Transatlantic cruises in the northern Atlantic often face medical situations requiring specialized care unavailable onboard, necessitating patient transfers to onshore facilities. The Portuguese archipelago of the Azores, strategically located along their routes, frequently receives such vessels. Currently, ship-to-shore communication relies on email, which is prone to errors and raises data security and privacy concerns. To address these challenges, this article describes the development of a 3-tier web application prototype with a React frontend, Express backend, and PostgreSQL database. This application adheres to the FHIR and openEHR healthcare standards, improving data modeling and creating comprehensive electronic health records. It transcends language barriers and streamlines clinical workflows, promoting seamless data exchange and greater efficiency in patient care. The benefits are substantial: error reduction through comprehensive patient overviews, mitigation of language barrier risks, optimized resource coordination, lowered costs, improved response times, and enhanced experiences for professionals and patients through more accurate, efficient, and secure medical care.

Abstract Poly‐medicated patients, especially those over 65, have increased. Multiple drug use and inappropriate prescribing increase drug–drug interactions, adverse drug reactions, morbidity, and mortality. This issue was addressed with recommendation systems. Health professionals have not followed these systems due to their poor alert quality and incomplete databases. Recent research shows a growing interest in using Text Mining via NLP to extract drug–drug interactions from unstructured data sources to support clinical prescribing decisions. NLP text mining and machine learning classifier training for drug relation extraction were used in this process. In this context, the proposed solution allows to develop an extraction system for drug–drug interactions from unstructured data sources. The system produces structured information, which can be inserted into a database that contains information acquired from three different data sources. The architecture outlined for the drug–drug interaction extraction system is capable of receiving unstructured text, identifying drug entities sentence by sentence, and determining whether or not there are interactions between them.

Abstract Object detection is one of the most fundamental problems to tackle in the computer vision research area. Recent advances in multimodal data streams and deep learning architectures have prompted a fast growth in the field of multimodal learning, which brings several advantages over single-modality approaches for object detection, such as improved accuracy, robustness to noise and ambiguity, handling of complex scenarios and adaptability to diverse data. Some of the biggest challenges when implementing a multimodal learning approach are the selection of the fusion strategy, design of processing architecture, modality alignment/synchronization and interpretability of such high-dimensional representations. To address this challenge, we propose a feature-level fusion architecture for object detection based on extracting YOLO features from images, spectral and rhythm features from sound using Mel-frequency cepstral coefficients, and general descriptors from radar modalities that, after timestamp and homography transformation matrix alignment, are combined with an attention mechanism into a single classification network. Preliminary experiments indicate that the proposed architecture can constitute itself as a base pipeline for several different multimodal object detection tasks in real-world applications.

Microsatellite Instability (MSI) is an important biomarker in cancer patients, showing a defective DNA mismatch repair system. Its detection allows the use of immunotherapy to treat cancer, an approach that is revolutionizing cancer treatment. MSI is especially relevant for three types of cancer: Colon Adenocarcinoma (COAD), Stomach Adenocarcinoma (STAD), and Uterus corpus endometrial cancer (UCEC). In this work, learning algorithms were employed to predict MSI using RNA-seq data from The Cancer Genome Atlas (TCGA) database, with a focus on the selection of the most informative genomic features. The Multi-Layer Perceptron (MLP) obtained the best score (AUC = 98.44%), showing that it is possible to exploit information from RNA-seq data to find relevant relationships with the instability levels of microsatellites (MS). The accurate prediction of MSI with transcription data from cancer patients will help with the correct determination of MSI status and adequate prescription of immunotherapy, creating more precise and personalized patient care. At the genetic level, the study revealed a high expression of genes related to cell regulation functions, and a low expression of genes responsible for Mismatch Repair functions, in patients with high instability.

The rapidly aging population has been a matter of concern over years since this problematic has been posing several challenges to healthcare systems worldwide. In Portugal, which is one of the countries with the largest aging population, nursing homes have been getting a higher demand, and health professionals are overloaded with work. Furthermore, the fact that few nursing homes use health information and communication technology (ICT) resorting to paper to record information and clinically manage their residents is a tremendous problem, since this method is more prone to errors and time-consuming. Thus, this paper proposes the design and development of a mobile application for health professionals working in a Portuguese nursing home with the intention of assisting them at the point-of-care, by recording and providing all the necessary information, and helping them to schedule, perform, and digitally record their tasks. This solution will help health professionals to provide better care, by reducing time-waste and errors, and, consequently, to improve elders’ quality of life. A mobile solution was chosen since a hand-held device, which can be used anywhere and anytime, is able to give access and store all the needed information at the point-of-care.

In this article, Data Mining classification techniques are employed, in order to classify as normal or not-normal the iron values from a patients’ blood analysis. The dataset used is relative to patients that were subjected to Continuous Ambulatory Peritoneal Dialysis (CAPD) treatment. Weka software was used for testing several classification algorithms into such data set. The main purpose is finding the best suitable classification algorithm, with a pleasing performance in classifying the instances of the data, whereas preserving low rate of false positives. The IBk algorithm achieved the best performance, being able to correctly classify 97.39% of the instances.

As a matter of fact, an Intensive Care Unit (ICU) stands for a hospital facility where patients require close observation and monitoring. Indeed, predicting Length-of-Stay (LoS) at ICUs is essential not only to provide them with improved Quality-of-Care, but also to help the hospital management to cope with hospital resources. Therefore, in this work one‘s aim is to present an Artificial Intelligence based Decision Support System to assist on the prediction of LoS at ICUs, which will be centered on a formal framework based on a Logic Programming acquaintance for knowledge representation and reasoning, complemented with a Case Based approach to computing, and able to handle unknown, incomplete, or even contradictory data, information or knowledge.

Parchment stands for a multifaceted material made from animal skin, which has been used for centuries as a writing support or as bookbinding. Due to the historic value of objects made of parchment, understanding their degradation and their condition is of utmost importance to archives, libraries and museums, i.e., the assessment of parchment degradation is mandatory, although it is hard to do with traditional methodologies and tools for problem solving. Hence, in this work we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate Parchment Degradation and the respective Degree-of-Confidence that one has on such a happening.

The emergency services are usually pressured to make quick decisions with incomplete information on most cases, and this situation has a significant impact on healthcare as well on increasing medical errors. On the other hand, there has been an increase of the Electronic Health at Maternity Care. The combination of these two factors allows the construction of a Decision Support System specific for Maternity Care Unit using Business Intelligence technology. This solution is supported by a Data Warehouse, that uses the dimensional structure snowflake and makes the modeling of the maternity care database. With this solution it is intended to turn possible a clinical evidence-based practice, allowing for real time medical decision making with pervasive and interoperable characteristics. This paper presents the architecture, KPIs and benefits of Business Intelligence solution for the real context. This platform has several modules of clinical importance. The Obstetric Gynecological Emergency and the Voluntary Interruption of Pregnancy modules are object of study. This solution has an innovative contribution to the medical and scientific community studying the problem in Maternity area.

In this paper we address the problem of modeling creativity in Artificial Intelligence using a Genetic or Evolutionary based approach to computing, where the universe of discourse is represented as theories or programs in an extension to the Logic Programming language, which makes possible to handle incomplete or even contradictory information in an evolutionary environment. Indeed, we present a new insight for the construction of evolutive systems that combines the potential of the knowledge representation and reasoning mechanisms, present in the logic programming languages. Here, in an evolutionary setting, the candidate solutions to model the universe of discourse are seen as evolutionary logic programs or theories, being the test whether a solution is optimal based on a measure of the quality-of-information carried by those logical theories or programs. From a point of view of the process, the quality-of-information of the universe of discourse is assessed on the fly, being therefore possible to select the best logical theory or program that models it, in terms of the same time line.

Recent trends in the field of Artificial Intelligence, brought along new ways of formalizing and expressing wills and declarations. Its application to Virtual Enterprises requires an analysis of the interactions among agents, frameworks and users, as well as technical and legal analysis, in order to discover the rules to be applied, to solve a particular problem under a prospective scenario. Credibility, trust and security issues must be taken under consideration, especially concerning authenticity, confidentiality, integrity and non-repudiation. In order to increase the use of agents in Virtual Enterprises, besides the analysis and research of legal solutions in the commercial arena, it is essential to assure that agents will meet requirements of credibility and trust, insuring a transparent and secure way for their commercial acting, now capable of generating legal relations. This paper shows how to construct a dynamic virtual world of complex and interacting entities or agents, in which fitness is judged by a quality of information criterion. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.