Dieter Hayn

• DI Dr., MBA
• Senior Scientist at Austrian Institute of Technology

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with severe complications such as ischemic stroke and heart failure. Early detection is essential for timely intervention; however, traditional diagnostic methods often lack scalability and accessibility. This project explores the use of photoplethysmography (PPG) signals recorde...

Background Telehealth has been effective in managing cardiovascular diseases like stroke and heart failure and has shown promising results in managing patients with peripheral arterial disease. However, more work is needed to fully understand the effect of telehealth-based predictive modeling on the physical fitness of patients with peripheral arte...

BACKGROUND Telehealth has been effective in managing cardiovascular diseases like stroke and heart failure and has shown promising results in managing patients with peripheral arterial disease (PAD). However, more work is needed to fully understand the effect of telehealth based predictive modelling on the physical fitness of PAD patients. OBJECTI...

Access to healthcare data for secondary use in clinical research is often restricted due to privacy concerns or business interests, hindering comprehensive analysis across patient pathways. The Smart FOX project seeks to address this challenge by developing concepts, methods, and tools to facilitate citizen/patient-driven donations of health data f...

Secondary use of data for research purposes is especially important in rare diseases (RD), since, per definition, data are sparse. The European Joint Programme on Rare Diseases (EJP RD) aims at developing an RD infrastructure which supports the secondary use of data. Significant amounts of RD data are a) distributed and b) available only in pseudon...

Background: The recent rise of large language models has triggered renewed interest in medical free text data, which holds critical information about patients and diseases. However, medical free text is also highly sensitive. Therefore, de-identification is typically required but is complicated since medical free text is mostly unstructured. With t...

Simple Summary Large datasets concerning childhood cancers are rare. Therefore, it is important to fully exploit all available data, which are distributed over several resources, including biomaterials, images, clinical trials, and registries. With privacy-preserving record linkage (PPRL), datasets can be merged, without disclosing the patients’ id...

Background: Guidelines recommend walking trainings for peripheral arterial disease (PAD) management. Supervised walking training is superior to walking advise to improve the walking distance. Telehealth service with nurse support may close this gap. Patients and methods: This study introduces a telehealth service, “Keep pace!”, which has been devel...

P38: Privacy-Preserving Record Linkage between the SIOPEN BIOPORTAL and the RDConnect Genome Phenome Analysis Platform via the EUPID Services

Background: This study focuses on the development of a neural network model to predict perceived sleep quality using data from wearable devices. We collected various physiological metrics from 18 participants over four weeks, including heart rate, physical activity, and both device-measured and self-reported sleep quality. Objectives: The primar...

BACKGROUND Home and telehealth-based interventions are increasingly used in cardiac rehabilitation (CR), a multidisciplinary model of health care. Digital tools such as wearables or digital training diaries are expected to support patients to adhere to recommended lifestyle changes, including physical exercise programmes. As previously published, t...

Background Home and telehealth-based interventions are increasingly used in cardiac rehabilitation, a multidisciplinary model of health care. Digital tools such as wearables or digital training diaries are expected to support patients to adhere to recommended lifestyle changes, including physical exercise programs. As previously published, the EPIC...

Telehealth services are becoming more and more popular, leading to an increasing amount of data to be monitored by health professionals. Machine learning can support them in managing these data. Therefore, the right machine learning algorithms need to be applied to the right data. We have implemented and validated different algorithms for selecting...

Artificial intelligence and machine learning have led to prominent and spectacular innovations in various scenarios. Application in medicine, however, can be challenging due to privacy concerns and strict legal regulations. Methods that centralize knowledge instead of data could address this issue. In this work, 6 different decentralized machine le...

Introduction Cardiovascular diseases are the leading cause of death worldwide and are partly caused by modifiable risk factors. Cardiac rehabilitation addresses several of these modifiable risk factors, such as physical inactivity and reduced exercise capacity. However, despite its proven short-term merits, long-term adherence to healthy lifestyle...

Heart failure is a common chronic disease which is associated with high re-hospitalization and mortality rates. Within the telemedicine-assisted transitional care disease management program HerzMobil, monitoring data such as daily measured vital parameters and various other heart failure related data are collected in a structured way. Additionally,...

Background: The aging population's need for treatment of chronic diseases is exhibiting a marked increase in urgency, with heart failure being one of the most severe diseases in this regard. To improve outpatient care of these patients and reduce hospitalization rates, the telemedical disease management program HerzMobil was developed in the past....

Background: The daily increasing amount of health data from different sources like electronic medical records and telehealth systems go hand in hand with the ongoing development of novel digital and data-driven analytics. Unifying this in a privacy-preserving data aggregation infrastructure can enable services for clinical decision support in pers...

Purpose: Long-term follow-up (LTFU) care for childhood cancer survivors (CCSs) is essential to improve and maintain their quality of life. The Survivorship Passport (SurPass) is a digital tool which can aid in the delivery of adequate LTFU care. During the European PanCareSurPass (PCSP) project, the SurPass v2.0 will be implemented and evaluated a...

Purpose: Artificial intelligence and machine learning have led to prominent and spectacular innovations in various scenarios. Application in medicine, however, can be challenging due to privacy concerns and strict legal regulations. Methods that centralize knowledge instead of data could address this issue. Methods: In this work, 6 different decent...

In this study, we investigated the effect of time shift in heartrate measurement by wearables, which might to be used in telehealth applications for patients suffering from heart failure. Six wearables commercially available on the market were tested in a 14-hour measurement. Each wearable was tested three times by five different test persons. A re...

Background: Clinical notes provide valuable data in telemonitoring systems for disease management. Such data must be converted into structured information to be effective in automated analysis. One way to achieve this is by classification (e.g. into categories). However, to conform with privacy regulations and concerns, text is usually de-identifi...

The demand for extended care for people suffering from heart failure is omnipresent. Wearables providing continuous heart rate measurement through optical sensors are of great interest due to their ease of use without the need for medical staff and their low cost. In this study, seven wearables were tested in fifteen measurement runs, with a durati...

Background: Python and MATLAB both are common tools used for predictive modelling applications, not only in healthcare. In our predictive modelling group, both tools are widely used. None of the two tools is optimal for all tasks along the value chain of predictive modelling in healthcare. Objectives: The aim of this study was to explore differe...

Background: Telehealth services for chronic diseases are becoming more and more popular since they are expected to improve health outcomes and reduce costs. Especially for diabetes patients, life-long disease management is required. However, there are situations in a patient's life, when motivation to continue the participation in disease manageme...

Frailty is one of the major problems associated with an aging society. Therefore, frailty assessment tools which support early detection and autonomous monitoring of the frailty status are heavily needed. One of the most used tests for functional assessment of the elderly is the “Timed Up-and-Go” test. In previous projects, we have developed an ult...

Heart failure is a serious disease which increases mortality as well as hospital admission rates for affected patients. Disease management programs supported by telehealth solutions are cost-effective approaches for reducing all-cause mortality and heart failure hospitalizations. A 6-minute walk test (6MWT) app could help heart failure patients to...

Frailty and falls are a major public health problem in older adults. Muscle weakness of the lower and upper extremities are risk factors for any, as well as recurrent falls including injuries and fractures. While the Timed Up-and-Go (TUG) test is often used to identify frail members and fallers, tensiomyography (TMG) can be used as a non-invasive t...

Telehealth services for long-term monitoring of chronically ill patients are becoming more and more common, leading to huge amounts of data collected by patients and healthcare professionals each day. While most of these data are structured, some information, especially concerning the communication between the stakeholders, is typically stored as u...

Objective: A third of people over 65 years experiences at least one fall a year. The Timed Up-and-Go test is commonly used to assess gait and balance and to evaluate an individual's risk of falling. Approach: We conducted a clinical study with 46 older participants for evaluating the fall risk assessment capabilities of an ultra-sound based Time...

Frailty and falls are the main causes of morbidity and disability in elderly people. The Timed Up-and-Go (TUG) test has been proposed as an appropriate method for evaluating elderly individuals' risk of falling. To analyze the TUG's potential for falls prediction, we conducted a clinical study with participants aged ≥ 65 years, living in nursing ho...

Machine Learning research and its application have gained enormous relevance in recent years. Their usage in medical settings could support patients, increase patient safety and assist health professionals in various tasks. However, medical data is often sparse, which renders big data analytics methods like deep learning ineffective. Data synthesis...

Background: Privacy-preserving record linkage (PPRL) is the process of detecting dataset entries that refer to the same individual within two independent datasets, without disclosing any personal information. While applied in different fields, it particularly attained importance in the medical sector. One popular PPRL method are Bloom filters. How...

Background: Heart failure is a chronic disease that affects around 26 million people worldwide. Projections assume a substantial increase in prevalence over the next years. To improve the survival rate and quality of life in patients suffering from heart failure, the European Society of Cardiology published guidelines for diagnosis and treatment....

Heart Failure is a severe chronic disease of the heart. Telehealth networks implement closed-loop healthcare paradigms for optimal treatment of the patients. For comprehensive documentation of medication treatment, health professionals create free text collaboration notes in addition to structured information. To make this valuable source of inform...

Lower-limb strength is a marker of functional decline in elders. This work studies the feasibility of using the quasi-periodic nature of the distance between a subjects’ back and the chair backrest during a 30-s chair–stand test (CST) to carry out unsupervised measurements based on readings from a low-cost ultrasound sensor. The device comprises an...

With the vast increase of digital healthcare data, there is an opportunity to mine the data for understanding inherent health patterns. Although machine-learning techniques demonstrated their applications in healthcare to answer several questions, there is still room for improvement in every aspect. In this paper, we are demonstrating a method that...

Life expectancy is rising in most parts of the world as is the prevalence of chronic diseases. Suboptimal adherence to long‐term medications is still rather the norm than the exception, although it is well known that suboptimal adherence compromises the therapeutic effectiveness. Information and communications technology provides new concepts for i...

Background: Huge amounts of data are collected by healthcare providers and other institutions. However, there are data protection regulations, which limit their utilisation for secondary use, e.g. Research: In scenarios, where several data sources are obtained without universal identifiers, record linkage methods need to be applied to obtain a c...

Background: Predictive modelling is becoming increasingly important in the healthcare sector. A comprehensive understanding of obtained models and their predictions is indispensable for the development and later acceptance of such systems. Objectives: A general concept of a toolset that supports data scientists in the development of predictive m...

Adoption of electronic medical records in hospitals generates a large amount of data. Health care professionals can easily lose their sight on the important insights of the patients' clinical and medical history. Although machine learning algorithms have already proved their significance in healthcare research, remains a challenge translation and d...

Background: In a database of electronic health records, the amount of available information varies widely between patients. In a real-time prediction scenario, a machine learning model may receive limited information for some patients. Objectives: Our aim was to evaluate the influence of missing data on real-time prediction of delirium, and dete...

Objective: Recent advantages in mHealth-enabled ECG recorders boosted the demand for algorithms, which are able to automatically detect cardiac anomalies with high accuracy. Approach: We present a combined method of classical signal analysis and machine learning which has been developed during the Computing in Cardiology Challenge (CinC) 2017. A...

Due to an ever-increasing amount of data generated in healthcare each day, healthcare professionals are more and more challenged with information. Predictive models based on machine learning algorithms can help to quickly identify patterns in clinical data. Requirements for data driven decision support systems for health and care ( DS4H ) are simil...

Delirium is an acute neuropsychiatric syndrome which is common in elderly patients during their hospitalisation and is associated with an increased mortality and morbidity. Since delirium is a) often underdiagnosed and b) preventable if early signs are detected,igh expectations are set in delirium risk assessment during hospital admission. In our l...

Digitalisation of health care for the purpose of medical documentation lead to huge amounts of data, hence having an opportunity to derive knowledge and associations of different attributes recorded. Many health care events can be prevented when identified. Machine learning algorithms could identify such events but there is ambiguity in understandi...

Hospital readmissions receive increasing interest, since they are burdensome for patients and costly for healthcare providers. For the calculation of reimbursement fees, in Germany there is the German-Diagnosis Related Groups (G-DRG) system. For every hospital stay, data are collected as a so-called "case", as the basis for the subsequent reimburse...

Systematic reviews are widely used as a tool for decision making to establish new clinical guidelines. Reviews can be time-consuming, potentially leaving authors with thousands of citations to screen. Software tools for assisting reviewers in this process are available, however, only few use text mining techniques to reduce screening time. In this...

Unplanned hospital readmissions are a burden to the healthcare system and to the patients. To lower the readmission rates, machine learning approaches can be used to create predictive models, with the intention to provide actionable information for caregivers. According to the German Diagnosis Related Groups (G-DRG) system, for every stay in a Germ...

Background: Blood transfusion is a highly prevalent procedure in hospitalized patients and in some clinical scenarios it has lifesaving potential. However, in most cases transfusion is administered to hemodynamically stable patients with no benefit, but increased odds of adverse patient outcomes and substantial direct and indirect cost. Therefore,...

Delirium is an acute confusion condition, which is common in elderly and often misdiagnosed in hospitalized patients. Early identification and prevention of delirium could reduce morbidity and mortality rates in those affected and reduce hospitalization costs. We have developed and validated a multivariate prediction model that predicts delirium an...

Background: Automatic event detection is used in telemedicine based heart failure disease management programs supporting physicians and nurses in monitoring of patients' health data. Objectives: Analysis of the performance of automatic event detection algorithms for prediction of HF related hospitalisations or diuretic dose increases. Methods:...

Background: Machine learning algorithms are a promising approach to help physicians to deal with the ever increasing amount of data collected in healthcare each day. However, interpretation of suggestions derived from predictive models can be difficult. Objectives: The aim of this work was to quantify the influence of a specific feature on an in...

Data from two contexts, i.e. the European Unresectable Neuroblastoma (EUNB) clinical trial and results from comparative genomic hybridisation (CGH) analyses from corresponding tumour samples shall be provided to existing repositories for secondary use. Utilizing the European Unified Patient IDentity Management (EUPID) as developed in the course of...

Research in blood transfusions mainly focuses on Donor Blood Management, including donation, screening, storage and transport. However, the last years saw an increasing interest in recipient related optimizations, i.e. Patient Blood Management (PBM). Although PBM already aims at reducing transfusion rates by pre- and intra-surgical optimization, th...

Opsoclonus Myoclonus Syndrome (OMS) is a rare disease in children which is often associated with neuroblastoma and, therefore, requires treatment by pediatric neurologists and oncologists. The ongoing OMS trial investigates questions related to OMS and potentially underlying neuroblastomas. To support this trial with an adequate IT infrastructure,...

Background: Electronic Patient Reported Outcomes (ePRO) gathered using telemonitoring solutions might be a valuable source of information in rare cancer research. Objectives: The objective of this paper was to develop a concept and implement a prototype for introducing ePRO into the existing neuroblastoma research network by applying Near Field...

Home-based monitoring might be useful to reduce the burden of long-lasting oncological treatment for children. Current telemonitoring applications focus on chronic diseases or elderly people. Based on the workflow for different stakeholders and the identification of parameters important in paediatric oncology, we developed a prototype of a smartpho...

Data that has been collected in the course of clinical trials are potentially valuable for additional scientific research questions in so called secondary use scenarios. This is of particular importance in rare disease areas like paediatric oncology. If data from several research projects need to be connected, so called Core Datasets can be used to...

Pressure ulcers are a common skin disease which is associated with pain, reduced autonomy, social isolation, and reduced quality of life. There are several systems for monitoring of pressure ulcer-related risk factors on the market, but up to now no satisfactory solution is available, especially for people with medium pressure ulcer risk. We presen...

The European Society of Cardiology guidelines for heart failure management are based on strong evidence that adherence to optimal medication is beneficial for heart failure patients. Telemonitoring with integrated clinical decision support enables physicians to adapt medication dose based on up to date vital parameters and reduces the number of hos...

We assessed a two-stage follow-up procedure for cardiac pacemakers, where in-clinic follow-ups were partly replaced by telemedical follow-ups. This was compared with the standard follow-up regime (in-clinic follow-up only). The new procedure required an electronic patient record, a telemedical follow-up unit for recording ECGs while the pacemaker w...

Heart failure is a common cardiac disease in elderly patients. After discharge, approximately 50% of all patients are readmitted to a hospital within six months. Recent studies show that home monitoring of heart failure patients can reduce the number of readmissions. Still, a large number of false positive alarms as well as underdiagnoses in other...

State-of-the-art decubitus prevention focuses mainly on special decubitus mattresses, which are indicated for extremely high risk only, while other risk factors such as nutrition or physical activity are hardly considered. Therefore, a monitoring system for decubitus prevention for persons with medium risk has been developed. The system consisted o...

This paper presents an algorithm for automated fetal QRS (fQRS) detection. The algorithm was developed with the Fetal ECG (FECG) Challenge Database from PhysioNet. This database provides noninvasive ECG signals recorded from the mother's abdomen, and expert annotations for fQRS locations.

Patients with diabetes were enrolled into a telemonitoring programme. They were offered the choice of collecting their health data either by using Near Field Communication (NFC) enabled mobile phones equipped with a dedicated application (App), or by means of a web-browser based user interface (Web). At the end of the study, each patient was catego...

Although immediate feedback concerning ECG signal quality during recording is useful, up to now not much literature describing quality measures is available. We have implemented and evaluated four ECG quality measures. Empty lead criterion (A), spike detection criterion (B) and lead crossing point criterion (C) were calculated from basic signal pro...

Poor patients' adherence to intake of prescribed medication has been identified as a serious problem in the treatment of chronically ill patients. Technical solutions are needed to measure and - if necessary - to increase the patients' adherence. A telemonitoring solution was developed to record a patient's medication intake based on smart blisters...

Decubitus is a skin disease caused by prolonged pressure exposure of single skin regions; it is a major problem in nursing and up to now no satisfying prevention systems exist. We prototypically implemented and tested the feasibility of several types of sensors in three different scenarios and conducted a survey in a group of specialists in the f...

Based on the current research, false alarm generation is a problem with patient health monitoring. Occurrence of high rates of irrelevant alarms might lead to ineffective therapy. The existing approaches dealing with the alarms are statistical or artificial intelligence. The presented results compare fixed manually adjustable vs. dynamic automatica...

This paper investigates the application of the enterprise information system (EIS) paradigm to standardized cardiovascular condition monitoring. There are many specifications in cardiology, particularly in the ECG standardization arena. The existence of ECG formats, however, does not guarantee the implementation of homogeneous, standardized solutio...

The study investigated weather influences on occurrence of alarm conditions among heart failure patients subjected to home telemonitoring. The telemonitored patients were located in the vicinities of five Austrian cities: Vienna, Graz, Innsbruck, Klagenfurt and Linz. The associated daily weather conditions were obtained from the Austrian Central In...

Heart failure consumes 2% of Austria's health budget. Re-hospitalization rates within 6 months after discharge are about 50% and earlier studies showed that even telemonitoring can only avoid up to 50% of these re-hospitalizations. Since leg edemas are a typical symptom, it was our aim to further reduce this rate, using new methods for edema detect...

Telemonitoring of vital signs is an established option in treatment of patients with chronic heart failure (CHF). In order to allow for early detection of atrial fibrillation (AF) which is highly prevalent in the CHF population telemonitoring programs should include electrocardiogram (ECG) signals. It was therefore the aim to extend our current hom...

Utilization of information and communication technologies such as mobile phones and wireless sensor networks becomes more and more common in the field of telemonitoring for chronic diseases. Providing elderly people with a mobile-phone-based patient terminal requires a barrier-free design of the overall user interface including the setup of wireles...

Patients with psoriasis who are being treated with biologics require intensive monitoring. However, the monitoring tool teledermatology is not commonly used. We investigated the applicability of a mobile phone based teledermatological system for monitoring psoriasis patients on biologic therapy. Nineteen patients were given mobile phones with built...

State-of-the-art mobile ECG recorders are usually not intended to be used by untrained personnel or by patients themselves. For that purpose, a suitable graphical user interface that provides real-time feedback concerning the signal quality is required. We have developed a measure for mobile ECG quality assessment based on a) basic signal quality p...