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 ...

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 ...

Machine learning methods have emerged as promising tools to predict antimicrobial resistance (AMR) and uncover resistance determinants from genomic ...

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 ...

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 ...

Analyzing several major pathology AI models designed to diagnose cancer, the researchers found unequal performance in ...

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 ...

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 ...

verybody has a favorite color and season—preferences that seem innate, always defying reason. Auditors are no different when it comes to haphazard sampling, a process in which—ideally—they choose ...