PRESTIGE: Prevalence estimation for interactive guesstimates

This study presents a direct prediction approach for estimating the prevalence of radiological findings from text, eliminating the need for computationally intensive similarity-based comparisons. The proposed method, named PRESTIGE, trades off apriori processing time in favor of real-time inference speed. PRESTIGE takes a single query example as input and estimates how many instances in a larger collection share similar characteristics. This approach is evaluated in both "global" settings (across an entire dataset) and "local" settings (within a relevant subset). In a clinical environment, the approach allows radiologists, hospital administrators, or researchers to quickly gauge the frequency of an observation occurring within their local patient population, without requiring manual chart reviews or text-mining. Experiments on radiology reports demonstrate that PRESTIGE achieves high accuracy in predicting prevalence, significantly reducing both inference time and storage requirements compared to naive or approximate nearest neighbor methods. Further efficiency gains are explored by incorporating low-rank adaptation (LoRA), a technique that updates only a small portion of the model's parameters, thereby reducing the model's size and accelerating training without introducing additional inference costs. Results indicate that LoRA can maintain strong performance while drastically lowering hardware demands, although a slight decrease in accuracy may occur depending on the dataset. Overall, these findings suggest that PRESTIGE can serve as an efficient and scalable solution for real-time, example-based prevalence estimation of radiological findings. By focusing on inference speed, this work facilitates interactive exploration of datasets and potentially streamlines research on determining how frequently specific observations appear in large document collections.