Evaluation of forensic data using logistic regression-based classification methods and an R Shiny implementation

We demonstrate the use of classification methods that are well-suited for forensic toxicology applications. The methods are based on penalized logistic regression, can be employed when separation occurs in a two-class classification setting, and allow for the calculation of likelihood ratios. A case study of this framework is demonstrated on alcohol biomarker data for classifying chronic alcohol drinkers. The approach can be extended to applications in the fields of analytical and forensic chemistry, where it is a common feature to have a large number of biomarkers, and allows for flexibility in model assumptions such as multivariate normality. While some penalized regression methods have been introduced previously in forensic applications, our study is meant to encourage practitioners to use these powerful methods more widely. As such, based upon our proof-of-concept studies, we also introduce an R Shiny online tool with an intuitive interface able to perform several classification methods. We anticipate that this open-source and free-of-charge application will provide a powerful and dynamic tool to infer the LR value in case of classification tasks

How can humans leverage machine learning? From Medical Data Wrangling to Learning to Defer to Multiple Experts

Mención Internacional en el título de doctorThe irruption of the smartphone into everyone’s life and the ease with which we digitise or record any data supposed an explosion of quantities of data. Smartphones, equipped with advanced cameras and sensors, have empowered individuals to capture moments and contribute to the growing pool of data. This data-rich landscape holds great promise for research, decision-making, and personalized applications. By carefully analyzing and interpreting this wealth of information, valuable insights, patterns, and trends can be uncovered. However, big data is worthless in a vacuum. Its potential value is unlocked only when leveraged to drive decision-making. In recent times we have been participants of the outburst of artificial intelligence: the development of computer systems and algorithms capable of perceiving, reasoning, learning, and problem-solving, emulating certain aspects of human cognitive abilities. Nevertheless, our focus tends to be limited, merely skimming the surface of the problem, while the reality is that the application of machine learning models to data introduces is usually fraught. More specifically, there are two crucial pitfalls frequently neglected in the field of machine learning: the quality of the data and the erroneous assumption that machine learning models operate autonomously. These two issues have established the foundation for the motivation driving this thesis, which strives to offer solutions to two major associated challenges: 1) dealing with irregular observations and 2) learning when and who should we trust. The first challenge originates from our observation that the majority of machine learning research primarily concentrates on handling regular observations, neglecting a crucial technological obstacle encountered in practical big-data scenarios: the aggregation and curation of heterogeneous streams of information. Before applying machine learning algorithms, it is crucial to establish robust techniques for handling big data, as this specific aspect presents a notable bottleneck in the creation of robust algorithms. Data wrangling, which encompasses the extraction, integration, and cleaning processes necessary for data analysis, plays a crucial role in this regard. Therefore, the first objective of this thesis is to tackle the frequently disregarded challenge of addressing irregularities within the context of medical data. We will focus on three specific aspects. Firstly, we will tackle the issue of missing data by developing a framework that facilitates the imputation of missing data points using relevant information derived from alternative data sources or past observations. Secondly, we will move beyond the assumption of homogeneous observations, where only one statistical data type (such as Gaussian) is considered, and instead, work with heterogeneous observations. This means that different data sources can be represented by various statistical likelihoods, such as Gaussian, Bernoulli, categorical, etc. Lastly, considering the temporal enrichment of todays collected data and our focus on medical data, we will develop a novel algorithm capable of capturing and propagating correlations among different data streams over time. All these three problems are addressed in our first contribution which involves the development of a novel method based on Deep Generative Models (DGM) using Variational Autoencoders (VAE). The proposed model, the Sequential Heterogeneous Incomplete VAE (Shi- VAE), enables the aggregation of multiple heterogeneous data streams in a modular manner, taking into consideration the presence of potential missing data. To demonstrate the feasibility of our approach, we present proof-of-concept results obtained from a real database generated through continuous passive monitoring of psychiatric patients. Our second challenge relates to the misbelief that machine learning algorithms can perform independently. However, this notion that AI systems can solely account for automated decisionmaking, especially in critical domains such as healthcare, is far from reality. Our focus now shifts towards a specific scenario where the algorithm has the ability to make predictions independently or alternatively defer the responsibility to a human expert. The purpose of including the human is not to obtain jsut better performance, but also more reliable and trustworthy predictions we can rely on. In reality, however, important decisions are not made by one person but are usually committed by an ensemble of human experts. With this in mind, two important questions arise: 1) When should the human or the machine bear responsibility and 2) among the experts, who should we trust? To answer the first question, we will employ a recent theory known as Learning to defer (L2D). In L2D we are not only interested in abstaining from prediction but also in understanding the humans confidence for making such prediction. thus deferring only when the human is more likely to be correct. The second question about who to defer among a pool of experts has not been yet answered in the L2D literature, and this is what our contributions aim to provide. First, we extend the two yet proposed consistent surrogate losses in the L2D literature to the multiple-expert setting. Second, we study the frameworks ability to estimate the probability that a given expert correctly predicts and assess whether the two surrogate losses are confidence calibrated. Finally, we propose a conformal inference technique that chooses a subset of experts to query when the system defers. Ensembling experts based on confidence levels is vital to optimize human-machine collaboration. In conclusion, this doctoral thesis has investigated two cases where humans can leverage the power of machine learning: first, as a tool to assist in data wrangling and data understanding problems and second, as a collaborative tool where decision-making can be automated by the machine or delegated to human experts, fostering more transparent and trustworthy solutions.La irrupción de los smartphones en la vida de todos y la facilidad con la que digitalizamos o registramos cualquier situación ha supuesto una explosión en la cantidad de datos. Los teléfonos, equipados con cámaras y sensores avanzados, han contribuido a que las personas puedann capturar más momentos, favoreciendo así el creciente conjunto de datos. Este panorama repleto de datos aporta un gran potencial de cara a la investigación, la toma de decisiones y las aplicaciones personalizadas. Mediante el análisis minucioso y una cuidada interpretación de esta abundante información, podemos descubrir valiosos patrones, tendencias y conclusiones Sin embargo, este gran volumen de datos no tiene valor por si solo. Su potencial se desbloquea solo cuando se aprovecha para impulsar la toma de decisiones. En tiempos recientes, hemos sido testigos del auge de la inteligencia artificial: el desarrollo de sistemas informáticos y algoritmos capaces de percibir, razonar, aprender y resolver problemas, emulando ciertos aspectos de las capacidades cognitivas humanas. No obstante, solemos centrarnos solo en la superficie del problema mientras que la realidad es que la aplicación de modelos de aprendizaje automático a los datos presenta desafíos significativos. Concretamente, se suelen pasar por alto dos problemas cruciales en el campo del aprendizaje automático: la calidad de los datos y la suposición errónea de que los modelos de aprendizaje automático pueden funcionar de manera autónoma. Estos dos problemas han sido el fundamento de la motivación que impulsa esta tesis, que se esfuerza en ofrecer soluciones a dos desafíos importantes asociados: 1) lidiar con datos irregulares y 2) aprender cuándo y en quién debemos confiar. El primer desafío surge de nuestra observación de que la mayoría de las investigaciones en aprendizaje automático se centran principalmente en manejar datos regulares, descuidando un obstáculo tecnológico crucial que se encuentra en escenarios prácticos con gran cantidad de datos: la agregación y el curado de secuencias heterogéneas. Antes de aplicar algoritmos de aprendizaje automático, es crucial establecer técnicas robustas para manejar estos datos, ya que est problemática representa un cuello de botella claro en la creación de algoritmos robustos. El procesamiento de datos (en concreto, nos centraremos en el término inglés data wrangling), que abarca los procesos de extracción, integración y limpieza necesarios para el análisis de datos, desempeña un papel crucial en este sentido. Por lo tanto, el primer objetivo de esta tesis es abordar el desafío normalmente paso por alto de tratar datos irregulare. Específicamente, bajo el contexto de datos médicos. Nos centraremos en tres aspectos principales. En primer lugar, abordaremos el problema de los datos perdidos mediante el desarrollo de un marco que facilite la imputación de estos datos perdidos utilizando información relevante obtenida de fuentes de datos de diferente naturalaeza u observaciones pasadas. En segundo lugar, iremos más allá de la suposición de lidiar con observaciones homogéneas, donde solo se considera un tipo de dato estadístico (como Gaussianos) y, en su lugar, trabajaremos con observaciones heterogéneas. Esto significa que diferentes fuentes de datos pueden estar representadas por diversas distribuciones de probabilidad, como Gaussianas, Bernoulli, categóricas, etc. Por último, teniendo en cuenta el enriquecimiento temporal de los datos hoy en día y nuestro enfoque directo sobre los datos médicos, propondremos un algoritmo innovador capaz de capturar y propagar la correlación entre diferentes flujos de datos a lo largo del tiempo. Todos estos tres problemas se abordan en nuestra primera contribución, que implica el desarrollo de un método basado en Modelos Generativos Profundos (Deep Genarative Model en inglés) utilizando Autoencoders Variacionales (Variational Autoencoders en ingés). El modelo propuesto, Sequential Heterogeneous Incomplete VAE (Shi-VAE), permite la agregación de múltiples flujos de datos heterogéneos de manera modular, teniendo en cuenta la posible presencia de datos perdidos. Para demostrar la viabilidad de nuestro enfoque, presentamos resultados de prueba de concepto obtenidos de una base de datos real generada a través del monitoreo continuo pasivo de pacientes psiquiátricos. Nuestro segundo desafío está relacionado con la creencia errónea de que los algoritmos de aprendizaje automático pueden funcionar de manera independiente. Sin embargo, esta idea de que los sistemas de inteligencia artificial pueden ser los únicos responsables en la toma de decisione, especialmente en dominios críticos como la atención médica, está lejos de la realidad. Ahora, nuestro enfoque se centra en un escenario específico donde el algoritmo tiene la capacidad de realizar predicciones de manera independiente o, alternativamente, delegar la responsabilidad en un experto humano. La inclusión del ser humano no solo tiene como objetivo obtener un mejor rendimiento, sino también obtener predicciones más transparentes y seguras en las que podamos confiar. En la realidad, sin embargo, las decisiones importantes no las toma una sola persona, sino que generalmente son el resultado de la colaboración de un conjunto de expertos. Con esto en mente, surgen dos preguntas importantes: 1) ¿Cuándo debe asumir la responsabilidad el ser humano o cuándo la máquina? y 2) de entre los expertos, ¿en quién debemos confiar? Para responder a la primera pregunta, emplearemos una nueva teoría llamada Learning to defer (L2D). En L2D, no solo estamos interesados en abstenernos de hacer predicciones, sino también en comprender cómo de seguro estará el experto para hacer dichas predicciones, diferiendo solo cuando el humano sea más probable en predecir correcatmente. La segunda pregunta sobre a quién deferir entre un conjunto de expertos aún no ha sido respondida en la literatura de L2D, y esto es precisamente lo que nuestras contribuciones pretenden proporcionar. En primer lugar, extendemos las dos primeras surrogate losses consistentes propuestas hasta ahora en la literatura de L2D al contexto de múltiples expertos. En segundo lugar, estudiamos la capacidad de estos modelos para estimar la probabilidad de que un experto dado haga predicciones correctas y evaluamos si estas surrogate losses están calibradas en términos de confianza. Finalmente, proponemos una técnica de conformal inference que elige un subconjunto de expertos para consultar cuando el sistema decide diferir. Esta combinación de expertos basada en los respectivos niveles de confianza es fundamental para optimizar la colaboración entre humanos y máquinas En conclusión, esta tesis doctoral ha investigado dos casos en los que los humanos pueden aprovechar el poder del aprendizaje automático: primero, como herramienta para ayudar en problemas de procesamiento y comprensión de datos y, segundo, como herramienta colaborativa en la que la toma de decisiones puede ser automatizada para ser realizada por la máquina o delegada a expertos humanos, fomentando soluciones más transparentes y seguras.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Joaquín Míguez Arenas.- Secretario: Juan José Murillo Fuentes.- Vocal: Mélanie Natividad Fernández Pradie

On the relative value of weak information of supervision for learning generative models: An empirical study

Weakly supervised learning is aimed to learn predictive models from partially supervised data, an easy-to-collect alternative to the costly standard full supervision. During the last decade, the research community has striven to show that learning reliable models in specific weakly supervised problems is possible. We present an empirical study that analyzes the value of weak information of supervision throughout its entire spectrum, from none to full supervision. Its contribution is assessed under the realistic assumption that a small subset of fully supervised data is available. Particularized in the problem of learning with candidate sets, we adapt Cozman and Cohen [1] key study to learning from weakly supervised data. Standard learning techniques are used to infer generative models from this type of supervision with both synthetic and real data. Empirical results suggest that weakly labeled data is helpful in realistic scenarios, where fully labeled data is scarce, and its contribution is directly related to both the amount of information of supervision and how meaningful this information is

Understanding travel mode choice: A new approach for city scale simulation

Understanding travel mode choice behaviour is key to effective management of transport networks, many of which are under increasing strain from rising travel demand. Conventional approaches to simulating mode choice typically make use of behavioural models either derived from stated preference choice experiments or calibrated to observed average mode shares. Whilst these models have played and continue to play a key role in economic, social, and environmental assessments of transport investments, there is growing need to gain a deeper understanding of how people interact with transport services, through exploiting available but fragmented data on passenger movements and transport networks. This thesis contributes to this need through developing a novel approach for urban mode choice prediction and applying it to historical trip records in the Greater London area. The new approach consists of two parts: (i) a data generation framework which combines multiple data-sources to build trip datasets containing the likely mode-alternative options faced by a passenger at the time of travel, and (ii) a modelling framework which makes use of these datasets to fit, optimise, validate, and select mode choice classifiers. This approach is used to compare the relative predictive performance of a complete suite of Machine Learning (ML) classification algorithms, as well as traditional utility-based choice models. Furthermore, a new assisted specification approach, where a fitted ML classifier is used to inform the utility function structure in a utility-based choice model, is then explored. The results identify three key findings. Firstly, the Gradient Boosting Decision Trees (GBDT) model is the highest performing classifier for this task. Secondly, the relative differences in predictive performance between classifiers are far smaller than has been suggested by previous research. In particular, there is a much smaller performance gap identified between Random Utility Models (RUMs) and ML classifiers. Finally, the assisted specification approach is successful in using the structure of a fitted ML classifier to improve the performance of a RUM. The resulting model achieves significantly better performance than all but the GBDT ML classifier, whilst maintaining a robust, interpretable behavioural model.Funding provided by UK Engineering and Physical Sciences Research Council via the Future Infrastructure and Built Environment Centre for Doctoral Training (EP/L016095/1)

Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods

The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this includes the importance of distinguishing between (at least) two different types of uncertainty, often referred to as aleatoric and epistemic. In this paper, we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular.Comment: 59 page

Advancing probabilistic and causal deep learning in medical image analysis

The power and flexibility of deep learning have made it an indispensable tool for tackling modern machine learning problems. However, this flexibility comes at the cost of robustness and interpretability, which can lead to undesirable or even harmful outcomes. Deep learning models often fail to generalise to real-world conditions and produce unforeseen errors that hinder wide adoption in safety-critical critical domains such as healthcare. This thesis presents multiple works that address the reliability problems of deep learning in safety-critical domains by being aware of its vulnerabilities and incorporating more domain knowledge when designing and evaluating our algorithms. We start by showing how close collaboration with domain experts is necessary to achieve good results in a real-world clinical task - the multiclass semantic segmentation of traumatic brain injuries (TBI) lesions in head CT. We continue by proposing an algorithm that models spatially coherent aleatoric uncertainty in segmentation tasks by considering the dependencies between pixels. The lack of proper uncertainty quantification is a robustness issue which is ubiquitous in deep learning. Tackling this issue is of the utmost importance if we want to deploy these systems in the real world. Lastly, we present a general framework for evaluating image counterfactual inference models in the absence of ground-truth counterfactuals. Counterfactuals are extremely useful to reason about models and data and to probe models for explanations or mistakes. As a result, their evaluation is critical for improving the interpretability of deep learning models.Open Acces

Novel Neural Network Applications to Mode Choice in Transportation: Estimating Value of Travel Time and Modelling Psycho-Attitudinal Factors

Whenever researchers wish to study the behaviour of individuals choosing among a set of alternatives, they usually rely on models based on the random utility theory, which postulates that the single individuals modify their behaviour so that they can maximise of their utility. These models, often identified as discrete choice models (DCMs), usually require the definition of the utilities for each alternative, by first identifying the variables influencing the decisions. Traditionally, DCMs focused on observable variables and treated users as optimizing tools with predetermined needs. However, such an approach is in contrast with the results from studies in social sciences which show that choice behaviour can be influenced by psychological factors such as attitudes and preferences. Recently there have been formulations of DCMs which include latent constructs for capturing the impact of subjective factors. These are called hybrid choice models or integrated choice and latent variable models (ICLV). However, DCMs are not exempt from issues, like, the fact that researchers have to choose the variables to include and their relations to define the utilities. This is probably one of the reasons which has recently lead to an influx of numerous studies using machine learning (ML) methods to study mode choice, in which researchers tried to find alternative methods to analyse travellers’ choice behaviour. A ML algorithm is any generic method that uses the data itself to understand and build a model, improving its performance the more it is allowed to learn. This means they do not require any a priori input or hypotheses on the structure and nature of the relationships between the several variables used as its inputs. ML models are usually considered black-box methods, but whenever researchers felt the need for interpretability of ML results, they tried to find alternative ways to use ML methods, like building them by using some a priori knowledge to induce specific constrains. Some researchers also transformed the outputs of ML algorithms so that they could be interpreted from an economic point of view, or built hybrid ML-DCM models. The object of this thesis is that of investigating the benefits and the disadvantages deriving from adopting either DCMs or ML methods to study the phenomenon of mode choice in transportation. The strongest feature of DCMs is the fact that they produce very precise and descriptive results, allowing for a thorough interpretation of their outputs. On the other hand, ML models offer a substantial benefit by being truly data-driven methods and thus learning most relations from the data itself. As a first contribution, we tested an alternative method for calculating the value of travel time (VTT) through the results of ML algorithms. VTT is a very informative parameter to consider, since the time consumed by individuals whenever they need to travel normally represents an undesirable factor, thus they are usually willing to exchange their money to reduce travel times. The method proposed is independent from the mode-choice functions, so it can be applied to econometric models and ML methods equally, if they allow the estimation of individual level probabilities. Another contribution of this thesis is a neural network (NN) for the estimation of choice models with latent variables as an alternative to DCMs. This issue arose from wanting to include in ML models not only level of service variables of the alternatives, and socio-economic attributes of the individuals, but also psycho-attitudinal indicators, to better describe the influence of psychological factors on choice behaviour. The results were estimated by using two different datasets. Since NN results are dependent on the values of their hyper-parameters and on their initialization, several NNs were estimated by using different hyper-parameters to find the optimal values, which were used to verify the stability of the results with different initializations

Challenges and prospects of spatial machine learning

The main objective of this thesis is to improve the usefulness of spatial machine learning for the spatial sciences and to allow its unused potential to be exploited. To achieve this objective, this thesis addresses several important but distinct challenges which spatial machine learning is facing. These are the modeling of spatial autocorrelation and spatial heterogeneity, the selection of an appropriate model for a given spatial problem, and the understanding of complex spatial machine learning models.Das wesentliche Ziel dieser Arbeit ist es, die Nützlichkeit des räumlichen maschinellen Lernens für die Raumwissenschaften zu verbessern und es zu ermöglichen, ungenutztes Potenzial auszuschöpfen. Um dieses Ziel zu erreichen, befasst sich diese Arbeit mit mehreren wichtigen Herausforderungen, denen das räumliche maschinelle Lernen gegenübersteht. Diese sind die Modellierung von räumlicher Autokorrelation und räumlicher Heterogenität, die Auswahl eines geeigneten Modells für ein gegebenes räumliches Problem und das Verständnis komplexer räumlicher maschineller Lernmodelle