Length-biased data analysis and survival modeling have become pivotal in accurately interpreting time-to-event data, particularly in epidemiology and clinical research. Traditional survival analyses ...

Biased sampling occurs frequently in economics, epidemiology, and medical studies either by design or due to data collecting mechanism. Failing to take into account the sampling bias usually leads to ...

Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial ...

AI holds the potential to revolutionize healthcare, but it also brings with it a significant challenge: bias. For instance, a dermatologist might use an AI-driven system to help identify suspicious ...

Artificial intelligence (AI) tools used in medicine, like AI used in other fields, work by detecting patterns in large volumes of data. AI tools are able to detect these patterns because they can ...

Your Artstor image groups were copied to Workspace. The Artstor website will be retired on Aug 1st. Diversity and Distributions Vol. 30, No. 6, June 2024 Causes and effects of sampling bias on m ...

In this special guest feature, Sinan Ozdemir, Director of Data Science at Directly, points out how algorithmic bias has been one of the most talked-about issues in AI for years, yet it remains one of ...

It's easy for biases to creep into different aspects of decision-making—even when you think you’re basing your decisions on objective facts. So how can you limit bias when it comes to making decisions ...

Machine learning methods have emerged as promising tools to predict antimicrobial resistance (AMR) and uncover resistance determinants from genomic data. This study shows that sampling biases driven ...

Study: American Life in Realtime: Benchmark, publicly available person-generated health data for equity in precision health. Image credit: Lomb/Shutterstock.com Their approach addresses the ...