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Oh S, Byeon H, Wijaya J. Machine learning surveillance of foodborne infectious diseases using wastewater microbiome, crowdsourced, and environmental data. WATER RESEARCH 2024; 265:122282. [PMID: 39178596 DOI: 10.1016/j.watres.2024.122282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 08/14/2024] [Accepted: 08/15/2024] [Indexed: 08/26/2024]
Abstract
Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, effective CP disease surveillance is essential for preventive and therapeutic interventions; however, conventional practices often entail complex, resource-intensive, and costly procedures. This study introduced a data-driven machine learning (ML) modeling framework for CP-related disease surveillance. It leveraged an integrated dataset of municipal wastewater microbiome (e.g., CP abundance), crowdsourced (CP-related web search keywords), and environmental data. Various optimization strategies, including data integration, data normalization, model selection, and hyperparameter tuning, were implemented to improve the ML modeling performance, leading to enhanced predictions of CP cases over time. Explainable artificial intelligence methods identified CP abundance as the most reliable predictor of CP disease cases. Multi-omics subsequently revealed the presence of CP and its genotypes/toxinotypes in wastewater, validating the utility of microbiome-data-enabled ML surveillance for foodborne diseases. This ML-based framework thus exhibits significant potential for complementing and reinforcing existing disease surveillance systems.
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Affiliation(s)
- Seungdae Oh
- Department of Civil Engineering, College of Engineering, Kyung Hee University, Yongin, Republic of Korea.
| | - Haeil Byeon
- Department of Civil Engineering, College of Engineering, Kyung Hee University, Yongin, Republic of Korea
| | - Jonathan Wijaya
- Department of Civil Engineering, College of Engineering, Kyung Hee University, Yongin, Republic of Korea
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Liu J, Bensimon J, Lu X. Frontiers of machine learning in smart food safety. ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 111:35-70. [PMID: 39103217 DOI: 10.1016/bs.afnr.2024.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Abstract
Integration of machine learning (ML) technologies into the realm of smart food safety represents a rapidly evolving field with significant potential to transform the management and assurance of food quality and safety. This chapter will discuss the capabilities of ML across different segments of the food supply chain, encompassing pre-harvest agricultural activities to post-harvest processes and delivery to the consumers. Three specific examples of applying cutting-edge ML to advance food science are detailed in this chapter, including its use to improve beer flavor, using natural language processing to predict food safety incidents, and leveraging social media to detect foodborne disease outbreaks. Despite advances in both theory and practice, application of ML to smart food safety still suffers from issues such as data availability, model reliability, and transparency. Solving these problems can help realize the full potential of ML in food safety. Development of ML in smart food safety is also driven by social and industry impacts. The improvement and implementation of legal policies brings both opportunities and challenges. The future of smart food safety lies in the strategic implementation of ML technologies, navigating social and industry impacts, and adapting to regulatory changes in the AI era.
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Affiliation(s)
- Jinxin Liu
- Department of Food Science and Agricultural Chemistry, McGill University, Montreal, QC, Canada
| | - Jessica Bensimon
- Department of Food Science and Agricultural Chemistry, McGill University, Montreal, QC, Canada
| | - Xiaonan Lu
- Department of Food Science and Agricultural Chemistry, McGill University, Montreal, QC, Canada.
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Mu W, Kleter GA, Bouzembrak Y, Dupouy E, Frewer LJ, Radwan Al Natour FN, Marvin HJP. 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. Compr Rev Food Sci Food Saf 2024; 23:e13296. [PMID: 38284601 DOI: 10.1111/1541-4337.13296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/25/2023] [Accepted: 12/15/2023] [Indexed: 01/30/2024]
Abstract
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.
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Affiliation(s)
- Wenjuan Mu
- Wageningen Food Safety Research, Wageningen University and Research, Wageningen, The Netherlands
| | - Gijs A Kleter
- Wageningen Food Safety Research, Wageningen University and Research, Wageningen, The Netherlands
| | - Yamine Bouzembrak
- Information Technology, Wageningen University, Wageningen University and Research, Wageningen, The Netherlands
| | - Eleonora Dupouy
- Food and Agriculture Organization of the United Nations, Rome, Italy
| | - Lynn J Frewer
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK
| | | | - H J P Marvin
- Hayan Group B.V., Research department, Rhenen, The Netherlands
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Dolatabadi E, Moyano D, Bales M, Spasojevic S, Bhambhoria R, Bhatti J, Debnath S, Hoell N, Li X, Leng C, Nanda S, Saab J, Sahak E, Sie F, Uppal S, Vadlamudi NK, Vladimirova A, Yakimovich A, Yang X, Kocak SA, Cheung AM. Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach. J Med Internet Res 2023; 25:e45767. [PMID: 37725432 PMCID: PMC10510753 DOI: 10.2196/45767] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 05/18/2023] [Accepted: 06/05/2023] [Indexed: 09/21/2023] Open
Abstract
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.
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Affiliation(s)
- Elham Dolatabadi
- Faculty of Health, School of Health Policy and Management, York University, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
- Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | | | | | - Rohan Bhambhoria
- Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada
| | | | | | | | - Xin Li
- Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | | | - Jad Saab
- TELUS Health, Montreal, QC, Canada
| | - Esmat Sahak
- Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Fanny Sie
- Hoffmann-La Roche Ltd, Toronto, ON, Canada
| | | | - Nirma Khatri Vadlamudi
- Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | | | | | | | | | - Angela M Cheung
- Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
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Tao D, Hu R, Zhang D, Laber J, Lapsley A, Kwan T, Rathke L, Rundensteiner E, Feng H. A Novel Foodborne Illness Detection and Web Application Tool Based on Social Media. Foods 2023; 12:2769. [PMID: 37509861 PMCID: PMC10379420 DOI: 10.3390/foods12142769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 07/18/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
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.
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Affiliation(s)
- Dandan Tao
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China
| | - Ruofan Hu
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Dongyu Zhang
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Jasmine Laber
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Anne Lapsley
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Timothy Kwan
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Liam Rathke
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Elke Rundensteiner
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Hao Feng
- College of Agricultural & Environmental Sciences, North Carolina A & T State University, Greensboro, NC 27411, USA
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Zhang Q, Li M, Dong W, Zuo M, Wei S, Song S, Ai D. An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7773259. [PMID: 35528358 PMCID: PMC9071985 DOI: 10.1155/2022/7773259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 11/17/2022]
Abstract
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.
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Affiliation(s)
- Qingchuan Zhang
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
- School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
| | - Menghan Li
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
- School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
| | - Wei Dong
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
- School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
| | - Min Zuo
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
- School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
| | - Siwei Wei
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
- School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
| | - Shaoyi Song
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
- School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
| | - Dongmei Ai
- Basic Experimental of Natural Science, University of Science and Technology Beijing, Beijing 100083, China
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