Probabilities of causation (PoC) are valuable concepts for explainable artificial intelligence and practical decision-making. PoC are originally defined for scalar binary variables. In this paper, we extend the concept of PoC to continuous treatment and outcome variables, and further generalize PoC to capture causal effects between multiple treatments and multiple outcomes. In addition, we consider PoC for a sub-population and PoC with multi-hypothetical terms to capture more sophisticated counterfactual information useful for decision-making. We provide a nonparametric identification theorem for each type of PoC we introduce. Finally, we illustrate the application of our results on a real-world dataset about education.

For an increasing number of women over 40, age really is just a number. It may not be possible to stop the passage of time -- but certain healthy habits can help slow down biological age, experts say. "As we age, our abilities to perform certain physical and cognitive tasks decline, while our risks for disease and ultimately death increase," Chris Mirabile, CEO and founder of NOVOS, a longevity supplements company in New York, told Fox News Digital. "Although these changes are correlated with chronological age, biological age is a more accurate predictor, because it looks at individuals and how well – or poorly – they are aging." If a 40-year-old woman has a biological age of 35, it implies that she is biologically in the same place as an average 35-year-old, Mirabile said – which means a significant reduction in risk for disease and death, plus an increased capacity for activities associated with a high quality of life.

Modern Software Engineering era is moving fast with the assistance of artificial intelligence (AI), especially Large Language Models (LLM). Researchers have already started automating many parts of the software development workflow. Requirements Engineering (RE) is a crucial phase that begins the software development cycle through multiple discussions on a proposed scope of work documented in different forms. RE phase ends with a list of user-stories for each unit task identified through discussions and usually these are created and tracked on a project management tool such as Jira, AzurDev etc. In this research we developed a tool "GeneUS" using GPT-4.0 to automatically create user stories from requirements document which is the outcome of the RE phase. The output is provided in JSON format leaving the possibilities open for downstream integration to the popular project management tools. Analyzing requirements documents takes significant effort and multiple meetings with stakeholders. We believe, automating this process will certainly reduce additional load off the software engineers, and increase the productivity since they will be able to utilize their time on other prioritized tasks.

In this paper, we introduce BiMediX, the first bilingual medical mixture of experts LLM designed for seamless interaction in both English and Arabic. Our model facilitates a wide range of medical interactions in English and Arabic, including multi-turn chats to inquire about additional details such as patient symptoms and medical history, multiple-choice question answering, and open-ended question answering. We propose a semi-automated English-to-Arabic translation pipeline with human refinement to ensure high-quality translations. We also introduce a comprehensive evaluation benchmark for Arabic medical LLMs. Furthermore, we introduce BiMed1.3M, an extensive Arabic-English bilingual instruction set covering 1.3 Million diverse medical interactions, resulting in over 632 million healthcare specialized tokens for instruction tuning. Our BiMed1.3M dataset includes 250k synthesized multi-turn doctor-patient chats and maintains a 1:2 Arabic-to-English ratio. Our model outperforms state-of-the-art Med42 and Meditron by average absolute gains of 2.5% and 4.1%, respectively, computed across multiple medical evaluation benchmarks in English, while operating at 8-times faster inference. Moreover, our BiMediX outperforms the generic Arabic-English bilingual LLM, Jais-30B, by average absolute gains of 10% on our Arabic medical benchmark and 15% on bilingual evaluations across multiple datasets. Our project page with source code and trained model is available at https://github.com/mbzuai-oryx/BiMediX .

More than 150,000 adults and children with type 1 diabetes are now eligible to get an'artificial pancreas'. NHS regulators have today approved hybrid closed-loop system technology, which experts say is the'biggest breakthrough since insulin'. The high-tech device continuously tracks blood sugar levels through a sensor stuck to the body. Readings are fed straight back to a body-worn insulin pump, with an algorithm then calculating how much of the hormone needs to be released. An artificial pancreas to manage type 1 diabetes could soon be offered to NHS patients after a major trial produced'blisteringly brilliant' early results.

Keeping blood sugar under control can improve outcomes in patients, but that's not always easy to do. Now, doctors are using artificial intelligence to help! Researchers are now using the technology to help people with Type 2 diabetes get their disease under control. "Diabetes is the leading cause of blindness," explained Mary Vouyiouklis Kellis, an endocrinologist at the Cleveland Clinic. "It can also affect kidney function. It can affect nerves, it can increase the risk for lower limb amputation."

Additionally, AI is helping to improve healthcare efficiency by providing tips on how to save time and money when treating patients. Additionally, AI can help doctors find new ways of treating diseases so that they don't become common occurrences again in future generations. By using machine learning, AI can help doctors and nurses spot problems early on, giving patients the best chance of getting the treatment they need. For example, AI could help to create a better understanding of patient symptoms and diagnose potential problems early on, which would then allow for earlier treatment and better outcomes. For example, if you have diabetes, your doctor might prescribe a glucose monitor that uses AI to measure your blood sugar levels and then alerts you when they're too high or too low--or perhaps it even administers insulin automatically!

Companies are experimenting with personalized eating apps and say the future of healthy eating lies in artificial intelligence. After 20 years with type 2 diabetes, Tom Idema has given up hope of getting his condition under control. He tried many diets without success and even considered bariatric surgery. When her employer gave her the opportunity to use a new diet app that uses artificial intelligence to monitor blood sugar, she accepted. READ MORE: What is Dark Matter?

The food-as-medicine paradigm has gained traction in recent years, partly due to physicians' and clinicians' growing understanding of the importance of using food in chronic illness therapy alongside drugs. Artificial Intelligence (AI) is a game-changing technology that is being used to learn more about individualized diets in the fields of nutrition and wellness. Linear and nonlinear interactions between numerous nutritional elements affecting us have been modeled by experts of AI development company. Food firms and tailored food product services use this approach to create better forecasts and healthy intake. It is accomplished by using deep learning to analyze the genetic information of individuals in order to produce precise forecasts and appropriate food recommendations.

The technological advancements in the global healthcare industry are hurtling at light speed. Making use of the technological edge and the rapidly increasing amount of data that can be discovered, analyzed, and applied is changing the day-to-day way medicine works. Today, edge computing devices are being used to monitor patients remotely, automate the delivery of care, bring artificial intelligence to improve the speed and accuracy of diagnoses, track the vaccine supply chain, and much more. The healthcare industry faces some common challenges such as data management issues, limited availability of IT administration talent, a jumble of legacy computing systems, and limited budgets. Edge-computing can put things in perspective by resolving budget issues and protecting data as well as guarding it against data breaches. Read on to know why Edge Computing is considered the game-changer for hospitals and clinics around the world.