A Practical Experience on the Amazon Alexa Integration in Smart Offices.

Smart offices are dynamically evolving spaces meant to enhance employees' efficiency, but also to create a healthy and proactive working environment. In a competitive business world, the challenge of providing a balance between the efficiency and wellbeing of employees may be supported with new technologies. This paper presents the work undertaken to build the architecture needed to integrate voice assistants into smart offices in order to support employees in their daily activities, like ambient control, attendance system and reporting, but also interacting with project management services used for planning, issue tracking, and reporting. Our research tries to understand what are the most accepted tasks to be performed with the help of voice assistants in a smart office environment, by analyzing the system based on task completion and sentiment analysis. For the experimental setup, different test cases were developed in order to interact with the office environment formed by specific devices, as well as with the project management tool tasks. The obtained results demonstrated that the interaction with the voice assistant is reasonable, especially for easy and moderate utterances.

Interoperability evaluation case study: an Obstetrics-Gynecology Department and related Information Systems.

The paper presents the steps and metrics for evaluating the interoperability of an Obstetrics-Gynecology Department Information System applied on Bega Clinic Timisoara regarding its readiness for interoperability in relation with similar systems. The developed OGD IS was modeled starting from the Generic Component Model and sends information to other medical units using the HL7 Clinical Document Architecture and Continuity of Care Document standards. The data for evaluation are real, collected between 2009 and 2010 from Bega Clinic Timisoara. The results were relatively good for the investigated data and structure.

Increasing Trust in AI Using Explainable Artificial Intelligence for Histopathology - An Overview.

Digital Pathology is an area that could benefit a lot from the automatic classification of scanned microscopic slides. One of the main problems with this is that the experts need to understand and trust the decisions of the system. This paper is an overview of the current state of the art methods used in histopathological practice for explaining CNN classification useful for histopathological experts and ML engineers that work with histopathological images. This paper is an overview of the current state of the art methods used in the histopathological practice for explain. The search was performed using SCOPUS database and revealed that there are few applications of CNNs for digital pathology. The 4-term search yielded 99 results. This research sheds light on the main methods that can be used for histopathology classification and offers a good starting point for future works.

Challenges in Early Diagnosis of Melanoma Using Tele-Dermatology.

Numerous classification systems have been developed over the years, systems which not only provide assistance to dermatologists, but also enable individuals, especially those living in areas with low medical access, to get a diagnosis. In this paper, a Machine Learning model, which performs a binary classification, and, which for the remainder of this paper will be abbreviated as ML model, is trained and tested, so as to evaluate its effectiveness in giving the right diagnosis, as well as to point out the limitations of the given method, which include, but are not limited to, the quality of smartphone images, and the lack of FAIR image datasets for model training. The results indicate that there are many measures to be taken and improvements to be made, if such a system were to become a reliable tool in real-life circumstances.

Explanation Relevance Index - A New Way of Assessing the Quality of Explanations Based on Learned Features.

The XAI methods began to emerge as a response for the black-box methods used to make decisions that could not be explained, even if checked by humans they were correct. This created the need for a better evaluation for these methods. In this paper we propose a new method for evaluation based on features. The main advantage of applying ERI to CNNs explanations are: a fully automated way to measure the quality of an explanation and the fact that the score uses the same information as the CNN, in this way being able to offer a measure of the quality of explanation that can be validated automatically.

Towards FAIR Data Standardization Using FHIR Genomics Resources Integration in Obstetrics-Gynecology Department Systems.

The paper presents a current situation of the FHIR Genomics resource and an assessment of FAIR data usage and possible future directions. FHIR Genomics forges a path towards data interoperability. By integrating both the FAIR principles and the FHIR resources, we can achieve a higher standardization across healthcare data collection and a smoother data exchange. By exemplifying on the FHIR Genomics resource, we want to pave the way towards the integration of genomic data into an Obstetrics-Gynecology Information system as a future direction to be able to identify possible disease predisposition in fetus.

Recovery Gestures Classification Using KNN and LDA Models.

Hand and joint mobility recovery involve performing a set of exercises. Gestures are often used in the hand mobility recovery process. This paper discusses the selection and the use of 2 models of neural networks for the classification of data that describe Leap Motion gestures. The gestures are: the hand opening and closing gesture and the palm rotation gesture. The purpose is the optimal selection of the neural network model to be used in the classification of the data describing the recovery gestures. The models chosen for the classification of the two gestures were: Linear Discriminant Analysis (LDA) and K-neighbors Classifier (KNN). The accuracies achieved in the classification of the gestures for each model are: 0.91 - LDA and 0.98 - KNN.

Comparison of Data Classification Results in Serious Gaming for Rehabilitation of Rheumatoid Arthritis.

Rheumatoid arthritis is a common disease which affects the joints of the wrist, fingers, feet and in the end the daily activities. Nowadays, gestures and virtual reality are used in many activities supporting recovery, games, learning as technology is present more and more in different fields. This paper presents results related to the grip movement detected by a Leap Motion device using binary classification and machine learning algorithms. We used 2 models to compare the results: Naïve Bayes and Random Forest Classifier. The metrics for comparison were: accuracy, precision, recall and f1-score. Also, we create a confusion matrix for a clear visualization of the results. We used 5000 data to train the algorithm and 1500 data to test. The accuracy and the precision were bigger than 97% in all the cases.

A Custom Image Recognition Model in the Cloud for Skin Diseases Identification.

In the context of global warming and increasing exposure to UV radiation, skin diseases are becoming more prevalent. Some of the most widespread skin conditions are solar lentigo and actinic keratosis. In this paper, we propose a technical approach related to the use of Azure Custom Vision services to classify these two conditions. The main advantage of using this service is the computational power offered by Azure. Additionally, generating a convolutional neural network model does not require a large dataset to achieve a good performance. For training the model, we used a dataset of 600 images from the ISIC database. The limitations of these approaches are imposed by the manual image labeling part that needs to be performed. As a result, we provide a trained model on a series of images that can be used for classifying images related to these two conditions. The performance of our neural network on the pre-trained images is 94%.

Application for Screening to Monitor and Assess Pregnancy Potential Risks.

Research in the field of maternal-fetal medicine brings a new approach, by involving several fields: genetics, informatics, teratology, imaging, obstetric diagnosis, maternal-fetal physiology, endocrinology, and aims to determine the relationships that appear between the maternal medical pathology and the fetal one. In this article, we present an application for monitoring and calculating risk in Trisomy-21 for pregnant women. To calculate the risk, we used 2 methods, one mathematical and one using neural networks to investigate which one offers higher precision. Following the experimental results, due to the use of several variables that increase the risk for Trisomy-21, the conclusion is that the method using neural networks is better, having an accuracy of 95%.

Mind Note - Application for Mental Health Support.

This paper presents a mobile application supporting mental health combining techniques to detect different mental health disorder symptoms. It provides different interactions, using affirmations, emotions, based on unloading techniques such as writing, drawing, and monitoring associated emotions, providing modalities to save and track progress.

Cloud computing technology applied in healthcare for developing large scale flexible solutions.

An extremely important area in which there is also vital information needed in different locations is the healthcare domain. In the areas of healthcare there is an important exchange of information since there are many departments where a patient can be sent for investigation. In this regard cloud computing is a technology that could really help supporting flexibility, seamless care and financial cuts.

Flexible solution for interoperable cloud healthcare systems.

It is extremely important for the healthcare domain to have a standardized communication because will improve the quality of information and in the end the resulting benefits will improve the quality of patients' life. The standards proposed to be used are: HL7 CDA and CCD. For a better access to the medical data a solution based on cloud computing (CC) is investigated. CC is a technology that supports flexibility, seamless care, and reduced costs of the medical act. To ensure interoperability between healthcare information systems a solution creating a Web Custom Control is presented. The control shows the database tables and fields used to configure the two standards. This control will facilitate the work of the medical staff and hospital administrators, because they can configure the local system easily and prepare it for communication with other systems. The resulted information will have a higher quality and will provide knowledge that will support better patient management and diagnosis.

Sharing the IT Educational Experience of Developing 3D Applications for Medical Students Training.

The importance of using the new technologies to develop educational and training applications for medical students is given by the times we live in and also by the continued development of the IT industry. New concepts used in 3D applications such as gamification bring added value to the use of learning applications. The introduction of technologies-based on virtual and augmented reality contributes to increasing of the interactivity. This paper presents an overview of the students' experience in developing a complex 3D application for medical students. The application development methodology is explained by splitting the design and development of the 3D learning application into steps. The main users' benefits provided by such applications are increased interactivity and learning benefits, and the continuous availability of the application wherein virtual laboratories within a medical clinic are simulated.

Comparison of Data Classification Results for Leap Motion Recovery Gestures.

Static and dynamic gestures are frequently used in activities supporting learning, recovery healthcare, engineering, and 3D games to increase the interactivity between man and machine. The gestures are detected via hardware devices and data is processed using different software methods. This paper presents the manner of detection and interpretation of two gestures, a hand rotation gesture and a palm closing and opening gesture, using the Leap Motion device. These two dynamic gestures are very often used in hand recovery exercises. For comparing the two gestures we use data classification methods, Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The data for the gesture classification were 80% training data and 20% testing data. The metrics for comparison are precision, recall, F1-score, and the total number of testing cases (support). The SVM classifier gives an accuracy of 99.4% and the MLP classifier a 96.2%. We built two confusion matrices for better visualizing the results.

Serious Gaming and Artificial Intelligence in Rehabilitation of Rheumatoid Arthritis.

This paper presents a current situation of studies and applications which are using serious games and artificial intelligence (AI) in rehabilitation of rheumatoid arthritis, and possible future directions. The objectives of this paper are: to highlight the technologies used for recovery of patients with rheumatoid arthritis (RA), to summarize the state of the art of existing applications and to present the authors work, a software application that aims contributing to the recovery of the specific patients. At this stage the application was tested by a group of 10 patients from Medical Centre Sf. Mary of Timisoara. Most of the patients reported that the physical and psychical effort were between easy-very easy. The patients confirmed that they would use the application in their rehabilitation process and are very excited about this type of rehabilitation that stimulates curiosity and a state of wellbeing. The application works based on the leap motion device that proved to be the most suitable device in terms of precision and the manner of interaction in virtual reality games.

Application Using Standard Communication Between Medical Facilities.

The web-based application described in this paper will support the patient to receive the treatment quicker and the physician to generate the prescription easier. The patient will have a real time information if their treatment/prescription is available in the pharmacy. Using a cloud solution will have all the information always available and without delays, the only requirement is the Internet connectivity. Using standardized communication as HL7 FHIR, the information exchanged is easier understanded by different medical units, and in the future other medical units can access the patient treatment/prescription and have a medical history, in this way the patient will receive better quality in treatment and health care.

Medical informatics and digital health multilingual ontology (MIMO): A tool to improve international collaborations.

BACKGROUND: Even if English is the leading language for international communication, it is essential to keep in mind that research runs at the local level by local teams generally communicating in their local/national language, especially in Europe among European projects. OBJECTIVE: Therefore, the European Federation for Medical Informatics - Working Group on Health Informatics for Inter-regional Cooperation" has one objective: To develop a multilingual ontology focusing on Health Informatics and Digital Health as a collaboration tool that improves international and, in particular, European collaborations. RESULTS: We have developed the Medical Informatics and Digital Health Multilingual Ontology (MIMO). Hosted on the Health Terminology/Ontology Portal (HeTOP), MIMO contains around 1,000 concepts, 460 MeSH Descriptors, 220 MeSH Concepts, and more than 300 newly created concepts. MIMO is continuously updated to comprise as recent as possible concepts and their translations in more than 30 languages. Moreover, the MIMO's development team constantly improves MIMO content and supporting information. Thus, during workshop discussions and one-on-one exchanges, the MIMO team has collected domain experts' opinions about the community's interests and suggestions for future enhancements. Moreover, MIMO will be integrated to support the annotation and categorization of research products into the HosmartAI European project involving more than 20 countries around Europe and worldwide. CONCLUSION: MIMO is hosted by HeTOP (Health Terminology/Ontology Portal), which integrates 100 terminologies and ontologies in 55 languages. MIMO is freely available online. MIMO is portable to other knowledge platforms as part of MIMO's main aims to facilitate communication between medical librarians, translators, and researchers as well as to support students' self-learning.

Cell seeding of tissue engineering scaffolds studied by Monte Carlo simulations.

Tissue engineering (TE) aims at building multicellular structures in the laboratory in order to regenerate, to repair or replace damaged tissues. In a well-established approach to TE, cells are cultured on a biocompatible porous structure, called scaffold. Cell seeding of scaffolds is an important first step. Here we study conditions that assure a uniform and rapid distribution of cells within the scaffold. The movement of cells has been simulated using the Metropolis Monte Carlo method, based on the principle that cellular system tends to achieve the minimum energy state. For different values of the model parameters, evolution of the cells' centre of mass is followed, which reflects the distribution of cells in the system. For comparison with experimental data, the concentration of the cells in the suspension adjacent to the scaffold is also monitored. Simulations of cell seeding are useful for testing different experimental conditions, which in practice would be very expensive and hard to perform. The computational methods presented here may be extended to model cell proliferation, cell death and scaffold degradation.

Extracting clinical information to support medical decision based on standards.

The paper presents a method connecting medical databases to a medical decision system, and describes a service created to extract the necessary information that is transferred based on standards. The medical decision can be improved based on many inputs from different medical locations. The developed solution is described for a concrete case concerning the management for chronic pelvic pain, based on the information retrieved from diverse healthcare databases.