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Articles 1 - 13 of 13
Full-Text Articles in Medicine and Health Sciences
A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb
A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb
Masters Theses
One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …
Investigations Of External Resources And The Impact Of Imaging On Patient Flow In The Emergency Department, Marisa Shehan
Investigations Of External Resources And The Impact Of Imaging On Patient Flow In The Emergency Department, Marisa Shehan
All Theses
The problems associated with Emergency Department (ED) crowding are numerous, varied, and complex. Though overcrowded Emergency Departments are frequently attributed to overcrowded hospitals, crowding is also impacted by bottlenecks in patient flow. While discrete-event simulation (DES) is commonly used to model ED flow, external resources are typically excluded from these models due to their complexity and the limited amount of known information for these processes. Instead, external resources such as consults, labs, and imaging are modeled using estimation and/or educated guesswork. In this study, the impact of imaging on patient flow was assessed through data analysis of specific imaging factors, …
Frequency Dependent Diffusion Kurtosis Measurement In The Human Brain With Oscillating Gradients, Kevin B. Borsos
Frequency Dependent Diffusion Kurtosis Measurement In The Human Brain With Oscillating Gradients, Kevin B. Borsos
Electronic Thesis and Dissertation Repository
Oscillating gradient spin-echo (OGSE) is an implementation of diffusion MRI that enables shorter effective diffusion times than the conventional pulse gradient spin-echo (PGSE) by periodically modulating the diffusion gradient. Measurements of the diffusion kurtosis, which reflects the degree of restricted diffusion, have previously been prohibited with OGSE due to technical limitations of clinical gradient systems. This thesis presents a novel oscillating gradient waveform that enables the measurement of kurtosis using OGSE without requiring advanced gradient hardware. Decreases of kurtosis are observed in OGSE acquisitions of healthy human subjects relative to PGSE, demonstrating the dependence of the kurtosis on oscillation frequency. …
Optimization Of Vaccine Supply Chains In Low- And Middle-Income Countries Utilizing Drones, Maximilian Kolter
Optimization Of Vaccine Supply Chains In Low- And Middle-Income Countries Utilizing Drones, Maximilian Kolter
Graduate Theses and Dissertations
Despite tremendous efforts from governments and humanitarian organizations, millions of children in low- and low-middle-income countries (LICs and LMICs) are still excluded from the benefits of immunization. The vaccine distribution in LICs and LMICs is challenging for several reasons, such as limited cold chain capacities, vaccine wastage, uncertain demand, and lack of access to immunization services. A promising avenue to address these issues is the utilization of drones for vaccine delivery. Drones can fly at high speed on direct paths and could enable on-demand deliveries to mitigate limited storage capacities. Further, their independence of road networks could allow them reaching …
Influence Of The Inherent Safety Principles On Quantitative Risk In Process Industry: Application Of Genetic Algorithm Process Optimization (Gapo), Mehdi Jahangiri, Abolfazl Moghadasi, Mojtaba Kamalinia, Farid Sadeghianjahromi, Sean Banaee
Influence Of The Inherent Safety Principles On Quantitative Risk In Process Industry: Application Of Genetic Algorithm Process Optimization (Gapo), Mehdi Jahangiri, Abolfazl Moghadasi, Mojtaba Kamalinia, Farid Sadeghianjahromi, Sean Banaee
Community & Environmental Health Faculty Publications
Inherent safety (IS) refers to a set of measures that enhance the safety level of processes and equipment, rendering additional equipment and/or add-ons. The early design phase of processes is suited best for implementation of IS strategies as some of such strategies either are impossible to be implemented at the operation phase or substantially increase costs. The purpose of this study is to present a new approach called genetic algorithm process optimization (GAPO), by which processes can be made inherently safer even at the operation phase. This study simulates the IS principle, assessing its impact on quantitative risk and the …
Impact Of A Localized Lean Six Sigma Implementation On Overall Patient Safety And Process Efficiency, Luvianca Gil, Pilar Pazos, Mamadou Seck, Rolando Delaguila
Impact Of A Localized Lean Six Sigma Implementation On Overall Patient Safety And Process Efficiency, Luvianca Gil, Pilar Pazos, Mamadou Seck, Rolando Delaguila
Engineering Management & Systems Engineering Faculty Publications
Continuous quality improvement tools have caught the attention of the Health Care Industry as a solution to process efficiency, patient safety and cost reduction. This research explores the impact of a Lean Six Sigma (LSS) process improvement initiative in overall process efficiency and patient safety in two Labor and Delivery (L+D) units of two large hospital providers. This study focuses on the application of modeling and simulation methodology to investigate the influence of a localized process improvement intervention on the overall L+D unit output, by considering patient flow, system capacity and unit performance. The simulation models capacity profiles and patient …
Improving Ventricular Catheter Design Through Computational Fluid Dynamics, Sofy Hefets Weisenberg
Improving Ventricular Catheter Design Through Computational Fluid Dynamics, Sofy Hefets Weisenberg
Masters Theses
Cerebrospinal fluid (CSF) shunts are fully implantable medical devices that are used to treat patients suffering from conditions characterized by elevated intracranial pressure, such as hydrocephalus. In cases of shunt failure or malfunction, patients are often required to endure one or more revision surgeries to replace all or part of the shunt. One of the primary causes of CSF shunt failure is obstruction of the ventricular catheter, a component of the shunt system implanted directly into the brain's ventricular system. This work aims to improve the design of ventricular catheters in order to reduce the incidence of catheter obstruction and …
Generating Optimal Control Simulations Of Musculoskeletal Movement Using Opensim And Matlab, Leng-Feng Lee, Brian R. Umberger
Generating Optimal Control Simulations Of Musculoskeletal Movement Using Opensim And Matlab, Leng-Feng Lee, Brian R. Umberger
Kinesiology Department Faculty Publication Series
Computer modeling, simulation and optimization are powerful tools that have seen increased use in biomechanics research. Dynamic optimizations can be categorized as either data-tracking or predictive problems. The data-tracking approach has been used extensively to address human movement problems of clinical relevance. The predictive approach also holds great promise, but has seen limited use in clinical applications. Enhanced software tools would facilitate the application of predictive musculoskeletal simulations to clinically-relevant research. The open-source software OpenSim provides tools for generating tracking simulations but not predictive simulations. However, OpenSim includes an extensive application programming interface that permits extending its capabilities with scripting …
Guidelines For Scheduling In Primary Care: An Empirically Driven Mathematical Programming Approach, Hyun Jung Alvarez Oh
Guidelines For Scheduling In Primary Care: An Empirically Driven Mathematical Programming Approach, Hyun Jung Alvarez Oh
Doctoral Dissertations
Primary care practices play a vital role in healthcare delivery since they are the first point of contact for most patients, and provide health prevention, counseling, education, diagnosis and treatment. Practices, however, face a complex appointment scheduling problem because of the variety of patient conditions, the mix of appointment types, the uncertain service times with providers and non-provider staff (nurses/medical assistants), and no-show rates which all compound into a highly variable and unpredictable flow of patients. The end result is an imbalance between provider idle time and patient waiting time. To understand the realities of the scheduling problem we analyze …
Developing An Optimal Model For Infant Home Visitation, Isaac Atuahene
Developing An Optimal Model For Infant Home Visitation, Isaac Atuahene
Doctoral Dissertations
The United States, Great Britain, Denmark, Canada and many other countries have accepted home visitation (HV) as a promising strategy for interventions for infants after births and for their mothers. Prior HV studies have focused on theoretical foundations, evaluations of programs, cost/benefit analysis and cost estimation by using hospital/payer/insurance data to prove its effectiveness and high cost. As governments and private organizations continue to fund HVs, it is an opportune time to develop and formulate operations research (OR) models of HV coverage, quality and cost so they might be used in program implementation as done for adult home healthcare (HHC) …
Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau
Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We study a real-world problem arising from the operations of a hospital service provider, which we term the master physician scheduling problem. It is a planning problem of assigning physicians’ full range of day-to-day duties (including surgery, clinics, scopes, calls, administration) to the defined time slots/shifts over a time horizon, incorporating a large number of constraints and complex physician preferences. The goals are to satisfy as many physicians’ preferences and duty requirements as possible while ensuring optimum usage of available resources. We propose mathematical programming models that represent different variants of this problem. The models were tested on a real …
Validating Pareto Optimal Operation Parameters Of Polyp Detection Algorithms For Ct Colonography, Jiang Li, Adam Huang, Nicholas Petrick, Jianhua Yao, Ronald M. Summers, Maryellen L. Giger (Ed.), Nico Karssemeijer (Ed.)
Validating Pareto Optimal Operation Parameters Of Polyp Detection Algorithms For Ct Colonography, Jiang Li, Adam Huang, Nicholas Petrick, Jianhua Yao, Ronald M. Summers, Maryellen L. Giger (Ed.), Nico Karssemeijer (Ed.)
Electrical & Computer Engineering Faculty Publications
We evaluated a Pareto front-based multi-objective evolutionary algorithm for optimizing our CT colonography (CTC) computer-aided detection (CAD) system. The system identifies colonic polyps based on curvature and volumetric based features, where a set of thresholds for these features was optimized by the evolutionary algorithm. We utilized a two-fold cross-validation (CV) method to test if the optimized thresholds can be generalized to new data sets. We performed the CV method on 133 patients; each patient had a prone and a supine scan. There were 103 colonoscopically confirmed polyps resulting in 188 positive detections in CTC reading from either the prone or …
Contrast Enhancement Of Medical Images Using Multiscale Edge Representation, Jian Lu, Dennis M. Healy Jr., John B. Weaver
Contrast Enhancement Of Medical Images Using Multiscale Edge Representation, Jian Lu, Dennis M. Healy Jr., John B. Weaver
Dartmouth Scholarship
Experience suggests the existence of a connection between the contrast of a gray-scale image and the gradient magnitude of intensity edges in the neighborhood where the contrast is measured. This observation motivates the development of edge-based contrast enhancement techniques. We present a simple and effective method for image contrast enhancement based on the multiscale edge representation of images. The contrast of an image can be enhanced simply by stretching or upscaling the multiscale gradient maxima of the image. This method offers flexibility to selectively enhance features of different sizes and ability to control noise magnification. We present some experimental results …