Blood and breath alcohol concentration from transdermal alcohol biosensor data: estimation and uncertainty quantification via forward and inverse filtering for a covariate-dependent, physics-informed, hidden Markov model*
DOI10.1088/1361-6420/AC5AC7zbMath1490.62369OpenAlexW4221008399MaRDI QIDQ5071179
Allison D. Rosen, Tianlan Shao, Chunming Wang, Susan E. Luczak, Clemens Oszkinat, Emily B. Saldich, I. G. Rosen
Publication date: 20 April 2022
Published in: Inverse Problems (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1088/1361-6420/ac5ac7
hidden Markov modelscovariatesblood alcohol concentrationbreath alcohol concentrationtransdermal alcohol concentrationforward and inverse filteringphysics informed regularization
Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10) Markov processes: estimation; hidden Markov models (62M05) Markov processes: hypothesis testing (62M02)
Cites Work
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- Using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensors
- Hidden Markov models for alcoholism treatment trial data
- Semigroups of linear operators and applications to partial differential equations
- Blind deconvolution for distributed parameter systems with unbounded input and output and determining blood alcohol concentration from transdermal biosensor data
- Hidden physics models: machine learning of nonlinear partial differential equations
- Hidden Markov models: inverse filtering, belief estimation and privacy protection
- Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach
- Adversarial uncertainty quantification in physics-informed neural networks
- Estimating the distribution of random parameters in a diffusion equation forward model for a transdermal alcohol biosensor
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Inference in hidden Markov models.
- Deconvolving an estimate of breath measured blood alcohol concentration from biosensor collected transdermal ethanol data
- Growth transformations for functions on manifolds
- Hidden Markov Models for Speech Recognition
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
- Deconvolving the input to random abstract parabolic systems: a population model-based approach to estimating blood/breath alcohol concentration from transdermal alcohol biosensor data
- Inverse Filtering for Hidden Markov Models With Applications to Counter-Adversarial Autonomous Systems
- On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs
- Statistical Inference for Probabilistic Functions of Finite State Markov Chains
- Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
- An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology
- A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
- Hidden Markov Models for Time Series
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