Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models: Quantitative Study Using Large Language Models.

ABSTRACT:

Background

Data collected in controlled settings typically results in high-quality datasets. However, in real-world applications, the quality of data collection is often compromised. It is well established that the quality of a dataset significantly impacts the performance of machine learning models. In this context, detailed information about individuals is often recorded in progress notes. Given the critical nature of health applications, it is essential to evaluate the impact of textual data quality, as any incorrect prediction can have serious, potentially life-threatening consequences.

Objective

This study aims to quantify the quality of textual datasets and systematically evaluate the impact of varying levels of errors on feature representation and machine learning mod

SUBMITTER: Sarwar T 

PROVIDER: S-EPMC12811037 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature