Electronic health records (EHR) are commonly institution-specific, provided by hospitals, insurance companies, or other institutions to fulfill their own objectives, thus, causing stored health information to be isolated, fragmented and duplicated across providers. Consequently, patients may lack complete access to their medical histories. As a solution, countries such as Denmark and Israel have long adopted nationwide EHR for their health care systemsa, in which the health information is well managed and digitally connected to avoid duplicate records and improve the quality and co-effectiveness of medical care as well as patient safety. In Germany, the “Appointment Service and Supply Act” adopted on 14th March 2019, requires the German statutory health insurance to provide EHR for all insured persons from 1st January 2021 onwardsb. As specified by German Health Care Information Technology Infrastructure in accordance with section 291a SGB V, the new EHR should store complete medical histories of patients such as previous diagnoses, therapeutic decisions, treatment reports and self-measurement values. Among other benefits, patients should have power to select freely between providers, hold data sovereignty for their EHR, and withdraw access rights at any time.
In line with those guidelines, OnePatientc is a patient-centered EHR system that stores data locally under the sovereignty of individual device owners, thereby enabling patients to take control of their health information, provide offline access to medical data, ensure privacy management and to avoid a single point of failure. The OnePatient EHR system can be provisioned on any of the patients’ devices; therefore, patients technically own their medical data while the device and software manage it. On the one hand, these developments simplify the technical and organizational challenges to implement data regulations such as the General Data Protection Regulation (GDPR) of the European Union. On the other hand, the data will not only be in isolated, heterogeneous and distributed environments but also pose a new challenge to the conventional data transaction procedures employed in machine learning (ML) today [4]. The traditional procedures for acquiring big data in ML involve several parties from collecting the data, transferring it to a central data repository and fusing it to build a model, whereas the data owners may be unclear about these procedures and the model future use cases, for that reason, may violate laws such as GDPR.
Therefore, to address these challenges, federated learning (FL) approaches can be leveraged to build ML models that can be sent to train locally–where the data is located. In this manner, only the model updates that contain anonymous results which cannot be reverse-engineered are returned to the central data repository. Leveraging FL and the account of the FL existing studies [1; 2; 3], although not focusing on the emerging EHR systems’ architecture like OnePatient, we aim to attain four objectives. The first is to investigate and design novel FL frameworks that enable local systems to collaboratively train a ML model that patients can benefit from without divulging their medical information to a central entity; moreover, medical practitioners will be able to access the training process of the FL frameworks to adjust the diagnostic criteria of the model, and therefore increase trust and accuracy of the model outcome. Secondly, we aim to investigate and compare the accuracy and performance of the model trained in a centralized way and the FL frameworks that will be proposed. Thirdly, to protect the data during training from potentially malicious models and participants, we aim to use countermeasures such as differential privacy and multi-party computation to ensure privacy guarantees. Finally, for proof of concept, we aim to demonstrate the effectiveness of FL frameworks using existing databases and suitable ML tasks with the data.
1. Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. Federated learning of predictive models from federated electronic health records. Int.J.Med.Inf. 2018; 112: 59-67.
2. Roy, A. G., Siddiqui, S., Pölsterl, S., Navab, N., & Wachinger, C. Braintorrent: A peer-to-peer environment for decentralized federated learning. arXiv preprint arXiv:1905.06731 2019;
3. Xu, J., & Wang, F. Federated learning for healthcare informatics. arXiv preprint arXiv:1911.06270 2019;
4. Yang, Q., Liu, Y., Chen, T., & Tong, Y. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 2019; 10: 1-19.
a. https://www.gesundheitsindustrie-bw.de/en/article/news/ehr-and-phr-digital-records-in-the-german-healthcare-system
b. https://www.gematik.de/anwendungen/e-patientenakte/#
c. https://refinio.net/software.html
We conduct theoretical and applied research for wearable computing systems and machine learning algorithms for engineering applications at the intersection of sports and health care. Our motivation is generating a positive impact on human wellbeing, be it through increasing performance, maintaining health, improving rehabilitation, or monitoring disease.
Research projects
Empatho-Kinaesthetic Sensor Technology
(Third Party Funds Group – Overall project)
Funding source: DFG / Sonderforschungsbereich / Transregio (SFB / TRR)
URL: https://empkins.de/
Holistic customer-oriented service optimization for fleet availability
(Third Party Funds Single)
Funding source: Industrie, andere Förderorganisation
Das Okulomotor Test System in der Virtuellen Realität -- Telemedizinische Detektion von Gehirnerschütterungen in VR
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
URL: https://www.mad.tf.fau.de/research/projects/vr-ots/
The aim of the project is to develop a rapid automated diagnosis (using artificial intelligence) of a fusion disorder in cases of e.g. concussion by measuring the oculomotor function (eye-tracking) for three-dimensional stimuli.
Tracking-based museum visitor research
(Third Party Funds Single)
Funding source: Industrie
Mobility in atypical parkinsonism: a randomized trial of physiotherapy
(Third Party Funds Single)
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
URL: https://gepris.dfg.de/gepris/projekt/438496663?context=projekt&task=showDetail&id=438496663&
Integratives Konzept zur personalisierten Präzisionsmedizin in Prävention, Früh-Erkennung, Therapie undRückfallvermeidung am Beispiel von Brustkrebs - DigiOnko
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Gesundheit und Pflege, StMGP (seit 2018)
Integratives Konzept zur personalisierten Präzisionsmedizin in Prävention, Früh-Erkennung, Therapie und Rückfallvermeidung am Beispiel von Brustkrebs
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Gesundheit und Pflege, StMGP (seit 2018)
Breast cancer is one of the leading causes of death in the field of oncology in Germany. For the successful care and treatment of patients with breast cancer, a high level of information for those affected is essential in order to achieve a high level of compliance with the established structures and therapies. On the one hand, the digitalisation of medicine offers the opportunity to develop new technologies that increase the efficiency of medical care. On the other hand, it can also strengthen patient compliance by improving information and patient integration through electronic health applications. Thus, a reduction in mortality and an improvement in quality of life can be achieved. Within the framework of this project, digital health programmes are going to be created that support and complement health care. The project aims to provide better and faster access to new diagnostic and therapeutic procedures in mainstream oncology care, to implement eHealth models for more efficient and effective cancer care, and to improve capacity for patients in oncologcal therapy in times of crisis (such as the SARS-CoV-2 pandemic). The Chair of Health Management is conducting the health economic evaluation and analysing the extent to which digitalisation can contribute to a reduction in the costs of treatment and care as well as to an improvement in the quality of life of breast cancer patients.
Activity Recognition using IMU Sensors integrated in Hearing Aids
(Third Party Funds Single)
Funding source: Industrie
The hearing aid of the future will be more than just an amplifying device. It may be used as fitness tracker to capture the user’s movements and activity level. Furthermore, it may be used as home monitoring device assessing the user’s vital parameters, tracking the user’s activity status or detecting falls. Hearing aids are becoming more complex and most modern hearing aids are already equipped with additional sensors such as inertial sensors. Acceleration signals are analyzed with signal processing algorithms to enhance speech intelligibility and audio quality. Furthermore, inertial sensors may be used to analyze the user’s movements and physical activity. Hearing aid amplification settings may be adapted according to the current activity. Moreover, given the user's explicit consent, activity recognition enables a long-term tracking of the user’s daily activity status.
The objective of this project is to investigate automatic activity recognition based on inertial sensor data. Therefore, data of different activities will be recorded using the IMU sensor integrated in the hearing aids. The hearing aids are provided by the cooperation partner WS Audiology. Machine learning algorithms will be developed to automatically classify different activity patterns.
Federated Machine Learning for Patient-Centered Electronic Health Records
(Third Party Funds Single)
Funding source: Deutscher Akademischer Austauschdienst (DAAD)
URL: https://www.mad.tf.fau.de/research/projects/federated-machine-learning-for-patient-centered-electronic-health-records/
Electronic health records (EHR) are commonly institution-specific, provided by hospitals, insurance companies, or other institutions to fulfill their own objectives, thus, causing stored health information to be isolated, fragmented and duplicated across providers. Consequently, patients may lack complete access to their medical histories. As a solution, countries such as Denmark and Israel have long adopted nationwide EHR for their health care systemsa, in which the health information is well managed and digitally connected to avoid duplicate records and improve the quality and co-effectiveness of medical care as well as patient safety. In Germany, the “Appointment Service and Supply Act” adopted on 14th March 2019, requires the German statutory health insurance to provide EHR for all insured persons from 1st January 2021 onwardsb. As specified by German Health Care Information Technology Infrastructure in accordance with section 291a SGB V, the new EHR should store complete medical histories of patients such as previous diagnoses, therapeutic decisions, treatment reports and self-measurement values. Among other benefits, patients should have power to select freely between providers, hold data sovereignty for their EHR, and withdraw access rights at any time.
In line with those guidelines, OnePatientc is a patient-centered EHR system that stores data locally under the sovereignty of individual device owners, thereby enabling patients to take control of their health information, provide offline access to medical data, ensure privacy management and to avoid a single point of failure. The OnePatient EHR system can be provisioned on any of the patients’ devices; therefore, patients technically own their medical data while the device and software manage it. On the one hand, these developments simplify the technical and organizational challenges to implement data regulations such as the General Data Protection Regulation (GDPR) of the European Union. On the other hand, the data will not only be in isolated, heterogeneous and distributed environments but also pose a new challenge to the conventional data transaction procedures employed in machine learning (ML) today [4]. The traditional procedures for acquiring big data in ML involve several parties from collecting the data, transferring it to a central data repository and fusing it to build a model, whereas the data owners may be unclear about these procedures and the model future use cases, for that reason, may violate laws such as GDPR.
Therefore, to address these challenges, federated learning (FL) approaches can be leveraged to build ML models that can be sent to train locally–where the data is located. In this manner, only the model updates that contain anonymous results which cannot be reverse-engineered are returned to the central data repository. Leveraging FL and the account of the FL existing studies [1; 2; 3], although not focusing on the emerging EHR systems’ architecture like OnePatient, we aim to attain four objectives. The first is to investigate and design novel FL frameworks that enable local systems to collaboratively train a ML model that patients can benefit from without divulging their medical information to a central entity; moreover, medical practitioners will be able to access the training process of the FL frameworks to adjust the diagnostic criteria of the model, and therefore increase trust and accuracy of the model outcome. Secondly, we aim to investigate and compare the accuracy and performance of the model trained in a centralized way and the FL frameworks that will be proposed. Thirdly, to protect the data during training from potentially malicious models and participants, we aim to use countermeasures such as differential privacy and multi-party computation to ensure privacy guarantees. Finally, for proof of concept, we aim to demonstrate the effectiveness of FL frameworks using existing databases and suitable ML tasks with the data.
1. Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. Federated learning of predictive models from federated electronic health records. Int.J.Med.Inf. 2018; 112: 59-67.
2. Roy, A. G., Siddiqui, S., Pölsterl, S., Navab, N., & Wachinger, C. Braintorrent: A peer-to-peer environment for decentralized federated learning. arXiv preprint arXiv:1905.06731 2019;
3. Xu, J., & Wang, F. Federated learning for healthcare informatics. arXiv preprint arXiv:1911.06270 2019;
4. Yang, Q., Liu, Y., Chen, T., & Tong, Y. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 2019; 10: 1-19.
a. https://www.gesundheitsindustrie-bw.de/en/article/news/ehr-and-phr-digital-records-in-the-german-healthcare-system
b. https://www.gematik.de/anwendungen/e-patientenakte/#
c. https://refinio.net/software.html
SMART Start: Smarte Sensorik in der Schwangerschaft - Ein integratives Konzept zur digitalen, präventiven Versorgung schwangerer Frauen
(Third Party Funds Single)
Funding source: Bundesministerium für Gesundheit (BMG)
Heisenberg-Förderung / Verlängerung - 2. Phase
(Third Party Funds Single)
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Biomarkers for immunotherapy in multiple sclerosis patients
(Third Party Funds Single)
Funding source: andere Förderorganisation
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) that is most common in young adult. The disease is due to an autoimmune reaction in which immune cells, which attack foreign pathogens, damage the body's own tissue. This project aims at identifying biomarkers in the form of gait characteristics in patients with multiple sclerosis which can be used both for the assessment of the current state of the disease and for the prediction of the further course of the disease in the form of relapses.
Development of neural networks and machine learning algorithms for online handwriting recognition
(Third Party Funds Group – Sub project)
Term: 2. September 2019 - 30. April 2022
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013), Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
The aim of this project is the development of a toolkit that is able to identify handwriting in real time. Using Stabilo Digipen with internal sensors that provides pen motion data in real time, machine learning algorithms are applied to track the pen movement on regular paper and digitize written sentences in real time.
Entwicklung intelligenter neuronaler Netze zur Schrifterkennung
(Third Party Funds Group – Sub project)
Term: 1. May 2019 - 30. April 2022
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement
(Third Party Funds Single)
Funding source: Europäische Union (EU)
URL: http://www.mobilise-d.eu/
Optimal treatment of the impaired mobility resulting from ageing and chronic disease is one of the 21st century's greatest challenges facing patients, society, governments, healthcare services, and science. New interventions are a key focus. However, to accelerate their development, we need better ways to detect and measure mobility loss. Digital technology, including body worn sensors, has the potential to revolutionise mobility assessment. The overarching objectives of MOBILISE-D are threefold: to deliver a valid solution (consisting of sensor, algorithms, data analytics, outcomes) for real-world digital mobility assessment; to validate digital outcomes in predicting clinical outcome in chronic obstructive pulmonary disease, Parkinson’s disease, multiple sclerosis, proximal femoral fracture recovery and congestive heart failure; and, to obtain key regulatory and health stakeholder approval for digital mobility assessment. The objectives address the call directly by linking digital assessment of mobility to clinical endpoints to support regulatory acceptance and clinical practice. MOBILISE-D consists of 35 partners from 13 countries with long, successful collaboration, combining the requisite expertise to address the technical and clinical challenges. To achieve the objectives, partners will jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes can successfully predict relevant clinical outcomes and provide a better, safer and quicker way to arrive at the development of innovative medicines. MOBILISE-D's results will directly facilitate drug development, and establish the roadmap for clinical implementation of new, complementary tools to identify, stratify and monitor disability, so enabling widespread, cost-effective access to optimal clinical mobility management through personalised healthcare.
Machine Learning for Predictive Analytics
(Third Party Funds Single)
Funding source: Industrie
The main goal of this project is to improve the overall system quality and customer satisfaction.
In this project, we analyze IoT data (machine logs and sensory data) sent by thousands of high-end medical devices every day. The extracted information can include physical parameters and additional extracted event patterns. This data can be used to predict the failure of specific components and correlate malfunction to machine usage. As a consequence, system stability can be improved and procedures for system testing can be recommended.
Furthermore, information from customer service data (e.g. tickets) is processed and fused with machine data to predict customer sentiment. With that, customer satisfaction can be improved via proactive service.
Methods designed and used include:
2022
2021
2020
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