ReviewBMC bioinformatics2022
Clinlabomics: leveraging clinical laboratory data by data mining strategies.
Review in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- Machine learning-based prediction of diabetic retinopathy using clinlabomics: a multi-center study.BMC medical informatics and decision making · 2026Article
- Identification of key metabolic indicators associated with the comorbidity of ischemic stroke and diabetes mellitus using an optimal interpretable clinlabomics model.Frontiers in cardiovascular medicine · 2026Article
- Early risk stratification for carbapenem resistance amongFrontiers in cellular and infection microbiology · 2026Article
- A Clinlabomics-Based Machine Learning Model Accurately Differentiates Atypical Hepatocellular Carcinoma from Atypical Benign Focal Hepatic Lesion.Journal of hepatocellular carcinoma · 2026Article
- Development and Validation of a Clinlabomics-Based Nomogram for Predicting the Prognosis of Small Cell Lung Cancer in China: A Multicenter, Retrospective Cohort Study.Cancer medicine · 2025Observational
- Developing and validating an explainable clinlabomics-based machine-learning model for screening primary angle-closure glaucoma in the context of PPPM.The EPMA journal · 2025Article
- Medical laboratory data-based models: opportunities, obstacles, and solutions.Journal of translational medicine · 2025Review
- Application of random survival forest to establish a nomogram combining clinlabomics-score and clinical data for predicting brain metastasis in primary lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Are we ready to integrate advanced artificial intelligence models in clinical laboratory?Biochemia medica · 2025Review
- A machine learning-based model for assessing community-acquired pneumonia severity using routine blood tests.Frontiers in cellular and infection microbiology · 2025Article
- Identifying metabolic parameters as key indicators of hyperuricemia and ischemic stroke comorbidity via interpretable Clinlabomics models.Frontiers in endocrinology · 2025Article
- A predictive model and mechanistic study of treatment effectiveness in patients newly diagnosed with small cell lung cancer.Frontiers in oncology · 2025Article
- Inference and analysis of cell-cell communication of non-myeloid circulating cells in late sepsis based on single-cell RNA-seq.IET systems biology · 2024Article
- Reduced heart rate variability is associated with altered clinical laboratory profile in people living with HIV.Brain, behavior, & immunity - health · 2024Article
- Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia.Journal of translational medicine · 2024Article
- Article
- Insights from semi-structured interviews on integrating artificial intelligence in clinical chemistry laboratory practices.BMC medical education · 2024Article
- Multitask Deep Learning for Joint Detection of Necrotizing Viral and Noninfectious Retinitis From Common Blood and Serology Test Data.Investigative ophthalmology & visual science · 2024Article
- Article
- Comparative Evaluation of Machine Learning Models for Subtyping Triple-Negative Breast Cancer: A Deep Learning-Based Multi-Omics Data Integration Approach.Journal of Cancer · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.