Machine Learning in Laboratory Diagnosis

This work explores the critical role of autoverification in laboratory medicine, where timely and accurate test results are imperative for effective patient care. Auto-verification systems can significantly reduce the time to report and enhance the reliability of test outcomes, which is particularly crucial in time-sensitive diagnostic environments. This study delves into meticulous preprocessing of clinical data to prepare them for analysis, addressing challenges such as data inconsistency and missing values. By integrating advanced machine learning (ML) and deep learning (DL) models, we develop robust algorithms aimed at automating the verification of laboratory test results. Furthermore, we demonstrate the feasibility of these algorithms by replicating the processes in KNIME, a data analytics platform. This not only substantiates the scalability of our approach but also underscores its potential for real-world application in improving diagnostic workflows and patient outcomes.