3-D pain drawings-mobile data collection using a PDA

A large number of the adult population suffers from some kind of back pain during their lifetime. Part of the process of diagnosing and treating such back pain is for a clinician to collect information as to the type and location of the pain that is being suffered.Traditional approaches to gathering and visualizing this pain data have relied on simple 2-D representations of the human body, where different types of sensation are recorded with various monochrome symbols. Although patients have been shown to prefer such drawings to traditional questionnaires, these pain drawings can be limited in their ability to accurately record pain. The work described in this paper proposes an alternative that uses a 3-D representation of the human body, which can be marked in color to visualize and record the pain data. This study has shown that the new approach is a promising development in this area of medical practice and has been positively received by patients and clinicians alike

Expanding the occupational health psychology methodology: an artificial neural network approach

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Psicología, Departamento de Psicología Social y Metodología. Fecha de Lectura: 22-01-202

Language modelling for clinical natural language understanding and generation

One of the long-standing objectives of Artificial Intelligence (AI) is to design and develop algorithms for social good including tackling public health challenges. In the era of digitisation, with an unprecedented amount of healthcare data being captured in digital form, the analysis of the healthcare data at scale can lead to better research of diseases, better monitoring patient conditions and more importantly improving patient outcomes. However, many AI-based analytic algorithms rely solely on structured healthcare data such as bedside measurements and test results which only account for 20% of all healthcare data, whereas the remaining 80% of healthcare data is unstructured including textual data such as clinical notes and discharge summaries which is still underexplored. Conventional Natural Language Processing (NLP) algorithms that are designed for clinical applications rely on the shallow matching, templates and non-contextualised word embeddings which lead to limited understanding of contextual semantics. Though recent advances in NLP algorithms have demonstrated promising performance on a variety of NLP tasks in the general domain with contextualised language models, most of these generic NLP algorithms struggle at specific clinical NLP tasks which require biomedical knowledge and reasoning. Besides, there is limited research to study generative NLP algorithms to generate clinical reports and summaries automatically by considering salient clinical information. This thesis aims to design and develop novel NLP algorithms especially clinical-driven contextualised language models to understand textual healthcare data and generate clinical narratives which can potentially support clinicians, medical scientists and patients. The first contribution of this thesis focuses on capturing phenotypic information of patients from clinical notes which is important to profile patient situation and improve patient outcomes. The thesis proposes a novel self-supervised language model, named Phenotypic Intelligence Extraction (PIE), to annotate phenotypes from clinical notes with the detection of contextual synonyms and the enhancement to reason with numerical values. The second contribution is to demonstrate the utility and benefits of using phenotypic features of patients in clinical use cases by predicting patient outcomes in Intensive Care Units (ICU) and identifying patients at risk of specific diseases with better accuracy and model interpretability. The third contribution is to propose generative models to generate clinical narratives to automate and accelerate the process of report writing and summarisation by clinicians. This thesis first proposes a novel summarisation language model named PEGASUS which surpasses or is on par with the state-of-the-art performance on 12 downstream datasets including biomedical literature from PubMed. PEGASUS is further extended to generate medical scientific documents from input tabular data.Open Acces

The synthesis of artificial neural networks using single string evolutionary techniques.

The research presented in this thesis is concerned with optimising the structure of Artificial Neural Networks. These techniques are based on computer modelling of biological evolution or foetal development. They are known as Evolutionary, Genetic or Embryological methods. Specifically, Embryological techniques are used to grow Artificial Neural Network topologies. The Embryological Algorithm is an alternative to the popular Genetic Algorithm, which is widely used to achieve similar results. The algorithm grows in the sense that the network structure is added to incrementally and thus changes from a simple form to a more complex form. This is unlike the Genetic Algorithm, which causes the structure of the network to evolve in an unstructured or random way. The thesis outlines the following original work: The operation of the Embryological Algorithm is described and compared with the Genetic Algorithm. The results of an exhaustive literature search in the subject area are reported. The growth strategies which may be used to evolve Artificial Neural Network structure are listed. These growth strategies are integrated into an algorithm for network growth. Experimental results obtained from using such a system are described and there is a discussion of the applications of the approach. Consideration is given of the advantages and disadvantages of this technique and suggestions are made for future work in the area. A new learning algorithm based on Taguchi methods is also described. The report concludes that the method of incremental growth is a useful and powerful technique for defining neural network structures and is more efficient than its alternatives. Recommendations are also made with regard to the types of network to which this approach is best suited. Finally, the report contains a discussion of two important aspects of Genetic or Evolutionary techniques related to the above. These are Modular networks (and their synthesis) and the functionality of the network itself

Machine learning models in decision support systems for diagnosing colorectal cancer based on metabolic profiles

In today’s ever-evolving technological landscape, the volume of data across sectors is grow ing, particularly in healthcare. Here, the gathering and processing of biochemical data aim to refine decision-making for patient treatments, especially using tools based on Machine Learning (ML). As a subset of Artificial Intelligence, ML harnesses algorithms to predict outcomes or unearth patterns that might otherwise remain concealed. The interpretability of ML models is pivotal, enabling healthcare professionals to place con fidence in and decipher the model’s predictions. This assumes particular significance when decisions could directly affect patient lives. This research embarked on an in-depth exploration of various ML algorithms and techniques to discern whether the combined metabolic profiles of amino acids and acylcarnitines might serve as new biochemical indicators for predicting colo-rectal cancer prognosis. Throughout this study, several algorithms and data preprocessing techniques were evaluated. Four distinct experiments validated the predictions of the models in different scenarios. These scenarios involved predicting Colorectal Cancer using amino acids with and without the age parameter, and similarly, using acylcarnitine with and without the age parameter. Each scenario’s predictions were elucidated using SHAP, both for overarching feature significance and individual instances. Preliminary analyses indicated that the constructed models demonstrated promising predic tive power, with notable variations for the different scenarios. Amongst the algorithms tested, Random Forest, Support Vector Machine, Gaussian Naive Bayes, and Gradient Boosting emerged as the top performers.No atual panorama tecnológico em constante evolução, o volume de dados em diversos setores está a aumentar, particularmente na saúde. Aqui, a recolha e processamento de dados bioquímicos visam aprimorar a tomada de decisão para tratamentos de pacientes, especialmente utilizando ferramentas baseadas em Aprendizagem Automática. Como um subconjunto da Inteligência Artificial, a Aprendizagem Automática utiliza algoritmos para prever resultados ou descobrir padrões que de outra forma poderiam permanecer ocultos. A interpretabilidade dos modelos de Aprendizagem Automática é fundamental, permitindo que os profissionais de saúde confiem e decifrem as previsões do modelo. Isto assume uma importância particular quando as decisões podem afetar diretamente a vida dos pacientes. Esta investigação levou a cabo uma exploração aprofundada de vários algoritmos e téc nicas de Aprendizagem Automática para determinar se os perfis metabólicos combinados de aminoácidos e acilcarnitinas poderiam servir como novos indicadores bioquímicos para a previsão e prognóstico do cancro colo-retal. Ao longo deste estudo, vários algoritmos e técnicas de pré-processamento de dados foram avaliados. Quatro experiências distintas validaram as previsões dos modelos em diferentes cenários. Estes cenários envolveram a previsão de Cancro Colorretal usando aminoácidos com e sem o atributo idade, e de forma semelhante, usando acilcarnitinas. As previsões de cada cenário foram elucidadas usando o SHAP, tanto para a importância geral dos atributos como para amostras individuais. Análises preliminares indicaram que os modelos construídos mostraram um poder preditivo promissor, com variações notáveis nos diferentes cenários. Entre os algoritmos testados, Random Forest, Support Vector Machines, Naive Bayes e Gradient Boosting destacaram-se com melhor desempenho

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications