暂无分享,去创建一个
Graciela Muniz-Terrera | Anja K. Leist | Matthias Klee | Jung Hyun Kim | David H. Rehkopf | St'ephane P. A. Bordas | Sara Wade | S. Bordas | A. Leist | G. Muniz-Terrera | D. Rehkopf | Sara Wade | Matthias Klee | Graciela Muniz-Terrera
[1] Jennifer L. Hill,et al. Examining treatment effect heterogeneity using BART , 2021, Observational Studies.
[2] M. Petersen,et al. Machine Learning in Causal Inference: How do I love thee? Let me count the ways. , 2021, American journal of epidemiology.
[3] Edward I. George,et al. Spike-and-slab Lasso biclustering , 2021 .
[4] B. Recht,et al. Patterns, predictions, and actions: A story about machine learning , 2021, ArXiv.
[5] Jessica G. Young,et al. Separating Algorithms from Questions and Causal Inference with Unmeasured Exposures: An Application to Birth Cohort Studies of Early BMI Rebound. , 2021, American journal of epidemiology.
[6] J. Thornton,et al. Data-driven identification of ageing-related diseases from electronic health records , 2021, Scientific Reports.
[7] Luca Oneto,et al. Fairness in Machine Learning , 2020, INNSBDDL.
[8] Kellyn F Arnold,et al. Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations , 2020, International journal of epidemiology.
[9] D. Llewellyn,et al. Identifying key features for dementia diagnosis using machine learning , 2020 .
[10] B. Reuter,et al. Identifying CBT non-response among OCD outpatients: A machine-learning approach , 2020, Psychotherapy research : journal of the Society for Psychotherapy Research.
[11] E. Ware,et al. A data-driven prospective study of dementia among older adults in the United States , 2020, PloS one.
[12] B. Woll,et al. A Multi-modal Machine Learning Approach and Toolkit to Automate Recognition of Early Stages of Dementia among British Sign Language Users , 2020, ECCV Workshops.
[13] Marzyeh Ghassemi,et al. Ethical Machine Learning in Health Care , 2020, Annual review of biomedical data science.
[14] F. Tylavsky,et al. Identification of Modifiable Social and Behavioral Factors Associated With Childhood Cognitive Performance. , 2020, JAMA pediatrics.
[15] Seong-Hoon Hwang,et al. Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach , 2020 .
[16] Ciarán M Lee,et al. Improving the accuracy of medical diagnosis with causal machine learning , 2020, Nature Communications.
[17] S. Vansteelandt,et al. The obesity paradox in critically ill patients: a causal learning approach to a casual finding , 2020, Critical Care.
[18] J. Walker,et al. Using gradient boosting with stability selection on health insurance claims data to identify disease trajectories in chronic obstructive pulmonary disease , 2020, Statistical methods in medical research.
[19] Mohammad Asif Emon,et al. Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia - lessons for translation into clinical practice , 2020, EPMA Journal.
[20] Klaus P. Ebmeier,et al. Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging , 2020, Human brain mapping.
[21] Nicholas C. Firth,et al. Sequences of cognitive decline in typical Alzheimer's disease and posterior cortical atrophy estimated using a novel event‐based model of disease progression , 2020, Alzheimer's & dementia : the journal of the Alzheimer's Association.
[22] Klaus P. Ebmeier,et al. Association of trajectories of depressive symptoms with vascular risk, cognitive function and adverse brain outcomes: The Whitehall II MRI sub-study , 2020, medRxiv.
[23] Yang Liu,et al. Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data , 2020, Int. J. Approx. Reason..
[24] Stephen R. Aichele,et al. Predicting Cognitive Impairment and Dementia: A Machine Learning Approach. , 2020, Journal of Alzheimer's disease : JAD.
[25] T. Wiemken,et al. Machine Learning in Epidemiology and Health Outcomes Research. , 2020, Annual review of public health.
[26] Sherri Rose. Intersections of machine learning and epidemiological methods for health services research , 2020, International journal of epidemiology.
[27] Jared S. Murray,et al. Bayesian Additive Regression Trees: A Review and Look Forward , 2020, Annual Review of Statistics and Its Application.
[28] Abigail R Cartus,et al. Machine learning as a strategy to account for dietary synergy: an illustration based on dietary intake and adverse pregnancy outcomes. , 2020, The American journal of clinical nutrition.
[29] G. Sanguinetti,et al. Robustness of Bayesian Neural Networks to Gradient-Based Attacks , 2020, NeurIPS.
[30] Nick C Fox,et al. The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up , 2020, Machine Learning for Biomedical Imaging.
[31] Ellicott C Matthay,et al. A Graphical Catalog of Threats to Validity , 2020, Epidemiology.
[32] Lorenz Kemper,et al. Predicting student dropout: A machine learning approach , 2020, European Journal of Higher Education.
[33] K. Yaffe,et al. Approximating dementia prevalence in population‐based surveys of aging worldwide: An unsupervised machine learning approach , 2020, Alzheimer's & dementia.
[34] Jaime Delgadillo,et al. Targeted prescription of cognitive-behavioral therapy versus person-centered counseling for depression using a machine learning approach. , 2020, Journal of consulting and clinical psychology.
[35] Justin Lessler,et al. What Is Machine Learning: a Primer for the Epidemiologist. , 2019, American journal of epidemiology.
[36] N. Kathmann,et al. Predicting cognitive behavioral therapy outcome in the outpatient sector based on clinical routine data: A machine learning approach. , 2019, Behaviour research and therapy.
[37] Ellicott C. Matthay,et al. Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence , 2019, SSM - population health.
[38] Audrey Renson,et al. Teaching yourself about structural racism will improve your machine learning. , 2019, Biostatistics.
[39] Uri Shalit,et al. Can we learn individual-level treatment policies from clinical data? , 2019, Biostatistics.
[40] Chandan Singh,et al. Definitions, methods, and applications in interpretable machine learning , 2019, Proceedings of the National Academy of Sciences.
[41] Valerio Baćak,et al. Principled Machine Learning Using the Super Learner: An Application to Predicting Prison Violence , 2019 .
[42] Tony Blakely,et al. Reflection on modern methods: when worlds collide-prediction, machine learning and causal inference. , 2019, International journal of epidemiology.
[43] D. Facal,et al. Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia , 2019, International journal of geriatric psychiatry.
[44] Alzheimer's Disease Neuroimaging Initiative,et al. Development and Validation of a Dementia Risk Prediction Model in the General Population: An Analysis of Three Longitudinal Studies. , 2019, The American journal of psychiatry.
[45] Fabio Stella,et al. A survey on Bayesian network structure learning from data , 2019, Progress in Artificial Intelligence.
[46] J. H. Rudd,et al. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants , 2019, PloS one.
[47] Tetsuji Katayama,et al. Modifiable Lifestyle Factors and Cognitive Function in Older People: A Cross-Sectional Observational Study , 2019, Front. Neurol..
[48] Hadi Kharrazi,et al. Exploring the use of machine learning for risk adjustment: A comparison of standard and penalized linear regression models in predicting health care costs in older adults , 2019, PloS one.
[49] Jie Ma,et al. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. , 2019, Journal of clinical epidemiology.
[50] Nicole Bohme Carnegie,et al. Variable Selection and Parameter Tuning for BART Modeling in the Fragile Families Challenge , 2019, Socius: Sociological Research for a Dynamic World.
[51] M. Howell,et al. Ensuring Fairness in Machine Learning to Advance Health Equity , 2018, Annals of Internal Medicine.
[52] Cynthia Rudin,et al. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.
[53] J. Whitwell,et al. Alzheimer's disease neuroimaging , 2018, Current opinion in neurology.
[54] M. van der Schaar,et al. Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning , 2018, Scientific Reports.
[55] Eleonore Bayen,et al. Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study , 2018, Journal of medical Internet research.
[56] A. Kaufman,et al. Targeted Estimation of the Relationship Between Childhood Adversity and Fluid Intelligence in a US Population Sample of Adolescents , 2018, American journal of epidemiology.
[57] Bernd Bischl,et al. iml: An R package for Interpretable Machine Learning , 2018, J. Open Source Softw..
[58] Dylan S. Small,et al. Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference Using Five Empirical Applications , 2018, The American Statistician.
[59] John Hsu,et al. A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks , 2018, CHANCE.
[60] Stephen J Mooney,et al. Big Data in Public Health: Terminology, Machine Learning, and Privacy. , 2018, Annual review of public health.
[61] Paolo Brunori,et al. The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees , 2018 .
[62] Scott M. Lundberg,et al. Consistent Individualized Feature Attribution for Tree Ensembles , 2018, ArXiv.
[63] Stéphane P. A. Bordas,et al. What makes Data Science different? A discussion involving Statistics2.0 and Computational Sciences , 2018, International Journal of Data Science and Analytics.
[64] M. Boustani,et al. Ongoing Medical Management to Maximize Health and Well-being for Persons Living With Dementia , 2018, The Gerontologist.
[65] Susan Athey,et al. The Impact of Machine Learning on Economics , 2018, The Economics of Artificial Intelligence.
[66] Shripad Tuljapurkar,et al. Machine learning approaches to the social determinants of health in the health and retirement study , 2017, SSM - population health.
[67] Samuel J Clark,et al. Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies. , 2017, Bayesian analysis.
[68] Pedro Rosa-Neto,et al. Identifying incipient dementia individuals using machine learning and amyloid imaging , 2017, Neurobiology of Aging.
[69] Kevin G. Stanley,et al. A glossary for big data in population and public health: discussion and commentary on terminology and research methods , 2017, Journal of Epidemiology & Community Health.
[70] P. A. Bradley,et al. Developing a practical suicide risk prediction model for targeting high‐risk patients in the Veterans health Administration , 2017, International journal of methods in psychiatric research.
[71] Hong-Woo Chun,et al. Longitudinal Study-Based Dementia Prediction for Public Health , 2017, International journal of environmental research and public health.
[72] T. Yarkoni,et al. Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning , 2017, Perspectives on psychological science : a journal of the Association for Psychological Science.
[73] Ashley I. Naimi,et al. Stacked generalization: an introduction to super learning , 2017, bioRxiv.
[74] Dionysis Goularas,et al. Distinguishing age-related cognitive decline from dementias: A study based on machine learning algorithms , 2017, Journal of Clinical Neuroscience.
[75] Hans-Peter Kriegel,et al. DBSCAN Revisited, Revisited , 2017, ACM Trans. Database Syst..
[76] Sara C. Madeira,et al. Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows , 2017, BMC Medical Informatics and Decision Making.
[77] Ramon Casanova,et al. INVESTIGATING PREDICTORS OF COGNITIVE DECLINE USING MACHINE LEARNING , 2017, Alzheimer's & Dementia.
[78] Christina Heinze-Deml,et al. Causal Structure Learning , 2017, 1706.09141.
[79] Sören R. Künzel,et al. Metalearners for estimating heterogeneous treatment effects using machine learning , 2017, Proceedings of the National Academy of Sciences.
[80] M. Hernán,et al. The value of explicitly emulating a target trial when using real world evidence: an application to colorectal cancer screening , 2017, European Journal of Epidemiology.
[81] Ricardo J. G. B. Campello,et al. A systematic comparative evaluation of biclustering techniques , 2017, BMC Bioinformatics.
[82] M. J. van der Laan,et al. Racial/Ethnic Differences in the Role of Childhood Adversities for Mental Disorders Among a Nationally Representative Sample of Adolescents , 2016, Epidemiology.
[83] Bo Shen,et al. MDBSCAN: Multi-level Density Based Spatial Clustering of Applications with Noise , 2016, KMO.
[84] Masataka Harada,et al. A flexible, interpretable framework for assessing sensitivity to unmeasured confounding , 2016, Statistics in medicine.
[85] Achim Zeileis,et al. Model-Based Recursive Partitioning for Subgroup Analyses , 2016, The international journal of biostatistics.
[86] James M Robins,et al. Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. , 2016, American journal of epidemiology.
[87] Stefan Wager,et al. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests , 2015, Journal of the American Statistical Association.
[88] Laura B. Balzer,et al. The roles of outlet density and norms in alcohol use disorder. , 2015, Drug and alcohol dependence.
[89] Svetha Venkatesh,et al. Is Demography Destiny? Application of Machine Learning Techniques to Accurately Predict Population Health Outcomes from a Minimal Demographic Dataset , 2015, PloS one.
[90] Reza Ebrahimpour,et al. Mixture of experts: a literature survey , 2014, Artificial Intelligence Review.
[91] Nick C Fox,et al. A data-driven model of biomarker changes in sporadic Alzheimer's disease , 2014, Alzheimer's & Dementia.
[92] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[93] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[94] Adam Kapelner,et al. bartMachine: Machine Learning with Bayesian Additive Regression Trees , 2013, 1312.2171.
[95] Jennifer L. Hill,et al. Assessing lack of common support in causal inference using bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children's cognitive outcomes , 2013, 1311.7244.
[96] A. Simmons,et al. Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment , 2013, Psychiatry Research: Neuroimaging.
[97] Max Kuhn,et al. Applied Predictive Modeling , 2013 .
[98] S. Rose. Mortality risk score prediction in an elderly population using machine learning. , 2013, American journal of epidemiology.
[99] Marc Ratkovic,et al. Estimating treatment effect heterogeneity in randomized program evaluation , 2013, 1305.5682.
[100] Pedro M. Domingos. A few useful things to know about machine learning , 2012, Commun. ACM.
[101] D. Green,et al. Modeling Heterogeneous Treatment Effects in Survey Experiments with Bayesian Additive Regression Trees , 2012 .
[102] Trevor J. Hastie,et al. The Graphical Lasso: New Insights and Alternatives , 2011, Electronic journal of statistics.
[103] Galit Shmueli,et al. To Explain or To Predict? , 2010, 1101.0891.
[104] Jennifer L. Hill,et al. Bayesian Nonparametric Modeling for Causal Inference , 2011 .
[105] Robert M. Groves,et al. Using proxy measures and other correlates of survey outcomes to adjust for non‐response: examples from multiple surveys , 2010 .
[106] Hemant Ishwaran,et al. Random Survival Forests , 2008, Wiley StatsRef: Statistics Reference Online.
[107] R. Tibshirani,et al. Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.
[108] H. Chipman,et al. BART: Bayesian Additive Regression Trees , 2008, 0806.3286.
[109] K. Hornik,et al. Unbiased Recursive Partitioning: A Conditional Inference Framework , 2006 .
[110] Robert P. Sheridan,et al. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..
[111] Sunil J Rao,et al. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2003 .
[112] Lucila Ohno-Machado,et al. Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.
[113] K. Anstey,et al. Selective non‐response to clinical assessment in the longitudinal study of aging: implications for estimating population levels of cognitive function and dementia , 2002, International journal of geriatric psychiatry.
[114] J. Friedman. Stochastic gradient boosting , 2002 .
[115] J. Robins,et al. Marginal Structural Models and Causal Inference in Epidemiology , 2000, Epidemiology.
[116] Payman Arabshahi,et al. Fundamentals of Artificial Neural Networks [Book Reviews] , 1996, IEEE Transactions on Neural Networks.
[117] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[118] Junfeng Jiao,et al. Predicting and mapping neighborhood-scale health outcomes: A machine learning approach , 2021, Comput. Environ. Urban Syst..
[119] E. LeDell,et al. H2O AutoML: Scalable Automatic Machine Learning , 2020 .
[120] M. Power,et al. Development of algorithmic dementia ascertainment for racial/ethnic disparities research in the U.S. Health and Retirement Study. , 2019, Epidemiology.
[121] Megan Kurka,et al. Machine Learning Interpretability with H2O Driverless AI , 2019 .
[122] Mihaela van der Schaar,et al. Demystifying Black-box Models with Symbolic Metamodels , 2019, NeurIPS.
[123] Alexander Galozy,et al. Towards Understanding ICU Procedures using Similarities in Patient Trajectories : An exploratory study on the MIMIC-III intensive care database , 2018 .
[124] Sherri Rose,et al. Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies , 2017, American journal of epidemiology.
[125] S. R. Bhagyashree,et al. Diagnosis of Dementia by Machine learning methods in Epidemiological studies: a pilot exploratory study from south India , 2017, Social Psychiatry and Psychiatric Epidemiology.
[126] Brandon M. Greenwell. pdp: An R Package for Constructing Partial Dependence Plots , 2017, R J..
[127] M. Glymour,et al. Evaluating Public Health Interventions: 5. Causal Inference in Public Health Research-Do Sex, Race, and Biological Factors Cause Health Outcomes? , 2017, American journal of public health.
[128] H. Soininen,et al. Generalizability of the disease state index prediction model for identifying patients progressing from mild cognitive impairment to Alzheimer's disease. , 2015, Journal of Alzheimer's disease : JAD.
[129] Kurt Hornik,et al. party with the mob : Model-Based Recursive Partitioning in R , 2009 .
[130] M. Glymour,et al. USING CAUSAL DIAGRAMS TO UNDERSTAND COMMON PROBLEMS IN SOCIAL EPIDEMIOLOGY , 2006 .
[131] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[132] Michael E. Tipping,et al. Probabilistic Principal Component Analysis , 1999 .
[133] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .