The developing field of enhanced diagnostic techniques in the diagnosis of
infectious diseases, constitutes a crucial domain in modern healthcare. By
utilizing Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) data and
incorporating machine learning algorithms into one platform, our research aims
to tackle the ongoing issue of precise infection identification. Inspired by
these difficulties, our goals consist of creating a strong data analytics
process, enhancing machine learning (ML) models, and performing thorough
validation for clinical applications. Our research contributes to the emerging
field of advanced diagnostic technologies by integrating Gas Chromatography-Ion
Mobility Spectrometry (GC-IMS) data and machine learning algorithms within a
unified Laboratory Information Management System (LIMS) platform. Preliminary
trials demonstrate encouraging levels of accuracy when employing various ML
algorithms to differentiate between infected and non-infected samples.
Continuing endeavors are currently concentrated on enhancing the effectiveness
of the model, investigating techniques to clarify its functioning, and
incorporating many types of data to further support the early detection of
diseases.

通过将气相色谱 - 离子迁移谱（GC-IMS）数据与机器学习算法整合到一个统一的实验室信息管理系统（LIMS）平台中，我们的研究目标在于解决精确感染识别的持续问题，为先进诊断技术领域做出贡献，并通过不同的机器学习算法区分感染和非感染样本，以支持疾病的早期检测。