Table 1.

The partial representative research of the application of Clinlabomics

Application fields	Year	Sample size	Best models of analysis	Objective and achievement
Clinical prediction	2019 [64]	149,000 physical samples	Deep Neural Network (DNN)	Biological aging prediction
	2016 [65]	62,419 physical samples	Deep Neural Network (DNN)	Biological aging and Smoking status prediction
	2021 [71]	285,965 diabetes patients and 1,221,598 healthy human samples	Extreme Gradient Boosting (XGBoost)	Risk prediction for diabetes
	2017 [72]	79 paraquat poisoning patients (41 living and 38 deceased)	Support Vector Machine (SVM)	Predicting the prognosis of paraquat poisoning patients
	2020 [73]	235 patients (89 benign ovarian tumors and 146 ovarian cancer)	Decision Tree Model	Predicting ovarian cancer
	2021 [80]	1823 COVID-19 patients	Extreme Gradient Boosting (XGBoost)	Predicting the mortality of patients with COVID-19
Clinical diagnosis	2012 [75]	203 iron deficiency anemia patients	Artificial Neural Network (ANN)	Iron deficiencyanemia diagnosis and iron serum level prediction
	2020 [76]	355 asthma patients and 1,480 Healthy individuals	Mahalanobis–Taguchi System (MTS)	Asthma diagnosis
	2019 [77]	551 chronic kidney disease patients	Logistic Regression Model (LR)	CKD severity diagnosis and surveillance
	2020 [79]	177 positive subjects and 102 negative subjects	Random Forest (RF)	COVID-19 infection diagnosis
	2019 [81]	15,176 Neurological patients	The Smart Blood Analytics (SBA) Machine Learning (ML) Algorithm	Brain tumors diagnosis
	2019 [82]	183 lung cancer patients and 94 patients without lung cancer	Random Forest (RF)	Lung cancer diagnosis
	2021 [83]	1168 colorectal cancer patients and 1269 healthy subjects	Logistic Regression Model (LR)	Colorectal cancer diagnosis
Clinical labortory management	2018 [85]	10,799 training samples and 9839 testing samples	Support Vector Machine (SVM)	Identifying wrong blood in tube errors prior to test reporting
	2021 [86]	141,396 samples	Artificial Neural Network (ANN)	Identifying mislabeled samples
	2021 [90]	192 clotted samples and 2889 normal blood samples	Back Propagation Neural Network (BPNN)	Identifying clotted specimens in coagulation testing
	2018 [91]	4619 samples of urine steroid profiles	Tree-based Model	Aiding the Interpretation of urine steroid profiles
	2022 [92]	202 consecutive chronic lymphocytic leukemia patients	Deep Neural Network (DNN)	Improving flow cytometry workflow efficiency for detecting of minimal residual disease of chronic lymphocytic leukemia
	2022 [95]	254 healthy samples, 8800 physical examination population and 7700 outpatient samples	Normally distributed data: Transformed Hoffmann, Transformed Bhattacahrya, Kosmic and RefineR Algorithms Data with obvious skewness: Expectation Maximization (EM) Algorithm combined with Box-Cox Transformation	Establishing reference intervals for thyroid-related hormones in older adults
	2019 [99]	212,554 urine samples	Extreme Gradient Boosting (XGBoost)	Screening urine microbiological inoculation samples