Clinlabomics: leveraging clinical laboratory data by data mining strategies

Abstract

The recent global focus on big data in medicine has been associated with the rise of artificial intelligence (AI) in diagnosis and decision-making following recent advances in computer technology. Up to now, AI has been applied to various aspects of medicine, including disease diagnosis, surveillance, treatment, predicting future risk, targeted interventions and understanding of the disease. There have been plenty of successful examples in medicine of using big data, such as radiology and pathology, ophthalmology cardiology and surgery. Combining medicine and AI has become a powerful tool to change health care, and even to change the nature of disease screening in clinical diagnosis. As all we know, clinical laboratories produce large amounts of testing data every day and the clinical laboratory data combined with AI may establish a new diagnosis and treatment has attracted wide attention. At present, a new concept of radiomics has been created for imaging data combined with AI, but a new definition of clinical laboratory data combined with AI has lacked so that many studies in this field cannot be accurately classified. Therefore, we propose a new concept of clinical laboratory omics (Clinlabomics) by combining clinical laboratory medicine and AI. Clinlabomics can use high-throughput methods to extract large amounts of feature data from blood, body fluids, secretions, excreta, and cast clinical laboratory test data. Then using the data statistics, machine learning, and other methods to read more undiscovered information. In this review, we have summarized the application of clinical laboratory data combined with AI in medical fields. Undeniable, the application of Clinlabomics is a method that can assist many fields of medicine but still requires further validation in a multi-center environment and laboratory.

iLABMED, why now and how in the future?

“…The role of LM is thus expanding. In addition to routine medical services (conventional diagnosis, treatment assessment, and monitoring), LM is also predicting future risks, engaging in targeted interventions, and contributing to a better understanding of the disease [13]. Moreover, it has been a bridge between the laboratory bench and the bedside.…”

Аpplication of Big Data in laboratory medicine. Russian Journal for Personalized Medicine

“…Стремясь создать идеальную цифровую модель доктора, компания разработала специализированный продукт, в частности Watson for Oncology (WFO), который путем анализа информации от онкологического пациента в совокупности с генетическими исследованиями представлял варианты терапии и противопоказания. Данные метаанализа ряда исследований [20], включивших 2 463 пациентов, говорят о том, что между междисциплинарными командами врачей и WFO коэффициент соответствия решений составил 81,52 % и зависел от стадии рака, региона. В России первой системой, прошедшей весной 2020 года независимые клинические и технические проверки (регистрационное удостоверение 2020/9958 Росздравнадзор) как программное медицинское изделие, стала платформа аналитики прогноза и управления рисками в сфере медицины на основе ИИ -Webiomed.…”

Data-Driven Medicine in the Diagnosis and Treatment of Infertility

“…The use of advanced next-generation sequencing (NGS) techniques—such as genomics, transcriptomics, proteomics and metabolomics—allows for the generation of a large volumes of biological and clinically useful data. These data can be used to support clinical decisions, uncover disease subtypes, associations, and prognostic or diagnostic biomarkers [ 31 ]. Figure 2 presents examples of the different types of data that are starting to be incorporated into an overall precision medicine picture in infertility.…”