2025
Predicting conversion to psychosis using machine learning: response to Cannon
Smucny J, Cannon T, Bearden C, Addington J, Cadenhead K, Cornblatt B, Keshavan M, Mathalon D, Perkins D, Stone W, Walker E, Woods S, Davidson I, Carter C. Predicting conversion to psychosis using machine learning: response to Cannon. Frontiers In Psychiatry 2025, 15: 1520173. PMID: 39882161, PMCID: PMC11775650, DOI: 10.3389/fpsyt.2024.1520173.Peer-Reviewed Original ResearchMachine learning algorithmsMachine learning modelsLearning algorithmsConversion to psychosisMachine learningLearning modelsStandard machine learning algorithmsClinical high riskNAPLS-2Overall performanceNaive BayesModel generalizationClinical high-risk individualsPredicting conversion to psychosisTest setIndependent datasetsRandom forest methodDataset
2024
Machine Learning-Based Prediction of Binge Drinking among Adults in the United State: Analysis of the 2022 Health Information National Trends Survey
Huang X, Dai Z, Wang K, Luo X. Machine Learning-Based Prediction of Binge Drinking among Adults in the United State: Analysis of the 2022 Health Information National Trends Survey. 2024, 2024: 1-10. PMID: 39834720, PMCID: PMC11745038, DOI: 10.1145/3670085.3670090.Peer-Reviewed Original ResearchHealth Information National Trends SurveyNational Trends SurveySupport vector machineTrends SurveyBinge drinkingMachine learningMultiple health behaviour interventionsRisk factors of binge drinkingHealth behavior interventionsImprove health outcomesPrevalence of binge drinkingK-Nearest NeighborHealth outcomesSocial mediaTobacco useU.S. adultsRadial basis functionBehavioral interventionsUnited StatesAlcohol useNaive BayesFeature selectionLogistic regressionK-nearestMachine learning-based prediction
2023
Stroke Prediction Using Machine Learning
D M A, Kumar A, Teja G, Sukesh M, S D, Chauhan N, Oviya I, Raja K. Stroke Prediction Using Machine Learning. 2023, 00: 1-5. DOI: 10.1109/icaecc59324.2023.10560191.Peer-Reviewed Original Research
2022
Generalizable prediction of COVID-19 mortality on worldwide patient data
Edelson M, Kuo T. Generalizable prediction of COVID-19 mortality on worldwide patient data. JAMIA Open 2022, 5: ooac036. PMID: 35663116, PMCID: PMC9129227, DOI: 10.1093/jamiaopen/ooac036.Peer-Reviewed Original Research
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
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