A Coggle Diagram about Bayesian mixed model
- Patient subgrouping
- Individualized disease trajectory (progression)
, Markov Switching model
, Markov decision process (for patient screening)
, State space model
(Wulsin, D.F., Fox, E.B. & Litt, B., 2014. Modeling the Complex Dynamics and Changing Correlations of Epileptic Events. arXiv.org, stat.ML, pp.55–75.
, Stanculescu, I., Williams, C. & Freer, Y., 2014. A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring. In Proceedings of the Thirtieth ….
, Lange, J.M. et al., 2015. A joint model for multistate disease processes and random informative observation times, with applications to electronic medical records data. Biometrics, 71(1), pp.90–101.
, Georgatzis, K., Williams, C.K.I. & Hawthorne, C., 2016. Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring.
, Grigorievskiy, A. & Karhunen, J., Gaussian Process Kernels for Popular State-Space Time Series Models. users.ics.aalto.fi .
and Xu, Y. et al., 2014. Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times. arXiv.org, stat.AP.
), Review papers
(Shivade, C. et al., 2014. A review of approaches to identifying patient phenotype cohorts using electronic health records. Journal of the American Medical Informatics Association, 21(2), pp.221–230.
, Johnson, A.E.W. et al., 2016. Machine Learning and Decision Support in Critical Care. Proceedings of the IEEE, 104(2), pp.444–466
, Yadav, P. et al., 2015. Mining Electronic Health Records (EHR): A Survey.
, Burtini, G., Loeppky, J. & Lawrence, R., 2015. A Survey of Online Experiment Design with the Stochastic Multi-Armed Bandit.
, Zhou, L., 2015. A Survey on Contextual Multi-armed Bandits.
and Shivade, C. et al., 2014. A review of approaches to identifying patient phenotype cohorts using electronic health records. Journal of the American Medical Informatics Association, 21(2), pp.221–230.
), Healthcare Application
, Deep learning
and Deep NN
), Gaussian process
(Liu, Z. & Hauskrecht, M., 2016. Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data. Thirtieth AAAI Conference on Artificial Intelligence.
, Lasko, T.A., Denny, J.C. & Levy, M.A., 2013. Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data J. Devaney, ed. PloS one, 8(6), p.e66341.
and Grigorievskiy, A. & Karhunen, J., Gaussian Process Kernels for Popular State-Space Time Series Models. users.ics.aalto.fi .
), (GP) Latent variable model
, High dimensional regression
, Markov modulated model
, Beyesian nonparametrics
, Casual inference
(van der Heijden, M., Velikova, M. & Lucas, P., 2014. Learning Bayesian networks for clinical time series analysis. Journal of Biomedical …, 48, pp.94–105.
), Ensemble model
(Turgeman, L. & May, J.H., 2016. A mixed-ensemble model for hospital readmission. Artificial Intelligence in Medicine.
), Undirected graph model
and Multi-armed bandit
(Wang, Y. & Powell, W., 2016. An optimal learning method for developing personalized treatment regimes. arXiv.org, stat.ML.