378 research outputs found
Latent representation for the characterisation of mental diseases
Mención Internacional en el título de doctorMachine learning (ML) techniques are becoming crucial in the field of health and, in particular,
in the analysis of mental diseases. These are usually studied with neuroimaging, which is
characterised by a large number of input variables compared to the number of samples available.
The main objective of this PhD thesis is to propose different ML techniques to analyse mental
diseases from neuroimaging data including different extensions of these models in order to adapt
them to the neuroscience scenario. In particular, this thesis focuses on using brainimaging latent
representations, since they allow us to endow the problem with a reduced low dimensional
representation while obtaining a better insight on the internal relations between the disease and
the available data. This way, the main objective of this PhD thesis is to provide interpretable
results that are competent with the state-of-the-art in the analysis of mental diseases.
This thesis starts proposing a model based on classic latent representation formulations,
which relies on a bagging process to obtain the relevance of each brainimaging voxel, Regularised
Bagged Canonical Correlation Analysis (RB-CCA). The learnt relevance is combined with a
statistical test to obtain a selection of features. What’s more, the proposal obtains a class-wise
selection which, in turn, further improves the analysis of the effect of each brain area on the
stages of the mental disease. In addition, RB-CCA uses the relevance measure to guide the
feature extraction process by using it to penalise the least informative voxels for obtaining the
low-dimensional representation. Results obtained on two databases for the characterisation of
Alzheimer’s disease and Attention Deficit Hyperactivity Disorder show that the model is able to
perform as well as or better than the baselines while providing interpretable solutions.
Subsequently, this thesis continues with a second model that uses Bayesian approximations
to obtain a latent representation. Specifically, this model focuses on providing different functionalities
to build a common representation from different data sources and particularities. For
this purpose, the proposed generative model, Sparse Semi-supervised Heterogeneous Interbattery
Bayesian Factor Analysis (SSHIBA), can learn the feature relevance to perform feature selection,
as well as automatically select the number of latent factors. In addition, it can also model heterogeneous
data (real, multi-label and categorical), work with kernels and use a semi-supervised
formulation, which naturally imputes missing values by sampling from the learnt distributions.
Results using this model demonstrate the versatility of the formulation, which allows these extensions
to be combined interchangeably, expanding the scenarios in which the model can be
applied and improving the interpretability of the results.
Finally, this thesis includes a comparison of the proposed models on the Alzheimer’s disease
dataset, where both provide similar results in terms of performance; however, RB-CCA provides
a more robust analysis of mental diseases that is more easily interpretable. On the other hand,
while RB-CCA is more limited to specific scenarios, the SSHIBA formulation allows a wider
variety of data to be combined and is easily adapted to more complex real-life scenarios.Las técnicas de aprendizaje automático (ML) están siendo cruciales en el campo de la salud y,
en particular, en el análisis de las enfermedades mentales. Estas se estudian habitualmente con
neuroimagen, que se caracteriza por un gran número de variables de entrada en comparación
con el número de muestras disponibles. El objetivo principal de esta tesis doctoral es proponer
diferentes técnicas de ML para el análisis de enfermedades mentales a partir de datos de neuroimagen
incluyendo diferentes extensiones de estos modelos para adaptarlos al escenario de la
neurociencia. En particular, esta tesis se centra en el uso de representaciones latentes de imagen
cerebral, ya que permiten dotar al problema de una representación reducida de baja dimensión
a la vez que obtienen una mejor visión de las relaciones internas entre la enfermedad mental y
los datos disponibles. De este modo, el objetivo principal de esta tesis doctoral es proporcionar
resultados interpretables y competentes con el estado del arte en el análisis de las enfermedades
mentales.
Esta tesis comienza proponiendo un modelo basado en formulaciones clásicas de representación
latente, que se apoya en un proceso de bagging para obtener la relevancia de cada
voxel de imagen cerebral, el Análisis de Correlación Canónica Regularizada con Bagging (RBCCA).
La relevancia aprendida se combina con un test estadístico para obtener una selección de
características. Además, la propuesta obtiene una selección por clases que, a su vez, mejora el
análisis del efecto de cada área cerebral en los estadios de la enfermedad mental. Por otro lado,
RB-CCA utiliza la medida de relevancia para guiar el proceso de extracción de características,
utilizándola para penalizar los vóxeles menos relevantes para obtener la representación de baja
dimensión. Los resultados obtenidos en dos bases de datos para la caracterización de la enfermedad
de Alzheimer y el Trastorno por Déficit de Atención e Hiperactividad demuestran que el
modelo es capaz de rendir igual o mejor que los baselines a la vez que proporciona soluciones
interpretables.
Posteriormente, esta tesis continúa con un segundo modelo que utiliza aproximaciones Bayesianas
para obtener una representación latente. En concreto, este modelo se centra en proporcionar
diferentes funcionalidades para construir una representación común a partir de diferentes
fuentes de datos y particularidades. Para ello, el modelo generativo propuesto, Sparse Semisupervised
Heterogeneous Interbattery Bayesian Factor Analysis (SSHIBA), puede aprender la
relevancia de las características para realizar la selección de las mismas, así como seleccionar
automáticamente el número de factores latentes. Además, también puede modelar datos heterogéneos
(reales, multietiqueta y categóricos), trabajar con kernels y utilizar una formulación
semisupervisada, que imputa naturalmente los valores perdidos mediante el muestreo de las
distribuciones aprendidas. Los resultados obtenidos con este modelo demuestran la versatilidad
de la formulación, que permite combinar indistintamente estas extensiones, ampliando los escenarios
en los que se puede aplicar el modelo y mejorando la interpretabilidad de los resultados. Finalmente, esta tesis incluye una comparación de los modelos propuestos en el conjunto de
datos de la enfermedad de Alzheimer, donde ambos proporcionan resultados similares en términos
de rendimiento; sin embargo, RB-CCA proporciona un análisis más robusto de las enfermedades
mentales que es más fácilmente interpretable. Por otro lado, mientras que RB-CCA está más
limitado a escenarios específicos, la formulación SSHIBA permite combinar una mayor variedad
de datos y se adapta fácilmente a escenarios más complejos de la vida real.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Manuel Martínez Ramón.- Secretario: Emilio Parrado Hernández.- Vocal: Sancho Salcedo San
Design and optimisation of the limaçon rotary compressor
The limaçon positive displacement machine is characterised by its internal geometry and unique mechanical motion; both based on a mathematical curve known as the limaçon of Pascal. The limaçon technology offers many advantages, such as compact size and double‐acting functionality, and its great potential for fluid processing applications has been proven by a number of patents and innovative designs in engines, expanders, and pumps. However, no commercial application of the limaçon technology in the field of positive displacement compressors has been reported in the literature. This could be attributed to the fact that the potential of the limaçon technology for gas compression has not been established as yet. The process of establishing potential is necessary before funds and resources are dedicated to investing in prototyping and testing. This process entails a considerable amount of modelling, coding and analysis as one must ensure the embodiment is geometrically capable of delivering suction and compression strokes, ports can be arranged to support the workings of these strokes, a number of measurable parameters can be identified as impacting compressor performance and it is possible to calculate a set of parameters which optimise this performance. To achieve this objective, a comprehensive mathematical model of a limaçon machine, implemented as a compressor,was first developed. The model, which is multi‐physical in nature, spans such domains as kinematics, fluid dynamics, characteristics of the port flow, internal leakage due to seal vibration, dynamics of the discharge valve, and thermodynamics. Subsequently, the simulation of the model has been performed to numerically study the operational characteristics of the limaçon compressor and to investigate the effect of various parameters on the compressor performance. It was found that the increase in the operating speed and pressure ratio would lead to negative effects on machine performance, especially on volumetric efficiency. Additionally, the results of simulations indicated that the level of fluid over‐compression is influenced by the characteristics of the discharge valve. To ensure the suitability of limaçon technology for use in positive displacement compressors, a study was undertaken to determine whether such an embodiment lent itself to optimisation efforts. For this purpose, the thorough mathematical model which has been developed to simulate compressor workings was then used for optimisation purposes whereby a Bayesian optimisation procedure was applied. The optimisation procedure was conducted in a two‐stage fashion where the first stage optimises the machine dimensions to meet volumetric requirements specified by the designer; and the second stage focuses on revealing the optimum combination of port geometries that improves machine performance. A numerical illustration was presented to prove the validity of the presented approach, and the results show that considerable improvements in the isentropic and volumetric efficiencies can be attained. Moreover, the optimised design was tested under different operating speeds and pressure ratios to investigate its robustness. It was found that the optimised design can exhibit relatively stable performance when the working conditions vary within a small bandwidth around that used in the optimisation procedure. The limaçon technology has three embodiments, namely the limaçon‐to‐limaçon (L2L), the limaçon‐to‐circular, and the circolimaçon. The circolimaçon embodiment features using circular arcs, rather than limaçon curves, to develop profiles for the rotor and housing. This embodiment simplifies the manufacturing process and reduces the production cost associated with producing a limaçon technology. A feasibility study of the circolimaçon embodiment was conducted by comparing its performance with that of the L2L type device. The machine dimensions and port geometries obtained from the optimisation procedure were used in the comparative study. A nonlinear three‐degree of freedom model was presented to describe the dynamic behaviour of the apex seal during the machine operation. Additionally, the leakage through the seal‐housing gap was formulated by considering the inertia and viscous effects of the flow. The results from the case study suggest that the circolimaçon embodiment exhibits comparable performance to the L2L‐type machine, despite having more significant seal vibrations. Moreover, it was also discovered that the circolimaçon compressor with a small capacity undergoes a lower level of seal dynamics, indicating better machine reliability.Doctor of Philosoph
Potentials and caveats of AI in Hybrid Imaging
State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research
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Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques
Medical imaging represents the utilisation of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient's life through a more precise and rapid diagnosis, and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this thesis is to propose novel tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CI') and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and classification. The fractal dimension (FO) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CI' modality, and quantitative performance analysis showed an accuracy of 83.30% in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour OM images via a set of statistical and model-based texture analysis algorithms. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations, achieving an overall class assignment classification accuracy of 92.50%. Also two new histopathological multi resolution approaches based on applying the FO as the best bases selection for discrete wavelet packet transform, and when fused with the Gabor filters' energy output improved the accuracy to 91.25% and 95.00%, respectively. While noise is quite common in all medical imaging modalities, the impact of noise on the applied texture measures was assessed as well. The developed lung and brain texture analysis techniques can improve the physician's ability to detect and analyse pathologies leading for a more reliable diagnosis and treatment of disease
Learning Bayesian network equivalence classes using ant colony optimisation
Bayesian networks have become an indispensable tool in the modelling of uncertain
knowledge. Conceptually, they consist of two parts: a directed acyclic graph called the
structure, and conditional probability distributions attached to each node known as the
parameters. As a result of their expressiveness, understandability and rigorous mathematical basis, Bayesian networks have become one of the first methods investigated,
when faced with an uncertain problem domain. However, a recurring problem persists
in specifying a Bayesian network. Both the structure and parameters can be difficult for
experts to conceive, especially if their knowledge is tacit.To counteract these problems, research has been ongoing, on learning both the structure
and parameters of Bayesian networks from data. Whilst there are simple methods for
learning the parameters, learning the structure has proved harder. Part ofthis stems from
the NP-hardness of the problem and the super-exponential space of possible structures.
To help solve this task, this thesis seeks to employ a relatively new technique, that has
had much success in tackling NP-hard problems. This technique is called ant colony
optimisation. Ant colony optimisation is a metaheuristic based on the behaviour of ants
acting together in a colony. It uses the stochastic activity of artificial ants to find good
solutions to combinatorial optimisation problems. In the current work, this method is
applied to the problem of searching through the space of equivalence classes of Bayesian
networks, in order to find a good match against a set of data. The system uses operators
that evaluate potential modifications to a current state. Each of the modifications is
scored and the results used to inform the search. In order to facilitate these steps, other
techniques are also devised, to speed up the learning process. The techniques includeThe techniques are tested by sampling data from gold standard networks and learning
structures from this sampled data. These structures are analysed using various goodnessof-fit measures to see how well the algorithms perform. The measures include structural
similarity metrics and Bayesian scoring metrics. The results are compared in depth
against systems that also use ant colony optimisation and other methods, including
evolutionary programming and greedy heuristics. Also, comparisons are made to well
known state-of-the-art algorithms and a study performed on a real-life data set. The
results show favourable performance compared to the other methods and on modelling
the real-life data
Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain
The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment
Development of an advanced artificial intelligent reliability analysis tool to enhance ship operations and maintenance activities
No Abstract availableNo Abstract availabl
ML-EWS: Machine Learning Early Warning System. The application of machine learning to predict in-hospital patient deterioration
Preventing hospitalised patients from suffering adverse event (AEs) (unexpected cardiac, arrest, intensive care unit admission, surgery or death) is a priority in healthcare. Almost 50% of these AEs, caused by mistakes/poor standards of care, are thought to be preventable. The identification and referral of a patient at risk of an AE to a dedicated rapid response team is a key mechanism for their reduction. Focussing on variables that are routinely collected and electronically stored (blood test data, and administrative data: demographics, date and method of admission, and co-morbidities), along with their trends, I have collected data on ~8 million admissions. I have explained how to navigate the complex ethical and legal landscape of performing such an ambitious data linkage and collection project. Analysing data on ~2 million hospital admissions with an in-hospital blood test result, I have 1. described how these variables (particularly urea and creatinine blood tests, method of admission, and date of admission) influence in-hospital mortality rate in different groups of patient. 2. created four machine learning (ML) models that have the highest accuracy yet described for identifying a patient at risk of an SAE, while at the same time capturing the majority of patients likely to die (high sensitivity). These models ML-Dehydration, ML-AKI, ML-Admission, and ML-Two- Tests, can be applied to admissions with limited data, specific syndromes, or on all patients in hospital at different time points in their hospital trajectory respectively. Their area under the receiver operator curves are 79.6%, 85.9%, 93% and 90.6% respectively. 3. built and deployed a technology platform Patient Rescue that allows for the automated application of any model in any hospital, as well as the communication of rich patient level reports to clinicians, all in real-time. The ML models and the Patient Rescue platform together form the ML – Early Warning System
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