Approximate dynamic programming for anemia management.

The focus of this dissertation work is the formulation and improvement of anemia management process involving trial-and-error. A two-stage method is adopted toward this objective. Given a medical treatment process, a discrete Markov representation is first derived as a formal translation of the treatment process to a control problem under uncertainty. A simulative numerical solution of the control problem is then obtained on-the-fly in the form of a control law maximizing the long-term benefit at each decision stage. Approximate dynamic programming methods are employed in the proposed solution. The motivation underlying this choice is that, in reality, some patient characteristics, which are critical for the sake of treatment, cannot be determined through diagnosis and remain unknown until early stages of treatment, when the patient demonstrates them upon actions by the decision maker. A review of these simulative control tools, which are studied extensively in reinforcement learning theory, is presented. Two approximate dynamic programming tools, namely SARSA and Q -learning, are introduced. Their performance in discovering the optimal individualized drug dosing policy is illustrated on hypothetical patients made up as fuzzy models for simulations. As an addition to these generic reinforcement learning methods, a state abstraction scheme for the considered application domain is also proposed. The control methods of this study, capturing the essentials of a drug delivery problem, constitutes a novel computational framework for model-free medical treatment. Experimental evaluation of the dosing strategies produced by the proposed methods against the standard policy, which is being followed actually by human experts in Kidney Diseases Program, University of Louisville, shows the advantages for use of reinforcement learning in the drug dosing problem in particular and in medical decision making in general

Mathematical Modeling of Oxygen Transport, Cell Killing and Cell Decision Making in Photodynamic Therapy of Cancer

In this study we present a model of in vitro cell killing through type II Photodynamic Therapy (PDT) for simulation of the molecular interactions leading to cell death in time domain in the presence of oxygen transport within a spherical cell. By coupling the molecular kinetics to cell killing, we develop a modeling method of PDT cytotoxicity caused by singlet oxygen and obtain the cell survival ratio as a function of light fluence or initial photosensitizer concentration with different photon density or irradiance of incident light and other parameters of oxygen transport. A systems biology model is developed to account for the detailed molecular pathways induced by PDT treatment leading to cell killing. We derive a mathematical model of cell decision making through a binary cell fate decision scheme on cell death or survival, during and after PDT treatment, and we employ a rate distortion theory as the logical design for this decision making proccess to understand the biochemical processing of information by a cell. Rate distortion theory is also used to design a time dependent Blahut-Arimoto algorithm of three variables where the input is a stimulus vector composed of the time dependent concentrations of three PDT induced signaling molecules and the output reflects a cell fate decision. The concentrations of molecules involved in the biochemical processes are determined by a group of rate equations which produce the probability of cell survival or death as the output of cell decision. The modeling of the cell decision strategy allows quantitative assessment of the cell survival probability, as a function of multiple parameters and coefficients used in the model, which can be modified to account for heterogeneous cell response to PDT or other killing or therapeutic agents. The numerical results show that the present model of type II PDT yields a powerful tool to quantify various processes underlying PDT at the molecular and cellular levels and to interpret experimental results of in vitro cell studies. Finally, following an alternative approach, the cell survival probability is modeled as a predator - prey equation where predators are cell death signaling molecules and prey is the cell survival. The two models can be compared to each other as well as directly to the experimental results of measured molecular concentrations and cell survival ratios for optimization of models, to gain insights on in vitro cell studies of PDT.  Ph.D

Transfer learning for medication adherence prediction from social forums self-reported data

Indiana University-Purdue University Indianapolis (IUPUI)Medication non-adherence and non-compliance left unaddressed can compound into severe medical problems for patients. Identifying patients that are likely to become non-adherent can help reduce these problems. Despite these benefits, monitoring adherence at scale is cost-prohibitive. Social forums offer an easily accessible, affordable, and timely alternative to the traditional methods based on claims data. This study investigates the potential of medication adherence prediction based on social forum data for diabetes and fibromyalgia therapies by using transfer learning from the Medical Expenditure Panel Survey (MEPS). Predictive adherence models are developed by using both survey and social forums data and different random forest (RF) techniques. The first of these implementations uses binned inputs from k-means clustering. The second technique is based on ternary trees instead of the widely used binary decision trees. These techniques are able to handle missing data, a prevalent characteristic of social forums data. The results of this study show that transfer learning between survey models and social forum models is possible. Using MEPS survey data and the techniques listed above to derive RF models, less than 5% difference in accuracy was observed between the MEPS test dataset and the social forum test dataset. Along with these RF techniques, another RF implementation with imputed means for the missing values was developed and shown to predict adherence for social forum patients with an accuracy >70%. This thesis shows that a model trained with verified survey data can be used to complement traditional medical adherence models by predicting adherence from unverified, self-reported data in a dynamic and timely manner. Furthermore, this model provides a method for discovering objective insights from subjective social reports. Additional investigation is needed to improve the prediction accuracy of the proposed model and to assess biases that may be inherent to self-reported adherence measures in social health networks

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Dynamic deep learning for automatic facial expression recognition and its application in diagnosis of ADHD & ASD

Neurodevelopmental conditions like Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) impact a significant number of children and adults worldwide. Currently, the means of diagnosing of such conditions is carried out by experts, who employ standard questionnaires and look for certain behavioural markers through manual observation. Such methods are not only subjective, difficult to repeat, and costly but also extremely time consuming. However, with the recent surge of research into automatic facial behaviour analysis and it's varied applications, it could prove to be a potential way of tackling these diagnostic difficulties. Automatic facial expression recognition is one of the core components of this field but it has always been challenging to do it accurately in an unconstrained environment. This thesis presents a dynamic deep learning framework for robust automatic facial expression recognition. It also proposes an approach to apply this method for facial behaviour analysis which can help in the diagnosis of conditions like ADHD and ASD. The proposed facial expression algorithm uses a deep Convolutional Neural Networks (CNN) to learn models of facial Action Units (AU). It attempts to model three main distinguishing features of AUs: shape, appearance and short term dynamics, jointly in a CNN. The appearance is modelled through local image regions relevant to each AU, shape is encoded using binary masks computed from automatically detected facial landmarks and dynamics is encoded by using a short sequence of image as input to CNN. In addition, the method also employs Bi-directional Long Short Memory (BLSTM) recurrent neural networks for modelling long term dynamics. The proposed approach is evaluated on a number of databases showing state-of-the-art performance for both AU detection and intensity estimation tasks. The AU intensities estimated using this approach along with other 3D face tracking data, are used for encoding facial behaviour. The encoded facial behaviour is applied for learning models which can help in detection of ADHD and ASD. This approach was evaluated on the KOMAA database which was specially collected for this purpose. Experimental results show that facial behaviour encoded in this way provide a high discriminative power for classification of people with these conditions. It is shown that the proposed system is a potentially useful, objective and time saving contribution to the clinical diagnosis of ADHD and ASD

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

PET-imaging in depression and antidepressant therapies : focus on the serotonin system and the cerebral glucose metabolism

The main scope of the research summarised in this dissertation comprises the use of positron emission tomography to investigate the role of the serotonin transporter in depression and antidepressant therapies. Hereby, several studies were performed using the PET radiotracer [11C]DASB, which specifically targets the serotonin transporter. To allow qualitative and safe research with this radiotracer, the first research topic focussed on the optimization of the radiotracer’s purification procedure and its quality control. Using this radiotracer, a first-in-dog study was carried out to investigate the radiotracer’s distribution and to define the appropriate image quantification methods. Subsequently, this radiotracer was used to perform a dose-occupancy in the dog to estimate the optimal dosing regimen to treat dogs with behavioural disorders with escitalopram. A second part of the dissertation focuses on rats and the current position of repetitive transcranial magnetic stimulation (rTMS) in the rat. Hereby, several additional objectives were put forward. The first objective comprised the evaluation of the accuracy of a for rodents adapted human neuronavigation system to perform rTMS in the rat. A second objective was the investigation of the construct validity of two depression models in terms of altered regional glucose metabolism. This was investigated via a PET study using the radiotracer [18F]FDG. Finally, for the preferred depression model, which was the one based on chronic corticosterone injections, the scope was extended from the serotonin transporter to the serotonin 5-HT1A and 5-HT2A receptors to explore the role of the serotonin system in the pathophysiology of this depression model in the rat. For this purpose, three radiotracers were applied: [11C]DASB, [18F]MPPF, and [18F]altanserin. This allowed to image the serotonin transporters, the 5-HT1A receptors, and the 5-HT2A receptors, respectively

Nonlinear Dynamics

This volume covers a diverse collection of topics dealing with some of the fundamental concepts and applications embodied in the study of nonlinear dynamics. Each of the 15 chapters contained in this compendium generally fit into one of five topical areas: physics applications, nonlinear oscillators, electrical and mechanical systems, biological and behavioral applications or random processes. The authors of these chapters have contributed a stimulating cross section of new results, which provide a fertile spectrum of ideas that will inspire both seasoned researches and students