Blood cultures are often over ordered without clear justification, straining healthcare resources and contributing to inappropriate antibiotic use pressures worsened by the global shortage. In study of 135483 emergency department (ED) blood culture orders, we developed machine learning (ML) models to predict the risk of bacteremia using structured electronic health record (EHR) data and provider notes via a large language model (LLM). The structured models AUC improved from 0.76 to 0.79 with note embeddings and reached 0.81 with added diagnosis codes. Compared to an expert recommendation framework applied by human reviewers and an LLM-based pipeline, our ML approach offered higher specificity without compromising sensitivity. The recommendation framework achieved sensitivity 86%, specificity 57%, while the LLM maintained high sensitivity (96%) but over classified negatives, reducing specificity (16%). These findings demonstrate that ML models integrating structured and unstructured data can outperform consensus recommendations, enhancing diagnostic stewardship beyond existing standards of care.

Antibiotics are often grouped by their mechanisms of action, such as blocking protein synthesis, disrupting folate biosynthesis, changing cell wall construction, compromising the cell membrane integrity and affecting DNA replication [93, 25]. These antibiotics, whether created in labs or found in nature, serve as the primary defence against bacterial infections. However, bacteria employ a series of strategies in response to resist these antibiotics, including inactivating antibiotics through enzymatic degradation, altering the antibiotic target, modifying cell membrane permeability, and using efflux pumps to maintain intracellular antibiotic concentrations of antibiotics below inhibitory levels [25]. Moreover, the gene transfer of antibiotic-resistant bacteria (ARB) further aggravates this challenge [92].

Whether you are just overwhelmed with data or just curious about what you will learn, you may be feeling the impulse to jump on the artificial intelligence (AI) bandwagon. Before you go too far down the road, please consider this Top 10 list of the most common mistakes mangers make when building an AI project. This comes from long, hard lessons learned across multiple missions and IT clients over the years. Mission owners have a lot to do. It is usually the most annoying or time-intensive tasks they want to automate the most. I never begrudge someone who is trying to better optimize the cognitive talent of their team.

This week's blogs are inspired by our global connector Marisol Menendez Tell me about Responsive and what you guys are all about? We're an advice-centric FinTech that's focused on financial advisors with better decisions and actions, that helps them grow customer wealth and loyalty. We do that by performing behavioural analytics on data and providing the advisors with insights in real-time. For the time being, that's the model we believe in. At this stage, we think that's just too valuable a process to be left to automation when it comes to the complexities of life and the weight of these decisions.

Innovations in artificial intelligence are made each year, but the effectiveness of an Intelligent Assistant is still determined by the quality of data fed into its machine learning algorithms. Thankfully, there is a field of study centered around creating, maintaining, and organizing quality data sources. According to a report from LinkedIn, the number one career field in America in 2019 is a data scientist. Data science also topped Glassdoor's list of Best Jobs in America for the past three years. Before we explore if you're a good fit for a career in data science, let's look at what a data scientist does. A data scientist leverages existing data sources, and aids in creating new ones, to extract meaningful information and actionable insights.

The scale, pace and breadth of technological development in artificial intelligence, robotics, computing, biotechnology, materials science and beyond have ushered in the Fourth Industrial Revolution. In an interview with journalist Thomas Friedman, Google executive Eric Teller argues that humanity's 21st century challenge is to become as good at shaping the positive impacts of technologies as we are at inventing the technologies in the first place. Teller says the problem is that the political, economic, legal, organizational and educational systems in which we operate are not agile enough to respond to the scale and pace of technological change. My professional life is focused on how to educate aspiring engineers to be agile. I teach ethics, professionalism and communication in the Faculty of Engineering and Applied Science at Memorial University.

The ability to manipulate and understand data is increasingly critical to discovery and innovation. As a result, we see the emergence of a new field--data science--that focuses on the processes and systems that enable us to extract knowledge or insight from data in various forms and translate it into action. In practice, data science has evolved as an interdisciplinary field that integrates approaches from such data-analysis fields as statistics, data mining, and predictive analytics and incorporates advances in scalable computing and data management. But as a discipline, data science is only in its infancy. The challenge of developing data science in a way that achieves its full potential raises important questions for the research and education community: How can we evolve the field of data science so it supports the increasing role of data in all spheres? How do we train a workforce of professionals who can use data to its best advantage? What should we teach them? What can government agencies do to help maximize the potential of data science to drive discovery and address current and future needs for a workforce with data science expertise?