Applications of artificial neural networks to neurophysiological studies in focal peripheral neuropathies

Introducción: La interpretación de estudios electrofisiológicos es esencialmente una tarea de clasificación. Las redes neuronales artificiales (ANN) son herramientas adecuadas para la clasificación porque son basado en técnicas de reconocimiento de patrones. Objetivos: Desarrollar un sistema informático para la detección automatizada de neuropatías focales. utilizando ANN. Métodos: El estudio se basó en 300 conjuntos de estudios de conducción nerviosa (NCS) de tres diferentes laboratorios de medicina de electrodiagnóstico. Cada conjunto de datos de entrada estaba formado por 11 parámetros, incluyendo latencias motoras y sensoriales, amplitudes, duraciones y velocidades de un solo nervio. Los conjuntos de entrada se clasificaron en 4 subgrupos de neuropatía focal (distal desmielinización, desmielinización proximal, desmielinización generalizada, pérdida de axones) según sobre el tipo de daño nervioso más 1 adicional para hallazgos normales. Los datos fueron presentados a una ANN de retropropagación con 1 capa oculta. La estructura de la red se modificó para lograr el error cuadrático medio más bajo posible. Los resultados de estas redes de primer nivel se presentaron a una red de segundo nivel para detectar neuropatías generalizadas. Después entrenando a las ANN, la precisión de la clasificación se probó utilizando otro conjunto de datos que se desconocido para las redes. Resultados: Se alcanzó una precisión de clasificación del 99% para la detección de patologías patrones. La precisión para la clasificación de neuropatías focales fue del 95,2%. Conclusiones: las redes neuronales clasifican subgrupos de neuropatía focal con alta precisión (> 95%). Este método puede conducir a la detección automática de neuropatía focal.Instituto Tecnológico de Estudios Superiores de Monterrey ITESMSUMMARY 11 INTRODUCCION 1 1 EL PROBLEMA 3 1.1 DESCRIPCIÓN DEL PROBLEMA 3 1.2 FORMULACIÓN DEL PROBLEMA 5 1.3 OBJETIVO GENERAL 5 1.4 OBJETIVOS ESPECIFICOS 5 1.5 JUSTIFICACIÓN 6 1.6 ALCANCES Y LIMITACIONES 7 2 MARCO DE REFERENCIA 9 2.1 ANTECEDENTES DE LA INVESTIGACIÓN 9 2.2 MARCO TEÓRICO CONCEPTUAL 11 2.2.1 Medicina Electrodiagnóstica 12 2.2.2 Inteligencia Artificial y Medicina 45 2.2.3 Redes Neuronales Artificiales 61 2.2.4 Aplicaciones de redes neuronales a Medicina 94 2.2.5 Aplicaciones de redes neuronales a electrodiagnóstico 104 3 METODOLOGÍA 106 3.1 DATOS 106 3.1.1 Salidas deseadas 106 3.1.2 Selección de los datos de entrada 107 3.1.3 Preprocesamiento de los datos de entrada 109 3.1.4 Datos Faltantes 110 3.1.5 Fuente de los datos 111 3.2 ARQUITECTURA DE LA RED 113 3.2.1 Tipo de red 114 3.2.2 Mejorar la Generalización 115 3.2.3 Arquitectura de la Red 1 116 3.2.4 Arquitectura de la Red 2 121 3.3 SOFTWARE 124 3.4 HARDWARE 125 3.5 ENTRENAMIENTO 125 3.6 VALIDACIÓN DE LA RED 126 4 RESULTADOS 127 4.1 RED 1 (ESTRUCTURA DE RED GENERAL) 127 4.2 RED 2 (RED NERVIO MEDIANO) 128 4.3 RED 3 (RED NERVIO ULNAR) 130 4.4 RED 4 (RED DE GENERALIZACIÓN) 132 4.5 VALIDACIÓN DE RESULTADOS 135 5 DISCUSIÓN 137 CONCLUSIONES 139 RECOMENDACIONES 141 BIBLIOGRAFIA 142 REFERENCIAS ELECTRONICASMaestríaIntroduction: Interpreting electrophysiological studies is essentially a classification task. Artificial neural networks (ANNs) are suitable tools for classification because they are based on pattern recognition techniques. Objectives: To develop a computer system for automated detection of focal neuropathies using ANNs. Methods: The study was based on 300 sets of nerve conduction studies (NCSs) from three different electrodiagnostic medicine laboratories. Each input data set was formed by 11 parameters, including motor and sensory latencies, amplitudes, durations, and velocities of a single nerve. The input sets were classified into 4 focal neuropathy subgroups (distal demyelination, proximal demyelination, generalized demyelination, axon loss) depending on the type of nerve damage plus 1 additional for normal findings. The data were presented to a backpropagation ANN with 1 hidden layer. The network structure was modified to achieve the lowest possible mean square error. The outputs from these first-level networks were presented to a second-level network in order to detect generalized neuropathies. After training the ANNs, the classification accuracy was tested using another data set that was unknown to the networks. Results: A classification accuracy of 99% was reached for the detection of pathologic patterns. The accuracy for focal neuropathies classification was 95.2%.Conclusions: Neural networks classify focal neuropathy subgroups with high accuracy (>95%). This method may lead to automated focal neuropathy detection.Modalidad Presencia

Development of an advanced artificial intelligent reliability analysis tool to enhance ship operations and maintenance activities

No Abstract availableNo Abstract availabl

Definition of a near real time microbiological monitor for space vehicles

Efforts to identify the ideal candidate to serve as the biological monitor on the space station Freedom are discussed. The literature review, the evaluation scheme, descriptions of candidate monitors, experimental studies, test beds, and culture techniques are discussed. Particular attention is given to descriptions of five candidate monitors or monitoring techniques: laser light scattering, primary fluorescence, secondary fluorescence, the volatile product detector, and the surface acoustic wave detector

Mathematics & Statistics 2017 APR Self-Study & Documents

UNM Mathematics & Statistics APR self-study report, review team report, response report, and initial action plan for Spring 2017, fulfilling requirements of the Higher Learning Commission

Interactive Visual Displays for Results Management in Complex Medical Workflows

Clinicians manage medical orders to ensure that the results are returned promptly to the correct physician and followed up on time. Delays in results management occur frequently, physically harm patients, and often cause malpractice litigation. Better tracking of medical orders that showed progress and indicated delays, could result in improved care, better safety, and reduced clinician effort. This dissertation presents novel displays of rich tables with an interaction technique called ARCs (Actions for Rapid Completion). Rich tables are generated by MStart (Multi-Step Task Analyzing, Reporting, and Tracking) from a workflow model that defines order processes. Rich tables help clinicians perceive each order's status, prioritize the critical ones, and act on results in a timely fashion. A second contribution is the design of an interactive visualization called MSProVis (Multi-Step Process Visualization), which is composed of several PCDs (Process Completion Diagrams) that show the number and duration of in-time, late, and not-completed orders. With MSProVis, managers perform retrospective analyses to make decisions by studying an overview of the order process, durations of order steps, and performances of individuals. I visited seven hospitals and clinics to define sample results management workflows. Iterative design reviews with clinicians, designers, and researchers led to refinements of the rich tables, ARCs, and design guidelines. A controlled experiment with 18 participants under time pressure and distractions tested two features (showing pending orders and prioritizing by lateness) of rich tables. These changes statistically significantly reduce the time from nine to one minute to correctly identify late orders compared to the traditional chronologically-ordered lists. Another study demonstrated that ARCs speed performance up by 25% compared to state-of-the-art systems. A usability study with two clinicians and five novices showed that participants were able to understand MSProVis and efficiently perform representative tasks. Two subjective preference surveys suggested new design choices for the PCDs. This dissertation provides designers of results management systems with clear guidance about showing pending results and prioritizing by lateness, and tested strategies for performing retrospective analyses. It also offers detailed design guidelines for results management, tables, and integrated actions on tables that speed performance for common tasks

Knowledge extraction from unstructured data

Data availability is becoming more essential, considering the current growth of web-based data. The data available on the web are represented as unstructured, semi-structured, or structured data. In order to make the web-based data available for several Natural Language Processing or Data Mining tasks, the data needs to be presented as machine-readable data in a structured format. Thus, techniques for addressing the problem of capturing knowledge from unstructured data sources are needed. Knowledge extraction methods are used by the research communities to address this problem; methods that are able to capture knowledge in a natural language text and map the extracted knowledge to existing knowledge presented in knowledge graphs (KGs). These knowledge extraction methods include Named-entity recognition, Named-entity Disambiguation, Relation Recognition, and Relation Linking. This thesis addresses the problem of extracting knowledge over unstructured data and discovering patterns in the extracted knowledge. We devise a rule-based approach for entity and relation recognition and linking. The defined approach effectively maps entities and relations within a text to their resources in a target KG. Additionally, it overcomes the challenges of recognizing and linking entities and relations to a specific KG by employing devised catalogs of linguistic and domain-specific rules that state the criteria to recognize entities in a sentence of a particular language, and a deductive database that encodes knowledge in community-maintained KGs. Moreover, we define a Neuro-symbolic approach for the tasks of knowledge extraction in encyclopedic and domain-specific domains; it combines symbolic and sub-symbolic components to overcome the challenges of entity recognition and linking and the limitation of the availability of training data while maintaining the accuracy of recognizing and linking entities. Additionally, we present a context-aware framework for unveiling semantically related posts in a corpus; it is a knowledge-driven framework that retrieves associated posts effectively. We cast the problem of unveiling semantically related posts in a corpus into the Vertex Coloring Problem. We evaluate the performance of our techniques on several benchmarks related to various domains for knowledge extraction tasks. Furthermore, we apply these methods in real-world scenarios from national and international projects. The outcomes show that our techniques are able to effectively extract knowledge encoded in unstructured data and discover patterns over the extracted knowledge presented as machine-readable data. More importantly, the evaluation results provide evidence to the effectiveness of combining the reasoning capacity of the symbolic frameworks with the power of pattern recognition and classification of sub-symbolic models

Enhancing Usability and Explainability of Data Systems

The recent growth of data science expanded its reach to an ever-growing user base of nonexperts, increasing the need for usability, understandability, and explainability in these systems. Enhancing usability makes data systems accessible to people with different skills and backgrounds alike, leading to democratization of data systems. Furthermore, proper understanding of data and data-driven systems is necessary for the users to trust the function of the systems that learn from data. Finally, data systems should be transparent: when a data system behaves unexpectedly or malfunctions, the users deserve proper explanation of what caused the observed incident. Unfortunately, most existing data systems offer limited usability and support for explanations: these systems are usable only by experts with sound technical skills, and even expert users are hindered by the lack of transparency into the systems\u27 inner workings and functions. The aim of my thesis is to bridge the usability gap between nonexpert users and complex data systems, aid all sort of users, including the expert ones, in data and system understanding, and provide explanations that help reason about unexpected outcomes involving data systems. Specifically, my thesis has the following three goals: (1) enhancing usability of data systems for nonexperts, (2) enable data understanding that can assist users in a variety of tasks such as achieving trust in data-driven machine learning, gaining data understanding, and data cleaning, and (3) explaining causes of unexpected outcomes involving data and data systems. For enhancing usability, we focus on example-driven user intent discovery. We develop systems based on example-driven interactions in two different settings: querying relational databases and personalized document summarization. Towards data understanding, we develop a new data-profiling primitive that can characterize tuples for which a machine-learned model is likely to produce untrustworthy predictions. We also develop an explanation framework to explain causes of such untrustworthy predictions. Additionally, this new data-profiling primitive enables interactive data cleaning. Finally, we develop two explanation frameworks, tailored to provide explanations in debugging data system components, including the data itself. The explanation frameworks focus on explaining the root cause of a concurrent application\u27s intermittent failure and exposing issues in the data that cause a data-driven system to malfunction

Non-rigid registration of contrast-enhanced dynamic MR mammography

Master'sMASTER OF ENGINEERIN

Fouille de données de santé

Dans le domaine de la santé, les techniques d’analyse de données sont de plus en plus populaires et se révèlent même indispensables pour gérer les gros volumes de données produits pour un patient et par le patient. Deux thématiques seront abordées dans cette présentation d'HDR.La première porte sur la définition, la formalisation, l’implémentation et la validation de méthodes d’analyse permettant de décrire le contenu de bases de données médicales. Je me suis particulièrement intéressée aux données séquentielles. J’ai fait évoluer la classique notion de motif séquentiel pour y intégrer des composantes contextuelles, spatiales et sur l’ordre partiel des éléments composant les motifs. Ces nouvelles informations enrichissent la sémantique initiale de ces motifs.La seconde thématique se focalise sur l’analyse des productions et des interactions des patients au travers des médias sociaux. J’ai principalement travaillé sur des méthodes permettant d’analyser les productions narratives des patients selon leurs temporalités, leurs thématiques, les sentiments associés ou encore le rôle et la réputation du locuteur s’étant exprimé dans les messages