A mobile and evolving tool to predict colorectal cancer survivability

In this work, a tool for the survivability prediction of patients with colon or rectal cancer, up to five years after diagnosis and treatment, is presented. Indeed, an accurate survivability prediction is a difficult task for health care professionals and of high concern to patients, so that they can make the most of the rest of their lives. The distinguishing features of the tool include a balance between the number of necessary inputs and prediction performance, being mobile-friendly, and featuring an online learning component that enables the automatic evolution of the prediction models upon the addition of new cases.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope UID/CEC/00319/2013. The work of Tiago Oliveira is supported by a FCT grant with the reference SFRH/BD/85291/2012.info:eu-repo/semantics/publishedVersio

A survival prediction model for colorectal cancer patients

Dissertação de mestrado integrado em Biomedical EngineeringThe importance of making predictions in health is mainly linked to the decision-making process. Make survival predictions accurately is a very difficult task for healthcare professionals and a major concern for patients. On the one hand, it can help physicians decide between palliative care or other medical practice for a patient. On the other hand, the notion of remaining lifetime could help patients in the realization of dreams. However, the prediction of survivability is directly related to the experience of health professionals and their ability to memorize. Most decisions are made based on probability and statistics, but these are based on large groups of people and may not be suitable to predict what will happen in particular cases. Consequently, the use of machine learning techniques have been explored in healthcare. Their ability to help solve diagnostic and prognosis problems has been increasingly exploited. The main contribution of this work is a prediction tool of survival of patients with cancer of the colon and/or rectum, after treatment and a few years after treatment. The characteristics that distinguishes it is the balance between the number of required inputs and their performance in terms of prediction. The tool is compatible with mobile devices, includes a online learning component that allows for automatic recalculation and flexibly of the prediction models, by adding new cases. The tool aims to facilitate the access of healthcare professionals for instruments that enrich their practice and improve their results. This increases the productivity of healthcare professionals, enabling them to make decisions faster and with a lower error rate.A importância de fazer previsões na área da saúde está sobretudo ligada ao processo de tomada de decisão. Fazer previsões de sobrevivência de forma precisa é uma tarefa muito difícil para os profissionais de saúde e uma grande preocupação para os pacientes. Por um lado, pode ajudar os médicos a decidir entre cuidados paliativos ou outra prática médica para um paciente. Por outro lado, a noção do tempo de vida remanescente poderia ajudar os pacientes na concretização de sonhos. No entanto, este tipo de previsão está diretamente relacionada com a experiência do profissional de saúde e da sua capacidade de memorizar. A maior parte das decisões são tomadas com base em probabilidades e estatística, mas estas têm como base grandes grupos de pessoas, podendo não ser adequadas para prever o que vai acontecer em casos particulares. Por conseguinte, a utilização de técnicas de machine learning têm sido exploradas na área da saúde. A sua capacidade para ajudar a resolver problemas de diagnóstico e prognóstico tem sido cada vez mais explorada. A principal contribuição deste trabalho é uma ferramenta de previsão da sobrevida de pacientes com cancro do cólon e/ou do reto, após o tratamento e alguns anos após o tratamento. As características que a distingue são o equilíbrio entre o número de entradas necessárias e o seu desempenho a nível da previsão. A ferramenta, compatível com dispositivos móveis, possui uma componente de aprendizagem em tempo real que permite recalcular de forma automática e evolutiva os modelos usados para fazer a previsão, através da adição de novos casos. A ferramenta tem como propósito facilitar o acesso dos profissionais de saúde a instrumentos capazes de enriquecer a sua prática e melhorar os seus resultados. Esta aumenta a produtividade dos profissionais de saúde, permitindo que estes tomem decisões mais rapidamente e com uma taxa de erro menor

Clinical decision support: Knowledge representation and uncertainty management

Programa Doutoral em Engenharia BiomédicaDecision-making in clinical practice is faced with many challenges due to the inherent risks of being a health care professional. From medical error to undesired variations in clinical practice, the mitigation of these issues seems to be tightly connected to the adherence to Clinical Practice Guidelines as evidence-based recommendations The deployment of Clinical Practice Guidelines in computational systems for clinical decision support has the potential to positively impact health care. However, current approaches to Computer-Interpretable Guidelines evidence a set of issues that leave them wanting. These issues are related with the lack of expressiveness of their underlying models, the complexity of knowledge acquisition with their tools, the absence of support to the clinical decision making process, and the style of communication of Clinical Decision Support Systems implementing Computer-Interpretable Guidelines. Such issues pose as obstacles that prevent these systems from showing properties like modularity, flexibility, adaptability, and interactivity. All these properties reflect the concept of living guidelines. The purpose of this doctoral thesis is, thus, to provide a framework that enables the expression of these properties. The modularity property is conferred by the ontological definition of Computer-Interpretable Guidelines and the assistance in guideline acquisition provided by an editing tool, allowing for the management of multiple knowledge patterns that can be reused. Flexibility is provided by the representation primitives defined in the ontology, meaning that the model is adjustable to guidelines from different categories and specialities. On to adaptability, this property is conferred by mechanisms of Speculative Computation, which allow the Decision Support System to not only reason with incomplete information but to adapt to changes of state, such as suddenly knowing the missing information. The solution proposed for interactivity consists in embedding Computer-Interpretable Guideline advice directly into the daily life of health care professionals and provide a set of reminders and notifications that help them to keep track of their tasks and responsibilities. All these solutions make the CompGuide framework for the expression of Clinical Decision Support Systems based on Computer-Interpretable Guidelines.A tomada de decisão na prática clínica enfrenta inúmeros desafios devido aos riscos inerentes a ser um profissional de saúde. Desde o erro medico até às variações indesejadas da prática clínica, a atenuação destes problemas parece estar intimamente ligada à adesão a Protocolos Clínicos, uma vez que estes são recomendações baseadas na evidencia. A operacionalização de Protocolos Clínicos em sistemas computacionais para apoio à decisão clínica apresenta o potencial de ter um impacto positivo nos cuidados de saúde. Contudo, as abordagens atuais a Protocolos Clínicos Interpretáveis por Maquinas evidenciam um conjunto de problemas que as deixa a desejar. Estes problemas estão relacionados com a falta de expressividade dos modelos que lhes estão subjacentes, a complexidade da aquisição de conhecimento utilizando as suas ferramentas, a ausência de suporte ao processo de decisão clínica e o estilo de comunicação dos Sistemas de Apoio à Decisão Clínica que implementam Protocolos Clínicos Interpretáveis por Maquinas. Tais problemas constituem obstáculos que impedem estes sistemas de apresentarem propriedades como modularidade, flexibilidade, adaptabilidade e interatividade. Todas estas propriedades refletem o conceito de living guidelines. O propósito desta tese de doutoramento é, portanto, o de fornecer uma estrutura que possibilite a expressão destas propriedades. A modularidade é conferida pela definição ontológica dos Protocolos Clínicos Interpretáveis por Maquinas e pela assistência na aquisição de protocolos fornecida por uma ferramenta de edição, permitindo assim a gestão de múltiplos padrões de conhecimento que podem ser reutilizados. A flexibilidade é atribuída pelas primitivas de representação definidas na ontologia, o que significa que o modelo é ajustável a protocolos de diferentes categorias e especialidades. Quanto à adaptabilidade, esta é conferida por mecanismos de Computação Especulativa que permitem ao Sistema de Apoio à Decisão não só raciocinar com informação incompleta, mas também adaptar-se a mudanças de estado, como subitamente tomar conhecimento da informação em falta. A solução proposta para a interatividade consiste em incorporar as recomendações dos Protocolos Clínicos Interpretáveis por Maquinas diretamente no dia a dia dos profissionais de saúde e fornecer um conjunto de lembretes e notificações que os auxiliam a rastrear as suas tarefas e responsabilidades. Todas estas soluções constituem a estrutura CompGuide para a expressão de Sistemas de Apoio à Decisão Clínica baseados em Protocolos Clínicos Interpretáveis por Máquinas.The work of the PhD candidate Tiago José Martins Oliveira is supported by a grant from FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) with the reference SFRH/BD/85291/ 2012

Stage-Specific Predictive Models for Cancer Survivability

Survivability of cancer strongly depends on the stage of cancer. In most previous works, machine learning survivability prediction models for a particular cancer, were trained and evaluated together on all stages of the cancer. In this work, we trained and evaluated survivability prediction models for five major cancers, together on all stages and separately for every stage. We named these models joint and stage-specific models respectively. The obtained results for the cancers which we investigated reveal that, the best model to predict the survivability of the cancer for one specific stage is the model which is specifically built for that stage. Additionally, we saw that for every stage of cancer, the most important features to predict survivability, differed from other stages. By evaluating the models separately on different stages we found that their performance differed on different stages. We also found that evaluating the models together on all stages, as was done in past, is misleading because it overestimates performance

Platform for AI-driven medical data analysis to support clinical decision

Cancer is one of the leading causes of death on the world and surviving its treatment does not mean that the process is over. Several patients that have undergone cancer treatment, feel insecure in relation to their health, due to the stress and anxiety of cancer reappearance and post-treatment symptoms such as: sleeping disorders, fatigue and memory problems, pain, anxiety, and stress. Patients that undergone cancer treatment are followed periodically by a clinician, that evaluates its clinical situation, but also, his Quality of Life. This information is vital to understand the patient well-being, since cancer as a huge impact on all aspects of the patient’s life. Nevertheless, clinicians lack on tools capable of measuring objectively the patient’s Quality of Life, nor tools that enable more data visualization that could improve the clinician’s decision-making. So, the purposed aim of this dissertation is to provide a Clinical Decision Support System Platform with visualization tools capable of giving information from patients, gathered from a wearable device and a smart scale, and using Fuzzy Logic, an Artificial Intelligence subset, to give new insights about patient well-being. The designed CDSS Platform was able to integrate commercially used smart device, with minimal human intervention required. Also, the data gathered from those devices was used to create a continuous monitoring system, associated with visualization tools that enhanced the clinician knowledge of the patient. Furthermore, an indicator denominated as Patient Progression Indicator was developed with the use of the Fuzzy Logic algorithm, that provides an indirect but objective measurement of the patient well-being. Although the results seem promising, more in-depth research is required such as a trial study capable of validating the results obtained.O cancro é umas das maiores causas de morte no mundo e sobreviver ao seu tratamento não significa que o processo tenha terminado. Vários pacientes que ultrapassaram o processo de tratamento permanecem inseguros em relação à sua saúde, devido ao stress e ansiedade causados pelo medo de reaparecimento do cancro e pelos efeitos do tratamento tais como: problemas de sono, cansaço e problemas de memória, dor, ansiedade e stress. Os pacientes que terminam o tratamento são seguidos periodicamente por clínicos, que avaliam a sua Qualidade de Vida. Esta informação é essencial para compreender o seu estado de saúde, dado que o cancro tem um impacto enorme em todos os aspetos da vida do paciente. No entanto, os clínicos têm à sua disposição poucas ferramentas capazes de mensurar objetivamente a Qualidade de Vida, ou de ferramentas que possibilitem uma maior visualização de dados que proporcione uma melhor tomada de decisão. Portanto, a solução proposta nesta dissertação é a de desenvolver um Sistema de Apoio à Decisão Clínica com ferramentas de visualização capazes de disponibilizar mais informação do paciente, obtidas com o uso de uma pulseira inteligente e uma balança inteligente. Também com o uso de Lógica Difusa, um subconjunto da Inteligência Artificial, proporcionar uma nova informação sobre o estado de saúde do paciente. A plataforma projetada foi capaz de integrar dispositivos inteligentes de uso comercial, de forma a necessitar o mínimo de interação humana. Além disso, os dados adquiridos pelos dispositivos foram usados para criar um sistema de monitorização contínuo, associado a ferramentas de visualização de dados que proporcionam mais informação em relação ao paciente. Mais ainda, foi desenvolvido um indicador designado por Indicador de Progresso do Paciente com a utilização do algoritmo de Lógica Difusa, que providência uma forma indireta, mas objetiva de mensurar o estado de saúde do paciente. Apesar dos resultados parecerem promissores, um estudo mais aprofundado é necessário, tal como um ensaio clínico capaz de validar os resultados obtidos

A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

The objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining, and machine learning to healthcare engineering systems. A systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest, and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors, and content. From the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field. The use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors’ previous knowledge and the nature of the publications were used to select different platforms. To the best of the authors’ knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining, and machine learning applied to healthcare engineering systems.N/

Seeing the Big Picture: System Architecture Trends in Endoscopy and LED-Based hyperspectral Subsystem Intergration

Early-stage colorectal lesions remain difficult to detect. Early development of neoplasia tends to be small (less than 10 mm) and flat and difficult to distinguish from surrounding mucosa. Additionally, optical diagnosis of neoplasia as benign or malignant is problematic. Low rates of detection of these lesions allow for continued growth in the colorectum and increased risk of cancer formation. Therefore, it is crucial to detect neoplasia and other non-neoplastic lesions to determine risk and guide future treatment. Technology for detection needs to enhance contrast of subtle tissue differences in the colorectum and track multiple biomarkers simultaneously. This work implements one such technology with the potential to achieve the desired multi-contrast outcome for endoscopic screenings: hyperspectral imaging. Traditional endoscopic imaging uses a white light source and a RGB detector to visualize the colorectum using reflected light. Hyperspectral imaging (HSI) acquires an image over a range of individual wavelength bands to create an image hypercube with a wavelength dimension much deeper and more sensitive than that of an RGB image. A hypercube can consist of reflectance or fluorescence (or both) spectra depending on the filtering optics involved. Prior studies using HSI in endoscopy have normally involved ex vivo tissues or xiv optics that created a trade-off between spatial resolution, spectral discrimination and temporal sampling. This dissertation describes the systems design of an alternative HSI endoscopic imaging technology that can provide high spatial resolution, high spectral distinction and video-rate acquisition in vivo. The hyperspectral endoscopic system consists of a novel spectral illumination source for image acquisition dependent on the fluorescence excitation (instead of emission). Therefore, this work represents a novel contribution to the field of endoscopy in combining excitation-scanning hyperspectral imaging and endoscopy. This dissertation describes: 1) systems architecture of the endoscopic system in review of previous iterations and theoretical next-generation options, 2) feasibility testing of a LED-based hyperspectral endoscope system and 3) another LED-based spectral illuminator on a microscope platform to test multi-spectral contrast imaging. The results of the architecture point towards an endoscopic system with more complex imaging and increased computational capabilities. The hyperspectral endoscope platform proved feasibility of a LED-based spectral light source with a multi-furcated solid light guide. Another LED-based design was tested successfully on a microscope platform with a dual mirror array similar to telescope designs. Both feasibility tests emphasized optimization of coupling optics and combining multiple diffuse light sources to a common output. These results should lead to enhanced imagery for endoscopic tissue discrimination and future optical diagnosis for routine colonoscopy

Awakenings: An Equine Assisted Learning Research Project

Objective. The purpose of this pilot study was to determine if and how the Awakenings Equine Assisted Learning program was effective at improving the professionalism, confidence, communication skills and adaptability of students preparing for careers as Anesthesiologist Assistants. Background. Equine Assisted Learning (EAL) is a rapidly growing experiential model that utilizes horses to enhance participants\u27 awareness of their own non-verbal language, communication styles, projection of self-confidence and competence, and problem-solving abilities (Chandler, 2012; Green, 2012, 2013; Kane, 2012; Trotter, 2012). Methods. As a part of their regular educational and clinical rotations, first year students in the Anesthesiologist Assistant (AA) program participated in a 6- week training that included weekly, 2 -hour Equine Assisted Learning (EAL) sessions. Each session, the AA students participated in a 2- hour experiential equine assisted activity, specifically designed to address a certain target area necessary for their development as professionals in this field. The participants completed a pre and post assessment with 93 items that measured development as they relate to the EAL sessions. Results. The data was analyzed using t-tests, exploratory factor analysis, and qualitative self-reports. Confidence, empathy, awareness, and communication were the most significant factors. Conclusion. The qualitative data reinforced the quantitative findings that showed significant improvement in the objective factors as a result of the EAL sessions

2013 IMSAloquium, Student Investigation Showcase

This year, we are proudly celebrating the twenty-fifth anniversary of IMSA’s Student Inquiry and Research (SIR) Program. Our first IMSAloquium, then called Presentation Day, was held in 1989 with only ten presentations; this year we are nearing two hundred.https://digitalcommons.imsa.edu/archives_sir/1005/thumbnail.jp

Artificial Intelligence in Surgery, Surgical Subspecialties, and Related Disciplines

Artificial intelligence (AI) and machine learning (ML) algorithms show promise in revolutionizing many aspects of surgical care. ML algorithms may be used to improve radiologic diagnosis of disease and predict peri-, intra-, and postoperative complications in patients based on their vital signs and other clinical characteristics. Computer vision may improve laparoscopic and minimally invasive surgical education by identifying and tracking the surgeon’s movements and providing real-time performance feedback. Eventually, AI and ML may be used to perform operative interventions that were not previously possible (nanosurgery or endoluminal surgery) with the utilization of fully autonomous surgical robots. Overall, AI will impact every surgical subspecialty, and surgeons must be prepared to facilitate the use of this technology to optimize patient care. This chapter will review the applications of AI across different surgical disciplines, the risks and limitations associated with AI and ML, and the role surgeons will play in implementing this technology into their practice