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Jose Machado is Associate Professor with Habilitation of the Department of Informatics, School of Engineering, University of Minho. He is at the University of Minho since 1988. He got his PhD in Computer Science in 2002 and Habilitation in 2011.
Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society. More specifically, Deep Learning (DL), with the ability to recognize audio, image, and time series predictions, has helped to solve various types of problems. This paper aims to introduce a new theory, Hierarchical Temporal Memory (HTM), that applies to stock market prediction. HTM is based on the biological functions of the brain as well as its learning mechanism. The results are of significant relevance and show a low percentage of errors in the predictions made over time. It can be noted that the learning curve of the algorithm is fast, identifying trends in the stock market for all seven data universes using the same network. Although the algorithm suffered at the time a pandemic was declared, it was able to adapt and return to good predictions. HTM proved to be a good continuous learning method for predicting time series datasets.
Regina Sousa; Tiago Lima; António Abelha; José Machado. Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting. Electronics 2021, 10, 1630 .
AMA StyleRegina Sousa, Tiago Lima, António Abelha, José Machado. Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting. Electronics. 2021; 10 (14):1630.
Chicago/Turabian StyleRegina Sousa; Tiago Lima; António Abelha; José Machado. 2021. "Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting." Electronics 10, no. 14: 1630.
Health is an information rich and complex environment, which makes it essential to implement interoperability in different health organizations and the consequent homogeneity among Health Information Systems (HIS). The Agency for the Integration, Dissemination and Archiving of Medical and Clinical Information (AIDA) is a consistent agent monitoring platform capable of guaranteeing the automation of information as well as the interoperability and integration of HIS. This platform was designed as a solution to the information islands that are commonly found in hospital systems, and it is currently being used in several hospitals throughout Portugal. However, like any technological innovation, the solution requires a constant health technology assessment (HTA) to ensure the absence of obsolescence and a continued efficiency and security of the platform. Hence, this article focuses on the relevance and the need for vigilance, culminating in the restructuring of certain intelligent agents that make up the AIDA platform.
Francisca Nogueira; Diana Ferreira; Regina Sousa; António Abelha; José Machado. Integrating a New Generation of Interoperability Agents into the AIDA Platform. Journal of Digital Science 2021, 3, 54 -64.
AMA StyleFrancisca Nogueira, Diana Ferreira, Regina Sousa, António Abelha, José Machado. Integrating a New Generation of Interoperability Agents into the AIDA Platform. Journal of Digital Science. 2021; 3 (1):54-64.
Chicago/Turabian StyleFrancisca Nogueira; Diana Ferreira; Regina Sousa; António Abelha; José Machado. 2021. "Integrating a New Generation of Interoperability Agents into the AIDA Platform." Journal of Digital Science 3, no. 1: 54-64.
The processing of information in real-time (through the processing of complex events) has become an essential task for the optimal functioning of manufacturing plants. Only in this way can artificial intelligence, data extraction, and even business intelligence techniques be applied, and the data produced daily be used in a beneficent way, enhancing automation processes and improving service delivery. Therefore, professionals and researchers need a wide range of tools to extract, transform, and load data in real-time efficiently. Additionally, the same tool supports or at least facilitates the visualization of this data intuitively and interactively. The review presented in this document aims to provide an up-to-date review of the various tools available to perform these tasks. Of the selected tools, a brief description of how they work, as well as the advantages and disadvantages of their use, will be presented. Furthermore, a critical analysis of overall operation and performance will be presented. Finally, a hybrid architecture that aims to synergize all tools and technologies is presented and discussed.
Regina Sousa; Rui Miranda; Ailton Moreira; Carlos Alves; Nicolas Lori; José Machado. Software Tools for Conducting Real-Time Information Processing and Visualization in Industry: An Up-to-Date Review. Applied Sciences 2021, 11, 4800 .
AMA StyleRegina Sousa, Rui Miranda, Ailton Moreira, Carlos Alves, Nicolas Lori, José Machado. Software Tools for Conducting Real-Time Information Processing and Visualization in Industry: An Up-to-Date Review. Applied Sciences. 2021; 11 (11):4800.
Chicago/Turabian StyleRegina Sousa; Rui Miranda; Ailton Moreira; Carlos Alves; Nicolas Lori; José Machado. 2021. "Software Tools for Conducting Real-Time Information Processing and Visualization in Industry: An Up-to-Date Review." Applied Sciences 11, no. 11: 4800.
The integration of Information Technology systems in healthcare is no new concept, however, the ever growing solutions offered by the IT field are pushing a revamp of older implementations of Hospital Information Systems. Contemporary web-based solutions are now readily available and promise independence from operating systems and desktop bound systems, while incorporating faster and more secure methods. The focus on interoperable systems has been setting new goals towards fully computerized hospital management and the progress of healthcare standards over the years has made interoperability an obligation. The work presented hereby reflects a FHIR web based application to overcome the problem presented by scheduling and appointment management.
António Chaves; Tiago Guimarães; Júlio Duarte; Hugo Peixoto; António Abelha; José Machado. Development of FHIR based web applications for appointment management in healthcare. Procedia Computer Science 2021, 184, 917 -922.
AMA StyleAntónio Chaves, Tiago Guimarães, Júlio Duarte, Hugo Peixoto, António Abelha, José Machado. Development of FHIR based web applications for appointment management in healthcare. Procedia Computer Science. 2021; 184 ():917-922.
Chicago/Turabian StyleAntónio Chaves; Tiago Guimarães; Júlio Duarte; Hugo Peixoto; António Abelha; José Machado. 2021. "Development of FHIR based web applications for appointment management in healthcare." Procedia Computer Science 184, no. : 917-922.
During COVID-19 pandemic crisis, healthcare institutions globally were experiencing a VUCA - Volatile, Uncertain, Complex, and Ambiguous - environment. Effcient clinical and administrative management had never been so emergent. To achieve this goal, different components of the Healthcare Information System (HIS) must cooperate and interoperate flawlessly. Data standardization is a necessary step towards normalization and interoperability between existing Legacy Systems (LSs), and provides for longitudinal, highly reliable and persistent Electronic Health Records (EHRs). The openEHR standard was chosen for its overall dual domain architecture, where the more dynamic clinical information model may evolve independently from the relatively stable Reference Model (RM). Its Information Model (IM) comprises demographic, administrative and clinical systems. Critical clinical terms have been aligned to the FHIR HL7 standard, as to further support interoperability.
Daniela Oliveira; Rui Miranda; Francini Hak; Nuno Abreu; Pedro Leuschner; António Abelha; José Machado. Steps towards an Healthcare Information Model based on openEHR. Procedia Computer Science 2021, 184, 893 -898.
AMA StyleDaniela Oliveira, Rui Miranda, Francini Hak, Nuno Abreu, Pedro Leuschner, António Abelha, José Machado. Steps towards an Healthcare Information Model based on openEHR. Procedia Computer Science. 2021; 184 ():893-898.
Chicago/Turabian StyleDaniela Oliveira; Rui Miranda; Francini Hak; Nuno Abreu; Pedro Leuschner; António Abelha; José Machado. 2021. "Steps towards an Healthcare Information Model based on openEHR." Procedia Computer Science 184, no. : 893-898.
The COVID-19 pandemic had put pressure on various national healthcare systems, due to the lack of health professionals and exhaustion of those avaliable, as well as lack of interoperability and inability to restructure their IT systems. Therefore, the restructuring of institutions at all levels is essential, especially at the level of their information systems. Furthermore, the COVID-19 pandemic had arrived in Portugal at March 2020, with a breakout on the northern region. In order to quickly respond to the pandemic, the CHUP healthcare institution, known as a research center, has embraced the challenge of developing and integrating a new approach based on the openEHR standard to interoperate with the institution’s existing information and its systems. An openEHR clinical modelling methodology was outlined and adopted, followed by a survey of daily clinical and technical requirements. With the arrival of the virus in Portugal, the CHUP institution has undergone through constant changes in their working methodologies as well as their openEHR modelling. As a result, an openEHR patient care workflow for COVID-19 was developed.
Daniela Oliveira; Rui Miranda; Pedro Leuschner; Nuno Abreu; Manuel Filipe Santos; Antonio Abelha; José Machado. OpenEHR modeling: improving clinical records during the COVID-19 pandemic. Health and Technology 2021, 1 -10.
AMA StyleDaniela Oliveira, Rui Miranda, Pedro Leuschner, Nuno Abreu, Manuel Filipe Santos, Antonio Abelha, José Machado. OpenEHR modeling: improving clinical records during the COVID-19 pandemic. Health and Technology. 2021; ():1-10.
Chicago/Turabian StyleDaniela Oliveira; Rui Miranda; Pedro Leuschner; Nuno Abreu; Manuel Filipe Santos; Antonio Abelha; José Machado. 2021. "OpenEHR modeling: improving clinical records during the COVID-19 pandemic." Health and Technology , no. : 1-10.
According to the World Cancer Research Fund, a leading authority on cancer prevention research, lung cancer is the most commonly occurring cancer in men and the third most commonly occurring cancer in women, with the 5-year relative survival percentage being significantly low. Smoking is the major risk factor for lung cancer and the symptoms associated with it include cough, fatigue, shortness of breath, chest pain, weight loss, and loss of appetite. In an attempt to build a model capable of identifying individuals with lung cancer, this study aims to build a data mining classification model to predict whether or not a patient has lung cancer based on crucial features such as the above mentioned symptoms. Through the CRISP-DM methodology and the RapidMiner software, different models were built, using different scenarios, algorithms, sampling methods, and data approaches. The best data mining model achieved an accuracy of 93%, a sensitivity of 96%, a specificity of 90% and a precision of 91%, using the Artificial Neural Network algorithm.
Eduarda Vieira; Diana Ferreira; Cristiana Neto; António Abelha; José Machado. Data Mining Approach to Classify Cases of Lung Cancer. Advances in Intelligent Systems and Computing 2021, 511 -521.
AMA StyleEduarda Vieira, Diana Ferreira, Cristiana Neto, António Abelha, José Machado. Data Mining Approach to Classify Cases of Lung Cancer. Advances in Intelligent Systems and Computing. 2021; ():511-521.
Chicago/Turabian StyleEduarda Vieira; Diana Ferreira; Cristiana Neto; António Abelha; José Machado. 2021. "Data Mining Approach to Classify Cases of Lung Cancer." Advances in Intelligent Systems and Computing , no. : 511-521.
In Human action recognition, the identification of actions is a system that can detect human activities. The types of human activity are classified into four different categories, depending on the complexity of the steps and the number of body parts involved in the action, namely gestures, actions, interactions, and activities [1]. It is challenging for video Human action recognition to capture useful and discriminative features because of the human body's variations. To obtain Intelligent Solutions for action recognition, it is necessary to training models to recognize which action is performed by a person. This paper conducted an experience on Human action recognition compare several deep learning models with a small dataset. The main goal is to obtain the same or better results than the literature, which apply a bigger dataset with the necessity of high-performance hardware. Our analysis provides a roadmap to reach the training, classification, and validation of each model.
Flávio Santos; Dalila Durães; Francisco Marcondes; Marco Gomes; Filipe Gonçalves; Joaquim Fonseca; Jochen Wingbermuehle; José Machado; Paulo Novais. Modelling a Deep Learning Framework for Recognition of Human Actions on Video. Advances in Intelligent Systems and Computing 2021, 104 -112.
AMA StyleFlávio Santos, Dalila Durães, Francisco Marcondes, Marco Gomes, Filipe Gonçalves, Joaquim Fonseca, Jochen Wingbermuehle, José Machado, Paulo Novais. Modelling a Deep Learning Framework for Recognition of Human Actions on Video. Advances in Intelligent Systems and Computing. 2021; ():104-112.
Chicago/Turabian StyleFlávio Santos; Dalila Durães; Francisco Marcondes; Marco Gomes; Filipe Gonçalves; Joaquim Fonseca; Jochen Wingbermuehle; José Machado; Paulo Novais. 2021. "Modelling a Deep Learning Framework for Recognition of Human Actions on Video." Advances in Intelligent Systems and Computing , no. : 104-112.
Increasingly, hospitals are collecting huge amounts of data through new storage methods. These data can be use to extract hidden knowledge, which can be crucial to estimate the length of stay of admitted patients in order to improve the management of hospital resources. Hence, this article portrays the performance analysis of different data mining techniques through the application of learning algorithms in order to predict patients’ length of stay when admitted to an Intensive Care Unit. The data used in this study contains about 60,000 records and 28 features with personal and medical information. A full analysis of the results obtained with different Machine Learning algorithms showed that the model trained with the Gradient Boosted Trees algorithm and using only the features that were strongly correlated to the patient’s length of stay, achieved the best performance with 99,19% of accuracy. In this sense, an accurate understanding of the factors associated with the length of stay in intensive care units was achieved.
Cristiana Neto; Gabriel Pontes; Alexandru Domente; Francisco Reinolds; José Costa; Diana Ferreira; José Machado. Step Towards Predicting Patient Length of Stay in Intensive Care Units. Advances in Intelligent Systems and Computing 2021, 287 -297.
AMA StyleCristiana Neto, Gabriel Pontes, Alexandru Domente, Francisco Reinolds, José Costa, Diana Ferreira, José Machado. Step Towards Predicting Patient Length of Stay in Intensive Care Units. Advances in Intelligent Systems and Computing. 2021; ():287-297.
Chicago/Turabian StyleCristiana Neto; Gabriel Pontes; Alexandru Domente; Francisco Reinolds; José Costa; Diana Ferreira; José Machado. 2021. "Step Towards Predicting Patient Length of Stay in Intensive Care Units." Advances in Intelligent Systems and Computing , no. : 287-297.
Diabetic retinopathy is one of the complications of diabetes that affects the small vessels of the retina, being the main cause of blindness in adults. An early detection of this disease is essential, as it can prevent blindness as well as other irreversible harmful outcomes. This article attempts to develop a data mining model capable of identifying diabetic retinopathy in patients based on features extracted from eye fundus images. The data mining process was carried out in the RapidMiner software and followed the CRISP-DM methodology. In particular, classification models were built by combining different scenarios, algorithms, and sampling methods. The data mining model which performed best achieved an accuracy of 76.90%, a precision of 85.92%, and a sensitivity of 67.40%, using the Logistic Regression algorithm and Split Validation as the sampling method.
Ana Abreu; Diana Ferreira; Cristiana Neto; António Abelha; José Machado. Diagnosis of Diabetic Retinopathy Using Data Mining Classification Techniques. Advances in Intelligent Systems and Computing 2021, 198 -209.
AMA StyleAna Abreu, Diana Ferreira, Cristiana Neto, António Abelha, José Machado. Diagnosis of Diabetic Retinopathy Using Data Mining Classification Techniques. Advances in Intelligent Systems and Computing. 2021; ():198-209.
Chicago/Turabian StyleAna Abreu; Diana Ferreira; Cristiana Neto; António Abelha; José Machado. 2021. "Diagnosis of Diabetic Retinopathy Using Data Mining Classification Techniques." Advances in Intelligent Systems and Computing , no. : 198-209.
Polycystic Ovary Syndrome is an endocrine abnormality that occurs in the female reproductive system and is considered a heterogeneous disorder because of the different criteria used for its diagnosis. Early detection and treatment are critical factors to reduce the risk of long-term complications, such as type 2 diabetes and heart disease. With the vast amount of data being collected daily in healthcare environments, it is possible to build Decision Support Systems using Data Mining and Machine Learning. Currently, healthcare systems have advanced skills like Artificial Intelligence, Machine Learning and Data Mining to offer intelligent and expert healthcare services. The use of efficient Data Mining techniques is able to reveal and extract hidden information from clinical and laboratory patient data, which can be helpful to assist doctors in maximizing the accuracy of the diagnosis. In this sense, this paper aims to predict, using the classification techniques and the CRISP-DM methodology, the presence of Polycystic Ovary Syndrome. This paper compares the performance of multiple algorithms, namely, Support Vector Machines, Multilayer Perceptron Neural Network, Random Forest, Logistic Regression and Gaussian Naïve Bayes. In the end, it was found that Random Forest provides the best classification, and the use of data sampling techniques also improves the results, allowing to achieve a sensitivity of 0.94, an accuracy of 0.95, a precision of 0.96 and a specificity of 0.96.
Cristiana Neto; Mateus Silva; Mariana Fernandes; Diana Ferreira; José Machado. Prediction Models for Polycystic Ovary Syndrome Using Data Mining. Advances in Intelligent Systems and Computing 2021, 210 -221.
AMA StyleCristiana Neto, Mateus Silva, Mariana Fernandes, Diana Ferreira, José Machado. Prediction Models for Polycystic Ovary Syndrome Using Data Mining. Advances in Intelligent Systems and Computing. 2021; ():210-221.
Chicago/Turabian StyleCristiana Neto; Mateus Silva; Mariana Fernandes; Diana Ferreira; José Machado. 2021. "Prediction Models for Polycystic Ovary Syndrome Using Data Mining." Advances in Intelligent Systems and Computing , no. : 210-221.
Despite advances in technology and health, the number of maternal and fetal deaths during and after pregnancy and childbirth remains significant. Most of these deaths could be avoided if there was prenatal care before and during pregnancy, which could assist in monitoring the fetal heart rate (FHR). Thus, medical methods have been developed for assisting fetal monitoring, such as cardiotocography (CTG). To collaborate with the methods developed, advances in the field of machine learning and computational intelligence made it possible to increase the effectiveness of classification and recognition systems and, thus, to predict possible maternal or fetal death. To this end, this paper tries to predict fetal well-being, through the classification of data resulting from fetal CTGs using two different types of classification, fetal state and morphological pattern. The classification by fetal state, using methods such as Decision Tree (DT) and k-Nearest Neighbors (kNN), presented high accuracy values, achieving values that range from 93% to 98%. However, although not expected, the classification by morphological standards also showed high accuracy values, achieving the best model a value of 93% of accuracy with the kNN. Therefore, the complementary between both classifications may guarantee success in predicting fetal well-being.
Maria Nogueira; Diana Ferreira; Cristiana Neto; António Abelha; José Machado. Data Mining for the Prediction of Fetal Malformation Through Cardiotocography Data. Advances in Intelligent Systems and Computing 2021, 60 -69.
AMA StyleMaria Nogueira, Diana Ferreira, Cristiana Neto, António Abelha, José Machado. Data Mining for the Prediction of Fetal Malformation Through Cardiotocography Data. Advances in Intelligent Systems and Computing. 2021; ():60-69.
Chicago/Turabian StyleMaria Nogueira; Diana Ferreira; Cristiana Neto; António Abelha; José Machado. 2021. "Data Mining for the Prediction of Fetal Malformation Through Cardiotocography Data." Advances in Intelligent Systems and Computing , no. : 60-69.
Over the years, mental illness has affected the life of numerous human beings and nowadays is a matter of great concern. The problems that arise with this clinical condition, such as social isolation, unemployment, and others, have been a subject of study. The purpose of this study is to use a survey that aims to assess the situation of unemployment among individuals with mental illness. Hence, this article focuses on using the result of this research to identify if there is a connection between having mental illness and being in a situation of unemployment, as well as, which factors can be determinant for such a relationship and also if there is any way to anticipate them. In this context, this research attempts to develop an accurate prediction mechanism, using Data Mining, capable of predicting, based on the answers of a similar questionnaire, if an individual will be in risk of unemployment. Throughout this research, the CRISP-DM methodology was adopted and the RapidMiner Studio software was the tool used for the learning process. The best percentages of accuracy were between 0.79 and 0.86, of sensitivity between 0.75 and 0.88, and of specificity between 0.66 and 0.93.
Cristiana Neto; Caroline Rodrigues; Emely Mendonça; Laercio Sartori; Rafaela De Pinho; Diana Ferreira; António Abelha; José Machado. Data Mining Approach to Understand the Association Between Mental Disorders and Unemployment. Advances in Intelligent Systems and Computing 2021, 70 -79.
AMA StyleCristiana Neto, Caroline Rodrigues, Emely Mendonça, Laercio Sartori, Rafaela De Pinho, Diana Ferreira, António Abelha, José Machado. Data Mining Approach to Understand the Association Between Mental Disorders and Unemployment. Advances in Intelligent Systems and Computing. 2021; ():70-79.
Chicago/Turabian StyleCristiana Neto; Caroline Rodrigues; Emely Mendonça; Laercio Sartori; Rafaela De Pinho; Diana Ferreira; António Abelha; José Machado. 2021. "Data Mining Approach to Understand the Association Between Mental Disorders and Unemployment." Advances in Intelligent Systems and Computing , no. : 70-79.
Hospitals generate large amounts of data on a daily basis, but most of the time that data is just an overwhelming amount of information which never transitions to knowledge. Through the application of Data Mining techniques it is possible to find hidden relations or patterns among the data and convert those into knowledge that can further be used to aid in the decision-making of hospital professionals. This study aims to use information about patients with diabetes, which is a chronic (long-term) condition that occurs when the body does not produce enough or any insulin. The main purpose is to help hospitals improve their care with diabetic patients and consequently reduce readmission costs. An hospital readmission is an episode in which a patient discharged from a hospital is admitted again within a specified period of time (usually a 30 day period). This period allows hospitals to verify that their services are being performed correctly and also to verify the costs of these re-admissions. The goal of the study is to predict if a patient who suffers from diabetes will be readmitted, after being discharged, using Machine Leaning algorithms. The final results revealed that the most efficient algorithm was Random Forest with 0.898 of accuracy.
Cristiana Neto; Fábio Senra; Jaime Leite; Nuno Rei; Rui Rodrigues; Diana Ferreira; José Machado. Different Scenarios for the Prediction of Hospital Readmission of Diabetic Patients. Journal of Medical Systems 2021, 45, 1 -9.
AMA StyleCristiana Neto, Fábio Senra, Jaime Leite, Nuno Rei, Rui Rodrigues, Diana Ferreira, José Machado. Different Scenarios for the Prediction of Hospital Readmission of Diabetic Patients. Journal of Medical Systems. 2021; 45 (1):1-9.
Chicago/Turabian StyleCristiana Neto; Fábio Senra; Jaime Leite; Nuno Rei; Rui Rodrigues; Diana Ferreira; José Machado. 2021. "Different Scenarios for the Prediction of Hospital Readmission of Diabetic Patients." Journal of Medical Systems 45, no. 1: 1-9.
Cardiovascular diseases (CVDs) aredisorders of the heart and blood vessels and are a major cause of disability and premature death worldwide. Individuals at higher risk of developing CVD must be noticed at an early stage to prevent premature deaths. Advances in the field of computational intelligence, together with the vast amount of data produced daily in clinical settings, have made it possible to create recognition systems capable of identifying hidden patterns and useful information. This paper focuses on the application of Data Mining Techniques (DMTs) to clinical data collected during the medical examination in an attempt to predict whether or not an individual has a CVD. To this end, the CRossIndustry Standard Process for Data Mining (CRISP-DM) methodology was followed, in which five classifiers were applied, namely DT, Optimized DT, RI, RF, and DL. The models were mainly developed using the RapidMiner software with the assist of the WEKA tool and were analyzed based on accuracy, precision, sensitivity, and specificity. The results obtained were considered promising on the basis of the research for effective means of diagnosing CVD, with the best model being Optimized DT, which achieved the highest values for all the evaluation metrics, 73.54%, 75.82%, 68.89%, 78.16% and 0.788 for accuracy, precision, sensitivity, specificity, and AUC, respectively.
Bárbara Martins; Diana Ferreira; Cristiana Neto; António Abelha; José Machado. Data Mining for Cardiovascular Disease Prediction. Journal of Medical Systems 2021, 45, 1 -8.
AMA StyleBárbara Martins, Diana Ferreira, Cristiana Neto, António Abelha, José Machado. Data Mining for Cardiovascular Disease Prediction. Journal of Medical Systems. 2021; 45 (1):1-8.
Chicago/Turabian StyleBárbara Martins; Diana Ferreira; Cristiana Neto; António Abelha; José Machado. 2021. "Data Mining for Cardiovascular Disease Prediction." Journal of Medical Systems 45, no. 1: 1-8.
In the last years, the increase of the average waiting times in waiting lists has been an issue felt in several health institutions worldwide. Therefore, this problematic situation creates the need to define and implement new administrative measures in order to improve the management of these organizations. In this context, this research project arose in an attempt to support the decision-making process in waiting lists, namely medical appointments and surgeries, in a hospital located in the north of Portugal. Hereupon, a pervasive business intelligence platform was designed and developed using recent technologies such as React, Node.js, and MySQL. The proposed information technology artifact allows the efficient and easy identification in real-time of average waiting times outside the outlined patterns. Thus, the aim is to enable the reduction of average waiting times through the analysis of business intelligence indicators in order to ensure patients' satisfaction by taking necessary and adequate measures.
Marisa Esteves; António Abelha; José Machado. A Proof of Concept of a Business Intelligence Platform to Support the Decision-Making Process of Health Professionals in Waiting Lists. Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering 2021, 1015 -1034.
AMA StyleMarisa Esteves, António Abelha, José Machado. A Proof of Concept of a Business Intelligence Platform to Support the Decision-Making Process of Health Professionals in Waiting Lists. Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering. 2021; ():1015-1034.
Chicago/Turabian StyleMarisa Esteves; António Abelha; José Machado. 2021. "A Proof of Concept of a Business Intelligence Platform to Support the Decision-Making Process of Health Professionals in Waiting Lists." Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering , no. : 1015-1034.
Mental illness is a concern these days, affecting people worldwide and across all kinds of ages. This article aims to predict mental illness and discover its association with unemployment as well as other possible causes behind the illness. In order to accomplish this goal, a Data Mining (DM) process was performed using the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology and the RapidMiner Studio software. In the end, the results obtained were considered promising since all the evaluation metrics, namely accuracy, sensitivity, and specificity, obtained values above 90%. The study also allowed, in the end, to identify the factors associated with the prediction of mental illness.
Carina Gonçalves; Diana Ferreira; Cristiana Neto; António Abelha; José Machado. Prediction of Mental Illness Associated with Unemployment Using Data Mining. Procedia Computer Science 2020, 177, 556 -561.
AMA StyleCarina Gonçalves, Diana Ferreira, Cristiana Neto, António Abelha, José Machado. Prediction of Mental Illness Associated with Unemployment Using Data Mining. Procedia Computer Science. 2020; 177 ():556-561.
Chicago/Turabian StyleCarina Gonçalves; Diana Ferreira; Cristiana Neto; António Abelha; José Machado. 2020. "Prediction of Mental Illness Associated with Unemployment Using Data Mining." Procedia Computer Science 177, no. : 556-561.
Chronic Kidney Disease (CKD) is a condition characterized by a gradual loss of kidney function over time. In national and international guidelines, CKD is organized into different degrees of risk stratification using commonly available markers. It is usually asymptomatic in its early stages, and early detection is important to reduce future risks. This study used the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and the WEKA software to build a system that can classify the chronic condition of kidney disease based on accuracy, sensitivity, specificity and precision. The results obtained were considered satisfactory, achieving the most suitable result of 97.66% of accuracy, 96.13% of sensitivity, 98.78% of specificity and 98.31% of precision with the J48 algorithm.
Ana Pinto; Diana Ferreira; Cristiana Neto; António Abelha; José Machado. Data Mining to Predict Early Stage Chronic Kidney Disease. Procedia Computer Science 2020, 177, 562 -567.
AMA StyleAna Pinto, Diana Ferreira, Cristiana Neto, António Abelha, José Machado. Data Mining to Predict Early Stage Chronic Kidney Disease. Procedia Computer Science. 2020; 177 ():562-567.
Chicago/Turabian StyleAna Pinto; Diana Ferreira; Cristiana Neto; António Abelha; José Machado. 2020. "Data Mining to Predict Early Stage Chronic Kidney Disease." Procedia Computer Science 177, no. : 562-567.
Driverless vehicles are more and more becoming a reality. However, people still have some concerns in using them, the main concern is fear, hence the importance of creating a surveillance system inside those vehicles. For the detection and classification of human movements to be possible it is necessary to train the system with data representative enough for all kinds of possibilities. Although the production of large quantities of data becomes an expensive process and adds the problem of data protection, the use of synthetic data once they are artificially generated allows lower costs and eliminates the problem of data protection. A bibliographic study was carried out in this paper with articles from 2017 or later on the use of synthetic data. In these studies, it is noted that synthetic data is widely used with good results. As far as image capture is concerned, they show that 3D cameras have better results, but they are more expensive, so 2D cameras are more often used with later conversion to 3D images. The stitched puppet (SP) model is capable of adapting to the most difficult poses having obtained good results in its application in the FAUST dataset.
Ana Coimbra; Cristiana Neto; Diana Ferreira; Júlio Duarte; Daniela Oliveira; Francini Hak; Filipe Gonçalves; Joaquim Fonseca; Nicolas Lori; António Abelha; José Machado. Review of Trends in Automatic Human Activity Recognition in Vehicle Based in Synthetic Data. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 368 -376.
AMA StyleAna Coimbra, Cristiana Neto, Diana Ferreira, Júlio Duarte, Daniela Oliveira, Francini Hak, Filipe Gonçalves, Joaquim Fonseca, Nicolas Lori, António Abelha, José Machado. Review of Trends in Automatic Human Activity Recognition in Vehicle Based in Synthetic Data. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():368-376.
Chicago/Turabian StyleAna Coimbra; Cristiana Neto; Diana Ferreira; Júlio Duarte; Daniela Oliveira; Francini Hak; Filipe Gonçalves; Joaquim Fonseca; Nicolas Lori; António Abelha; José Machado. 2020. "Review of Trends in Automatic Human Activity Recognition in Vehicle Based in Synthetic Data." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 368-376.
Improving customer experience is crucial in any industry, especially in telecommunications, where competition is a constant factor. Today, all telecommunications companies rely on the massive amount of data generated daily to get to know the customer or study their behavior and thus create new effective strategies for their business. Within the most varied user experiences, the process of installing new services can be an event that raises doubts about their operation, degrade the user experience, or, in extreme cases, lead to maintenance interventions. Therefore, the use of advanced predictive models that can predict such occurrences become vital. With this, the company can anticipate the cases that will be problematic and reduce the number of negative experiences. The main objective of this work is to create a predictive model that, through all the available data history, can predict which customers will contact the customer service with problems derived from the installation process and have a following maintenance intervention. After analyzing an unbalanced dataset with approximately 560K entries from a Portuguese telecommunications company, and resorting to the CRISP-DM methodology for modeling, the best results were found with LightGBM which obtained an AUPRC of 0.11 and AUROC of 0.62. The best trade-off between precision and recall was found with a threshold model of 0.43 in order to maximize recall while still avoiding a large number of false negatives.
Diana Costa; Carlos Pereira; Hugo Peixoto; José Machado. Anticipating Maintenance in Telecom Installation Processes. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 322 -334.
AMA StyleDiana Costa, Carlos Pereira, Hugo Peixoto, José Machado. Anticipating Maintenance in Telecom Installation Processes. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():322-334.
Chicago/Turabian StyleDiana Costa; Carlos Pereira; Hugo Peixoto; José Machado. 2020. "Anticipating Maintenance in Telecom Installation Processes." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 322-334.