Composite large margin classifiers with latent subclasses for heterogeneous biomedical data: Composite Large Margin Classifiers with Latent Subclasses

High dimensional classification problems are prevalent in a wide range of modern scientific applications. Despite a large number of candidate classification techniques available to use, practitioners often face a dilemma of choosing between linear and general nonlinear classifiers. Specifically, simple linear classifiers have good interpretability, but may have limitations in handling data with complex structures. In contrast, general nonlinear classifiers are more flexible, but may lose interpretability and have higher tendency for overfitting. In this paper, we consider data with potential latent subgroups in the classes of interest. We propose a new method, namely the Composite Large Margin Classifier (CLM), to address the issue of classification with latent subclasses. The CLM aims to find three linear functions simultaneously: one linear function to split the data into two parts, with each part being classified by a different linear classifier. Our method has comparable prediction accuracy to a general nonlinear classifier, and it maintains the interpretability of traditional linear classifiers. We demonstrate the competitive performance of the CLM through comparisons with several existing linear and nonlinear classifiers by Monte Carlo experiments. Analysis of the Alzheimer’s disease classification problem using CLM not only provides a lower classification error in discriminating cases and controls, but also identifies subclasses in controls that are more likely to develop the disease in the future

Towards generalizable machine learning models for computer-aided diagnosis in medicine

Hidden stratification represents a phenomenon in which a training dataset contains unlabeled (hidden) subsets of cases that may affect machine learning model performance. Machine learning models that ignore the hidden stratification phenomenon--despite promising overall performance measured as accuracy and sensitivity--often fail at predicting the low prevalence cases, but those cases remain important. In the medical domain, patients with diseases are often less common than healthy patients, and a misdiagnosis of a patient with a disease can have significant clinical impacts. Therefore, to build a robust and trustworthy CAD system and a reliable treatment effect prediction model, we cannot only pursue machine learning models with high overall accuracy, but we also need to discover any hidden stratification in the data and evaluate the proposing machine learning models with respect to both overall performance and the performance on certain subsets (groups) of the data, such as the ‘worst group’. In this study, I investigated three approaches for data stratification: a novel algorithmic deep learning (DL) approach that learns similarities among cases and two schema completion approaches that utilize domain expert knowledge. I further proposed an innovative way to integrate the discovered latent groups into the loss functions of DL models to allow for better model generalizability under the domain shift scenario caused by the data heterogeneity. My results on lung nodule Computed Tomography (CT) images and breast cancer histopathology images demonstrate that learning homogeneous groups within heterogeneous data significantly improves the performance of the computer-aided diagnosis (CAD) system, particularly for low-prevalence or worst-performing cases. This study emphasizes the importance of discovering and learning the latent stratification within the data, as it is a critical step towards building ML models that are generalizable and reliable. Ultimately, this discovery can have a profound impact on clinical decision-making, particularly for low-prevalence cases

Statistical Learning for Biomedical Data under Various Forms of Heterogeneity

In modern biomedical research, an emerging challenge is data heterogeneity. Ignoring such heterogeneity can lead to poor modeling results. In cancer research, clustering methods are applied to find subgroups of homogeneous individuals based on genetic profiles together with heuristic clinical analysis. A notable drawback of existing clustering methods is that they ignore the possibility that the variance of gene expression profile measurements can be heterogeneous across subgroups, leading to inaccurate subgroup prediction. In Chapter 2, we present a statistical approach that can capture both mean and variance structure in gene expression data. We demonstrate the strength of our method in both synthetic data and two cancer data sets. For a binary classification problem, there can be potential subclasses within the two classes of interest. These subclasses are latent and usually heterogeneous. We propose the Composite Large Margin Classifier (CLM) to address the issue of classification with latent subclasses in Chapter 3. The CLM aims to find three linear functions simultaneously: one linear function to split the data into two parts, with each part being classified by a different linear classifier. Our method has comparable prediction accuracy to a general nonlinear kernel classifier without overfitting the training data while at the same time maintaining the interpretability of traditional linear classifiers. There is a growing recognition of the importance of considering individual level heterogeneity when searching for optimal treatment doses. Such optimal individualized treatment rules (ITRs) for dosing should maximize the expected clinical benefit. In Chapter 4, we consider a randomized trial design where the candidate dose levels are continuous. To find the optimal ITR under such a design, we propose an outcome weighted learning method which directly maximizes the expected beneficial clinical outcome. This method converts the individualized dose selection problem into a nonstandard weighted regression problem. A difference of convex functions (DC) algorithm is adopted to efficiently solve the associated non-convex optimization problem. The consistency and convergence rates for the estimated ITR are derived and small-sample performance is evaluated via simulation studies. We illustrate the method using data from a clinical trial for Warfarin dosing.Doctor of Philosoph

Diagnosis Code Assignment Using Sparsity-Based Disease Correlation Embedding

© 1989-2012 IEEE. With the latest developments in database technologies, it becomes easier to store the medical records of hospital patients from their first day of admission than was previously possible. In Intensive Care Units (ICU), modern medical information systems can record patient events in relational databases every second. Knowledge mining from these huge volumes of medical data is beneficial to both caregivers and patients. Given a set of electronic patient records, a system that effectively assigns the disease labels can facilitate medical database management and also benefit other researchers, e.g., pathologists. In this paper, we have proposed a framework to achieve that goal. Medical chart and note data of a patient are used to extract distinctive features. To encode patient features, we apply a Bag-of-Words encoding method for both chart and note data. We also propose a model that takes into account both global information and local correlations between diseases. Correlated diseases are characterized by a graph structure that is embedded in our sparsity-based framework. Our algorithm captures the disease relevance when labeling disease codes rather than making individual decision with respect to a specific disease. At the same time, the global optimal values are guaranteed by our proposed convex objective function. Extensive experiments have been conducted on a real-world large-scale ICU database. The evaluation results demonstrate that our method improves multi-label classification results by successfully incorporating disease correlations

Kernel Methods and Measures for Classification with Transparency, Interpretability and Accuracy in Health Care

Support vector machines are a popular method in machine learning. They learn from data about a subject, for example, lung tumors in a set of patients, to classify new data, such as, a new patient’s tumor. The new tumor is classified as either cancerous or benign, depending on how similar it is to the tumors of other patients in those two classes—where similarity is judged by a kernel. The adoption and use of support vector machines in health care, however, is inhibited by a perceived and actual lack of rationale, understanding and transparency for how they work and how to interpret information and results from them. For example, a user must select the kernel, or similarity function, to be used, and there are many kernels to choose from but little to no useful guidance on choosing one. The primary goal of this thesis is to create accurate, transparent and interpretable kernels with rationale to select them for classification in health care using SVM—and to do so within a theoretical framework that advances rationale, understanding and transparency for kernel/model selection with atomic data types. The kernels and framework necessarily co-exist. The secondary goal of this thesis is to quantitatively measure model interpretability for kernel/model selection and identify the types of interpretable information which are available from different models for interpretation. Testing my framework and transparent kernels with empirical data I achieve classification accuracy that is better than or equivalent to the Gaussian RBF kernels. I also validate some of the model interpretability measures I propose

Entity-centric knowledge discovery for idiosyncratic domains

Technical and scientific knowledge is produced at an ever-accelerating pace, leading to increasing issues when trying to automatically organize or process it, e.g., when searching for relevant prior work. Knowledge can today be produced both in unstructured (plain text) and structured (metadata or linked data) forms. However, unstructured content is still themost dominant formused to represent scientific knowledge. In order to facilitate the extraction and discovery of relevant content, new automated and scalable methods for processing, structuring and organizing scientific knowledge are called for. In this context, a number of applications are emerging, ranging fromNamed Entity Recognition (NER) and Entity Linking tools for scientific papers to specific platforms leveraging information extraction techniques to organize scientific knowledge. In this thesis, we tackle the tasks of Entity Recognition, Disambiguation and Linking in idiosyncratic domains with an emphasis on scientific literature. Furthermore, we study the related task of co-reference resolution with a specific focus on named entities. We start by exploring Named Entity Recognition, a task that aims to identify the boundaries of named entities in textual contents. We propose a newmethod to generate candidate named entities based on n-gram collocation statistics and design several entity recognition features to further classify them. In addition, we show how the use of external knowledge bases (either domain-specific like DBLP or generic like DBPedia) can be leveraged to improve the effectiveness of NER for idiosyncratic domains. Subsequently, we move to Entity Disambiguation, which is typically performed after entity recognition in order to link an entity to a knowledge base. We propose novel semi-supervised methods for word disambiguation leveraging the structure of a community-based ontology of scientific concepts. Our approach exploits the graph structure that connects different terms and their definitions to automatically identify the correct sense that was originally picked by the authors of a scientific publication. We then turn to co-reference resolution, a task aiming at identifying entities that appear using various forms throughout the text. We propose an approach to type entities leveraging an inverted index built on top of a knowledge base, and to subsequently re-assign entities based on the semantic relatedness of the introduced types. Finally, we describe an application which goal is to help researchers discover and manage scientific publications. We focus on the problem of selecting relevant tags to organize collections of research papers in that context. We experimentally demonstrate that the use of a community-authored ontology together with information about the position of the concepts in the documents allows to significantly increase the precision of tag selection over standard methods

Establishment of predictive blood-based signatures in medical large scale genomic data sets : Development of novel diagnostic tests

Increasing data has led to tremendous success in discovering molecular biomarkers based on high throughput data. However, the translation of these so-called genomic signatures into clinical practice has been limited. The complexity and volume of genomic profiling requires heightened attention to robust design, methodological details, and avoidance of bias. During this thesis, novel strategies aimed at closing the gap from initially promising pilot studies to the clinical application of novel biomarkers are evaluated. First, a conventional process for genomic biomarker development comprising feature selection, algorithm and parameter optimization, and performance assessment was established. Using this approach, a RNA-stabilized whole blood diagnostic classifier for non-small cell lung cancer was built in a training set that can be used as a biomarker to discriminate between patients and control samples. Subsequently, this optimized classifier was successfully applied to two independent and blinded validation sets. Extensive permutation analysis using random feature lists supports the specificity of the established transcriptional classifier. Next, it was demonstrated that a combined approach of clinical trial simulation and adaptive learning strategies can be used to speed up biomarker development. As a model, genome-wide expression data derived from over 4,700 individuals in 37 studies addressing four clinical endpoints were used to assess over 1,800,000 classifiers. In addition to current approaches determining optimal classifiers within a defined study setting, randomized clinical trial simulation unequivocally uncovered the overall variance in the prediction performance of potential disease classifiers to predict the outcome of a large biomarker validation study from a pilot trial. Furthermore, most informative features were identified by feature ranking according to an individual classification performance score. Applying an adaptive learning strategy based on data extrapolation led to a datadriven prediction of the study size required for larger validation studies based on small pilot trials and an estimate of the expected statistical performance during validation. With these significant improvements, exceedingly robust and clinically applicable gene signatures for the diagnosis and detection of acute myeloid leukemia, active tuberculosis, HIV infection, and non-small cell lung cancer are established which could demonstrate disease-related enrichment of the obtained signatures and phenotype-related feature ranking. In further research, platform requirements for blood-based biomarker development were exemplarily examined for micro RNA expression profiling. The performance as well as the technical sample handling to provide reliable strategies for platform implementation in clinical applications were investigated. Overall, all introduced methods improve and accelerate the development of biomarker signatures for molecular diagnostics and can easily be extended to other high throughput data and other disease settings

Learning for text mining : tackling the cost of feature and knowledge engineering.

Over the last decade, the state-of-the-art in text mining has moved towards the adoption of machine learning as the main paradigm at the heart of approaches. Despite significant advances, machine learning based text mining solutions remain costly to design, develop and maintain for real world problems. An important component of such cost (feature engineering) concerns the effort required to understand which features or characteristics of the data can be successfully exploited in inducing a predictive model of the data. Another important component of the cost (knowledge engineering) has to do with the effort in creating labelled data, and in eliciting knowledge about the mining systems and the data itself. I present a series of approaches, methods and findings aimed at reducing the cost of creating and maintaining document classification and information extraction systems. They address the following questions: Which classes of features lead to an improved classification accuracy in the document classification and entity extraction tasks? How to reduce the amount of labelled examples needed to train machine learning based document classification and information extraction systems, so as to relieve domain experts from this costly task? How to effectively represent knowledge about these systems and the data that they manipulate, in order to make systems interoperable and results replicable? I provide the reader with the background information necessary to understand the above questions and the contributions to the state-of the- art contained herein. The contributions include: the identification of novel classes of features for the document classification task which exploit the multimedia nature of documents and lead to improved classification accuracy; a novel approach to domain adaptation for text categorization which outperforms standard supervised and semi-supervised methods while requiring considerably less supervision; and a well-founded formalism for declaratively specifying text and multimedia mining systems

Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8

Efficient Learning Machines

Computer scienc