Patient Preferences: An Unexplored Area in the Post-Pandemic Era

The rapid advancement of clinical research has resulted into numerous therapeutic options currently available for most of the diseases. During the patient therapeutic journey, many health-related decisions are necessary requiring patients to choose between the potential health benefits of an intervention, versus the countervailing risk of serious adverse health outcomes. Studies focusing exactly on those patient preferences aim to elicit preferences with the common objective to generate information that facilitates comparing the importance of attributes of interest. The world has experienced a dramatic change in patient's preferences during the pandemic. Despite the importance of patient preference studies in healthcare decision making, there is a lack of effective storage and accessibility of relevant data for wider use. In this paper the authors present the design of a platform to systematically collect, curate, annotate, index, synthesize and make available pertinent information of patient preference studies so that they can be further exploited by decision support tools. © 2021 IEEE.

Report on the Third International Workshop on Semantic Web Meets Health Data Management (SWH 2020)

Creating a holistic view of patient data comes with many challenges but also brings many benefits for disease prediction, prevention, diagnosis, and treatment. Especially in the COVID-19 era, this is more important than ever before. The third International Workshop on Semantic Web Meets Health Data Management (SWH) was aimed at bringing together an interdisciplinary audience who was interested in the fields of Semantic Web, data management, and health informatics. The workshop goal was to discuss the challenges in healthcare data management and to propose new solutions for the next generation of data-driven healthcare systems. In this article, we summarize the outcomes of the workshop, and we present a number of key observations and research directions that emerged from presentations.

Predictive analytics based on open source technologies for acute respiratory distress syndrome

The continuous growth of high volumes of biomedical data in healthcare generates significant challenges for their efficient management. This data requires efficient management and analysis in order to derive meaningful and actionable information. Especially in the current situation of the COVID-19 pandemic, complications that might occur after the onset of this disease are important. Such a complication is Acute Respiratory Distress Syndrome (ARDS), which is a serious respiratory condition with high mortality and associated morbidity. A large number of basic and clinical studies demonstrated that early diagnosis and intervention are keys to improve the survival rate of patients with ARDS. Therefore, there is a pressing need for the development and clinical testing of predictive models for ARDS events, which might improve the clinical diagnosis or the management of ARDS. In this paper, we focus on two distinct objectives;namely a) to design a scalable data science platform, built on open source technologies able to streamline the development of such models, and b) to exploit the platform using publicly available big datasets to develop such models. To this direction, we employ random forests and logistic regression algorithmic models for the early prediction and diagnosis of ARDS. Our approach achieves better results in all metrics, when compared to relevant published efforts using the MIMIC III dataset. © 2021 IEEE.

An Outbreak Response Tool to Effectively Support Surveillance of Suspect, Probable and Confirmed Incidence Cases while Staying Safe in COVID-19

During the burst of the coronavirus pandemic, in early-midst 2020, public health authorities worldwide considered appropriate identification, isolation and contact tracing as the most appropriate strategy for infection containment. This work presents an outbreak response tool, designed for public health authorities to effectively track suspect, probable and confirmed incidence cases in a pandemic by means of a mobile app used by citizens to provide immediate feedback. It is developed based on an already existing personal health record app, which has been extended to properly accommodate specific needs that emerged during the crisis. The aim is to better support human tracers and should not be confused with proximity tracking apps. It respects safety and security regulations, while at the same time it conforms to international standards and widely accepted medical protocols. Issues relevant to privacy concerns, and interoperability with available patient registries and data analytics tools are also examined to better support public healthcare delivery and contain the spread of the infection.

Prediction of COVID-19 Infection Based on Symptoms and Social Life Using Machine Learning Techniques

COVID-19 pandemic has affected nearly every aspect of life. Observing online the spread of the virus can offer a complementary view to the cases that are daily officially recorded and reported. In this article, we present an approach that exploits information available on social media to predict whether a patient has been infected with COVID-19. Our approach is based on a Bayesian model that is trained using data collected online. Then the trained model can be used for evaluating the possibility that new patients are infected with COVID-19. The experimental evaluation presented shows the high quality of our approach. In addition, our model can be incrementally retrained, so that it becomes more robust in an efficient way. © 2021 ACM.

Staying Safe in COVID-19

We protect the community. We protect ourselves. We decongest the health system. We stay safe in COVID-19. One of the many responses to the global call against the world pandemic of COVID-19 resulted in "Safe in COVID-19", an electronic platform developed by the Institute of Computer Science of the Foundation for Research and Technology - Hellas (FORTH-ICS), which is intended for tracing suspect, probable and confirmed incidence cases.

A Probabilistic Logic Program that Predicts COVID-19 Infection and the Possibility a Patient to Recover

COVID-19 is a disease that has infected almost the whole world and has been pronounced as a global pandemic. The digital health domain has already tried to respond to the pandemic challenges by developing algorithms and applications that make predictions on the infection and the corresponding outcome in case of infection. In this direction, in this paper, we present "COVID-19 Detect & Predict", an application that can detect and predict COVID-19 infection through probabilistic logic reasoning, and in case of infection, it can also predict the possibility a patient to recover. We demonstrate the effectiveness of our solution on realistic datasets, showing its potential benefits. Our approach is the first to detect and predict COVID-19 infection through probabilistic logic reasoning to the best of our knowledge. © 2020 ACM.

Data management, semantics and personal health apps for staying safe in COVID-19

The ongoing coronavirus pandemic, is affecting the lives of millions of people, while changing our society by establishing new norms for social life, business, and traveling. The digital health domain has already tried to respond to the pandemic challenges, by the rapid development and release of mobile apps aiming to “flatten the curve” of the increasing number of COVID-19 cases. In this paper, starting from our own developed app to support citizens staying “Safe in COVID-19”, we present our vision on how semantics and data management could highly contribute, along with personal health apps, to the secondary usage of available data in order to support effective disease management, prediction and increase the collective knowledge on the disease. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).