A soft computing decision support framework for e-learning

Tesi per compendi de publicacions.Supported by technological development and its impact on everyday activities, e-Learning and b-Learning (Blended Learning) have experienced rapid growth mainly in higher education and training. Its inherent ability to break both physical and cultural distances, to disseminate knowledge and decrease the costs of the teaching-learning process allows it to reach anywhere and anyone. The educational community is divided as to its role in the future. It is believed that by 2019 half of the world's higher education courses will be delivered through e-Learning. While supporters say that this will be the educational mode of the future, its detractors point out that it is a fashion, that there are huge rates of abandonment and that their massification and potential low quality, will cause its fall, assigning it a major role of accompanying traditional education. There are, however, two interrelated features where there seems to be consensus. On the one hand, the enormous amount of information and evidence that Learning Management Systems (LMS) generate during the e-Learning process and which is the basis of the part of the process that can be automated. In contrast, there is the fundamental role of e-tutors and etrainers who are guarantors of educational quality. These are continually overwhelmed by the need to provide timely and effective feedback to students, manage endless particular situations and casuistics that require decision making and process stored information. In this sense, the tools that e-Learning platforms currently provide to obtain reports and a certain level of follow-up are not sufficient or too adequate. It is in this point of convergence Information-Trainer, where the current developments of the LMS are centered and it is here where the proposed thesis tries to innovate. This research proposes and develops a platform focused on decision support in e-Learning environments. Using soft computing and data mining techniques, it extracts knowledge from the data produced and stored by e-Learning systems, allowing the classification, analysis and generalization of the extracted knowledge. It includes tools to identify models of students' learning behavior and, from them, predict their future performance and enable trainers to provide adequate feedback. Likewise, students can self-assess, avoid those ineffective behavior patterns, and obtain real clues about how to improve their performance in the course, through appropriate routes and strategies based on the behavioral model of successful students. The methodological basis of the mentioned functionalities is the Fuzzy Inductive Reasoning (FIR), which is particularly useful in the modeling of dynamic systems. During the development of the research, the FIR methodology has been improved and empowered by the inclusion of several algorithms. First, an algorithm called CR-FIR, which allows determining the Causal Relevance that have the variables involved in the modeling of learning and assessment of students. In the present thesis, CR-FIR has been tested on a comprehensive set of classical test data, as well as real data sets, belonging to different areas of knowledge. Secondly, the detection of atypical behaviors in virtual campuses was approached using the Generative Topographic Mapping (GTM) methodology, which is a probabilistic alternative to the well-known Self-Organizing Maps. GTM was used simultaneously for clustering, visualization and detection of atypical data. The core of the platform has been the development of an algorithm for extracting linguistic rules in a language understandable to educational experts, which helps them to obtain patterns of student learning behavior. In order to achieve this functionality, the LR-FIR algorithm (Extraction of Linguistic Rules in FIR) was designed and developed as an extension of FIR that allows both to characterize general behavior and to identify interesting patterns. In the case of the application of the platform to several real e-Learning courses, the results obtained demonstrate its feasibility and originality. The teachers' perception about the usability of the tool is very good, and they consider that it could be a valuable resource to mitigate the time requirements of the trainer that the e-Learning courses demand. The identification of student behavior models and prediction processes have been validated as to their usefulness by expert trainers. LR-FIR has been applied and evaluated in a wide set of real problems, not all of them in the educational field, obtaining good results. The structure of the platform makes it possible to assume that its use is potentially valuable in those domains where knowledge management plays a preponderant role, or where decision-making processes are a key element, e.g. ebusiness, e-marketing, customer management, to mention just a few. The Soft Computing tools used and developed in this research: FIR, CR-FIR, LR-FIR and GTM, have been applied successfully in other real domains, such as music, medicine, weather behaviors, etc.Soportado por el desarrollo tecnológico y su impacto en las diferentes actividades cotidianas, el e-Learning (o aprendizaje electrónico) y el b-Learning (Blended Learning o aprendizaje mixto), han experimentado un crecimiento vertiginoso principalmente en la educación superior y la capacitación. Su habilidad inherente para romper distancias tanto físicas como culturales, para diseminar conocimiento y disminuir los costes del proceso enseñanza aprendizaje le permite llegar a cualquier sitio y a cualquier persona. La comunidad educativa se encuentra dividida en cuanto a su papel en el futuro. Se cree que para el año 2019 la mitad de los cursos de educación superior del mundo se impartirá a través del e-Learning. Mientras que los partidarios aseguran que ésta será la modalidad educativa del futuro, sus detractores señalan que es una moda, que hay enormes índices de abandono y que su masificación y potencial baja calidad, provocará su caída, reservándole un importante papel de acompañamiento a la educación tradicional. Hay, sin embargo, dos características interrelacionadas donde parece haber consenso. Por un lado, la enorme generación de información y evidencias que los sistemas de gestión del aprendizaje o LMS (Learning Management System) generan durante el proceso educativo electrónico y que son la base de la parte del proceso que se puede automatizar. En contraste, está el papel fundamental de los e-tutores y e-formadores que son los garantes de la calidad educativa. Éstos se ven continuamente desbordados por la necesidad de proporcionar retroalimentación oportuna y eficaz a los alumnos, gestionar un sin fin de situaciones particulares y casuísticas que requieren toma de decisiones y procesar la información almacenada. En este sentido, las herramientas que las plataformas de e-Learning proporcionan actualmente para obtener reportes y cierto nivel de seguimiento no son suficientes ni demasiado adecuadas. Es en este punto de convergencia Información-Formador, donde están centrados los actuales desarrollos de los LMS y es aquí donde la tesis que se propone pretende innovar. La presente investigación propone y desarrolla una plataforma enfocada al apoyo en la toma de decisiones en ambientes e-Learning. Utilizando técnicas de Soft Computing y de minería de datos, extrae conocimiento de los datos producidos y almacenados por los sistemas e-Learning permitiendo clasificar, analizar y generalizar el conocimiento extraído. Incluye herramientas para identificar modelos del comportamiento de aprendizaje de los estudiantes y, a partir de ellos, predecir su desempeño futuro y permitir a los formadores proporcionar una retroalimentación adecuada. Así mismo, los estudiantes pueden autoevaluarse, evitar aquellos patrones de comportamiento poco efectivos y obtener pistas reales acerca de cómo mejorar su desempeño en el curso, mediante rutas y estrategias adecuadas a partir del modelo de comportamiento de los estudiantes exitosos. La base metodológica de las funcionalidades mencionadas es el Razonamiento Inductivo Difuso (FIR, por sus siglas en inglés), que es particularmente útil en el modelado de sistemas dinámicos. Durante el desarrollo de la investigación, la metodología FIR ha sido mejorada y potenciada mediante la inclusión de varios algoritmos. En primer lugar un algoritmo denominado CR-FIR, que permite determinar la Relevancia Causal que tienen las variables involucradas en el modelado del aprendizaje y la evaluación de los estudiantes. En la presente tesis, CR-FIR se ha probado en un conjunto amplio de datos de prueba clásicos, así como conjuntos de datos reales, pertenecientes a diferentes áreas de conocimiento. En segundo lugar, la detección de comportamientos atípicos en campus virtuales se abordó mediante el enfoque de Mapeo Topográfico Generativo (GTM), que es una alternativa probabilística a los bien conocidos Mapas Auto-organizativos. GTM se utilizó simultáneamente para agrupamiento, visualización y detección de datos atípicos. La parte medular de la plataforma ha sido el desarrollo de un algoritmo de extracción de reglas lingüísticas en un lenguaje entendible para los expertos educativos, que les ayude a obtener los patrones del comportamiento de aprendizaje de los estudiantes. Para lograr dicha funcionalidad, se diseñó y desarrolló el algoritmo LR-FIR, (extracción de Reglas Lingüísticas en FIR, por sus siglas en inglés) como una extensión de FIR que permite tanto caracterizar el comportamiento general, como identificar patrones interesantes. En el caso de la aplicación de la plataforma a varios cursos e-Learning reales, los resultados obtenidos demuestran su factibilidad y originalidad. La percepción de los profesores acerca de la usabilidad de la herramienta es muy buena, y consideran que podría ser un valioso recurso para mitigar los requerimientos de tiempo del formador que los cursos e-Learning exigen. La identificación de los modelos de comportamiento de los estudiantes y los procesos de predicción han sido validados en cuanto a su utilidad por los formadores expertos. LR-FIR se ha aplicado y evaluado en un amplio conjunto de problemas reales, no todos ellos del ámbito educativo, obteniendo buenos resultados. La estructura de la plataforma permite suponer que su utilización es potencialmente valiosa en aquellos dominios donde la administración del conocimiento juegue un papel preponderante, o donde los procesos de toma de decisiones sean una pieza clave, por ejemplo, e-business, e-marketing, administración de clientes, por mencionar sólo algunos. Las herramientas de Soft Computing utilizadas y desarrolladas en esta investigación: FIR, CR-FIR, LR-FIR y GTM, ha sido aplicadas con éxito en otros dominios reales, como música, medicina, comportamientos climáticos, etc.Postprint (published version

Strategies for annotation and curation of translational databases: the eTUMOUR project

The eTUMOUR (eT) multi-centre project gathered in vivo and ex vivo magnetic resonance (MR) data, as well as transcriptomic and clinical information from brain tumour patients, with the purpose of improving the diagnostic and prognostic evaluation of future patients. In order to carry this out, among other work, a database—the eTDB—was developed. In addition to complex permission rules and software and management quality control (QC), it was necessary to develop anonymization, processing and data visualization tools for the data uploaded. It was also necessary to develop sophisticated curation strategies that involved on one hand, dedicated fields for QC-generated meta-data and specialized queries and global permissions for senior curators and on the other, to establish a set of metrics to quantify its contents. The indispensable dataset (ID), completeness and pairedness indices were set. The database contains 1317 cases created as a result of the eT project and 304 from a previous project, INTERPRET. The number of cases fulfilling the ID was 656. Completeness and pairedness were heterogeneous, depending on the data type involved

Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes.

The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.Funding from project MARESCAN (SAF2011-23870) from Ministerio de Economia y Competitividad in Spain. This work was also partially funded by CIBER-BBN, which is an initiative of the VI National R&D&i Plan 2008-2011, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund. JRG acknowledges support from Cancer Research UK, the University of Cambridge and Hutchison Whampoa Ltd.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/nbm.343

Late effects of cancer at a young age: Registry-based studies of the health of cancer patients and their off-spring

Modern cancer therapy has resulted in increased survival among patients diagnosed with cancer at a young age. These improvements have led to the investigation of late morbidity and mortality associated with cancer and its treatments. The aim of this study was to evaluate late effects of cancer treated at a young age on the health of patients and their offspring. Utilising the nationwide population-based registries in Finland, we evaluated the risk of hypothyroidism and the probability of parenthood in cancer survivors as well as preterm birth, neonatal outcomes, and the risk of cancer among offspring of patients. The survivor cohort, identified from the Finnish Cancer Registry, consisted of 25,784 cancer patients diag-nosed between ages 0 and 34 in 1953–2004. By linkage to the population register, siblings of these patients were identified for comparison. The prevalence of hypothyroidism was higher among former childhood cancer (aged 0–16) patients than in the general population. The probability of parenthood following early onset cancer was overall significantly reduced compared to siblings. Offspring of female cancer survivors were at an increased risk of preterm birth, this risk being highest among patients diagnosed in childhood and early adulthood (aged 20–34 years). The offspring were not, however, at a significantly increased risk of neonatal death or stillbirth, though they were more likely to need monitoring or intensive care in the neonatal period. The risk of sporadic cancer among offspring of male and female cancer survivors was not elevated in comparison to the general population. The study showed that former cancer patients are at risk of certain adverse endocrine and reproductive health outcomes and should be followed for timely intervention. The offspring of cancer survivors do not appear to be at risk for adverse health outcomes.Siirretty Doriast

UWOMJ Volume 70, Number 1, Spring 2000

Schulich School of Medicine & Dentistryhttps://ir.lib.uwo.ca/uwomj/1018/thumbnail.jp

UWOMJ Volume 66, No 2, Summer 1997

An interdisciplinary medical science publication, established in 1930.https://ir.lib.uwo.ca/uwomj/1029/thumbnail.jp

Molecular testing for Lynch syndrome in people with colorectal cancer: systematic reviews and economic evaluation

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND: Inherited mutations in deoxyribonucleic acid (DNA) mismatch repair (MMR) genes lead to an increased risk of colorectal cancer (CRC), gynaecological cancers and other cancers, known as Lynch syndrome (LS). Risk-reducing interventions can be offered to individuals with known LS-causing mutations. The mutations can be identified by comprehensive testing of the MMR genes, but this would be prohibitively expensive in the general population. Tumour-based tests - microsatellite instability (MSI) and MMR immunohistochemistry (IHC) - are used in CRC patients to identify individuals at high risk of LS for genetic testing. MLH1 (MutL homologue 1) promoter methylation and BRAF V600E testing can be conducted on tumour material to rule out certain sporadic cancers. OBJECTIVES: To investigate whether testing for LS in CRC patients using MSI or IHC (with or without MLH1 promoter methylation testing and BRAF V600E testing) is clinically effective (in terms of identifying Lynch syndrome and improving outcomes for patients) and represents a cost-effective use of NHS resources. REVIEW METHODS: Systematic reviews were conducted of the published literature on diagnostic test accuracy studies of MSI and/or IHC testing for LS, end-to-end studies of screening for LS in CRC patients and economic evaluations of screening for LS in CRC patients. A model-based economic evaluation was conducted to extrapolate long-term outcomes from the results of the diagnostic test accuracy review. The model was extended from a model previously developed by the authors. RESULTS: Ten studies were identified that evaluated the diagnostic test accuracy of MSI and/or IHC testing for identifying LS in CRC patients. For MSI testing, sensitivity ranged from 66.7% to 100.0% and specificity ranged from 61.1% to 92.5%. For IHC, sensitivity ranged from 80.8% to 100.0% and specificity ranged from 80.5% to 91.9%. When tumours showing low levels of MSI were treated as a positive result, the sensitivity of MSI testing increased but specificity fell. No end-to-end studies of screening for LS in CRC patients were identified. Nine economic evaluations of screening for LS in CRC were identified. None of the included studies fully matched the decision problem and hence a new economic evaluation was required. The base-case results in the economic evaluation suggest that screening for LS in CRC patients using IHC, BRAF V600E and MLH1 promoter methylation testing would be cost-effective at a threshold of £20,000 per quality-adjusted life-year (QALY). The incremental cost-effectiveness ratio for this strategy was £11,008 per QALY compared with no screening. Screening without tumour tests is not predicted to be cost-effective. LIMITATIONS: Most of the diagnostic test accuracy studies identified were rated as having a risk of bias or were conducted in unrepresentative samples. There was no direct evidence that screening improves long-term outcomes. No probabilistic sensitivity analysis was conducted. CONCLUSIONS: Systematic review evidence suggests that MSI- and IHC-based testing can be used to identify LS in CRC patients, although there was heterogeneity in the methods used in the studies identified and the results of the studies. There was no high-quality empirical evidence that screening improves long-term outcomes and so an evidence linkage approach using modelling was necessary. Key determinants of whether or not screening is cost-effective are the accuracy of tumour-based tests, CRC risk without surveillance, the number of relatives identified for cascade testing, colonoscopic surveillance effectiveness and the acceptance of genetic testing. Future work should investigate screening for more causes of hereditary CRC and screening for LS in endometrial cancer patients. STUDY REGISTRATION: This study is registered as PROSPERO CRD42016033879. FUNDING: The National Institute for Health Research Health Technology Assessment programme.Funding for this study was provided by the Health Technology Assessment programme of the National Institute for Health Researc

Positron emission tomography/computerised tomography imaging in detecting and managing recurrent cervical cancer: systematic review of evidence, elicitation of subjective probabilities and economic modelling.

© Queen’s Printer and Controller of HMSO 2013. This work was produced by Meads et al. under the terms of a commissioning contract issued by the Secretary of State for Health. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising.Cancer of the uterine cervix is a common cause of mortality in women. After initial treatment women may be symptom free, but the cancer may recur within a few years. It is uncertain whether it is more clinically effective to survey asymptomatic women for signs of recurrence or to await symptoms or signs before using imaging.National Institute for Health Research Health Technology Assessment programm