2020
Robust Machine Learning for Colorectal Cancer Risk Prediction and Stratification
Nartowt BJ, Hart GR, Muhammad W, Liang Y, Stark GF, Deng J. Robust Machine Learning for Colorectal Cancer Risk Prediction and Stratification. Frontiers In Big Data 2020, 3: 6. PMID: 33693381, PMCID: PMC7931964, DOI: 10.3389/fdata.2020.00006.Peer-Reviewed Original ResearchArtificial neural networkNeural networkOne-hot encodingSupport vector machineNational Health Interview SurveyExpectation maximization imputationNaive BayesSupervised machineRobust machineVector machineRandom forestDecision treeCRC riskColorectal cancerPLCO datasetMachineScreening datasetsNetworkColorectal cancer risk predictionImputation methodsPrevention of CRCDatasetHealth Interview SurveyListwise deletionMethod combination
2019
A Model of Risk of Colorectal Cancer Tested between Studies: Building Robust Machine Learning Models for Colorectal Cancer Risk Prediction
Nartowt B, Hart G, Muhammad W, Liang Y, Deng J. A Model of Risk of Colorectal Cancer Tested between Studies: Building Robust Machine Learning Models for Colorectal Cancer Risk Prediction. International Journal Of Radiation Oncology • Biology • Physics 2019, 105: e132. DOI: 10.1016/j.ijrobp.2019.06.2265.Peer-Reviewed Original ResearchScoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data
Nartowt BJ, Hart GR, Roffman DA, Llor X, Ali I, Muhammad W, Liang Y, Deng J. Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data. PLOS ONE 2019, 14: e0221421. PMID: 31437221, PMCID: PMC6705772, DOI: 10.1371/journal.pone.0221421.Peer-Reviewed Original ResearchConceptsNational Health Interview SurveyUnited States Preventative Services Task ForceColorectal cancerPredictive valueDiagnosis of CRCColorectal cancer riskHealth Interview SurveyHigh-risk categoryNegative predictive valuePositive predictive valueMultivariable prediction modelHealth dataUSPSTF guidelinesRisk score methodCRC riskFamily historyCancer riskHigh riskAge 50Individual prognosisLower riskPersonal health dataClinical applicabilityInterview SurveyCancer