100 research outputs found
Learning Provably Useful Representations, with Applications to Fairness
Representation learning involves transforming data so that it is useful for solving a particular supervised learning problem. The aim is to learn a representation function which maps inputs to some representation space, and an hypothesis which maps the representation space to targets. It is possible to learn a representation function using unlabeled data or data from a probability distribution other than that of the main problem of interest, which is helpful if labeled data is scarce. This approach has been successfully applied in practice, for example through pre-trained neural networks in computer vision and word embeddings in natural language processing. This thesis explores when it is possible to learn representations that are provably useful.
We consider learning a representation function from unlabeled data, and propose an approach to identifying conditions where this technique will be useful for a subsequent supervised learning task. The approach requires shared structure in the labeled and unlabeled distributions, as well as a compatible representation function class and hypothesis class. We provide an example where representation learning can exploit cluster structure present in the data.
We also consider learning a representation function from a source task distribution and re-using it on a target task of interest, and again propose conditions where this approach will be successful. In this case the conditions depend on shared structure between source and target task distributions. We provide an example involving the transfer of weights in a two-layer feedforward neural network.
Representation learning can be applied to another topic of interest: fairness in machine learning. The issue of fairness arises when machine learning systems make or provide advice on decisions about people. A common approach to defining fairness is measuring differences in decisions made by an algorithm for one demographic group compared to another. One approach to preventing discrimination against particular groups is to learn a representation of the data from which it is not possible for an adversary to determine an individual's group membership, but which preserves other useful information. We quantify the costs and benefits of such an approach with respect to several possible fairness definitions. We also examine the relationships between different definitions of fairness and show cases where they cannot simultaneously be satisfied.
We explore the use of representation learning for fairness through two case studies: predicting domestic violence recidivism while avoiding discrimination on the basis of race, and predicting student outcomes at university while avoiding discrimination on the basis of gender. Our case studies reveal both the utility of fair representation learning and the trade-offs between accuracy and the definitions of fairness considered
Predictive modelling for health and health-care utilisation : an observational study for Australians aged 45 and up
The burden of chronic disease is growing at a fast pace, leading to poor quality of life and high healthcare expenditures in a large portion of the Australian population. Much of the burden is borne by hospitals, and therefore there is an ever-increasing interest in preventative interventions that can keep people out of hospitals and healthier for longer periods. There is a wide range of potential interventions that may be able to achieve this goal, and policy makers need to decide which one should be funded and implemented. This task is difficult for two reasons: first it is often not clear what is the short-term effectiveness of an intervention, and how it varies in specific sub-populations, and second it is also not clear what the long-term intended and unintended consequences might be. In this thesis I make contributions to address both these difficulties. On the short-term side I focus on the use of physical activity to prevent the development of chronic disease and to reduce hospital costs. Increasing physical activity has been long heralded as a way to achieve these goals but evidence of its effectiveness has been elusive. In this thesis I provide data driven evidence to justify policies that encourage higher levels of physical activity (PA) in middle age and older Australian population. I use data from the â45 and upâ and the Social, Economic and Environmental Factors (SEEF) study, linked with the Admitted Patient Data Collection (APDC), to identify and study the cost and health trajectories of individuals with different levels of physical activity. The results show a clear statistically significant association between PA and lower hospitalisation cost, as well as between PA and reduced risk of heart disease, diabetes and stroke. On the long-term side of the analysis, I placed this thesis in the context of a larger program of work performed at Western Sydney University that aims to build a microsimulation model for the analysis of health policy interventions. In this framework I studied predictive models that use survey and/or administrative data to predict hospital costs and resource utilisation. I placed particular emphasis on the application of methods borrowed from Natural Language Processing to understand how to use the thousands of diagnosis and procedure codes found in administrative data as input to predictive models. The methods developed in this thesis go beyond the application to hospital data and can be used in any predictive model that relies on complex coding of healthcare information
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Surrogate Model Optimisation for PWR Fuel Management
Pressurised Water Reactor (PWR) fuel management is an operational problem for nuclear operators, requiring solutions on a regular basis throughout the life of the plant. A variety of conflicting factors and changing goals mean that fuel loading pattern design problems are multiobjective and, by design, have many input variables. This causes a combinatorial explosion, known as the âcurse of dimensionalityâ, which makes these complex problems difficult to investigate.
In this thesis, the method of surrogate model optimisation is adapted to PWR loading pattern generation. Surrogate models are developed based around three approaches: deep learning methods (convolutional neural networks and multi-layer perceptrons), the fission matrix and simulated quantum annealing. The models are used to predict core parameters of reactors in simplified optimisation scenarios for a microcore, a small modular reactor, and a âstandardâ PWR. The experiments with deep learning models show that competitive results can be obtained for training sets using a much lower number of simulations than direct optimisation. Fission matrix experiments demonstrate the method to predict core parameters for the first time, with interesting preliminary results. Novel experiments using simulated quantum annealing demonstrate the technique is able to generate loading patterns by following heuristic rules and is suitable for application to custom optimisation hardware.
The principal contribution of this work is to show that surrogate model optimisation can be used to augment fuel loading pattern optimisation, generating competitive results and providing enormous computational cost reduction and thus permitting more investigation within a given computational budget. These methods can also make use of new computational hardware such as neural chips and quantum annealers. The promising methods developed in this thesis thus provide candidate implementations that can bring the benefits of these innovations to the sphere of nuclear engineering
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Mathematical Modeling of Malaria: Theories of Malaria Elimination
This dissertation describes the development and application of a new mathematical model for simulating the progression of Plasmodium falciparum infections in individuals with no malarial acquired immunity. The model allows for stochastic simulation of asexual and sexual parasitemias as well as the onset of fever and human to mosquito infectivity on a daily time scale. The model components for the asexual and sexual stages were developed elsewhere but are here extended to allow for simulation of the full range of dynamics observed in a subset of malaria therapy patients. As a first application of the model, I calculate the human component of malarial R0, the basic reproductive number. I then compare this value to those from three other models and describe how this quantity can be used to model malaria transmission. The second application of the model incorporates the effects of drug treatment on progression of infection by utilizing modeled pharmacokinetic and pharmacodynamic properties of a variety of antimalarials. I utilize a stage specific proportional killing model for sexual stages, informed from recent in vitro data. The relationship of effect sizes to treatment coverage and type of treatment in both early and late treatment seeking settings is calculated. In the third chapter, I consider the economic and epidemiological ramifications of antimalarial and rapid diagnostic subsidization for malaria control. For the epidemiological modeling I utilize a semi-mechanistic model of the spread of drug resistance parameterized from historical malaria mortality data; for the economic model I consider the effect of rapid diagnostics on the intensive and extensive margins of antibiotics and antimalarials, as well as the benefits to improved targeting of both. I find that rapid diagnostic testing is justified given our baseline assumptions for areas with low proportions of malarious individuals among all treatment-seekers, but that caution is necessary before deployment worldwide. For antimalarial subsidization, we find that this is a cost-effective method for reducing mortality in developing countries, though efforts to delay the onset and slow the spread of resistance are urgently needed
Statistical methods for NHS incident reporting data
The National Reporting and Learning System (NRLS) is the English and Welsh NHSâ national repository of incident reports from healthcare. It aims to capture details of incident reports, at national level, and facilitate clinical review and learning to improve patient safety. These incident reports range from minor ânear-missesâ to critical incidents that may lead to severe harm or death. NRLS data are currently reported as crude counts and proportions, but their major use is clinical review of the free-text descriptions of incidents. There are few well-developed quantitative analysis approaches for NRLS, and this thesis investigates these methods. A literature review revealed a wealth of clinical detail, but also systematic constraints of NRLSâ structure, including non-mandatory reporting, missing data and misclassification. Summary statistics for reports from 2010/11 â 2016/17 supported this and suggest NRLS was not suitable for statistical modelling in isolation. Modelling methods were advanced by creating a hybrid dataset using other sources of hospital casemix data from Hospital Episode Statistics (HES). A theoretical model was established, based on âexposureâ variables (using casemix proxies), and âcultureâ as a random-effect. The initial modelling approach examined Poisson regression, mixture and multilevel models. Overdispersion was significant, generated mainly by clustering and aggregation in the hybrid dataset, but models were chosen to reflect these structures. Further modelling approaches were examined, using Generalized Additive Models to smooth predictor variables, regression tree-based models including Random Forests, and Artificial Neural Networks. Models were also extended to examine a subset of death and severe harm incidents, exploring how sparse counts affect models. Text mining techniques were examined for analysis of incident descriptions and showed how term frequency might be used. Terms were used to generate latent topics models used, in-turn, to predict the harm level of incidents. Model outputs were used to create a âStandardised Incident Reporting Ratioâ (SIRR) and cast this in the mould of current regulatory frameworks, using process control techniques such as funnel plots and cusum charts. A prototype online reporting tool was developed to allow NHS organisations to examine their SIRRs, provide supporting analyses, and link data points back to individual incident reports
Intelligent Systems for Sustainable Person-Centered Healthcare
This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregiversâ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life
An Empirical and Computational Investigation of Neurocognitive Performance Underlying Dimensional Psychopathology
Deficits in neurocognitive abilities have been claimed to be an aetiological feature of psychopathology. Recently, dimensional structural models of psychopathology have been developed that view psychopathological experience on a dimension across multiple higher and lower order factors and indicators. This thesis explored limitations of the dimensional approach, such as the factorsâ substantive meaning, and examined the functional associations between neurocognition and dimensional psychopathology. Dimensional psychopathology is best explained by a non-linear interactive conceptualisation of neurocognition
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