Machine Learning representation of loss of eye regularity in a drosophila neurodegenerative model

The fruit fly compound eye is a premier experimental system for modeling human neurodegenerative diseases. The disruption of the retinal geometry has been historically assessed using time-consuming and poorly reliable techniques such as histology or pseudopupil manual counting. Recent semiautomated quantification approaches rely either on manual region-of-interest delimitation or engineered features to estimate the extent of degeneration. This work presents a fully automated classification pipeline of bright-field images based on orientated gradient descriptors and machine learning techniques. An initial region-of-interest extraction is performed, applying morphological kernels and Euclidean distance-to-centroid thresholding. Image classification algorithms are trained on these regions (support vector machine, decision trees, random forest, and convolutional neural network), and their performance is evaluated on independent, unseen datasets. The combinations of oriented gradient C gaussian kernel Support Vector Machine [0.97 accuracy and 0.98 area under the curve (AUC)] and fine-tuned pre-trained convolutional neural network (0.98 accuracy and 0.99 AUC) yielded the best results overall. The proposed method provides a robust quantification framework that can be generalized to address the loss of regularity in biological patterns similar to the Drosophila eye surface and speeds up the processing of large sample batche

Using Natural Language Processing to Detect Breast Cancer Recurrence in Clinical Notes: A Hierarchical Machine Learning Approach

The vast amount of data amassed in the electronic health records (EHRs) creates needs and opportunities for automated extraction of information from EHRs using machine learning techniques. Natural language processing (NLP) has the potential to substantially reduce the burden of manual chart reviewing to extract risk factors, adverse events, or outcomes, that are documented in unstructured clinical reports and progress notes. In this thesis, an NLP pipeline was built using open-source software to process a corpus of electronic clinical notes extracted from an integrated health care system in Cancer Care Manitoba (CCMB) which contains a cohort of women with early-stage incident breast cancers. The goal is to identify whether and when recurrences were diagnosed. We developed and evaluated the system using 117,365 clinical notes from 892 patients receiving EHR-documented care at CCMB between 2004 to 2007. We used a hierarchical architecture, where a model is built to provide the patient-level recurrence status, then the NLP pipeline is used to detect notes which contains information about recurrence and the date of recurrence. Class imbalance was a significant issue as the proportion of positive to negative notes was at approximately 1:22 ratio. Various techniques including undersampling and cost-based classification were used to mitigate this issue. The XGBoost classifier was the best performing model which achieved a balanced accuracy of 0.924, with sensitivity of 0.867, specificity of 0.981, precision of 0.886 and ROC of 0.924. In addition, more data was collected from the years 2008 to 2012 in a similar cohort. This dataset was used to validate the performance of the models, which include 615 patients with 78,460 notes. The model performed well with a balanced accuracy of 0.909, sensitivity of 0.843, specificity of 0.974, precision of 0.575 and Area Under the ROC Curve (AUC) value of 0.909. The study has demonstrated the ability to use natural language processing and machine learning techniques to assist in chart review by 1) excluding a large amount of notes which contain no relevant information, 2) identifying notes that most likely contain relevant recurrence information, in order to accurately identify the timing of recurrence

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Predicting the price of Bitcoin using the sentiment of popular Bitcoin-related Tweets

In little over a decade, cryptocurrencies have become a highly speculative asset class in global financial markets, with Bitcoin leading the way. Throughout its relatively brief history, the price of bitcoin has gone through multiple cycles of growth and decline. As a consequence, Bitcoin has become a widely discussed – and polarizing – topic on Twitter. This work studies whether the sentiment of popular Bitcoin-related tweets can be used to predict the future price movements of bitcoin. In total, seven different algorithms are evaluated: Vector Autoregression, Vector Autoregression Moving-Average, Random Forest, XGBoost, LightGBM, Long Short-Term Memory, and Gated Recurrent Unit. By applying lexicon-based sentiment analysis, and heuristic filtering of tweets, it was discovered that sentiment-based features of popular tweets improve the prediction accuracy over baseline features (open-high-low-close data) in five of the seven algorithms tested. The tree-based algorithms (Random Forest, XGBoost, LightGBM) generally had the lowest prediction errors, while the neural network algorithms (Light Short-Term Memory and Gated Recurrent Unit) had the poorest performance. The findings suggest that the sentiment of popular Bitcoin-related tweets can be an important feature in predicting the future price movements of bitcoin

Structuring the Unstructured: Unlocking pharmacokinetic data from journals with Natural Language Processing

The development of a new drug is an increasingly expensive and inefficient process. Many drug candidates are discarded due to pharmacokinetic (PK) complications detected at clinical phases. It is critical to accurately estimate the PK parameters of new drugs before being tested in humans since they will determine their efficacy and safety outcomes. Preclinical predictions of PK parameters are largely based on prior knowledge from other compounds, but much of this potentially valuable data is currently locked in the format of scientific papers. With an ever-increasing amount of scientific literature, automated systems are essential to exploit this resource efficiently. Developing text mining systems that can structure PK literature is critical to improving the drug development pipeline. This thesis studied the development and application of text mining resources to accelerate the curation of PK databases. Specifically, the development of novel corpora and suitable natural language processing architectures in the PK domain were addressed. The work presented focused on machine learning approaches that can model the high diversity of PK studies, parameter mentions, numerical measurements, units, and contextual information reported across the literature. Additionally, architectures and training approaches that could efficiently deal with the scarcity of annotated examples were explored. The chapters of this thesis tackle the development of suitable models and corpora to (1) retrieve PK documents, (2) recognise PK parameter mentions, (3) link PK entities to a knowledge base and (4) extract relations between parameter mentions, estimated measurements, units and other contextual information. Finally, the last chapter of this thesis studied the feasibility of the whole extraction pipeline to accelerate tasks in drug development research. The results from this thesis exhibited the potential of text mining approaches to automatically generate PK databases that can aid researchers in the field and ultimately accelerate the drug development pipeline. Additionally, the thesis presented contributions to biomedical natural language processing by developing suitable architectures and corpora for multiple tasks, tackling novel entities and relations within the PK domain

Extreme multi-label deep neural classification of Spanish health records according to the International Classification of Diseases

111 p.Este trabajo trata sobre la minería de textos clínicos, un campo del Procesamiento del Lenguaje Natural aplicado al dominio biomédico. El objetivo es automatizar la tarea de codificación médica. Los registros electrónicos de salud (EHR) son documentos que contienen información clínica sobre la salud de unpaciente. Los diagnósticos y procedimientos médicos plasmados en la Historia Clínica Electrónica están codificados con respecto a la Clasificación Internacional de Enfermedades (CIE). De hecho, la CIE es la base para identificar estadísticas de salud internacionales y el estándar para informar enfermedades y condiciones de salud. Desde la perspectiva del aprendizaje automático, el objetivo es resolver un problema extremo de clasificación de texto de múltiples etiquetas, ya que a cada registro de salud se le asignan múltiples códigos ICD de un conjunto de más de 70 000 términos de diagnóstico. Una cantidad importante de recursos se dedican a la codificación médica, una laboriosa tarea que actualmente se realiza de forma manual. Los EHR son narraciones extensas, y los codificadores médicos revisan los registros escritos por los médicos y asignan los códigos ICD correspondientes. Los textos son técnicos ya que los médicos emplean una jerga médica especializada, aunque rica en abreviaturas, acrónimos y errores ortográficos, ya que los médicos documentan los registros mientras realizan la práctica clínica real. Paraabordar la clasificación automática de registros de salud, investigamos y desarrollamos un conjunto de técnicas de clasificación de texto de aprendizaje profundo