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Intelligence artificielle pour la détection des intoxications alimentaires

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UNIVERSITY OF MARILAND 18/11/20 How AI Tactics May Spare Your GI Tract - NIFA Grant Supports New Approach to Fight Foodborne Illness. Keep meat and dairy foods refrigerated, wash your hands, cook thoroughly—these are some of the best ways to stave off foodborne illnesses, which each year sicken an estimated one in six Americans and cause about 3,000 deaths—but there’s only so much even the most cautious consumer can do.

UNIVERSITY OF MARILAND 18/11/20 How AI Tactics May Spare Your GI Tract - NIFA Grant Supports New Approach to Fight Foodborne Illness

Now, supported by a $500,000 National Institute of Food and Agriculture grant, University of Maryland researchers are looking to enhance food safety risk assessment models, starting with that reliable “stomach flu” pairing—salmonella and chicken. THE SPOON 29/05/19 Will More Artificial Intelligence Lead to Fewer Foodborne Illnesses at Restaurants? Chick-fil-A is now using AI to monitor social media feedback from customers in order to detect and prevent foodborne illness, QSR reports.

THE SPOON 29/05/19 Will More Artificial Intelligence Lead to Fewer Foodborne Illnesses at Restaurants?

At last week’s ReWork Deep Learning Summit in Boston, Chick-fil-A’s senior principle IT leader of food safety and product quality, Davis Addy, explained how this tech works. For most QSRs, gathering feedback from social media is key for getting insights into what’s working and what isn’t with the business. That includes spotting any mentions of food safety issues or potential foodborne illnesses like norovirus, which on average causes 19–21 million cases of acute gastroenteritis annually in the U.S. Restaurants and catered events are one of the most common settings for norovirus, according to the CDC.

Hence, more AI. Chick-fil-A says its system currently operates on 78 percent accuracy and that the company is working with Amazon to improve those numbers. Once it’s done that, AI’s job ends. BIORXIV 23/02/21 Machine learning to predict the source of campylobacteriosis using whole genome data. Sensors and Actuators B: Chemical Volume 309, 15 April 2020, Single-cell classification of foodborne pathogens using hyperspectral microscope imaging coupled with deep learning frameworks. Rui Kang received his M.S. degree in 2015 from College of Engineering of Nanjing Agricultural University, Nanjing, China.

Sensors and Actuators B: Chemical Volume 309, 15 April 2020, Single-cell classification of foodborne pathogens using hyperspectral microscope imaging coupled with deep learning frameworks

Currently, he is a PhD student in Nanjing Agricultural University and a visiting student in the United States Department of Agriculture (USDA), Agricultural Research Service (ARS), U.S. National Poultry Research Center (USNPRC), Athens, GA, USA. His research focuses on developing advanced analytical methods for hyperspectral microscope imaging technology for quality and safety assurance in food and agricultural products. Bosoon Park received his Ph. D. degree from the Texas A&M University in 1991. Matthew B. Qin Ouyang received her Ph.D. degree in Food Science and Engineering of Jiangsu University, Zhenjiang, China, in 2015. Kunjie Chen received his Ph.D. degrees in 2005 from College of Engineering of Nanjing Agricultural University, Nanjing, China.

FOOD SAFETY MAAZINE - DEC 2019 - Artificial Intelligence and Food Safety: Hype vs. Reality. Cover Story | December 2019/January 2020 By Kristen M.

FOOD SAFETY MAAZINE - DEC 2019 - Artificial Intelligence and Food Safety: Hype vs. Reality

Altenburger, A.M., and Daniel E. Ho, Ph.D. STATNEWS 22/10/19 Restaurants are using AI to listen in on social media. They want to know how you are feeling. You probably don’t know it yet, but you’ve got some new social media followers.

STATNEWS 22/10/19 Restaurants are using AI to listen in on social media. They want to know how you are feeling

And they’re very interested in how you’re feeling right this instant. Chick-fil-A recently deployed a new artificial intelligence-based system to monitor millions of social media accounts for food safety issues at its 2,400-plus locations nationwide. The company’s custom algorithm tracks troublesome health mentions around its restaurants, listening in on Twitter, Facebook, and other platforms for “puke,” “got sick” and similar targeted keywords that could indicate a potential outbreak of foodborne illness.

It’s similar to the Harvard T.H. MAA 22/11/18 Intelligence artificielle dans les services publics : le ministère de l'Agriculture se distingue. FRONT. MICROBIOL. 06/08/19 Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks. 1.

FRONT. MICROBIOL. 06/08/19 Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks

Introduction. SWISS MEDICAL WEEKLY 08/04/17 Precision global health in the digital age. QSR MAGAZINE - MAI 2019 - Chick-fil-A Turns to AI to Fight Foodborne Illness - The chicken chain has developed custom technology to monitor social media and identify outbreaks. Chick-fil-A is turning to a high-tech method to detect foodborne illness.

QSR MAGAZINE - MAI 2019 - Chick-fil-A Turns to AI to Fight Foodborne Illness - The chicken chain has developed custom technology to monitor social media and identify outbreaks.

In a proactive move, the chain is deploying an artificial intelligence program to monitor social media to spot signs of an outbreak. Per VentureBeat, Davis Addy, Chick-fil-A’s senior principal IT leader of food safety and product quality, outlined May 23 how the company plans to use the technology during a presentation at the ReWork Deep Learning Summit in Boston. “For us in this journey with analytics and food safety, we’re going from a place of hindsight to insight … and eventually foresight so we can be more proactive in helping our restaurants better identify and address food safety risks,” Addy said.

Every year, norovirus causes 19­–21 million illnesses and between 570–800 deaths, according to the CDC. Most of these outbreaks occur in foodservice settings—restaurants, schools, nursing homes, hospitals, day care centers, military barracks, universities and cruise ships. JMIR Public Health Surveill. 2019 Apr 1;5(2):e11477. Wet Markets and Food Safety: TripAdvisor for Improved Global Digital Surveillance. MAA CEP 14/12/18 Machine Learning au service de l'épidémiologie : ciblage des restaurants à inspecter par les services sanitaires aux États-Unis. En novembre 2018 ont été diffusés les résultats de travaux menés, aux États-Unis, par Google, l'université Harvard et les départements de santé et d'innovation de Las Vegas et Chicago, visant à améliorer le ciblage des restaurants à contrôler par les services sanitaires.

MAA CEP 14/12/18 Machine Learning au service de l'épidémiologie : ciblage des restaurants à inspecter par les services sanitaires aux États-Unis

L'équipe de recherche a tout d'abord mis en place un algorithme de détection des requêtes, lancées sur le navigateur web Google, concernant des problèmes de santé consécutifs à la consommation d'aliments dans des conditions sanitaires médiocres. L'algorithme permet de distinguer les personnes effectivement malades de celles faisant des recherches dans un autre cadre : par exemple, les médecins et les étudiants peuvent se renseigner sur certains symptômes sans être eux-mêmes atteints. 52,3 % des restaurants, identifiés par FINDER et contrôlés par les services sanitaires, se sont avérés non conformes lors des inspections, contre 24,7 % en temps normal.

Source : npj Digital Medicine. UNIVERSITY OF NEVADA - MAI 2017 - Thèse en ligne : Determining the Effects of Social Media Monitoring to Identify Potential Foodborne Illness in Southern Nevada. CONSUMER DATA RESEARCH CENTER - Using Big Data to Identify Outbreaks of Food PoisoningUsing New Data and Novel Methods to Undertake Syndromic Surveillance to Identify Outbreaks of Food Poisoning. Twenty-Eighth AAAI Conference on Innovative Applications - 2016 - Deploying nEmesis: Preventing Foodborne Illness by Data Mining Social Media. SPRINGER 15/06/17 Prospective Detection of Foodborne Illness Outbreaks Using Machine Learning Approaches.

SLATE 08/11/18 Google sait quel restaurant vous a rendu malade. Temps de lecture: 2 min — Repéré sur Quartz Une intoxication alimentaire n’est jamais une expérience agréable.

SLATE 08/11/18 Google sait quel restaurant vous a rendu malade

Pour lutter contre, les épidémiologistes cherchent à savoir comment identifier le plus rapidement possible les sources de maladies causées par de la nourriture. D’après un article récemment paru dans le NPJ Digital Medicine, la solution pourrait résider dans l’intelligence artificielle (comme, semble-t-il, pour tous les problèmes actuels). Une équipe de recherche employée par Google a travaillé avec des spécialistes en santé publique pour développer un algorithme pouvant identifier des restaurants suspectés de causer des intoxications alimentaires.

Appelé FINDER pour Foodborn IllNess DEtector in Real time (et gagnant par la même occasion la médaille de l’acronyme le plus tiré par les cheveux), le programme est très efficace. Données personnelles FINDER utilise à la fois l’historique de recherche Google et son service de géolocalisation. SCIENCE NEWS 06/11/18 Computer model identifies sources of foodborne illnesses more accurately. A new computer model that uses machine learning and de-identified and aggregated search and location data from logged-in Google users was significantly more accurate in identifying potentially unsafe restaurants when compared with existing methods of consumer complaints and routine inspections, according to new research led by Google and Harvard T.H.

SCIENCE NEWS 06/11/18 Computer model identifies sources of foodborne illnesses more accurately

Chan School of Public Health. The findings indicate that the model can help identify lapses in food safety in near real time. "Foodborne illnesses are common, costly, and land thousands of Americans in emergency rooms every year. This new technique, developed by Google, can help restaurants and local health departments find problems more quickly, before they become bigger public health problems," said corresponding author Ashish Jha, K.T.

ROCHESTER INSTITUTE OF TECHNOLOGY 19/05/16 Thèse en ligne : Developing a Prototype System for Syndromic Surveillance and Visualization Using Social Media Data. PUBLIC HEALTH INFORMATICS - 2016 - ISDS 2015 Conference Abstracs - A Digital Platform for Local Foodborne Illness and Outbreak Surveillance. Online J Public Health Inform. 2018; 10(1): e120. Evaluating Twitter for Foodborne Illness Outbreak Detection in New York City. NATURE 06/11/18 Machine-learned epidemiology: real-time detection of foodborne illness at scale. Experimental design FINDER is a machine-learned model for real-time foodborne illness detection. To determine the ability of FINDER to detect potentially unsafe restaurants, we introduced FINDER into two local health departments in Chicago and Las Vegas. In each city, FINDER-identified restaurants were inspected following the same protocol used in other restaurant inspections.

The results of the FINDER inspections were then compared to the overall baseline inspection results, as well as to two subsets of baseline inspections, complaint-based inspections, and routine inspections that are conducted at certain time intervals. Analyses were further stratified by restaurant risk level. NATIONAL SCIENCE FOUNDATION 07/03/16 Fighting food poisoning in Las Vegas with machine learning. News Release 16-025 University of Rochester-developed app helps health departments track foodborne illnesses and improves inspections March 7, 2016 This material is available primarily for archival purposes. Telephone numbers or other contact information may be out of date; please see current contact information at media contacts. It's happened to many of us. Foodborne illness afflicts 48 million people annually in the U.S. alone; 120,000 individuals are hospitalized annually, and 3,000 die from the illness. Journal of the American Medical Informatics Association, 00(0), 2018, Discovering foodborne illness in online restaurant reviews.

JMIR Public Health Surveill. 2018 Apr-Jun; 4(2): e57. Identifying Methods for Monitoring Foodborne Illness: Review of Existing Public Health Surveillance Techniques. Int. J. Environ. Res. Public Health 2018, 15(5), 833; Evaluating the Implementation of a Twitter-Based Foodborne Illness Reporting Tool in the City of St. Louis Department of Health. HARVARD_EDU 06/11/18 Computer model more accurate at identifying potential sources of foodborne illnesses than traditional methods. For immediate release: November 6, 2018 Boston, MA – A new computer model that uses machine learning and de-identified and aggregated search and location data from logged-in Google users was significantly more accurate in identifying potentially unsafe restaurants when compared with existing methods of consumer complaints and routine inspections, according to new research led by Google and Harvard T.H.

Chan School of Public Health. The findings indicate that the model can help identify lapses in food safety in near real time. “Foodborne illnesses are common, costly, and land thousands of Americans in emergency rooms every year. This new technique, developed by Google, can help restaurants and local health departments find problems more quickly, before they become bigger public health problems,” said corresponding author Ashish Jha, K.T. The study was published online November 6, 2018 in npj Digital Medicine. In Las Vegas, the model was deployed between May and August 2016. Photo: iStock. FOOD SAFETY MAGAZINE 06/11/17 Technology’s Role in Eradicating Foodborne Illness.

Signature Series | November 6, 2017 By Mahni Ghorashi Food recalls cost an average of $15 million per incident and cause significant harm to brands’ reputation and credibility. They can also cause significant harm to individuals. In the U.S. alone, foodborne illnesses make 48 million people sick and are responsible for 3,000 fatalities every year.[1] Investment in best practices, effective consumer education, and responsible regulation continue to be vital to combating these costly outbreaks. FEDERALREPORTER_NIH_GOV - Projet de recherche 2015-2020 - INCORPORATING SOCIAL MEDIA MONITORING SOFTWARE INTO HEALTH DEPARTMENT FOODBORNE ILLNESS SURVEILLANCE AND RESPONSE.

EFSA - 2018 - e-Inventory of Research Ideas 2018 Utrecht. ECONOMIC INQUIRY 12/07/16 BIG DATA AND BIG CITIES: THE PROMISES AND LIMITATIONS OF IMPROVED MEASURES OF URBAN LIFE. Historically, most research on urban areas has relied on coarse aggregate statistics and smaller‐scale surveys. Over the past decade, however, digitization of records, expansion of sensor networks, and the computerization of society has produced a wealth of city data at high temporal frequencies and low levels of spatial and temporal aggregation.

The “big data” revolution will fundamentally change urban science. Big data turns a cross section of space into living data, offering a broader and finer picture of urban life than has ever been available before. Moreover, in combination with predictive algorithms, big data may allow us to extrapolate outcome variables to previously unmeasured parts of the population. Nevertheless, classical issues of causal inference remain—big data rarely solves identification problems on its own. Big data is also improving city management. DAILYMAIL_CO_UK 08/11/18 Google algorithm can monitor searches for 'diarrhea' and other symptoms of food poisoning in real time to warn you of unsafe restaurant.

COLUMBIA_EDU 10/01/18 NYC Health Department Identifies 10 Outbreaks of Foodborne Illness Using Yelp Reviews Since 2012. CLEMSON_EDU - 2017 - Detecting and monitoring foodborne illness outbreaks: Twitter communications and the 2015 U.S. Salmonella outbreak linked to. CENTRE DE COLLABORATION NATIONALE DES MALADIES INFECTIEUSES - 2017 - Utilité des mégadonnées en surveillance des maladies infectieuses et contribution possible aux enquêtes sur les maladies d’origine alimentaire au Canada. ARIZONA INFECTIOUS DISEASE TRAINING 19/07/17 Présentation : Improving foodborne complaint and outbreak detection using social media, New York City.