Identifying evolving multivariate dynamics in individual and cohort time series, with application to physiological control systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 117-125).Physiological control systems involve multiple interacting variables operating in feedback loops that enhance an organism's ability to self-regulate and respond to internal and external disturbances. The resulting multivariate time-series often exhibit rich dynamical patterns, which are altered under pathological conditions. However, model identification for physiological systems is complicated by measurement artifacts and changes between operating regimes. The overall aim of this thesis is to develop and validate computational tools for identification and analysis of structured multivariate models of physiological dynamics in individual and cohort time-series. We first address the identification and stability of the respiratory chemoreflex system, which is key to the pathogenesis of sleep-induced periodic breathing and apnea. Using data from both an animal model of periodic breathing, as well as human recordings from clinical sleep studies, we demonstrate that model-based analysis of the interactions involved in spontaneous breathing can characterize the dynamics of the respiratory control system, and provide a useful tool for quantifying the contribution of various dynamic factors to ventilatory instability. The techniques have suggested novel approaches to titration of combination therapies, and clinical evaluations are now underway. We then study shared multivariate dynamics in physiological cohort time-series, assuming that the time-series are generated by switching among a finite collection of physiologically constrained dynamical models. Patients whose time-series exhibit similar dynamics may be grouped for monitoring and outcome prediction. We develop a novel parallelizable machine-learning algorithm for outcome-discriminative identification of the switching dynamics, using a probabilistic dynamic Bayesian network to initialize a deterministic neural network classifier. In validation studies involving simulated data and human laboratory recordings, the new technique significantly outperforms the standard expectation-maximization approach for identification of switching dynamics. In a clinical application, we show the prognostic value of assessing evolving dynamics in blood pressure time-series to predict mortality in a cohort of intensive care unit patients. A better understanding of the dynamics of physiological systems in both health and disease may enable clinicians to direct therapeutic interventions targeted to specific underlying mechanisms. The techniques developed in this thesis are general, and can be extended to other domains involving multi-dimensional cohort time-series.by Shamim Nemati.Ph.D

Data Informed Health Simulation Modeling

Combining reliable data with dynamic models can enhance the understanding of health-related phenomena. Smartphone sensor data characterizing discrete states is often suitable for analysis with machine learning classifiers. For dynamic models with continuous states, high-velocity data also serves an important role in model parameterization and calibration. Particle filtering (PF), combined with dynamic models, can support accurate recurrent estimation of continuous system state. This thesis explored these and related ideas with several case studies. The first employed multivariate Hidden Markov models (HMMs) to identify smoking intervals, using time-series of smartphone-based sensor data. Findings demonstrated that multivariate HMMs can achieve notable accuracy in classifying smoking state, with performance being strongly elevated by appropriate data conditioning. Reflecting the advantages of dynamic simulation models, this thesis has contributed two applications of articulated dynamic models: An agent-based model (ABM) of smoking and E-Cigarette use and a hybrid multi-scale model of diabetes in pregnancy (DIP). The ABM of smoking and E-Cigarette use, informed by cross-sectional data, supports investigations of smoking behavior change in light of the influence of social networks and E-Cigarette use. The DIP model was evidenced by both longitudinal and cross-sectional data, and is notable for its use of interwoven ABM, system dynamics (SD), and discrete event simulation elements to explore the interaction of risk factors, coupled dynamics of glycemia regulation, and intervention tradeoffs to address the growing incidence of DIP in the Australia Capital Territory. The final study applied PF with an SD model of mosquito development to estimate the underlying Culex mosquito population using various direct observations, including time series of weather-related factors and mosquito trap counts. The results demonstrate the effectiveness of PF in regrounding the states and evolving model parameters based on incoming observations. Using PF in the context of automated model calibration allows optimization of the values of parameters to markedly reduce model discrepancy. Collectively, the thesis demonstrates how characteristics and availability of data can influence model structure and scope, how dynamic model structure directly affects the ways that data can be used, and how advanced analysis methods for calibration and filtering can enhance model accuracy and versatility

A Nonlinear Dynamic Approach for Evaluating Postural Control

Recent research suggests that traditional biomechanical models of postural stability do not fully characterise the nonlinear properties of postural control. In sports medicine, this limitation is manifest in the postural steadiness assessment approach, which may not be sufficient for detecting the presence of subtle physiological change after injury. The limitation is especially relevant given that return-to-play decisions are being made based on assessment results. This update first reviews the theoretical foundation and limitations of the traditional postural stability paradigm. It then offers, using the clinical example of athletes recovering from cerebral concussion, an alternative theoretical proposition for measuring changes in postural control by applying a nonlinear dynamic measure known as ‘approximate entropy’. Approximate entropy shows promise as a valuable means of detecting previously unrecognised, subtle physiological changes after concussion. It is recommended as an important supplemental assessment tool for determining an athlete’s readiness to resume competitive activity

Progression Modeling of Cognitive Disease Using Temporal Data Mining: Research Landscape, Gaps and Solution Design

Dementia is a cognitive disorder whose diagnosis and progression monitoring is very difficult due to a very slow onset and progression. It is difficult to detect whether cognitive decline is due to ageing process or due to some form of dementia as MRI scans of the brain cannot reliably differentiate between ageing related volume loss and pathological changes. Laboratory tests on blood or CSF samples have also not proved very useful. Alzheimer�s disease (AD) is recognized as the most common cause of dementia. Development of sensitive and reliable tool for evaluation in terms of early diagnosis and progression monitoring of AD is required. Since there is an absence of specific markers for predicting AD progression, there is a need to learn more about specific attributes and their temporal relationships that lead to this disease and determine progression from mild cognitive impairment to full blown AD. Various stages of disease and transitions from one stage to the have be modelled based on longitudinal patient data. This paper provides a critical review of the methods to understand disease progression modelling and determine factors leading to progression of AD from initial to final stages. Then the design of a machine learning based solution is proposed to handle the gaps in current research

Improving outcomes in interstitial lung disease through the application of bioinformatics and systems biology

Idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are two distinct respiratory diseases whose features including pathogenesis and progression are not fully understood. However, both clinicians utilise changes in serial pulmonary function measurements to gain an insight into disease severity and control. More accurate prediction of disease progression would be beneficial, particularly for IPF given the variability in its clinical course as an unknown factor at the time of diagnosis. Home-based, real-time monitoring of disease progression by spirometry has provided an opportunity to optimise the delivery of treatment and reduce the length of clinical trials. Therefore, the potential to understand the mechanisms underlying disease progression and generate effective treatment has been improved. In light of this, the motivation for this project is to understand the mathematical features within daily pulmonary function time series generated by IPF patients. Hopefully, statistical models of pulmonary function time series would aid the identification of significant clinical events such as acute exacerbation. The mathematical techniques used to identify potentially important features within pulmonary function time series involved the autocorrelation function, critical transitions and detrended fluctuation analysis (DFA). Temporal properties, such as the serial correlation, abrupt changes in trends and complexity, were assessed using time series from the PROFILE clinical trial and London COPD cohort. Forced vital capacity (FVC) measurements were found to be correlated to the previous day’s reading which may inform the sampling rate of lung function during clinical trials. The presence of short-term memory within FVC time series will influence the management of missing data within clinical trials, particularly methods of imputation. Also, FVC time series’ exhibit long-term memory and adaptability supporting the role of FVC as a surrogate marker for IPF disease progression.Open Acces

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of Machine Learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. ML has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management

Modelling change and stability of physical and technical performance in young soccer players.

Esta tese investigou a relação entre mudanças no crescimento e forma do corpo, composição corporal, maturação biológica, desempenho físico, habilidades técnicas, conhecimento de jogo e historial do treino de jovens futebolistas no contexto de seus clubes. O estudo tem um delineamento longitudinal-misto; amostrou 150 futebolistas com idades compreendidas entre os 12 e 14 anos no baseline, oriundos de seis clubes de futebol da área metropolitana do Porto que foram selecionados do estudo Em Busca da Excelência no Desporto - Um Estudo Longitudinal Misto em Jovens Atletas (INEX) (2016-2019). Os jogadores foram divididos em três coortes de idade (12, 13 e 14 anos), e seguidos consecutivamente durante 4 anos. Os dados foram coletados com protocolos padronizados, e a informação está dividida em dois domínios: Individual - biológico, aptidão física, habilidades específicas da modalidade/proficiência do jogo, psicológico, e contextual - clubes. Os procedimentos estatísticos foram realizados nos softwares Somatotype calculation, SPSS, Timepath, MATLAB e STATA. Os resultados mostraram que: (1) jogadores mais velhos são mais altos e pesados e têm valores de composição corporal correspondentes à maturação biológica avançada; (2) jogadores mais velhos têm melhor desempenho físico e são mais habilidosos que os seus pares mais jovens; (3) estas componentes estão relacionadas com maturidade avançada e mais tempo de treino; (4) as habilidades táticas e características psicológicas dos jogadores não diferem entre as coortes de idade; (5) os clubes oferecem uma variedade de condições para aumentar o sucesso dos jogadores na resposta ao treino e à competição; (6) o desenvolvimento da velocidade e agilidade apresentou uma tendência linear em função do tempo; (7) a puberdade é considerada um momento crucial para nutrir futuras vocações de qualidade dos jogadores de futebol dos futebolistas sobretudo no seu desempenho físico; (8) este desenvolvimento está positivamente associado com a maturidade, níveis mais elevados de força explosiva dos membros inferiores e horas acumuladas de treino; (9) velocidade de condução de bola, passe curto com tabela e o precisão de remate melhoraram com a idade, os níveis de atleticismo e horas acumuladas de treino; (10) o crescimento do corpo e a maturidade não explicaram, de forma independente, as diferenças nas trajetórias das habilidades técnicas dos jogadores; (11) o remate de precisão não está associado, significativamente, com os preditores considerados no estudo. Espera-se que os resultados deste estudo possam contribuir para uma atitude mais esclarecida e abrangente na preparação dos jovens futebolistas em termos da sua resposta ao treino e competição ao longo da sua carreira desportiva. PALAVRAS-CHAVE: futebol jovem; atuação; Habilidades; longitudinal; multinível; desenvolvimento; treino; maturidade.This thesis investigates the relationship between changes in young soccer players' physical growth, body shape and composition, biological maturation, physical performance, technical skills, game knowledge and training history within the context of their clubs. A total of 150 players aged 12 to 14 years, at baseline, were selected from participants in the In Search of Excellence in Sport - A Mixed-Longitudinal Study in Young Athletes (INEX) study (2016-2019). INEX was conducted in six soccer clubs of the Porto metropolitan area. They clustered into three age-cohorts (12, 13 and 14 years), were followed consecutively over 4-years using a mixed-longitudinal study design. All data were collected using standardized protocols and the information was distributed over two nested domains: individual - biological, skill/ game proficiency, psychological, and contexts - clubs. Statistical procedures were done in Somatotype calculation, SPSS, LDA, MATLAB, and STATA. Results showed that: (1) older players were more advanced in body physique and their body composition were in line with their advanced biological maturation; (2) older players outperformed their younger peers in all physical performance and technical skills components; (3) these components were related to both advanced maturity and increased training; (4) young soccer players' tactical skills as well as psychological characteristics did not differ across age-cohorts; (5) clubs offer a variety of conditions aiming to enhance players success in their response to training and competition; (6) young soccer players development in speed and agility showed a linear trend, i.e., they improve as time passes; (7) puberty has been found to be a crucial time for nourishing soccer players' future quality vocations in physical performance development; (8) physical performance development is positively associated with biological maturation, higher levels of explosive leg strength, and accumulated hours of soccer specific training; (9) dribbling speed, short-pass rebound and shooting accuracy linearly improved with age, levels of athleticism, and years of official soccer training had positive affect on dribbling speed development; (10) body growth and biological maturation did not independently explain differences in players' trajectories in technical skills; (11) shooting accuracy technique had no significant association with any predictors. We hope that these results may be useful for a more comprehensive approach in young soccer players' long-term preparation in terms of their responses to training and competition