges, and practical implications. It is worth noting that reviews on epidemiology and public health within the Al/big data/data analytics areas are not in short supply, and that the previous two editions of the IMIA Yearbook have dealt with this specific subject 22 , 23 , providing a very interesting perspective of the worldwide efforts that have been done in the field in the last years. Therefore, and for the sake of providing a different perspective, we have decided to focus this paper on two specific areas which fall within the scope of public health and epidemiology, but, to the best of our knowledge, have not been previously addressed in depth.…”

Section: Introductionmentioning

confidence: 99%

Public Health and Epidemiology Informatics: Can Artificial Intelligence Help Future Global Challenges? An Overview of Antimicrobial Resistance and Impact of Climate Change in Disease Epidemiology

2019

View full text Add to dashboard Cite

Objectives: To provide an oveiview of the current application of artificial intelligence (AI) in the field of public health and epidemiology, with a special focus on antimicrobial resistance and the impact of climate change in disease epidemiology. Both topics are of vital importance and were included in the “Ten threats to global health in 2019“ report published by the World Health Organization. Methods: We analysed publications that appeared in the last two years, between January 2017 and October 2018. Papers were searched using Google Scholar with the following keywords: public health, epidemiology, machine learning, data analytics, artificial intelligence, disease surveillance, climate change, antimicrobial resistance, and combinations thereof. Selected articles were organised by theme. Results: In spite of a large interest in AI generated both within and outside the scientific community, and of the many opinions pointing towards the importance of a better use of data in public health, few papers have been published on the selected topics in the last two years. We identify several potential reasons, including the complexity of the integration of heterogeneous data, and the lack of sound and unbiased validation procedures. Conclusions: As there is a better comprehension of AI and more funding available, artificial intelligence will become not only the centre of attention in informatics, but more importantly the source of innovative solutions for public health.

show abstract

“…As compared to 2017 literature analyzed in the Public Health and Epidemiology Informatics section of the International Medical Informatics Association (IMIA) Yearbook 1 , in addition to Precision Public Health or Digital epidemiology, a new term has appeared in 2018: infodemiology and infoveillance 2 3 4 . A large number of the papers published in Public Health informatics is about the epidemiological surveillance based on the new data generated in the current digital era.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Surveillance in Public Health

Thiébaut

Cossin

Informatics³

2019

Yearb Med Inform

View full text Add to dashboard Cite

Objectives: To introduce and summarize current research in the field of Public Health and Epidemiology Informatics. Methods: The 2018 literature concerning public health and epidemiology informatics was searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 15 candidate best papers. These papers were then peer-reviewed by external reviewers to give the editorial team an enlightened selection of the best papers. Results: Among the 805 references retrieved from PubMed and Web of Science, three were finally selected as best papers. All three papers are about surveillance using digital tools. One study is about the surveillance of flu, another about emerging animal infectious diseases and the last one is about foodborne illness. The sources of information are Google news, Twitter, and Yelp restaurant reviews. Machine learning approaches are most often used to detect signals. Conclusions: Surveillance is a central topic in public health informatics with the growing use of machine learning approaches in regards of the size and complexity of data. The evaluation of the approaches developed remains a serious challenge.

show abstract

Public Health and Epidemiology Informatics

Cited by 10 publications

References 19 publications

Leveraging Machine Learning Approaches for Predicting Potential Lyme Disease Cases and Incidence Rates in United States Using Twitter

Leveraging Machine Learning Approaches for Predicting Potential Lyme Disease Cases and Incidence Rates in United States Using Twitter

Public Health and Epidemiology Informatics: Can Artificial Intelligence Help Future Global Challenges? An Overview of Antimicrobial Resistance and Impact of Climate Change in Disease Epidemiology

Artificial Intelligence for Surveillance in Public Health

Contact Info

Product

Resources

About