Identifying diabetic patient profile through machine learning-based clustering analysis

Given the rapid growth over the past 15 years, Diabetes is currently a key issue in medical science and healthcare administration. Considering the importance of the health sector in our society, it is critical to correctly diagnose and treat Diabetes in order to avoid immediate difficulties and reduce the chance of long-term issues. The analysis of vast amounts of data that are available in organizations is an important factor to describing their internal factors, predicting future trends, and prescribing the best course of action to improve their performance in light of the increasing technological evolution and the emergence of Artificial Intelligence (AI). The main objective of this project, which is being carried out in collaboration with the Unidade Local de Saúde do Alto Minho (ULSAM), is to define a typology of diabetic patients by building Machine Learning (ML) models from registered clinical information, medication, complementary diagnostic tools, therapeutic and monitoring data, and registered medication data.(undefined

Adaptive business intelligence: a new architectural approach

Healthcare systems face enormous challenges, fundamentally due to the amount of data generated daily in a hospital environment, which forces entities to reflect on how to organize and use the same data. Currently, the number of studies at this level is growing, focusing on the innovation to be implemented, so that this same sector can adopt new methodologies, architectures and technologies that allow a more efficient support of existing hospital processes, as well as the results to be provided to all professionals involved in this area. In this research, an Adaptive Business Intelligence architecture is proposed, whose contribution was supported by the realization of an adequate conceptual and technological framework describing its development at different levels. Thus, a possible modernization of several working methods is initiated, with the introduction of an architecture capable of contributing to several factors, both at clinical and administrative levels, meeting the needs of a hospital system, regarding the design, development, implementation and demonstration of results.FCT – Fundação para a Ciência e Tecnologi

Predictive and prescriptive analytics in healthcare: a survey

Over the years, health area has received numerous studies on how to improve its management and administration activities and, fundamentally, the Healthcare provided to its patients. Currently, there is an exponential growth of data in the health system. In this sense, it is crucial the implementation of technologies capable of using it in a beneficial way for the organization, helping it to fulfill its strategic objectives. Subsequently, this same data, if used correctly, has the capacity to assist the organization at an administrative level, as well as at the level of patient care, using predictive and optimization models capable of revolutionizing the current health system. Thus, this article aims to identify the advances that have been made in this area, focusing on the development of predictive and optimization techniques, applied in Health, and how these can improve the lives of managers, doctors and patients.FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/20

Business Analytics components for public healthinstitution - Clinical decision area

The practice of evidence-based medicine has been gaining ground in the most diverse health institutions and consequently in the work of their health professionals. Thus, the Clinical Decision Support Systems appear, which aim to ensure that health professionals are supported with the best evidence in order to improve the clinical decision-making process, reducing the occurrence of adverse events through the availability and sharing of quality information by health professionals. Business Intelligence (BI) and Business Analytics (BA) technologies are increasingly being used to extract knowledge and turn it into useful information and analytical tools for healthcare professionals. This article is part of a dissertation that has as main objective the extraction of knowledge from the hospital data of the obstetrics service in Centro Materno-Infantil do Norte (CMIN) from Centro Hospitalar Universitário do Porto (CHUP) with focus on the area of clinical decision.FCT - Fundação para a Ciência e a Tecnologia (UIDB/00319/2020

Predictive analytics for hospital discharge flow determination

In recent years, hospitals around the world are faced with large patient flows, which negatively affect the quality of patient care and become a crucial factor to consider in inpatient management. The main objective of this management is to maximize the number of available beds, using efficient planning. Intensive Care Units (ICU) are hospital units with a higher monetary consumption, and the importance of indicators that allow the achievement of useful information for a correct management is critical. This study allowed the prediction of the Length of Stay (LOS) based on their demographic data, information collected at the time of admission and clinical conditions, which can help health professionals in conducting a more assertive planning and a better quality service. The results obtained show that Machine Learning (ML) models, using demographic information simultaneously with the patient's pathway, as well as clinical data, drugs, tests and analysis, introduce a greater predictive ability for LOS.FCT -Fundação para a Ciência e a Tecnologia(DSAIPA/DS/0084/2018

Business Intelligence platform for COVID-19 Monitoring: A case study

With the emergence of the COVID-19 virus, the need to effectively and flexibly plan and manage the measures to be applied within a Healthcare institution has become imperative. New knowledge about the disease and legislative changes arose frequently and with high impacts on clinical practice and management. The platform developed under this case study aggregates data from multiple sources within the Healthcare Institution and also from external entities, such as clinical analysis laboratories. Thus, it provides a set of dashboards that seeks to present in a simple and intuitive way, data analysis ranging from COVID tests and their results, bed occupancy, with an exceptional focus in occupancy in Intensive Care Units, to monitoring the positive movements of COVID patients in the Centro Hospitalar e Universitário do Porto (CHUP), in almost real time. The development of this data collection, analysis and demonstration platform was created in response to a critical need for reliable information so that clinical and management decisions could be supported by solid foundations based on facts and current and reliable data.The work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Projects Scope: DSAIPA/DS/0084/201

Towards a multi-marker prognostic strategy in acute heart failure: a role for GDF-15

AIMS: Growth differentiation factor (GDF)-15 mirrors inflammation and oxidative stress in cardiovascular diseases. Brain natriuretic peptide (BNP) is associated with cardiomyocyte stretch in heart failure (HF). The objective of this study was to evaluate the prognostic impact of plasma GDF-15 and BNP in acute HF. METHODS AND RESULTS: We studied a subgroup of patients prospectively recruited in an acute HF registry (follow-up: 2 years; endpoint: all-cause mortality). Cox regression multivariate models were built to study the association of GDF-15 and mortality. Further cross-classification according to discharge GDF-15 (mean) and BNP (mean) and association with mortality was studied. We studied 158 patients: seventy-nine were male, mean age was 75 years, 55.1% had left ventricular ejection fraction < 40%, mean discharge BNP was 1000 pg/mL, and mean GDF-15 was 3013 ng/mL. Higher BNP and GDF-15 predicted 2-year mortality. Patients with GDF-15 ≥ 3000 ng/mL had a multivariate adjusted 2-year death risk of 1.86 (1.08-3.18). Patients discharged with both BNP and GDF-15 above the mean had an adjusted hazard ratio of 4.33 (2.07-9.06) when compared with those with both <mean. CONCLUSIONS: Higher GDF-15 associated with worse prognosis in acute HF independently of BNP. When both biomarkers GDF-15 and BNP were elevated at discharge, the 2-year mortality risk increased over four-fold. Biomarkers related to different pathophysiological pathways can provide incremental prognostic information in acute HF.info:eu-repo/semantics/publishedVersio

OpenEHR and business intelligence in healthcare: an overview

Healthcare systems are a constant concern in today's society. The healthcare sector, like any other, collects a large amount of information on a daily basis. This information has recently been stored in electronic health records, the EHR. These records are a way to store a patient's information electronically, consequently improving its availability for better management in the institution. On the other hand, it is important to realise that there is an interoperability in this data, since there is a large interaction of various systems, and it is important to ensure interaction between these systems. To combat this interoperability problem, OpenEHR modelling emerges. To improve the management of the existing information in these records, it is interesting to integrate with Business Intelligence. Therefore, the objective of this article is to present an overview of OpenEHR and Business Intelligence systems, as more specific to health sector.(undefined

Permissioned blockchain approach using open data in healthcare

Digital health records play a key role in the area. However, it is difficult to obtain a unified view of your data, as it is distributed among different providers, spread over several places, and is not integrated. To address these problems, blockchain technology and the openEHR interoperability standard have emerged. Blockchain is a new wave of disruption that has come to redesign interactions that involve any form of exchange of values, with the potential to improve healthcare, bringing a new perspective on security, resilience, and effectiveness of systems. In turn, with the use of openEHR, the standardization of electronic records is guaranteed, offering fine-grained access permissions for stakeholders. In addition to the use of archetypes as a reference to make the templates, where they are integrated to build a module with compatible standards. Based on an open data framework, OpenEHR, and blockchain technology, this paper has conceptualised a proposed two architectures that will be implemented within a Portuguese hospital, at the ICU, to increase and provide support for clinical decision-making, ensuring interoperability between systems, as well as the veracity, privacy and security of the data being used.FCT -Fundação para a Ciência e a Tecnologia(DSAPI A/DS/0084/2018