Sequentially spherical data modeling with hidden Markov models and its application to fMRI data analysis

2020 ◽  
Vol 206 ◽  
pp. 106341
Author(s):  
Wentao Fan ◽  
Lin Yang ◽  
Nizar Bouguila ◽  
Yewang Chen
Keyword(s):  
Data Analysis ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Data Modeling ◽  
Fmri Data ◽  
Spherical Data ◽  
Fmri Data Analysis

scholarly journals Intelligent Data Analysis to Model and Understand Live Cell Time-lapse Sequences

2012 ◽  
Vol 51 (04) ◽  
pp. 332-340 ◽  
Author(s):  
A. Paterson ◽  
M. Ashtari ◽  
D. Ribé ◽  
G. Stenbeck ◽  
A. Tucker
Keyword(s):  
Data Analysis ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Lapse ◽  
Living Cells ◽  
Live Cell ◽  
Intelligent Data Analysis ◽  
Stable States ◽  
Experimental Conditions

SummaryBackground: One important aspect of cellular function, which is at the basis of tissue homeostasis, is the delivery of proteins to their correct destinations. Significant advances in live cell microscopy have allowed tracking of these pathways by following the dynamics of fluorescently labelled proteins in living cells.Objectives: This paper explores intelligent data analysis techniques to model the dynamic behavior of proteins in living cells as well as to classify different experimental conditions.Methods: We use a combination of decision tree classification and hidden Markov models. In particular, we introduce a novel approach to “align” hidden Markov models so that hidden states from different models can be cross-compared.Results: Our models capture the dynamics of two experimental conditions accurately with a stable hidden state for control data and multiple (less stable) states for the experimental data recapitulating the behaviour of particle trajectories within live cell time-lapse data.Conclusions: In addition to having successfully developed an automated framework for the classification of protein transport dynamics from live cell time-lapse data our model allows us to understand the dynamics of a complex trafficking pathway in living cells in culture.

scholarly journals Big Data Analysis of Electronic Health Records: Clinically interpretable representations of older adult inpatient trajectories using time-series numerical data and Hidden Markov Models

2021 ◽  
Author(s):  
Maria Herrero-Zazo ◽  
Victoria L Keevil ◽  
Vince Taylor ◽  
Helen Street ◽  
Afzal N Chaudhry ◽  
...  
Keyword(s):  
Time Series ◽  
Big Data ◽  
Data Analysis ◽  
Electronic Health Records ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Big Data Analysis ◽  
Health Records ◽  
State Spaces

The implementation of Electronic Health Records (EHR) in UK hospitals provides new opportunities for clinical 'big data' analysis. The representation of observations routinely recorded in clinical practice is the first step to use these data in several research tasks. Anonymised data were extracted from 11 158 first emergency admission episodes (AE) in older adults. Irregular records from 23 laboratory blood tests and vital signs were normalized and regularised into daily bins and represented as numerical multivariate time-series (MVTS). Unsupervised Hidden Markov Models (HMM) were trained to represent each day of each AE as one of 17 state spaces. The visual clinical interpretation of these states showed remarkable differences between patients who died at the end of the AE and those who were discharged. All states had marked features that allowed their clinical interpretation and differentiation between those associated with the patients' disease burden, their physiological response to this burden or the stage of admission. The most evident relationships with hold-out clinical information were also confirmed by Chi-square tests, with two states strongly associated with inpatient mortality (IM) and 12 states (71%) associated with at least one admission diagnosis. The potential of these data representations on prediction of hospital outcomes was also explored using Logistic Regression (LR) and Random Forest (RF) models, with higher prediction performance observed when models were trained with MVTS data compared to HMM state spaces. However, the outputs of generative and discriminative analyses were complementary. For example, highest ranking features of the best performing RF model for IM (ROC-AUC 0.851) resembled the laboratory blood test and vital sign variables characterising the 'Early Inflammatory Response-like' state, itself strongly associated with IM. These results provide evidence of the capability of generative models to extract biological signals from routinely collected clinical data and their potential to represent interpretable patients' trajectories for future research in hypothesis generation or prediction modelling.

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