In order to build a national injury surveillance system based on emergency room (ER) visits we are developing a coding system to classify their causes from clinical notes content. Supervised learning techniques have shown good results in this area but require to manually build a large learning annotated dataset. New levels of performance have been recently achieved in neural language models (NLM) with the use of models based on the Transformer architecture with an unsupervised generative pre-training step. Our hypothesis is that methods involving a generative self-supervised pre-training step significantly reduce the number of annotated samples required for supervised fine-tuning. In this case study, we assessed whether we could predict from free text clinical notes whether a visit was the consequence of a traumatic or a non-traumatic event. We compared two strategies: Strategy A consisted in training the GPT-2 NLM on the full 161 930 samples dataset with all labels (trauma/non-trauma). In Strategy B, we split the training dataset in two parts, a large one of 151 930 samples without any label for the self-supervised pre-training phase and a smaller one (up to 10 000 samples) for the supervised fine-tuning with labels. While strategy A needed to process 40 000 samples to achieve good performance (AUC>0.95), strategy B needed only 500 samples, a gain of 80. Moreover, an AUC of 0.93 was measured with only 30 labeled samples processed 3 times (3 epochs). To conclude, it is possible to adapt a multi-purpose NLM model such as the GPT-2 to create a powerful tool for classification of free-text notes with the need of a very small number of labeled samples. Only two modalities (trauma/non-trauma) were predicted for this case study but the same method can be applied for multimodal classification tasks such as diagnosis/disease terminologies.

ABBOTT PARK, Ill., Sept. 12, 2019 -- Abbott announced that new research, published in the journal Circulation, found its algorithm could help doctors in hospital emergency rooms more accurately determine if someone is having a heart attack or not, so that they can receive faster treatments or be safely discharged.1 In this study, researchers from the U.S., Germany, U.K., Switzerland, Australia and New Zealand looked at more than 11,000 patients to determine if Abbott's technology developed using artificial intelligence (AI) could provide a faster, more accurate determination that someone is having a heart attack or not. The study found that the algorithm provided doctors a more comprehensive analysis of the probability that a patient was having a heart attack or not, particularly for those who entered the hospital within the first three hours of when their symptoms started. "With machine learning technology, you can go from a one-size-fits-all approach for diagnosing heart attacks to an individualized and more precise risk assessment that looks at how all the variables interact at that moment in time," said Fred Apple, Ph.D., Hennepin HealthCare/ Hennepin County Medical Center, professor of Laboratory Medicine and Pathology at the University of Minnesota, and one of the study authors. "This could give doctors in the ER more personalized, timely and accurate information to determine if their patient is having a heart attack or not." A team of physicians and statisticians at Abbott developed the algorithm* using AI tools to analyze extensive data sets and identify the variables most predictive for determining a cardiac event, such as age, sex and a person's specific troponin levels (using a high sensitivity troponin-I blood test**) and blood sample timing.

Artificial intelligence is giving medical professionals the ability to create a digital replica of patients, allowing doctors to predict how effective surgery will be. Australia's peak science agency CSIRO is developing AI-powered tools to diagnose mental health disorders more accurately and safeguard patients' genetic data in DNA testing. Northeast Health Wangaratta is already using AI to stop cyber attacks. Using its public hospital in regional Victoria, Northeast Health Wangaratta is investigating the use of AI to create a digital replica of its patients. Doctors then use the replica to trial treatment.

Zygmunt received his PhD degree in Computer Science from the University of Adelaide, Australia in 2013, and his MSc degree in Computer Science from the University of KwaZulu-Natal, South Africa in 2009. He is a senior research fellow at the Australian Institute for Machine Learning. His research lies at the interface of computer vision, machine learning, and challenging industry problems. He develops algorithms that allow computers to perform tasks typically associated with human intelligence. In the last couple of years, his work has focused on the application of machine learning and image processing techniques for the development of smart medical devices.

Over three decades, the Internet has grown from a small network of computers used by research scientists to communicate and exchange data to a technology that has penetrated almost every aspect of our day-to-day lives. Today, it is hard to imagine a life without online access for business, shopping, and socialising. A technology that has connected humanity at a scale never before possible has also amplified some of our worst qualities. Online hate speech spreads virally across the globe with short- and long-term consequences for individuals and societies. These consequences are often difficult to measure and predict. Online social media websites and mobile apps have inadvertently become the platform for the spread and proliferation of hate speech.

Co-organized by Belarus and the United Nations Office of Counter-Terrorism (UNOCT), "Countering terrorism through innovative approaches and the use of new and emerging technologies" concluded on Wednesday in Minsk. The internet "expands technological boundaries literally every day" and AI, 3D printing biotechnology innovations, can help to achieve the Sustainable Development Goals (SDGs), said Vladimir Voronkov, the first-ever Under Secretary-General for the UN Counter-Terrorism Office. The Co-Chair's Summary issued at the end of the @BelarusMFA & @UN_OCT Conference stresses the urgent need to strengthen international cooperation to tackle terrorist abuse of #NewTechnologies & share innovative approaches to counter this threathttps://t.co/55b4vVUq1Y#BY2019UN But it also provides "live video broadcasting of brutal killings", he continued, citing the recent attack in the New Zealand city of Christchurch, where dozens of Muslim worshippers were killed by a self-avowed white supremacist. "This is done in order to spread fear and split society", maintained the UNOCT chief, warning of more serious developments, such as attempts by terrorists to create home-made biological weapons.

So, a few months ago, I was walking down the street in Shenzhen, in the Guangdong Province of southeastern China. I was hungry and looking for lunch. Armed with my credit card and plenty of the local currency, I strode out of my hotel to check out the many street vendors selling delicious-smelling food. Using Google Translate, I was able to order a fried fish dish, but when I went to pay, the vendor refused my credit card. Undaunted I pulled out cash, but that too was refused. The guy pointed me to a large QR code and asked me to pay using the WeChat app. As this was my first day in China, I hadn't yet set the app to pay for things, so I walked away, a little embarrassed. Still hungry, I came to a large junction and saw a promising looking restaurant across a busy street.

An algorithm combining high sensitive troponin testing with personal details can help A&E doctors better determine whether patients are having a heart attack, according to new research. The study, published in medical journal Circulation today, used Abbott's algorithm on 11,000 patients from the UK, the US, Germany, Switzerland, Australia and New Zealand, to see whether it could help deliver faster and more accurate evidence as to whether patients were suffering from a heart attack. Developed using machine learning – a branch of artificial intelligence – the algorithm uses a high sensitivity troponin-I blood test, and the time it was taken, to assess the patient's blood troponin protein levels, combining the results with personal details, such as age and sex, to deliver a bespoke assessment. It is thought this will help get around two current obstacles in heart attack diagnoses. The first is that women are currently at greater risk of misdiagnosis, because their troponin protein levels can be lower than those of men, and international guidelines for the use high sensitive troponin tests do not always account for sex in results.

SimplyGo signals change for ticketing in Singapore There is no doubting the significance of smart ticketing technology for cities worldwide as transport providers strive to create a seamless passenger experience. Intelligent Transport spoke to Yeo Teck Guan, Senior Group Director (Public Transport) at Singapore's Land Transport Authority (LTA), to discuss the country's account-based ticketing system, SimplyGo. Fare's fair: using AI to combat fare evasion At stations all over the world, fare evaders are often difficult to catch and just as difficult to stop. In Catalonia, however, the implementation of AI-based technology has led to the near-eradication of tailgating at fare gates – Oriol Juncadella i Fortuny, Director of FGC Operadora, the operations division of Ferrocarrils de la Generalitat de Catalunya (FGC), tells us more. How Queensland is earning its place among the smart ticketing elite Intelligent Transport sat down with Queensland Transport and Main Roads Minister, Mark Bailey, to learn how the Australian state will touch the future of travel through one of the most advanced ticketing systems in the world, joining the likes of Singapore, New York, London and Chicago on the global stage.

While we know many technology and solutions providers are building machine learning and artificial intelligence (AI) into their solutions, apps and platforms, the concept of what employees think about AI is less well understood. In the contact center, many solutions – most notably quality monitoring and workforce management – employ AI to analyze data, make predictions, and recommend next-best actions. In workforce management, the goal is to find hidden patterns in the historical data used to generate forecasts for volume and work time. These are all very employee-centric solutions, yet new research from Genesys (News - Alert) has found that few employees understand how AI affects them. The Genesys research was drawn from a study of broad attitudes of 1,103 employers and 4,207 employees in the United States, Germany, the United Kingdom, Japan, Australia and New Zealand regarding the current and future effects of AI on their workplaces.