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Exam questions and solutions in LaTex

MA230exam98pdf

Exam questions and solutions in PDF

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Exam questions and solutions in LaTex

Análisis predictivo de los eventos adversos relacionados con los indicadores de eficiencia hospitalaria de una IPS de Bogotá

En este estudio se realizó un análisis predictivo de la aparición de eventos adversos de los pacientes de una IPS de Bogotá, Mederi Hospital Universitario de Barrios Unidos (HUBU) durante el año 2013; relacionados con los indicadores de eficiencia hospitalaria (Porcentaje de ocupación hospitalaria, número de egresos hospitalarios, promedio de estancia hospitalaria, número de egresos de urgencias, promedio de estancia en urgencias). Los datos fueron exportados a una matriz de análisis de las variables cualitativas; fueron presentadas con frecuencias absolutas y relativas, las variables cuantitativas (edad, tiempos de estancia) fueron presentadas con media, desviaciones estándar. Se agruparon los datos de eventos adversos y de eficiencia hospitalaria en una nueva matriz que permitiera el análisis predictivo la nueva matriz fue exportada al software de modelación estadístico Eviews 6.5; se especificaron modelos predictivos multivariados para la variable número de eventos adversos, respecto de los indicadores de eficiencia hospitalaria y se estimaron las probabilidades de ocurrencia, análisis de correlación y multicolinealidad; los resultados se presentaron en tablas de estimación para cada modelo, se restringieron los eventos adversos prevenibles y no prevenibles información obtenida a través de un sistema de información que registra los factores relacionados con la ocurrencia de eventos adversos en salud, a través del sistema de reporte de eventos en salud, reporte en las historias clínicas, reporte individual, reporte por servicio, análisis de datos y estudios de caso, de la misma forma fueron extraídos los datos de eficiencia hospitalaria para el mismo periodo. El análisis y gestión de eventos adversos pretende establecer estrategias de mejoramiento continuo y análisis de resultados frente a los indicadores de eficiencia que permitan intervención de los factores de riesgo operativo de los servicios del Hospital Universitario de Barrios Unidos (HUBU), relacionados con eventos adversos en la atención de los pacientes en especial se debe enfocar en la gestión de los egresos de pacientes de acuerdo a los resultados obtenidos con el fin de alinearse y fortalecer las políticas de seguridad del paciente para brindar una atención integral con calidad y eficiencia, disminuyendo las quejas en la atención, las glosas, los riesgos jurídicos, de acuerdo al modelo predictivo estudiado.

Desenvolupament d'un sistema de suport a la decisió ambiental per a la gestió de les infraestructures hidràuliques, amb l'objectiu de garantir la qualitat de l'aigua a la Conca del Besòs

Aquesta tesi presenta un projecte de gestió integral d'infraestructures hidràuliques de sanejament a la Conca del riu Besòs. S'han considerat dos sistemes de sanejament (La Garriga i Granollers) amb les seves respectives xarxes de clavegueram i Estacions Depuradores d'Aigües Residuals (EDAR), i un tram del riu Congost, afluent del Besòs, com a medi receptor de les seves aigües residuals. Amb aquesta finalitat es construeix i s'utilitza un Sistema de Suport a la Decisió Ambiental (SSDA). Aquesta eina incorpora l'ús de models de simulació de qualitat de l'aigua pels sistemes de clavegueram, EDAR i riu, com a forma d'extracció de coneixement sobre la gestió integrada d'aquests elements. Aquest coneixement es conceptualitza, posteriorment, en forma d'arbres de decisió, que proporcionaran a l'usuari les actuacions a realitzar davant de les diferents situacions reals de gestió diària.

A novel shape feature for fast region-based pedestrian recognition

A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people detection using a pedestrian dataset.

PMCRI: a parallel modular classification rule induction framework

In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction of Decision Trees (TDIDT) algorithm is a very widely used technology to predict the classification of newly recorded data. However alternative technologies have been derived that often produce better rules but do not scale well on large datasets. Such an alternative to TDIDT is the PrismTCS algorithm. PrismTCS performs particularly well on noisy data but does not scale well on large datasets. In this paper we introduce Prism and investigate its scaling behaviour. We describe how we improved the scalability of the serial version of Prism and investigate its limitations. We then describe our work to overcome these limitations by developing a framework to parallelise algorithms of the Prism family and similar algorithms. We also present the scale up results of a first prototype implementation.

Parallel rule induction with information theoretic pre-pruning

In a world where data is captured on a large scale the major challenge for data mining algorithms is to be able to scale up to large datasets. There are two main approaches to inducing classification rules, one is the divide and conquer approach, also known as the top down induction of decision trees; the other approach is called the separate and conquer approach. A considerable amount of work has been done on scaling up the divide and conquer approach. However, very little work has been conducted on scaling up the separate and conquer approach.In this work we describe a parallel framework that allows the parallelisation of a certain family of separate and conquer algorithms, the Prism family. Parallelisation helps the Prism family of algorithms to harvest additional computer resources in a network of computers in order to make the induction of classification rules scale better on large datasets. Our framework also incorporates a pre-pruning facility for parallel Prism algorithms.

P-Prism: a computationally efficient approach to scaling up classification rule induction

Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

Towards a computationally efficient approach to modular classification rule induction

Induction of classification rules is one of the most important technologies in data mining. Most of the work in this field has concentrated on the Top Down Induction of Decision Trees (TDIDT) approach. However, alternative approaches have been developed such as the Prism algorithm for inducing modular rules. Prism often produces qualitatively better rules than TDIDT but suffers from higher computational requirements. We investigate approaches that have been developed to minimize the computational requirements of TDIDT, in order to find analogous approaches that could reduce the computational requirements of Prism.

J-PMCRI: a methodology for inducing pre-pruned modular classification rules

Inducing rules from very large datasets is one of the most challenging areas in data mining. Several approaches exist to scaling up classification rule induction to large datasets, namely data reduction and the parallelisation of classification rule induction algorithms. In the area of parallelisation of classification rule induction algorithms most of the work has been concentrated on the Top Down Induction of Decision Trees (TDIDT), also known as the ‘divide and conquer’ approach. However powerful alternative algorithms exist that induce modular rules. Most of these alternative algorithms follow the ‘separate and conquer’ approach of inducing rules, but very little work has been done to make the ‘separate and conquer’ approach scale better on large training data. This paper examines the potential of the recently developed blackboard based J-PMCRI methodology for parallelising modular classification rule induction algorithms that follow the ‘separate and conquer’ approach. A concrete implementation of the methodology is evaluated empirically on very large datasets.