Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.

Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach

BACKGROUND

While scientific knowledge of post-COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians.

OBJECTIVE

In this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline's potential as a surveillance tool.

METHODS

We used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several countries.

RESULTS

UmlsBERT-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuropsychiatric (such as anxiety and brain fog), and respiratory (such as shortness of breath). In addition, we also found novel symptom and condition terms that had not been categorized in previous studies, such as infection and pain. Regarding the co-occurring symptoms, the pair of fatigue and headaches was among the most co-occurring term pairs across both platforms. Based on the temporal analysis, the neuropsychiatric terms were the most prevalent, followed by the systemic category, on both social media platforms. Our spatial analysis concluded that 42% (10,938/26,247) of the analyzed terms included location information, with the majority coming from the United States, United Kingdom, and Canada.

CONCLUSIONS

The outcome of our social media-derived pipeline is comparable with the results of peer-reviewed articles relevant to PCC symptoms. Overall, this study provides unique insights into patient-reported health outcomes of PCC and valuable information about the patient's journey that can help health care providers anticipate future needs.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1101/2022.12.14.22283419.

A Novel Foodborne Illness Detection and Web Application Tool Based on Social Media

Foodborne diseases and outbreaks are significant threats to public health, resulting in millions of illnesses and deaths worldwide each year. Traditional foodborne disease surveillance systems rely on data from healthcare facilities, laboratories, and government agencies to monitor and control outbreaks. Recently, there is a growing recognition of the potential value of incorporating social media data into surveillance systems. This paper explores the use of social media data as an alternative surveillance tool for foodborne diseases by collecting large-scale Twitter data, building food safety data storage models, and developing a novel frontend foodborne illness surveillance system. Descriptive and predictive analyses of the collected data were conducted in comparison with ground truth data reported by the U.S. Centers for Disease Control and Prevention (CDC). The results indicate that the most implicated food categories and the distributions from both Twitter and the CDC were similar. The system developed with Twitter data could complement traditional foodborne disease surveillance systems by providing near-real-time information on foodborne illnesses, implicated foods, symptoms, locations, and other information critical for detecting a potential foodborne outbreak.

An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain

Dealing with food safety issues in time through online public opinion incidents can reduce the impact of incidents and protect human health effectively. Therefore, by the smart technology of extracting the entity relationship of public opinion events in the food field, the knowledge graph of the food safety field is constructed to discover the relationship between food safety issues. To solve the problem of multi-entity relationships in food safety incident sentences for few-shot learning, this paper adopts the pipeline-type extraction method. Entity relationship is extracted from Bidirectional Encoder Representation from Transformers (BERTs) joined Bidirectional Long Short-Term Memory (BLSTM), namely, the BERT-BLSTM network model. Based on the entity relationship types extracted from the BERT-BLSTM model and the introduction of Chinese character features, an entity pair extraction model based on the BERT-BLSTM-conditional random field (CRF) is established. In this paper, several common deep neural network models are compared with the BERT-BLSTM-CRF model with a food public opinion events dataset. Experimental results show that the precision of the entity relationship extraction model based on BERT-BLSTM-CRF is 3.29%∼23.25% higher than that of other models in the food public opinion events dataset, which verifies the validity and rationality of the model proposed in this paper.