De MARC a CEPAL o cualquier otro formato y viceversa

El propósito de este artículo es práctico: enseñar al usuario de Winisis a reformatear las bases de datos diseñadas e implementadas en cualquier formato, normalizado o no, convirtiéndolas al formato de interés de la unidad de información.Winisis es un paquete que permite utilizar cualquier nomenclatura en la definición de las etiquetas que identifican los campos. Este aspecto es una ventaja y una desventaja porque permite libertad en la selección del formato a utilizar, pero, se generan una gran diversidad de formatos en las unidades de información, dificultando la compatibilidad en los sistemas automatizados de catalogación, la normalización y la transferencia efectiva de la información entre unidades de información.Algunos problemas que enfrentan los bibliotecólogos en su trabajo cotidiano al normalizar la información son por ejemplo:a) la diversidad de bases de datos sin normalización, b) la variación entre formatos, c) los diferentes números, etiquetas y nombres de los campos que integran las bases de datos, e incluso bases de datos creadas con diferentes versiones de Microisis; d) un catálogo automatizado en un formato sin normalización; e) para la participación en una red y exige la reconversión del catálogo al formato común de la red. En algunos casos se ha optado por digitar de nuevo la información en el nuevo formato, sin embargo, ante esta situación, Microisis y Winisis dispone de un recurso denominado "Tabla de Selección de Campos para reformateo".

Some misspecfication problems in long-memory time series models

Doctor of Philosophy in Mathematics

Predicting risk of suicide attempt using history of physical illnesses from electronic medical records

BACKGROUND: Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk.

OBJECTIVE: The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data.

METHODS: We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC).

RESULTS: The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians.

CONCLUSIONS: This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk.

School students and part-time work: Workplace problems and challenges.

This paper uses data from a large national project on student-working to examine problems and challenges for school students working in part-time jobs. While literature has identified some potential problems and challenges, and some potential difficulties can be extrapolated from the nature of a young teenage workforce and the nature of the workplaces, these were largely absent in the two companies researched because the companies already had policies in place that addressed the potential problems. Some suggestions are made about how problems and challenges could be avoided in a wider range of adolescent workplaces.

Using data mining to improve building life cycle

The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.