06. 2010 IMSAloquium Student Investigation Showcase

https://digitalcommons.imsa.edu/class_of_2010/1004/thumbnail.jp

2010 IMSAloquium, Student Investigation Showcase

IMSA students engage in investigations in nanotechnology, particle physics, law, neonatal medicine, literature, transplantation biology, water purity, the educational achievement gap, neurobiology and memory, ethics, theatre, discrete mathematics, economics, and more.https://digitalcommons.imsa.edu/archives_sir/1002/thumbnail.jp

2023 IMSAloquium

Welcome to IMSAloquium 2023. This is IMSA’s 36 th year of leading in educationalinnovation, and the 35th year of the IMSA Student Inquiry and Research (SIR) Program.https://digitalcommons.imsa.edu/archives_sir/1033/thumbnail.jp

Contemporary Topics in Patient Safety

As healthcare systems continue to evolve, it is clear that providing safe, high-quality care to patients is an extremely complex process. Ranging from multi-disciplinary teams to bedside care, virtually every aspect of the patient-care experience provides us with an opportunity for doing things better, from improving efficiency, safety, and overall outcomes to reducing costs and promoting team synergy. This book, the fifth in our patient safety series collection, consists of chapters that help explore key concepts related to both the safety and quality of care. In a departure from the vignette-driven format of our earlier books, this installment gravitates toward discussing frameworks, theoretical considerations, team-centric approaches, and a variety of other concepts that are critical to both our understanding and the implementation of safer and better-performing health systems. We also feel that the knowledge presented herein increasingly applies across the world, especially as global health systems evolve and mature over time. It is our goal to improve the recognition of potential opportunities that will highlight various aspects of the delivery of healthcare and thus contribute to better patient experiences, with safety at the forefront. Topics covered in this volume, as well as the previous volumes, highlight the critical importance of identifying and addressing opportunities for improvement, not as one-time events, but rather as continuous, hardwired institutional processes

Evolution and challenges in the design of computational systems for triage assistance

AbstractCompared with expert systems for specific disease diagnosis, knowledge-based systems to assist decision making in triage usually try to cover a much wider domain but can use a smaller set of variables due to time restrictions, many of them subjective so that accurate models are difficult to build. In this paper, we first study criteria that most affect the performance of systems for triage assistance. Such criteria include whether principled approaches from machine learning can be used to increase accuracy and robustness and to represent uncertainty, whether data and model integration can be performed or whether temporal evolution can be modeled to implement retriage or represent medication responses. Following the most important criteria, we explore current systems and identify some missing features that, if added, may yield to more accurate triage systems

EUDONORGAN a blended-learning programme to improve organ donation knowledge in the European Union and Neighbouring countries: Prospective study

[eng] Developing training actions in organ donation and transplantation is key to improving viability, efficiency and donation rates. The European Union and neighbouring countries have promoted educational initiatives, such as EUDONORGAN, a 36-month project promoted by the European Commission and as an initiative of the European Parliament. The project developed by an international consortium involved four countries in central and southern Europe: Croatia, Italy, Slovenia and Spain, with similar models of organ donation and successful transplant rates, and pioneers in the development of educational training programs in organ and tissue donation with outstanding results. The project was carried out in two consecutive phases with the aim of providing, on the one hand, training for health professionals (HCPs) and other key actors (OKPs) such as patients and patient support groups, representatives of public and government bodies, representatives of health institutions, opinion leaders and the media in the field of organ and tissue donation. On the other hand, to organize, with the support of trained professionals, awareness-raising and dissemination events as well as monitoring and evaluation strategies to guarantee the maximum possible impact. The proposed prospective study focuses on the study of the first part of the project according to the hypothesis: The training actions improve knowledge and change the attitude and perceptions of HCPs and OKPs towards a positive perspective, helping to organize donation activities and promoting awareness in hospitals and the rest of society. The specific objectives of the study consisted, on the one hand, in assessing the knowledge, skills and attitudes of HCPs and OKPs, providing a tailor-made training programme based on a blended training methodology. On the other hand, to assess whether the development of a training programme promotes changes in the knowledge, attitude and perception of health professionals and other key actors towards a positive outlook on organ and tissue donation. The methodology used was based on the analysis of trends in education and literature research to ensure effective educational strategies, using blended learning methods. The training was provided through a WebApp created specifically for the project, which followed storytelling and microlearning methodologies. In addition, the face-to-face training sessions incorporated learning strategies for adults based on the principles of andragogy, transformative learning, experiential learning and situated cognition. These strategies encouraged hands-on learning, networking, and interactivity. To evaluate the effectiveness of the training, the evaluation model proposed by Kirkpatrick was used. In this case, the model was partially adapted to fit the design of the customized program "Train the trainers". Satisfaction and learning levels were taken into account , including the knowledge, attitudes and perceptions of the participants. The training was divided into seven modules and a 5-point Likert scale was used to assess medical aspects, educational advice and practical activities. The results of the evaluation showed that the overall average satisfaction scores were higher than 4 in each module, with no significant differences between the HCPs and the OKPs. In addition, in the survey carried out during the face-to-face training sessions, similar scores above 4 were obtained for most of the items. In terms of learning, a significant improvement was observed in both HCPs and OKPs, as well as transplant/donor coordinators, doctors, registered nurses, anaesthesiologists/intensivists and intensive care nurses. In addition, improvements in attitudes and perceptions regarding organ donation were observed, especially among HCPs. Organ donation continues to be a complex process that affects both health professionals and society as a whole. EU-funded projects and other educational initiatives play a key role in providing lifelong learning to increase knowledge and promote positive attitudes and perceptions towards organ donation and transplantation. EUDONORGAN was one of the innovative and pioneering initiatives that offered successful training at the European level with the main objective of emphasizing the positive aspects of organ donation and promoting public awareness on this issue. This study shows that the educational methodology used in healthcare professionals is also applied to other relevant key actors, and highlights the need for ongoing education of experts involved in organ donation and transplantation.[cat] Desenvolupar accions formatives en donació i trasplantament d’òrgans és clau per millorar viabilitat, eficiència i taxes de donació. La Unió Europea i països veïns han impulsat iniciatives educatives, com EUDONORGAN, un projecte de 36 mesos de durada promogut per la Comissió Europea i com a iniciativa del Parlament Europeu. El projecte desenvolupat per un consorci internacional va implicar quatre països del centre i el sud d’Europa: Croàcia, Itàlia, Eslovènia i Espanya, amb models similars de donació d’òrgans i taxes de trasplantaments d’èxit, i pioners en el desenvolupament de programes de formació educativa en donació d’òrgans i teixits amb resultats destacats. El projecte es va dur a terme en dues fases consecutives amb l’objectiu d’oferir, per una banda, una formació pels professionals de la salut (HCPs) i altres actors clau (OKPs) com pacients i grups de suport al pacient, representants d’organismes públics i governamentals, representants d’institucions de salut, líders d’opinió i mitjans de comunicació en l’àmbit de la donació d’òrgans i teixits. Per una altra, organitzar, amb el suport dels professionals formats, actes de sensibilització i de difusió així com estratègies de seguiment i avaluació per garantir el màxim impacte possible. L’estudi prospectiu proposat s’enfoca en l’estudi de la primera part del projecte segons la hipòtesis: Les accions formatives milloren el coneixement i canvien l’actitud i de les percepcions dels HCPs i OKPs cap a una perspectiva positiva, ajudant a organitzar activitats de donació i promovent la conscienciació als hospitals i a la resta de la societat. Els objectius específics de l’estudi van consistir, per una banda, en avaluar els coneixements, les habilitats i les actituds dels HCP i OKP, proporcionant un programa de formació a mida basat en una metodologia de formació mixta. Per una altra, avaluar si el desenvolupament d’un programa de formació promou canvis en el coneixement, l’actitud i la percepció dels professionals de la salut i altres actors claus cap a una perspectiva positiva de la donació d’òrgans i teixits. La metodologia utilitzada es va basar en l’anàlisi de les tendències en educació i la investigació de la literatura per garantir estratègies educatives efectives, utilitzant mètodes d’aprenentatge semipresencials. La formació es va proporcionar a través d’una WebApp creada específicament per al projecte, que va seguir metodologies de storytelling i microaprenentatge. A més, les sessions formatives presencials van incorporar estratègies d’aprenentatge per a adults basades en els principis d’andragogia, aprenentatge transformador, aprenentatge vivencial i cognició situada. Aquestes estratègies van fomentar l’aprenentatge pràctic, la col·laboració en xarxa i la interactivitat. Per avaluar l’eficàcia de la formació, es va utilitzar el model d’avaluació proposat per Kirkpatrick. En aquest cas, el model es va adaptar parcialment per ajustar-se al disseny del programa personalitzat “Train the trainers”. Es van tenir en compte els nivells de satisfacció i aprenentatge, incloent coneixements, actituds i percepcions dels participants. La formació es va dividir en set mòduls i es va utilitzar una escala Likert de 5 punts per avaluar els aspectes mèdics, els consells educatius i les activitats pràctiques. Els resultats de l’avaluació van mostrar que les puntuacions mitjanes globals de satisfacció van ser superiors a 4 en cada mòdul, sense diferències significatives entre els HCPs i els OKPs. A més, en l’enquesta realitzada durant les sessions formatives presencials, es van obtenir puntuacions similars per sobre de 4 per a la majoria dels ítems. Pel que fa a l’aprenentatge, es va observar una millora significativa tant en els HCPs com en els OKPs, així com en els coordinadors de trasplantaments/donants, metges, infermeres col·legiats, anestesiòlegs/intensivistes i infermeres de cures intensives. A més, es van observar millores en les actituds i percepcions respecte a la donació d’òrgans, especialment entre els HCPs. La donació d’òrgans continua sent un procés complex que afecta tant als professionals de la salut com a tota la societat. Els projectes finançats per la UE i altres iniciatives educatives representen un paper clau a l’hora d’oferir formació contínua per augmentar el coneixement i promoure actituds i percepcions positives cap a la donació i el trasplantament d’òrgans. EUDONORGAN va ser una de les iniciatives, innovadora i pionera, que a nivell europeu va oferir una formació d’èxit amb l’objectiu principal d’emfatitzar els aspectes positius de la donació d’òrgans i fomentar la conscienciació pública sobre aquest tema. Aquest estudi mostra que la metodologia educativa utilitzada en els professionals sanitaris també s’aplica a altres actors clau rellevants, i destaca la necessitat d’una educació permanent dels experts implicats en la donació i el trasplantament d’òrgans

Agrupamiento, predicción y clasificación ordinal para series temporales utilizando técnicas de machine learning: aplicaciones

In the last years, there has been an increase in the number of fields improving their standard processes by using machine learning (ML) techniques. The main reason for this is that the vast amount of data generated by these processes is difficult to be processed by humans. Therefore, the development of automatic methods to process and extract relevant information from these data processes is of great necessity, giving that these approaches could lead to an increase in the economic benefit of enterprises or to a reduction in the workload of some current employments. Concretely, in this Thesis, ML approaches are applied to problems concerning time series data. Time series is a special kind of data in which data points are collected chronologically. Time series are present in a wide variety of fields, such as atmospheric events or engineering applications. Besides, according to the main objective to be satisfied, there are different tasks in the literature applied to time series. Some of them are those on which this Thesis is mainly focused: clustering, classification, prediction and, in general, analysis. Generally, the amount of data to be processed is huge, arising the need of methods able to reduce the dimensionality of time series without decreasing the amount of information. In this sense, the application of time series segmentation procedures dividing the time series into different subsequences is a good option, given that each segment defines a specific behaviour. Once the different segments are obtained, the use of statistical features to characterise them is an excellent way to maximise the information of the time series and simultaneously reducing considerably their dimensionality. In the case of time series clustering, the objective is to find groups of similar time series with the idea of discovering interesting patterns in time series datasets. In this Thesis, we have developed a novel time series clustering technique. The aim of this proposal is twofold: to reduce as much as possible the dimensionality and to develop a time series clustering approach able to outperform current state-of-the-art techniques. In this sense, for the first objective, the time series are segmented in order to divide the them identifying different behaviours. Then, these segments are projected into a vector of statistical features aiming to reduce the dimensionality of the time series. Once this preprocessing step is done, the clustering of the time series is carried out, with a significantly lower computational load. This novel approach has been tested on all the time series datasets available in the University of East Anglia and University of California Riverside (UEA/UCR) time series classification (TSC) repository. Regarding time series classification, two main paths could be differentiated: firstly, nominal TSC, which is a well-known field involving a wide variety of proposals and transformations applied to time series. Concretely, one of the most popular transformation is the shapelet transform (ST), which has been widely used in this field. The original method extracts shapelets from the original time series and uses them for classification purposes. Nevertheless, the full enumeration of all possible shapelets is very time consuming. Therefore, in this Thesis, we have developed a hybrid method that starts with the best shapelets extracted by using the original approach with a time constraint and then tunes these shapelets by using a convolutional neural network (CNN) model. Secondly, time series ordinal classification (TSOC) is an unexplored field beginning with this Thesis. In this way, we have adapted the original ST to the ordinal classification (OC) paradigm by proposing several shapelet quality measures taking advantage of the ordinal information of the time series. This methodology leads to better results than the state-of-the-art TSC techniques for those ordinal time series datasets. All these proposals have been tested on all the time series datasets available in the UEA/UCR TSC repository. With respect to time series prediction, it is based on estimating the next value or values of the time series by considering the previous ones. In this Thesis, several different approaches have been considered depending on the problem to be solved. Firstly, the prediction of low-visibility events produced by fog conditions is carried out by means of hybrid autoregressive models (ARs) combining fixed-size and dynamic windows, adapting itself to the dynamics of the time series. Secondly, the prediction of convective cloud formation (which is a highly imbalance problem given that the number of convective cloud events is much lower than that of non-convective situations) is performed in two completely different ways: 1) tackling the problem as a multi-objective classification task by the use of multi-objective evolutionary artificial neural networks (MOEANNs), in which the two conflictive objectives are accuracy of the minority class and the global accuracy, and 2) tackling the problem from the OC point of view, in which, in order to reduce the imbalance degree, an oversampling approach is proposed along with the use of OC techniques. Thirdly, the prediction of solar radiation is carried out by means of evolutionary artificial neural networks (EANNs) with different combinations of basis functions in the hidden and output layers. Finally, the last challenging problem is the prediction of energy flux from waves and tides. For this, a multitask EANN has been proposed aiming to predict the energy flux at several prediction time horizons (from 6h to 48h). All these proposals and techniques have been corroborated and discussed according to physical and atmospheric models. The work developed in this Thesis is supported by 11 JCR-indexed papers in international journals (7 Q1, 3 Q2, 1 Q3), 11 papers in international conferences, and 4 papers in national conferences

Organ Transplantation Management

Organ transplantation is a widespread and effective technique to treat important diseases and can often make the difference between life and death of some patients. Given a donor, finding the best recipient for one of his organs means finding, in the shortest time possible, the patient in the waiting list that best represents the compromise between numerous constraints related to donor-recipient physical compatibility and logistical arrangements. Currently, this process is still done in a non-automated, non-coordinated way, often leading to a non-optimal choice of the eventual recipient. This project aims at easing the organ transplantation management by representing it into a multi-agent system and by completely delegating the operation of matchmaking to the agents in the system