Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a class-imbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world \textit{TWEET-FID} data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts.

Scientists at a university in Scotland have developed a technique which could help to identify the source of food poisoning in a better way than curre

Moreover, poor quality can become dangerous if faults cause injuries or other complications. Fortunately, no matter what kind of company you have, artificial intelligence or AI may assist with your quality assurance needs. Here are six examples, along with AI and QA tips you could adopt for your organisation. One of the areas where AI is proving its worth for quality assurance is in the software development sector. AI seems particularly well-suited to regression testing.

Artificial intelligence could one day be used to tailor education to the needs of each individual child.Credit: Suzanne Kreiter/The Boston Globe/Getty People produce more than 2.5 quintillion bytes of data each day. Businesses are harnessing these riches using artificial intelligence (AI) to add trillions of dollars in value to goods and services each year. Amazon dispatches items it anticipates customers will buy to regional hubs before they are purchased. Thanks to the vast extractive might of Google and Facebook, every bakery and bicycle shop is the beneficiary of personalized targeted advertising. But governments have been slow to apply AI to hone their policies and services.

Google may soon tell you which restaurants could give you food poisoning. The tech giant is working with Harvard University to develop an algorithm that analyzes Google searches to spot which restaurants might have food safety issues. Researchers say it's capable of flagging possible offenders in'near real time.' They created a machine-learning based algorithm to identify unsafe restaurants, training it to look for specific search terms and location data. The model is called FINDER, or Foodborne Illness Detector in Real Time.

Worried your go-to hole-in-the-wall might not have a stellar food safety record? Google's new artificially intelligent (AI) system can help lay your fears to rest -- or confirm the worst of them. A study led by researchers at the Mountain View company and Harvard's T.H. Chan School of Public Health describes a machine learning model -- FINDER (Foodborne IllNess DEtector in Real time) -- that leverages search and location data to identify "potentially unsafe" restaurants. Their paper ("Machine-learned epidemiology: real-time detection of foodborne illness at scale") was published today in the journal npj Digital Medicine. "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," Ashish Jha, K.T. Li Professor of Global Health at Harvard Chan School and director of the Harvard Global Health Institute, said.

Google could soon tell you which restaurants are more likely to give you food poisoning, thanks to an algorithm that can identify lapses in food safety in near real time. Working with researchers from Harvard University, Google tested a machine-learned model in Chicago and Las Vegas to identify user search queries such as "stomach cramps" or "diarrhea", and then cross-referenced them with saved location history data -- in particular recently-visited food establishments -- from the smartphones used to make those searches. Health inspectors were then sent to a number of restaurants: some identified by Google's model as potentially unsafe, and others identified by traditional methods, such as consumer complaints -- the inspectors didn't know which. When the researchers compared the model with routine inspections by health departments in Las Vegas and Chicago, they found that the overall rate across both cities of unsafe restaurants detected by the model was 52.3 percent, whereas the overall rate of detection of unsafe restaurants via routine inspections across the two cities was 22.7 percent. Study co-author and Google research scientist Evgeniy Gabrilovich said the model could play a major role in combatting the persistent problem of foodborne illness.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.

Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection. The group of bacteria known as Salmonella includes many different types that vary in the severity of the disease they cause. Some types cause food poisoning, known as gastrointestinal Salmonella, whereas others cause severe disease by spreading beyond the gut, for example Salmonella Typhi which causes typhoid fever.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.