Development of a Web Platform for Surgical Oncologists in Portugal

In an age of enormous access to clinical data and rapid technological development, ensuring that physicians have computational tools to navigate a sea of information and improve health outcomes is vital. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support the healthcare team in clinical decision making, thus improving the quality of decisions and overall patient care, while minimizing costs. Postsurgical complications of cancer surgery are hard to predict, although there are several traditional risk scores available. However, there is an urgent need to improve perioperative risk assessment to reduce the growing postoperative burden in the Portuguese population. Understanding the individual risks of performing surgical procedures is essential to customizing preparatory, intervention, and aftercare protocols to minimize post-surgical complications. This knowledge is essential in oncology, given the nature of the interventions, the fragile profile of patients with comorbidities and drug exposure, and the possible recurrence of cancer. This thesis aims to develop an user-friendly web platform to support the collaboration and manage clinical data among oncologists at the Portuguese Institute of Oncology, Porto. The work integrates both a database to register/store the clinical data of cancer patients in a structured format, visualization tools and computational methods to calculate a specific risk score of postoperative outcomes for the Portuguese population. The platform named IPOscore will not only to manage the clinic data but also offer a predictive healthcare system, as an valuable instrument for the oncologists.Numa época de grande acesso a dados e rápido desenvolvimento tecnológico, garantir que os médicos tenham as ferramentas de apoio à decisão clínica para se deslocar em um mar de informação para encontrar o que é mais relevante para as necessidades dos pacientes é vital para otimizar os resultados de saúde. Um grande avanço na prática médica é a incorporação de Sistemas de Apoio à Decisão Clínica (CDSSs) para auxiliar e apoiar a equipe de saúde na tomada de decisão clínica, melhorando assim a qualidade das decisões e o atendimento geral ao paciente, minimizando custos. As complicações pós-operatórias da cirurgia do cancro ainda são difíceis de prever, embora existam muitos scores de risco destinados a fazer tais previsões. Compreender os riscos individuais de realizar procedimentos cirúrgicos é essencial para personalizar os protocolos preparatórios, de intervenção e pós-atendimento para minimizar as complicações pós-cirúrgicas. Esse conhecimento é fundamental em oncologia, dada a natureza das intervenções, o perfil frágil dos pacientes com comorbidades e exposição a drogas e a possível recorrência do cancro. Este trabalho propõe a construção duma plataformaweb de fácil utilização para apoiar a colaboração e dispor uma gestão de dados clínicos entre oncologistas. O trabalho integra uma base de dados para registrar / armazenar os dados clínicos, fisiológicos e biopatológicos de pacientes com cancro num formato estruturado e métodos computacionais para calcular um grau de risco específico de complicações pós-operatórias para a população portuguesa. A plataforma denominada IPOscore servirá para a gestão de dados clinicos, mas também oferecerá um sistema preditivo e preventivo, como uma ferramenta de apoio à decisão médica no contexto clínico diário

BlogForever: D2.5 Weblog Spam Filtering Report and Associated Methodology

This report is written as a first attempt to define the BlogForever spam detection strategy. It comprises a survey of weblog spam technology and approaches to their detection. While the report was written to help identify possible approaches to spam detection as a component within the BlogForver software, the discussion has been extended to include observations related to the historical, social and practical value of spam, and proposals of other ways of dealing with spam within the repository without necessarily removing them. It contains a general overview of spam types, ready-made anti-spam APIs available for weblogs, possible methods that have been suggested for preventing the introduction of spam into a blog, and research related to spam focusing on those that appear in the weblog context, concluding in a proposal for a spam detection workflow that might form the basis for the spam detection component of the BlogForever software

Conversational AI for Serving Fact-Checks

The purpose of this thesis was to create a conversational AI for serving fact-checks, using a collection of already existing fact-checking articles. The conversational AI uses a hybrid system, combining both a question answering agent, chitchat agent, and multiple non-AI based skills to perform the task. The program created consists of a user interface, broker, and seven different skills. For the implementation multiple existing pre-trained deep learning models were used, where many are based on the Transformer architecture. Already fine-tuned versions of these models were used. The conversational AI can present fact-checking articles in multiple ways, fact-check a claim presented, and has some multi-turn capabilities. The result is a functional conversational AI which is capable of serving fact-checks from a collection of fact-checking articles. Although the conversational AI is functional, there are several issues that should be addressed, and further work to be done

Classical and Probabilistic Information Retrieval Techniques: An Audit

Information retrieval is acquiring particular information from large resources and presenting it according to the user’s need. The incredible increase in information resources on the Internet formulates the information retrieval procedure, a monotonous and complicated task for users. Due to over access of information, better methodology is required to retrieve the most appropriate information from different sources. The most important information retrieval methods include the probabilistic, fuzzy set, vector space, and boolean models. Each of these models usually are used for evaluating the connection between the question and the retrievable documents. These methods are based on the keyword and use lists of keywords to evaluate the information material. In this paper, we present a survey of these models so that their working methodology and limitations are discussed. This is an important understanding because it makes possible to select an information retrieval technique based on the basic requirements. The survey results showed that the existing model for knowledge recovery is somewhere short of what was planned. We have also discussed different areas of IR application where these models could be used

Privacy-preserving efficient searchable encryption

Data storage and computation outsourcing to third-party managed data centers, in environments such as Cloud Computing, is increasingly being adopted by individuals, organizations, and governments. However, as cloud-based outsourcing models expand to society-critical data and services, the lack of effective and independent control over security and privacy conditions in such settings presents significant challenges. An interesting solution to these issues is to perform computations on encrypted data, directly in the outsourcing servers. Such an approach benefits from not requiring major data transfers and decryptions, increasing performance and scalability of operations. Searching operations, an important application case when cloud-backed repositories increase in number and size, are good examples where security, efficiency, and precision are relevant requisites. Yet existing proposals for searching encrypted data are still limited from multiple perspectives, including usability, query expressiveness, and client-side performance and scalability. This thesis focuses on the design and evaluation of mechanisms for searching encrypted data with improved efficiency, scalability, and usability. There are two particular concerns addressed in the thesis: on one hand, the thesis aims at supporting multiple media formats, especially text, images, and multimodal data (i.e. data with multiple media formats simultaneously); on the other hand the thesis addresses client-side overhead, and how it can be minimized in order to support client applications executing in both high-performance desktop devices and resource-constrained mobile devices. From the research performed to address these issues, three core contributions were developed and are presented in the thesis: (i) CloudCryptoSearch, a middleware system for storing and searching text documents with privacy guarantees, while supporting multiple modes of deployment (user device, local proxy, or computational cloud) and exploring different tradeoffs between security, usability, and performance; (ii) a novel framework for efficiently searching encrypted images based on IES-CBIR, an Image Encryption Scheme with Content-Based Image Retrieval properties that we also propose and evaluate; (iii) MIE, a Multimodal Indexable Encryption distributed middleware that allows storing, sharing, and searching encrypted multimodal data while minimizing client-side overhead and supporting both desktop and mobile devices

Efficient On-the-fly Category Retrieval using ConvNets and GPUs

We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval - where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. We make three contributions: (i) we present an evaluation of state-of-the-art image representations for object category retrieval over standard benchmark datasets containing 1M+ images; (ii) we show that ConvNets can be used to obtain features which are incredibly performant, and yet much lower dimensional than previous state-of-the-art image representations, and that their dimensionality can be reduced further without loss in performance by compression using product quantization or binarization. Consequently, features with the state-of-the-art performance on large-scale datasets of millions of images can fit in the memory of even a commodity GPU card; (iii) we show that an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel with downloading the new training images, allowing for a continuous refinement of the model as more images become available, and simultaneous training and ranking. The outcome is an on-the-fly system that significantly outperforms its predecessors in terms of: precision of retrieval, memory requirements, and speed, facilitating accurate on-the-fly learning and ranking in under a second on a single GPU.Comment: Published in proceedings of ACCV 201

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Measuring Short Text Semantic Similarity with Deep Learning Models

Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken, which is a subfield of artificial intelligence (AI). The development of NLP applications is challenging because computers traditionally require humans to speak" to them in a programming language that is precise, unambiguous and highly structured, or through a limited number of clearly enunciated voice commands. We study the use of deep learning models, the state-of-the-art artificial intelligence (AI) method, for the problem of measuring short text semantic similarity in NLP area. In particular, we propose a novel deep neural network architecture to identify semantic similarity for pairs of question sentence. In the proposed network, multiple channels of knowledge for pairs of question text can be utilized to improve the representation of text. Then a dense layer is used to learn a classifier for classifying duplicated question pairs. Through extensive experiments on the Quora test collection, our proposed approach has shown remarkable and significant improvement over strong baselines, which verifies the effectiveness of the deep models as well as the proposed deep multi-channel framework