Ebook: pHealth 2021

pHealth 2021 is the 18th conference in a series of scientific events that has brought together expertise from medicine, technology, politics, administration, and social domains, and even from philosophy and linguistics. It opens a new chapter in the success story of this series of international conferences on wearable or implantable micro and nano technologies for personalized medicine.

Begun in 2003 as a Dissemination Activity in the framework of a European Project on Wearable Micro and Nano Technologies for Personalized Health with personal health management systems, pHealth conferences have evolved to become truly interdisciplinary and global events. As comprehensively represented in the conference series, pHealth also covers technological and biomedical facilities, legal, ethical, social and organizational requirements and impacts, as well as the basic research necessary for the enabling of future-proof care paradigms. It thereby combines medical services with public health, prevention, social and elderly care, wellness and personal fitness to establish participatory, predictive, personalized, preventive, and effective care settings. In this way, it has attracted scientists, developers, and practitioners from various technologies, medical and health disciplines, legal affairs, politics, and administration from all over the world. The conference brings together health-service vendor and provider institutions, funding organizations, government departments, academic institutions, professional bodies, and also patients and citizens representatives.

Smart mobile systems, such as microsystems, smart textiles, smart implants, sensor-controlled medical devices, and innovative sensor and actuator principles and techniques, as well as related body, local and wide-area networks up to cloud services, have become important enablers for telemedicine and ubiquitous pervasive health as the next generation of healthcare services. Social media and gamification have added even further knowledge to pHealth as an eco-system.

The OECD has defined four basic areas on which to focus in the new care model: addressing the challenges of big data; fostering meaningful innovation; understanding and addressing the potential new risks; and supporting a concerted effort to un-silo communities for a virtual care future. The benefits of pHealth technologies offer enormous potential for all stakeholder communities, including patients, citizens, health professionals, politicians, healthcare establishments, and companies from the biomedical technology, pharmaceutical, and telecommunications domain, not only in terms of improvements in medical quality and industrial competitiveness, but also for the management of healthcare costs.

The pHealth 2021 conference benefits from the experience of and the lessons learned by the organizing committees of previous pHealth events, particularly 2009 in Oslo, 2010 in Berlin, 2011 in Lyon, 2012 in Porto, 2013 in Tallinn, 2014 in Vienna, 2015 in Västerås, 2016 in Heraklion, 2017 in Eindhoven, 2018 in Gjøvik, 2019 in Genoa, and 2020 in Prague. The 2009 conference raised the interesting idea of having special sessions focusing on a particular topic and organized by a mentor/moderator. The Berlin event in 2010 initiated workshops on particular topics taking place before to the official start of the conference. Lyon, in 2011, launched so-called dynamic demonstrations which allowed participants to demonstrate software and hardware solutions on the fly without the need for a booth. Implementing pre-conference events, pHealth 2012 in Porto gave attendees a platform for presenting and discussing recent developments and provocative ideas that helped to animate the sessions. The highlight of pHealth 2013 in Tallinn was the special session on European project success stories, and also presentations on up and coming paradigm changes and challenges associated with Big Data, Analytics, Translational and Nano Medicine, etc. Vienna, in 2014, focused on lessons learned from national and international R&D activities and practical solutions, particularly from Horizon 2020, the new EU Framework Program for Research and Innovation. Alongside reports about technology transfer support and building ecosystems and value chains to ensure better time to market and higher impact of knowledge-based technologies, the acceptability of solutions, particularly considering security and privacy aspects were presented and deeply discussed. pHealth 2015, held in Västerås, addressed mobile technologies, knowledge-driven applications and computer-assisted decision support, as well as apps designed to support the elderly and chronic patients in daily and possibly independent living. The fundamental scientific and methodological challenges of adaptive, autonomous, and intelligent pHealth approaches, the new role of patients as consumers and active parties with growing autonomy and related responsibilities, as well as the requirements and solutions for mHealth in low- and medium-income countries were also considered. The 2016 pHealth conference was aimed at the integration of biology and medical data, the deployment of mobile technologies through the development of micro-nano-bio smart systems, the emphasis on personalized health, virtual care, precision medicine, big bio-data management and analytics. The pHealth 2017 event in Eindhoven provided an inventory of the former conferences by summarizing requirements and solutions for pHealth systems, highlighting the importance of trust, and focused afresh on the behavioral aspects of designing and using pHealth systems. A specific aspect addressed was the need for flexible, adaptive and knowledge-based systems, as well as decision intelligence. pHealth 2018 established national and European satellite workshops, so complementing the more theoretical consideration of the majority of the papers with organizational and practical experiences. Borrowing good experiences from former events, pHealth 2018 responded to the national and regional need for advancing healthcare systems and their services to citizens and health professionals. pHealth 2019 placed a particular focus on artificial intelligence (AI) and machine learning (ML) and their deployment for decision support, and ethical challenges and related international manifests were discussed in depth in that context. pHealth 2020 – organized as a virtual event – addressed AI and robots, bio-data management and analytics for health and social care, security, privacy and safety challenges, integrated care, and also the intelligent management of specific diseases including the Covid-19 pandemic. The 2021 edition of the pHealth conference series – again a virtual event – focuses on digital health ecosystems in the transformation of healthcare towards personalized, participative, preventive, predictive precision medicine (5P medicine). The deployment of mobile technologies, micro-nano-bio smart systems, bio-data management and analytics, autonomous and intelligent systems, as well as the Health Internet of Things (HIoT) for personalized health, systems medicine, public health and virtual care are thereby especially considered. The conference also addresses new potential risks for security and privacy as well as safety chances and challenges, trustworthiness of partners and processes, and the motivation and empowerment of patients in care processes. The multilateral benefits of pHealth technologies offer enormous potential for all stakeholder communities, not only in terms of improvements in medical quality and industrial competitiveness, but also for managing health care costs and, last but not least, improving patient experiences.

The conference is organized under the patronage of the City of Genoa and the Liguria Regional Authority, the University of Genoa and the Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS) in particular, and Healthtrophy srl as a University of Genoa’s Spin-Off. Following a long-standing tradition, the Working Groups “Electronic Health Records (EHR)”, “Personal Portable Devices (PPD)”, “Security, Safety and Ethics (SSE)”, and “Translational Health Informatics” of the European Federation for Medical Informatics (EFMI) have also been actively involved in the preparation and realization of the pHealth 2021 event.

Neither the pHealth 2021 Conference nor the publication of the pHealth 2021 Proceedings by IOS Press would have been possible without the aforementioned financial and spiritual supporters and sponsors. This also includes the Italian Scientific Society of Biomedical Informatics, the IEEE Engineering in Medicine and Biology Society (EMBS), the Camber of Engineers Genoa, and the European Federation for Medical Informatics (EFMI) and standard-setting organizations such as HL7 International, ISO/TC215 or CEN/TC251.

The editors are also grateful to the Members of the international Scientific Program Committee, but especially for the dedicated efforts of members of the Local Organizing Committee and their supporters for the careful preparation and the smooth operation of the conference.

Bernd Blobel, Mauro Giacomini

(Editors)

For meeting the challenge of aging, multi-diseased societies, cost containment, workforce development and consumerism by improved care quality and patient safety as well as more effective and efficient care processes, health and social care systems around the globe undergo an organizational, methodological and technological transformation towards personalized, preventive, predictive, participative precision medicine (P5 medicine). This paper addresses chances, challenges and risks of specific disruptive methodologies and technologies for the transformation of health and social care systems, especially focusing on the deployment of intelligent and autonomous systems.

The ‘patient summary’ has an important role in delivering continuity and coordination of a person’s health and care. ‘patient summary’ implementations are pervasive and important to both healthcare providers and to their subjects of care. The digital version of the patient summary, however, often falls short of its intended functionality and its potential value. The requirements of summarization and what they mean for the communication situation in which the summarization of health and care data takes place has been analyzed. The purpose is to understand the limitations and potential of current digital solutions for communicating a ‘patient summary’. The International Patient Summary (IPS) standard is a step towards communicating safe, relevant patient summaries for use throughout the world. To meet this grand challenge, the IPS can capitalize upon the inherent capacity and competence of all people to produce and consume summaries.

The Covid-19 pandemic has only accelerated the need and desire to deal more openly with mortality, because the effect on survival is central to the comprehensive assessment of harms and benefits needed to meet a ‘reasonable patient’ legal standard. Taking the view that this requirement is best met through a multi-criterial decision support tool, we offer our preferred answers to the questions of What should be communicated about mortality in the tool, and How, given preferred answers to Who for, Who by, Why, When, and Where. Summary measures, including unrestricted Life Expectancy and Restricted Mean Survival Time are found to be reductionist and relative, and not as easy to understand and communicate as often asserted. Full lifetime absolute survival curves should be presented, even if they cannot be ‘evidence-based’ beyond trial follow-up limits, along with equivalent measures for other criteria in the (necessarily) multi-criterial decision. A decision support tool should relieve the reasonable person of the resulting calculation burden.

pHealth is a data (personal health information) driven approach that use communication networks and platforms as technical base. Often it’ services take place in distributed multi-stakeholder environment. Typical pHealth services for the user are personalized information and recommendations how to manage specific health problems and how to behave healthy (prevention). The rapid development of micro- and nano-sensor technology and signal processing makes it possible for pHealth service provider to collect wide spectrum of personal health related information from vital signs to emotions and health behaviors. This development raises big privacy and trust challenges especially because in pHealth similarly to eCommerce and Internet shopping it is commonly expected that the user automatically trust in service provider and used information systems. Unfortunately, this is a wrong assumption because in pHealth’s digital environment it almost impossible for the service user to know to whom to trust, and what the actual level of information privacy is. Therefore, the service user needs tools to evaluate privacy and trust of the service provider and information system used. In this paper, the authors propose a solution for privacy and trust as results of their antecedents, and for the use of computational privacy and trust. To answer the question, which antecedents to use, two literature reviews are performed and 27 privacy and 58 trust attributes suitable for pHealth are found. A proposal how to select a subset of antecedents for real life use is also provided.

The paper describes some aspects of precision medicine and shows the importance of pharmacokinetics and pharmacodynamics for the therapeutic drug monitoring and model-informed precision dosing. A key element in the design of the pharmacokinetics and pharmacodynamics (PKPD) models is relevant literature search that represents an essential step in the procurement and validation of a new drug. Available search engine resources do not offer specific functionalities that are required for efficient and relevant search in reliable literature sources. We present a prototype of such an intelligent search engine and show its results on real project data.

In this ongoing fall of the year 2021, many disciplines are frightened by the Covid-19 situation. A generalized sense of Scientific and administrative impotence, – in keeping the pandemic under real control, – is felt widely in Society. In this Invited Lecture the author reminds us of the blows suffered, recalls pertinent elements present in our social organization, browses selected eHealth experiences and proposes an open agenda of actions to allow the eHealth to help the population segments better, and individuals as well.

The Coronavirus pandemic has surprised the world and social media was extremely used to express frustrations and development of the cases found. Social media tools, such as Twitter, show a comparable impact with the number of tweets related to COVID-19 indicating remarkable development in a limited ability to focus time. The purpose of this paper is to investigate the impact of Coronavirus on the United States of America (USA) and New Zealand (NZ), and how that is reflected in a sentiment analysis through the examination of American and New Zealand tweets. We have gathered tweets from a March 2020 – August 2020 and used sentiment extraction on the tweets. The major finding of this sentiment extraction is the fact that the overall average sentiment over the 5-month period stayed in a negative range in the USA and NZ. This paper aims to analyze these trends, identify patterns, and determine whether these trends were caused by the COVID-19 pandemic or outside sources. One trend that was analyzed was the spike of COVID-19 results in relation to the number of protests occurring in the USA.

Digital information consists of sequences of numbers that are selections. So far, these are defined by context. We can globalize this by using an efficient global pointer (UL) as “context”. The article explains new globally identified and defined “Domain Vectors” (DVs) for transporting digital information. They have the structure “UL plus sequence of numbers”, where UL is an efficient identifier and global pointer (link) to the unified online definition of the sequence of numbers. Thus, the format of the number sequence and its meaning is defined online. This opens up far-reaching new possibilities for the efficient exchange, comparison and search of information. It can form the basis for a new global framework that improves the reproducibility, search, and exchange of data across systems, borders, and languages.

The global pandemic over the past two years has reset societal agendas by identifying both strengths and weaknesses across all sectors. Focusing in particular on global health delivery, the ability of health care facilities to scale requirements and to meet service demands has detected the need for some national services and organisations to modernise their organisational processes and infrastructures. Core to requirements for modernisation is infrastructure to share information, specifically structural standardised approaches for both operational procedures and terminology services. Problems of data sharing (aka interoperability) is a main obstacle when patients are moving across healthcare facilities or travelling across border countries in cases where emergency treatment is needed. Experts in healthcare service delivery suggest that the best possible way to manage individual care is at home, using remote patient monitoring which ultimately reduces cost burden both for the citizen and service provider. Core to this practice will be advancing digitalisation of health care underpinned with safe integration and access to relevant and timely information. To tackle the data interoperability issue and provide a quality driven continuous flow of information from different health care information systems semantic terminology needs to be provided intact. In this paper we propose and present ContSonto a formal ontology for continuity of care based on ISO 13940:2015 ContSy and W3C Semantic Web Standards Language OWL (Web Ontology Language). ContSonto has several benefits including semantic interoperability, data harmonization and data linking. It can be use as a base model for data integration for different healthcare information models to generate knowledge graph to support shared care and decision making.

This article describes the results of feature extraction from unstructured medical records and prediction of postoperative complications for patients with thoracic aortic aneurysm operations using machine learning algorithms. The datasets from two different medical centers were integrated. Seventy-two features were extracted from Russian unstructured medical records. We formulated 8 target features: Mortality, Temporary neurological deficit (TND), Permanent neurological deficit (PND), Prolonged (> 7 days) lung ventilation (LV), Renal replacement therapy (RRT), Bleeding, Myocardial infarction (MI), Multiple organ failure (MOF). XGBoost showed the best performance for most target variables (F-measure 0.74–0.95) which is comparable to recent results in cardiovascular postoperative risks prediction.

Electronic Medical Records (EMR) contain a lot of valuable data about patients, which is however unstructured. There is a lack of labeled medical text data in Russian and there are no tools for automatic annotation. We present an unsupervised approach to medical data annotation. Morphological and syntactical analyses of initial sentences produce syntactic trees, from which similar subtrees are then grouped by Word2Vec and labeled using dictionaries and Wikidata categories. This method can be used to automatically label EMRs in Russian and proposed methodology can be applied to other languages, which lack resources for automatic labeling and domain vocabularies.

The relevance of this study lies in improvement of machine learning models understanding. We present a method for interpreting clustering results and apply it to the case of clinical pathways modeling. This method is based on statistical inference and allows to get the description of the clusters, determining the influence of a particular feature on the difference between them. Based on the proposed approach, it is possible to determine the characteristic features for each cluster. Finally, we compare the method with the Bayesian inference explanation and with the interpretation of medical experts [1].

In this paper, we present a framework, which aims at facilitating the choice of the best strategy related to the treatment of periprosthetic joint infection (PJI). The framework includes two models: a detailed non-Markovian model based on the decision tree approach, and a general Markov model, which captures the most essential states of a patient under treatment. The application of the framework is demonstrated on the dataset provided by Russian Scientific Research Institute of Traumatology and Orthopedics “R.R. Vreden”, which contains records of patients with PJI occurred after total hip arthroplasty. The methods of cost-effectiveness analysis of treatment strategies and forecasting of individual treatment outcomes depending on the selected strategy are discussed.

Today pneumonia is one of the main problems of all countries around the world. This disease can lead to early disability, serious complications, and severe cases of high probabilities of lethal outcomes. A big part of cases of pneumonia are complications of COVID-19 disease. This type of pneumonia differs from ordinary pneumonia in symptoms, clinical course, and severity of complications. For optimal treatment of disease, humans need to study specific features of providing 19 pneumonia in comparison with well-studied ordinary pneumonia. In this article, the authors propose a new approach to identifying these specific features. This method is based on creating dynamic disease models for COVID and non-COVID pneumonia based on Bayesian Network design and Hidden Markov Model architecture and their comparison. We build models using real hospital data. We created a model for automatically identifying the type of pneumonia (COVID-19 or ordinary pneumonia) without special COVID tests. And we created dynamic models for simulation future development of both types of pneumonia. All created models showed high quality. Therefore, they can be used as part of decision support systems for medical specialists who work with pneumonia patients.

Business process modeling aims to construct digital representations of processes being executed in the company. However, models derived from the event logs of their execution tend to overcomplicate the desired representation, making them difficult to apply. The most accurate recovery of the business process model requires a comprehensive study of the various artifacts stored in the company’s information system. This paper, however, aims to explore the possibility to automatically obtain the most accurate model of business process, using mutual optimization of models recovered from a set of event logs. Further, the obtained models are executed in multi-agent simulation model of company, and the resulting event logs are examined to determine patterns that are specific to distinct employees and those that generally characterize business process.

In this paper, we propose a health data sharing infrastructure which aims to empower a democratic health data sharing ecosystem. Our project, named Health Democratization (HD), aims to enable seamless data mobility of health data across trust boundaries, through addressing structural and functional challenges of its underlying infrastructure with a throughout core concept of data democratization. A programmatic design of HD platform was elaborated, followed by an introduction about one of our exploratory designs —an “reverse onus” mechanism that aims to incentivize creditable data accessing behaviors. This scheme shows a promising prospect of enabling a democratic health data sharing platform.

According to different systematic reviews incidence of thoracic aortic aneurysms (TAA) in the general population is increasing in frequency ranging from 5 to 10.4 per 100000 patients. However, only few studies have illustrated the role of different risk factors in the onset and progression of ascending aortic dilatation. Currently, noninvasive imaging techniques are used to assess the progression rate of aortic and aortic valve disease. Transthoracic (TT) Echocardiographic examination routinely includes evaluation of the aorta It is the most available screening method for diagnosis of proximal aortic dilatation. Since the predominant area of dilation is the proximal aorta, TT-echo is often sufficient for screening. We retrospectively analyzed the ECHO database with 78499 echocardiographic records in the Almazov National Medical Research Centre to identify patients with aneurysm. Detailed information including demographic characteristics, ECHO results and comorbidities were extracted from outpatient clinic and from hospital charts related to hospitalizations occurring within a year before index echocardiography was performed. Comorbid diseases were similarly extracted from outpatient clinic and/or hospital admissions. The classifier showed an AUC-ROC for predicting of aneurism detection after a repeated ECHO at 82%.

Systematizing and conceptualizing of the components of the patient’s diagnosis at definite Clinical Situations are the important steps in constructing the medical electronic platforms to monitor the drug treatment quality and to realize the process of risk-management of drug care. The risk-management is a hard and expensive process for the non-profit hospitals. The Information technologies have a high potential for solving these problems. The conceptual schemes to construct the multimodal medical electronic platform were discussed. The Information Space of Clinical Practice, Information Field for formulating the detailed patient’s diagnosis at the definite Clinical Situations, Information Environment for the components of the detailed patient’s diagnosis were described.

In this paper we propose a new definition of digital phenotype to enrich the formulation with information stored in the Electronic Health Records (EHR) plus data obtained using wearables. On this basis, we describe how to use this formalism to represent the health state of a patient in a given moment (retrospective, present, or future) and how can it be applied for personalized medicine to find out the mutations that should be introduced at present to reach a better health status in the future.

The paper introduces a multilayer long short-term memory (LSTM) based auto-encoder network to spot abnormalities in fetal ECG. The LSTM network was used to detect patterns in the time series, reconstruct errors and classify a given segment as an anomaly or not. The proposed anomaly detection method provides a filtering procedure able to reproduce ECG variability based on the semi-supervised paradigm. Experiments show that the proposed method can learn better features than the traditional approach without any prior knowledge and subject to proper signal identification can facilitate the analysis of fetal ECG signals in daily life.

According to the “Istituto Superiore di Sanita‘” (ISS), hospital infections are the most frequent and serious complication of health care. This constitutes a real health emergency which requires incisive and joint action at all levels of the local and national health organization. Most of the valuable information related to the presence of a specific microorganism in the blood are written into the notes field of the laboratory exams results. The main objective of this work is to build a Natural Language Processing (NLP) pipeline for the automatic extraction of the names of microorganisms present in the clinical texts. A sample of 499 microbiological notes have been analysed with the developed system and all the microorganisms names have been extracted correctly, according to the labels given by the expert.