Burnout is a psychological syndrome marked by emotional exhaustion, depersonalization, and reduced personal accomplishment, with a significant impact on individual well-being and organizational performance. This study proposes a machine learning approach to predict burnout risk using the HackerEarth Employee Burnout Challenge dataset. Three supervised algorithms were evaluated: nearest neighbors (KNN), random forest, and support vector machine (SVM), with model performance evaluated through 30-fold cross-validation using the determination coefficient (R2). Among the models tested, SVM achieved the highest predictive performance (R2 = 0.84) and was statistically superior to KNN and Random Forest based on paired $t$-tests. To ensure practical applicability, an interactive interface was developed using Streamlit, allowing non-technical users to input data and receive burnout risk predictions. The results highlight the potential of machine learning to support early detection of burnout and promote data-driven mental health strategies in organizational settings.

Clinician burnout poses a substantial threat to patient safety, particularly in high-acuity intensive care units (ICUs). Existing research predominantly relies on retrospective survey tools or broad electronic health record (EHR) metadata, often overlooking the valuable narrative information embedded in clinical notes. In this study, we analyze 10,000 ICU discharge summaries from MIMIC-IV, a publicly available database derived from the electronic health records of Beth Israel Deaconess Medical Center. The dataset encompasses diverse patient data, including vital signs, medical orders, diagnoses, procedures, treatments, and deidentified free-text clinical notes. We introduce a hybrid pipeline that combines BioBERT sentiment embeddings fine-tuned for clinical narratives, a lexical stress lexicon tailored for clinician burnout surveillance, and five-topic latent Dirichlet allocation (LDA) with workload proxies. A provider-level logistic regression classifier achieves a precision of 0.80, a recall of 0.89, and an F1 score of 0.84 on a stratified hold-out set, surpassing metadata-only baselines by greater than or equal to 0.17 F1 score. Specialty-specific analysis indicates elevated burnout risk among providers in Radiology, Psychiatry, and Neurology. Our findings demonstrate that ICU clinical narratives contain actionable signals for proactive well-being monitoring.

The term "cosy game" typically inspires one of two responses in those of us who play video games regularly. It will either call you in with the promise of soft, resource-management oriented gameplay whose slower pace offers a gentle escape and a bucolic alternative to gunslinging and high-stress adventure. Or it will repel you – as admittedly, it repels me. Cosy is often a kind of code for twee, low-stakes domestic adventures where drama is eschewed in favour of repetitive tasks intended to generate comfort, or imitate lightning-in-a-bottle resource management sims such as Stardew Valley or Animal Crossing. So when faced with Wanderstop, a colourful game in which a fallen warrior trades in her fighting life for managing a tea shop, I was hesitant.

Burnout, classified as a syndrome in the ICD-11, arises from chronic workplace stress that has not been effectively managed. It is characterized by exhaustion, cynicism, and reduced professional efficacy, and estimates of its prevalence vary significantly due to inconsistent measurement methods. Recent advancements in Natural Language Processing (NLP) and machine learning offer promising tools for detecting burnout through textual data analysis, with studies demonstrating high predictive accuracy. This paper contributes to burnout detection in German texts by: (a) collecting an anonymous real-world dataset including free-text answers and Oldenburg Burnout Inventory (OLBI) responses; (b) demonstrating the limitations of a GermanBERT-based classifier trained on online data; (c) presenting two versions of a curated BurnoutExpressions dataset, which yielded models that perform well in real-world applications; and (d) providing qualitative insights from an interdisciplinary focus group on the interpretability of AI models used for burnout detection. Our findings emphasize the need for greater collaboration between AI researchers and clinical experts to refine burnout detection models. Additionally, more real-world data is essential to validate and enhance the effectiveness of current AI methods developed in NLP research, which are often based on data automatically scraped from online sources and not evaluated in a real-world context. This is essential for ensuring AI tools are well suited for practical applications.

FOX News' Eben Brown reports on AI going mainstream in healthcare, which doctors say has the potential to create stronger relationships with patients. The use of generative AI may not be helpful in reducing burnout in health care, new research suggests. Previous research indicated that increased time spent using electronic health record (EHR) systems and handling administrative responsibilities has been a burden on doctors. So some people had heralded artificial intelligence as a potential solution -- yet recent investigations by U.S. health systems found that large language models (LLMs) did not simplify clinicians' day-to-day responsibilities. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

White-collar workers are so overwhelmed with emails, web chats, and meetings that they are using AI tools to get their jobs done--even if their companies haven't trained them to do so, according to a work trends index published Wednesday by Microsoft and LinkedIn. Seventy-five percent of people in desk jobs are already using AI at work, and the amount of people using AI has nearly doubled over the past six months, the report found. The vast majority of workers using AI--regardless of whether they are baby boomers or Gen Z--are "bringing their own AI tools" rather than waiting for their companies to guide them. "People are overwhelmed with digital debt and under duress at work," Colette Stallbaumer, general manager of Microsoft's chatbot Copilot and cofounder of Workload, said in a video announcing the report's results. "And they are turning to AI for relief." Microsoft (which also owns LinkedIn) stands to win from the adoption of AI, and is already cashing in on its generative AI tools.

Jordi Van Den Bussche used to devote every waking hour to building his presence on social media. He did this while courting brand deals and doing the other work integral to his survival on the platform. Five years ago, he ran into a problem. "Every time I wanted to take a holiday or I needed some time for myself, I couldn't really do that, because my entire business would stop," he says. It's an issue known as the "key person problem."

As a venture capitalist, Jim Breyer has invested in many breakthrough technology ideas in recent decades, names we all know and interact with on a daily basis like Meta and Spotify. But the biggest one of all may be next, he says, through the combination of artificial intelligence and branches of science involved in medicine. Since 2017, Breyer says his No. 1 task as a venture investor has focused on finding the best disease and medical data from leading research hospitals such as Memorial Sloan Kettering, MD Anderson, and Johns Hopkins -- highly proprietary, significant data to license into startups Breyer Capital is backing. "AI and medicine is perhaps the most attractive new investment opportunity I've ever seen," Breyer, founder and CEO of Breyer Capital, said at last week's CNBC Healthy Returns virtual summit. Breyer says he is not alone among tech leaders holding this view, citing a fireside chat he recently conducted with Michael Dell, during which the PC pioneer agreed, and private conversations he has had with tech CEOs.

In this article, I will present you the deep learning project that I wanted to perform, then I'll present the techniques and approach that I used to tacle this. And I will end up that article with some meaningful reflections, that I hope would help some of you. I wanted to build a smartphone app which can recognize flower from taken picture. Basically the app is splitted into two parts, the front-end part which is basically the mobile development. I wanted to build from scratch a deep learning model without deep learning framework, to help me understand the inner working process of image classification (I know it sounds crazy).

Have you noticed that our rhetoric surrounding the epidemic is still concentrated on "going back" rather than "moving forward"? "During the pandemic, many people felt their lives had been thrown off course. So understandably, people desire to get back on track. However, much of the transformation during and after the pandemic has been positive. Might we think about it as "moving forward?" Author Heather McGowan's new book, The Empathy Advantage: Leading the Empowered Workforce, co-written with Chris Shipley, points out many of the ways we've changed for the good--moving forward--since the pandemic. But there is still a way to go. Once Heather pointed out the "going back" language during our interview, I couldn't help but notice that it is present in many conversations regarding the future of work. So many leaders are asking how to get things back to the way they once were rather than asking how to harness the change to achieve greater things. Insert almost any hot topic, be it generational differences in career priorities, gender norms, or attitudes toward how work fits into our lives. You'll see as many people pushing back on the "return" language as you do pushing for "moving forward" change. As Heather says, "You can't put the toothpaste back into the tube now." Gender norms are something applicable to all organizations when it comes to the future of work. When surveyed, Millennials and Gen Z say you shouldn't have fixed, exclusionary gender markers in your language, in your restrooms, in your customer offerings."