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- Artificial intelligence and mapping a new direction in laboratory medicine: a review.Clin Chem. 2021; 67: 1466-1482
- Applications of artificial intelligence in clinical microbiology testing.Clin Microbiol Newsl. 2020; 42: 61-70
- Pre-analytical phase management: a review of the procedures from patient preparation to laboratory analysis.Scand J Clin Lab Invest. 2017; 77: 153-163
- Challenges in preanalytical phase of laboratory medicine: rate of blood sample nonconformity in a tertiary care hospital.EJIFCC. 2020; 31: 21-27
- Errors in a stat laboratory: types and frequencies 10 years later.Clin Chem. 2007; 53: 1338-1342
- An international study of the performance of sample collection from patients.Vox Sang. 2003; 85: 40-47
- Using machine learning-based multianalyte delta checks to detect wrong blood in tube errors.Am J Clin Pathol. 2018; 150: 555-566
- Highly accurate and explainable detection of specimen mix-up using a machine learning model.Clin Chem Lab Med. 2020; 58: 375-383
- A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory.Clin Chem Lab Med. 2021; https://doi.org/10.1515/cclm-2021-1171
- Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors.Clin Chem Lab Med. 2021; https://doi.org/10.1515/cclm-2021-0873
- Identifying mislabelled samples: machine learning models exceed human performance.Ann Clin Biochem. 2021; 58: 650-652
- Detection of preanalytic laboratory testing errors using a statistically guided protocol.Am J Clin Pathol. 2012; 138: 406-413
- Detection of falsely elevated point-of-care potassium results due to hemolysis using predictive analytics.Am J Clin Pathol. 2020; 154: 242-247
- Machine learning algorithms for the detection of spurious white blood cell differentials due to erythrocyte lysis resistance.J Clin Pathol. 2019; 72: 431-437
- A deep learning-based system for assessment of serum quality using sample images.Clin Chim Acta. 2022; 531: 254-260
- A hemolysis image detection method based on GAN-CNN-ELM.Comput Math Methods Med. 2022; 2022: 1558607
- Using machine learning to identify clotted specimens in coagulation testing.Clin Chem Lab Med. 2021; 59: 1289-1297
- Optimization of a moving averages program using a simulated annealing algorithm: the goal is to monitor the process not the patients.Clin Chem. 2016; 62: 1361-1371
- Moving average quality control: principles, practical application and future perspectives.Clin Chem Lab Med. 2019; 57: 773-782
- Ten-month evaluation of the routine application of patient moving average for real-time quality control in a hospital setting.J Appl Lab Med. 2020; 5: 1184-1193
- A direct comparison of patient-based real-time quality control techniques: the importance of the analyte distribution.Ann Clin Biochem. 2020; 57: 206-214
- Recommendation for performance verification of patient-based real-time quality control.Clin Chem Lab Med. 2020; 58: 1205-1213
- CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.Clin Biochem. 2016; 49: 201-207
- Quality assessment and interference detection in targeted mass spectrometry data using machine learning.Clin Proteomics. 2018; 15: 33
- Intra-batch effect correction in liquid chromatography-mass spectrometry using quality control samples and support vector regression (QC-SVRC).Analyst. 2015; 140: 7810-7817
- Predicting abnormal laboratory blood test results in the intensive care unit using novel features based on information theory and historical conditional probability: observational study.JMIR Med Inform. 2022; 10: e35250
- Assessment of machine-learning techniques on large pathology data sets to address assay redundancy in routine liver function test profiles.Diagnosis (Berl). 2015; 2: 41-51
- Using machine learning to predict laboratory test results.Am J Clin Pathol. 2016; 145: 778-788
- Automated prediction of low ferritin concentrations using a machine learning algorithm.Clin Chem Lab Med. 2022; https://doi.org/10.1515/cclm-2021-1194
- 3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data.J Am Med Inform Assoc. 2018; 25: 645-653
- Biomarkers for prostate cancer: prostate-specific antigen and beyond.Clin Chem Lab Med. 2020; 58: 326-339
- A multicenter study of [-2]pro-prostate specific antigen combined with prostate specific antigen and free prostate specific antigen for prostate cancer detection in the 2.0 to 10.0 ng/ml prostate specific antigen range.J Urol. 2011; 185: 1650-1655
- A multi-institutional prospective trial in the USA confirms that the 4Kscore accurately identifies men with high-grade prostate cancer.Eur Urol. 2015; 68: 464-470
- Identification of a support vector machine-based biomarker panel with high sensitivity and specificity for nonalcoholic steatohepatitis.Clin Chim Acta. 2012; 414: 154-157
- Validation of the accuracy of the FAST score for detecting patients with at-risk nonalcoholic steatohepatitis (NASH) in a North American cohort and comparison to other non-invasive algorithms.PLoS One. 2022; 17: e0266859
- Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease.Diabetologia. 2021; 64: 1504-1515
- Analytical validation of a multi-biomarker algorithmic test for prediction of progressive kidney function decline in patients with early-stage kidney disease.Clin Proteomics. 2021; 18: 26
- Development and validation of a machine learning model to predict near-term risk of iatrogenic hypoglycemia in hospitalized patients.JAMA Netw Open. 2021; 4: e2030913
- A clinically applicable approach to continuous prediction of future acute kidney injury.Nature. 2019; 572: 116-119
- Automated continuous acute kidney injury prediction and surveillance: a random forest model.Mayo Clin Proc. 2019; 94: 783-792
- Predicting mortality in diabetic ICU patients using machine learning and severity indices.AMIA Jt Summits Transl Sci Proc. 2018; 2017: 310-319
- Using machine learning to aid the interpretation of urine steroid profiles.Clin Chem. 2018; 64: 1586-1595
- A machine learning approach for the automated interpretation of plasma amino acid profiles.Clin Chem. 2020; 66: 1210-1218
- Use of steroid profiling combined with machine learning for identification and subtype classification in primary aldosteronism.JAMA Netw Open. 2020; 3: e2016209
- ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia.BMC Med Inform Decis Mak. 2019; 19: 212
- Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning.Nat Med. 2022; 28: 164-174
- Machine learning for antibiotic resistance prediction: a prototype using off-the-shelf techniques and entry-level data to guide empiric antimicrobial therapy.Healthc Inform Res. 2021; 27: 214-221
- Using machine learning techniques to predict antimicrobial resistance in stone disease patients.World J Urol. 2022; https://doi.org/10.1007/s00345-022-04043-x
- Machine learning algorithms for objective remission and clinical outcomes with thiopurines.J Crohns Colitis. 2017; 11: 801-810
- Invention and validation of an automated camera system that uses optical character recognition to identify patient name mislabeled samples.Clin Chem. 2014; 60: 463-470
- Progress in automated urinalysis.Ann Lab Med. 2019; 39: 15-22
- DIP/U.S. Urine analysis test system K173327 approval letter.(Available at:) (Accessed July 5, 2022)
- Robust dipstick urinalysis using a low-cost, micro-volume slipping manifold and mobile phone platform.Lab Chip. 2016; 16: 2069-2078
- Evaluation of the analytical performances of Cobas 6500 and Sysmex UN series automated urinalysis systems with manual microscopic particle counting.Biochem Med (Zagreb). 2018; 28: 020712
- An end-to-end system for automatic urinary particle recognition with convolutional neural network.J Med Syst. 2018; 42: 165
- The comparison of automated urine analyzers with manual microscopic examination for urinalysis automated urine analyzers and manual urinalysis.Pract Lab Med. 2016; 5: 14-20
- UriSed 3 and UX-2000 automated urine sediment analyzers vs manual microscopic method: a comparative performance analysis.J Clin Lab Anal. 2018; 32https://doi.org/10.1002/jcla.22249
- Analytical performance of the Iris iQ200 automated urine microscopy analyzer.Clin Chim Acta. 2006; 372: 54-64
- Very deep convolutional neural networks for morphologic classification of erythrocytes.Clin Chem. 2017; 63: 1847-1855
- Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells.Lab Invest. 2020; 100: 98-109
- Applications of digital microscopy and densely connected convolutional neural networks for automated quantification of babesia-infected erythrocytes.Clin Chem. 2021; 68: 218-229
- Clinical interpretation of antinuclear antibody tests in systemic rheumatic diseases.Mod Rheumatol. 2009; 19: 219-228
- Recent evolutions of machine learning applications in clinical laboratory medicine.Crit Rev Clin Lab Sci. 2021; 58: 131-152
- Automated versus conventional microscopic interpretation of antinuclear antibody indirect immunofluorescence test.Ann Clin Lab Sci. 2019; 49: 127-133
- Anti-neutrophil cytoplasmic antibody testing by indirect immunofluorescence: computer-aided versus conventional microscopic evaluation of routine diagnostic samples from patients with vasculitis or other inflammatory diseases.Clin Chim Acta. 2020; 511: 117-124
- Application of supervised machine learning to recognize competent level and mixed antinuclear antibody patterns based on ICAP international consensus.Diagnostics (Basel). 2021; 11https://doi.org/10.3390/diagnostics11040642
- Applications of machine learning in the chemical pathology laboratory.J Clin Pathol. 2021; 74: 435-442
Li H, Racine-Brzostek S, Xi N, Luo J, Zhao Z, Yuan J. Learning to Detect Monoclonal Protein in Electrophoresis Images. 2021 International Conference on Visual Communications and Image Processing (VCIP). 2021:1-5. doi:10.1109/VCIP53242.2021.9675332
- Deep collocative learning for immunofixation electrophoresis image analysis.IEEE Trans Med Imaging. 2021; 40: 1898-1910
- Achieving expert-level interpretation of serum protein electrophoresis through deep learning driven by human reasoning.Clin Chem. 2021; 67: 1406-1414
- Application of intraoperative mass spectrometry and data analytics for oncological margin detection, a review.IEEE Trans Biomed Eng. 2022; 69: 2220-2232
- Machine learning for antimicrobial resistance prediction: current practice, limitations, and clinical perspective.Clin Microbiol Rev. 2022; : e0017921https://doi.org/10.1128/cmr.00179-21
- Automation of chromatographic peak review and order to result data transfer in a clinical mass spectrometry laboratory.Clin Chim Acta. 2019; 498: 84-89
- automRm: an r package for fully automatic LC-QQQ-MS data preprocessing powered by machine learning.Anal Chem. 2022; 94: 6163-6171
- Streamlining quality review of mass spectrometry data in the clinical laboratory by use of machine learning.Arch Pathol Lab Med. 2019; 143: 990-998
- Reducing false-positive results in newborn screening using machine learning.Int J Neonatal Screen. 2020; 6https://doi.org/10.3390/ijns6010016
- Application of machine learning algorithms for the differential diagnosis of peroxisomal disorders.J Biochem. 2019; 165: 67-73
- Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles.CPT Pharmacometrics Syst Pharmacol. 2022; https://doi.org/10.1002/psp4.12810
- Mycophenolic acid exposure prediction using machine learning.Clin Pharmacol Ther. 2021; 110: 370-379
- The use of machine learning algorithms and the mass spectrometry lipidomic profile of serum for the evaluation of tacrolimus exposure and toxicity in kidney transplant recipients.Biomedicines. 2022; 10https://doi.org/10.3390/biomedicines10051157
- Diagnosis of Acute Poisoning using explainable artificial intelligence.Comput Biol Med. 2021; 134: 104469
- Autoverification of test results in the core clinical laboratory.Clin Biochem. 2019; 73: 11-25
- Artificial neural network approach in laboratory test reporting: learning algorithms.Am J Clin Pathol. 2016; 146: 227-237https://doi.org/10.1093/ajcp/aqw104
- Using machine learning to develop an autoverification system in a clinical biochemistry laboratory.Clin Chem Lab Med. 2021; 59: 883-891
- Statistics in the practice of medicine.JAMA. 1963; 185: 864-873
- A simple method of resolution of a distribution into Gaussian components.Biometrics. 1967; 23: 115-135
- Indirect methods for reference interval determination - review and recommendations.Clin Chem Lab Med. 2018; 57: 20-29
- An unsupervised learning method to identify reference intervals from a clinical database.J Biomed Inform. 2016; 59: 276-284
- Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate.Int J Health Geogr. 2013; 12: 11
- The ethics of artificial intelligence in pathology and laboratory medicine: principles and practice.Acad Pathol. 2021; 8 (2374289521990784)https://doi.org/10.1177/2374289521990784
- Validation and regulation of clinical artificial intelligence.Clin Chem. 2019; 65: 1336-1337
- Identifying ethical considerations for machine learning healthcare applications.Am J Bioeth. 2020; 20: 7-17
- Machine learning in medicine: addressing ethical challenges.PLoS Med. 2018; 15: e1002689
- Predictive models for identifying risk of readmission after index hospitalization for heart failure: a systematic review.Eur J Cardiovasc Nurs. 2018; 17: 675-689
- Machine learning in healthcare: mapping a path to title 21.Clin Chem. 2022; 68: 609-610
- Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial.BMJ. 2021; 372: m4786
- Association between e-alert implementation for detection of acute kidney injury and outcomes: a systematic review.Nephrol Dial Transpl. 2017; 32: 265-272
- Dissecting racial bias in an algorithm used to manage the health of populations.Science. 2019; 366: 447-453