Privacy-preserving federated machine learning on FAIR health data: A real-world application.

Objective: This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an architecture for executing classification algorithms in a federated manner, enabling collaborative model-building among health data owners without sharing their datasets. Materials and methods: Utilizing an agent-based architecture, a privacy-preserving federated ML algorithm was developed to create a global predictive model from various local models. This involved formally defining the algorithm in two steps: data preparation and federated model training on FAIR health data and constructing the architecture with multiple components facilitating algorithm execution. The solution was validated by five healthcare organizations using their specific health datasets. Results: Five organizations transformed their datasets into Health Level 7 Fast Healthcare Interoperability Resources via a common FAIRification workflow and software set, thereby generating FAIR datasets. Each organization deployed a Federated ML Agent within its secure network, connected to a cloud-based Federated ML Manager. System testing was conducted on a use case aiming to predict 30-day readmission risk for chronic obstructive pulmonary disease patients and the federated model achieved an accuracy rate of 87%. Discussion: The paper demonstrated a practical application of privacy-preserving federated ML among five distinct healthcare entities, highlighting the value of FAIR health data in machine learning when utilized in a federated manner that ensures privacy protection without sharing data. Conclusion: This solution effectively leverages FAIR datasets from multiple healthcare organizations for federated ML while safeguarding sensitive health datasets, meeting legislative privacy and security requirements.

A Nationwide Chronic Disease Management Solution via Clinical Decision Support Services: Software Development and Real-Life Implementation Report.

BACKGROUND: The increasing population of older adults has led to a rise in the demand for health care services, with chronic diseases being a major burden. Person-centered integrated care is required to address these challenges; hence, the Turkish Ministry of Health has initiated strategies to implement an integrated health care model for chronic disease management. We aim to present the design, development, nationwide implementation, and initial performance results of the national Disease Management Platform (DMP). OBJECTIVE: This paper's objective is to present the design decisions taken and technical solutions provided to ensure successful nationwide implementation by addressing several challenges, including interoperability with existing IT systems, integration with clinical workflow, enabling transition of care, ease of use by health care professionals, scalability, high performance, and adaptability. METHODS: The DMP is implemented as an integrated care solution that heavily uses clinical decision support services to coordinate effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines and, hence, to increase the quality of health care delivery. The DMP is designed and implemented to be easily integrated with the existing regional and national health IT systems via conformance to international health IT standards, such as Health Level Seven Fast Healthcare Interoperability Resources. A repeatable cocreation strategy has been used to design and develop new disease modules to ensure extensibility while ensuring ease of use and seamless integration into the regular clinical workflow during patient encounters. The DMP is horizontally scalable in case of high load to ensure high performance. RESULTS: As of September 2023, the DMP has been used by 25,568 health professionals to perform 73,715,269 encounters for 16,058,904 unique citizens. It has been used to screen and monitor chronic diseases such as obesity, cardiovascular risk, diabetes, and hypertension, resulting in the diagnosis of 3,545,573 patients with obesity, 534,423 patients with high cardiovascular risk, 490,346 patients with diabetes, and 144,768 patients with hypertension. CONCLUSIONS: It has been demonstrated that the platform can scale horizontally and efficiently provides services to thousands of family medicine practitioners without performance problems. The system seamlessly interoperates with existing health IT solutions and runs as a part of the clinical workflow of physicians at the point of care. By automatically accessing and processing patient data from various sources to provide personalized care plan guidance, it maximizes the effect of evidence-based decision support services by seamless integration with point-of-care electronic health record systems. As the system is built on international code systems and standards, adaptation and deployment to additional regional and national settings become easily possible. The nationwide DMP as an integrated care solution has been operational since January 2020, coordinating effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines.

Interoperable E-Health System Using Structural and Semantic Interoperability Approaches in CAREPATH.

Technical and semantic interoperability are broadly used components of interoperability technology in healthcare. Technical Interoperability provides interoperability interfaces to enable data exchange within different healthcare systems, despite any underlying heterogeneity. Semantic interoperability make different healthcare systems understand and interpret the meaning of the data that is exchanged, by using and mapping standardized terminologies, coding systems, and data models to describe the concept and structure of data. We propose a solution using Semantic and Structural Mapping techniques within CAREPATH; a research project designed to develop ICT solutions for the care management of elderly multimorbid patients with mild cognitive impairment or mild dementia. Our technical interoperability solution supplies a standard-based data exchange protocol to enable information exchange between local care systems and CAREPATH components. Our semantic interoperability solution supplies programmable interfaces, in order to semantically mediate different clinical data representation formats and incorporating data format and terminology mapping features. The solution offers a more reliable, flexible and resource efficient method across EHRs.

Significant Prostate Cancer in Patients with PI-RADS Category 3 Lesions: A Single-Center, Retrospective Cohort Study.

PURPOSE:  The Prostate Imaging-Reporting and Data System (PI-RADS) category 3 is the most ambiguous lesion with a variable clinically significant prostate cancer (CsPCa) detection rate. Prostate-specific antigen density (PSAD) has been investigated as an adjunctive factor to improve the diagnostic efficiency of PI-RADS categories. This study aimed to investigate the utility of PSAD as an adjunctive factor in predicting CsPCA risk in patients with PI-RADS 3 lesions. MATERIALS AND METHODS: The patients with an initial PI-RADS 3 category lesion (n=142) scheduled for systematic and magnetic resonance imaging-guided prostate biopsy between 2018 and 2022 were retrospectively evaluated. Demographic and clinical variables, including PSAD, were collected. The rate of CsPCa was the primary outcome. The impact of PSAD on the CsPCa detection rate was the secondary outcome. RESULTS: The median age was 62 years. The rate of CsPCa was 8.5% (n=12). The patients with CsPCa have significantly lower prostate volüme and higher PSAD levels than those without CsPCa (p=0.016 and p=0.012). The cut-off values of PSAD in predicting CsPCa in all PI-RADS 3 patients and patients with CsPCa and clinically insignificant prostate cancer (n=26) were ≥0.181 ng/ml2. The sensitivity and specificity values for PSAD ≥0.181 ng/ml2 were of 75% (95% CI: 42.8%-94.5%) and 81.5% (95% CI: 73.4%-88.0%) in predicting CsPCa among PI-RADS 3 category.      Conclusion: PSAD values higher than 0.181 ng/ml2 can be used as an adjunctive clinical parameter in predicting CsPCa in patients with PI-RADS 3 lesions and differentiating CsPCa from clinically insignificant prostate cancer cases.

Implementation of HL7 FHIR-Based Interoperability Profiles to Manage Care Plans for Multimorbid Patients with Mild Dementia.

Management of multimorbidity in patients with mild dementia and mild cognitive impairment introduces additional challenges. The CAREPATH project provides an integrated care platform to assist both healthcare professionals and patients and their informal caregivers in the day-to-day management of care plans for this patient population. This paper introduces an HL7 FHIR-based interoperability approach for exchanging care plan action and goals with the patients and collecting feedback and adherence information from patients. In this way, seamless information exchange between healthcare professionals, patients and their informal care givers is achieved to support patients in their self-care management journey and increase their adherence to their care plans despite the burdens of mild dementia.

A Data Transformation Methodology to Create Findable, Accessible, Interoperable, and Reusable Health Data: Software Design, Development, and Evaluation Study.

BACKGROUND: Sharing health data is challenging because of several technical, ethical, and regulatory issues. The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have been conceptualized to enable data interoperability. Many studies provide implementation guidelines, assessment metrics, and software to achieve FAIR-compliant data, especially for health data sets. Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is a health data content modeling and exchange standard. OBJECTIVE: Our goal was to devise a new methodology to extract, transform, and load existing health data sets into HL7 FHIR repositories in line with FAIR principles, develop a Data Curation Tool to implement the methodology, and evaluate it on health data sets from 2 different but complementary institutions. We aimed to increase the level of compliance with FAIR principles of existing health data sets through standardization and facilitate health data sharing by eliminating the associated technical barriers. METHODS: Our approach automatically processes the capabilities of a given FHIR end point and directs the user while configuring mappings according to the rules enforced by FHIR profile definitions. Code system mappings can be configured for terminology translations through automatic use of FHIR resources. The validity of the created FHIR resources can be automatically checked, and the software does not allow invalid resources to be persisted. At each stage of our data transformation methodology, we used particular FHIR-based techniques so that the resulting data set could be evaluated as FAIR. We performed a data-centric evaluation of our methodology on health data sets from 2 different institutions. RESULTS: Through an intuitive graphical user interface, users are prompted to configure the mappings into FHIR resource types with respect to the restrictions of selected profiles. Once the mappings are developed, our approach can syntactically and semantically transform existing health data sets into HL7 FHIR without loss of data utility according to our privacy-concerned criteria. In addition to the mapped resource types, behind the scenes, we create additional FHIR resources to satisfy several FAIR criteria. According to the data maturity indicators and evaluation methods of the FAIR Data Maturity Model, we achieved the maximum level (level 5) for being Findable, Accessible, and Interoperable and level 3 for being Reusable. CONCLUSIONS: We developed and extensively evaluated our data transformation approach to unlock the value of existing health data residing in disparate data silos to make them available for sharing according to the FAIR principles. We showed that our method can successfully transform existing health data sets into HL7 FHIR without loss of data utility, and the result is FAIR in terms of the FAIR Data Maturity Model. We support institutional migration to HL7 FHIR, which not only leads to FAIR data sharing but also eases the integration with different research networks.

A Formal Model for the FAIR4Health Information Architecture.

In the EU project FAIR4Health, a ETL pipeline for the FAIRification of structured health data as well as an agent-based, distributed query platform for the analysis of research hypotheses and the training of machine learning models were developed. The system has been successfully tested in two clinical use cases with patient data from five university hospitals. Currently, the solution is also being considered for use in other hospitals. However, configuring the system and deploying it in the local IT architecture is non-trivial and meets with understandable concerns about security. This paper presents a model for describing the information architecture based on a formal approach, the 3LGM metamodel. The model was evaluated by the developers. As a result, the clear separation of tasks and the software components that implement them as well as the rich description of interactions via interfaces were positively emphasized.

CAREPATH: Developing Digital Integrated Care Solutions for Multimorbid Patients with Dementia.

CAREPATH project is focusing on providing an integrated solution for sustainable care for multimorbid elderly patients with dementia or mild cognitive impairment. The project has a digitally enhanced integrated patient-centered care approach clinical decision and associated intelligent tools with the aim to increase patients' independence, quality of life and intrinsic capacity. In this paper, the conceptual aspects of the CAREPATH project, in terms of technical and clinical requirements and considerations, are presented.

Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study.

BACKGROUND: Owing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers. OBJECTIVE: The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD). METHODS: The application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies. RESULTS: Clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87% (87/100) of cases. CONCLUSIONS: Implementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles.

Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm.

The current availability of electronic health records represents an excellent research opportunity on multimorbidity, one of the most relevant public health problems nowadays. However, it also poses a methodological challenge due to the current lack of tools to access, harmonize and reuse research datasets. In FAIR4Health, a European Horizon 2020 project, a workflow to implement the FAIR (findability, accessibility, interoperability and reusability) principles on health datasets was developed, as well as two tools aimed at facilitating the transformation of raw datasets into FAIR ones and the preservation of data privacy. As part of this project, we conducted a multicentric retrospective observational study to apply the aforementioned FAIR implementation workflow and tools to five European health datasets for research on multimorbidity. We applied a federated frequent pattern growth association algorithm to identify the most frequent combinations of chronic diseases and their association with mortality risk. We identified several multimorbidity patterns clinically plausible and consistent with the bibliography, some of which were strongly associated with mortality. Our results show the usefulness of the solution developed in FAIR4Health to overcome the difficulties in data management and highlight the importance of implementing a FAIR data policy to accelerate responsible health research.

FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research.

Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance.

End User Evaluation of the FAIR4Health Data Curation Tool.

The aim of this study is to build an evaluation framework for the user-centric testing of the Data Curation Tool. The tool was developed in the scope of the FAIR4Health project to make health data FAIR by transforming them from legacy formats into a Common Data Model based on HL7 FHIR. The end user evaluation framework was built by following a methodology inspired from the Delphi method. We applied a series of questionnaires to a group of experts not only in different roles and skills, but also from various parts of Europe. Overall, 26 questions were formulated for 16 participants. The results showed that the users are satisfied with the capabilities and performance of the tool. The feedbacks were considered as recommendations for technical improvement and fed back into the software development cycle of the Data Curation Tool.

From Raw Data to FAIR Data: The FAIRification Workflow for Health Research.

BACKGROUND: FAIR (findability, accessibility, interoperability, and reusability) guiding principles seek the reuse of data and other digital research input, output, and objects (algorithms, tools, and workflows that led to that data) making them findable, accessible, interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governed initiative - defined a seven-step FAIRification process focusing on data, but also indicating the required work for metadata. This FAIRification process aims at addressing the translation of raw datasets into FAIR datasets in a general way, without considering specific requirements and challenges that may arise when dealing with some particular types of data. OBJECTIVES: This scientific contribution addresses the architecture design of an open technological solution built upon the FAIRification process proposed by "GO FAIR" which addresses the identified gaps that such process has when dealing with health datasets. METHODS: A common FAIRification workflow was developed by applying restrictions on existing steps and introducing new steps for specific requirements of health data. These requirements have been elicited after analyzing the FAIRification workflow from different perspectives: technical barriers, ethical implications, and legal framework. This analysis identified gaps when applying the FAIRification process proposed by GO FAIR to health research data management in terms of data curation, validation, deidentification, versioning, and indexing. RESULTS: A technological architecture based on the use of Health Level Seven International (HL7) FHIR (fast health care interoperability resources) resources is proposed to support the revised FAIRification workflow. DISCUSSION: Research funding agencies all over the world increasingly demand the application of the FAIR guiding principles to health research output. Existing tools do not fully address the identified needs for health data management. Therefore, researchers may benefit in the coming years from a common framework that supports the proposed FAIRification workflow applied to health datasets. CONCLUSION: Routine health care datasets or data resulting from health research can be FAIRified, shared and reused within the health research community following the proposed FAIRification workflow and implementing technical architecture.

Brachial Artery Wall Stiffness Assessment by Shear Wave Elastography: A Promising New Diagnostic Tool for Endothelial Dysfunction Detection.

OBJECTIVES: This study was designed to measure the changes in brachial artery wall stiffness by shear wave elastography (SWE) and evaluate the accuracy of SWE changes for detection of endothelial dysfunction. METHODS: Sixty-five consecutive participants (19 patients with atherosclerosis proven by coronary angiography, 16 healthy young adults, 15 patients with cardiovascular risk factors, and 15 healthy older adults between 50 and 60 years) were prospectively included in this study. They were examined in the same week by SWE, and flow-mediated dilatation was evaluated for each patient. RESULTS: The mean flow-mediated dilatation values ± 2 SDs after forearm occlusion were 8.54% ± 1.4% in healthy young adults, 7.61% ± 1.4% in healthy older adults, 5.83% ± 0.7% in patients with risk factors (P < .001), and 3.81% ± 2.4% in patients with atherosclerosis (P < .001, with respect to the risk factor group). There was a significant decrease in stiffness measurements in parallel with the increase in flow-mediated dilatation: 19.9% ± 6.3% in healthy young adults, 16.3% ± 5.1% in healthy older adults, 9.8% ± 5.4% in patients with risk factors (P < .05 with respect to the group with no risk factors), and 7.8% ± 6.4% in patients with atherosclerosis (P < .001 with respect to the healthy older adults). CONCLUSIONS: Shear wave elastography in combination with flow-mediated dilatation could be a promising, widely available noninvasive diagnostic tool for detecting endothelial dysfunction.

[Idiopathic bilateral basal ganglia calcification (Fahr's disease) presenting with psychotic depression and criminal violence: a case report with forensic aspect]. / Psikotik depresyon ve siddet davranisi ile basvuran idiyopatik bilateral bazal ganglion kalsifikasyonu (Fahr hastaligi): adli yönü dolayisiyla bir olgu sunumu.

Fahr's disease is a rare neuropsychiatric disease characterized by bilateral intracranial calcification, primarily in the basal ganglia. The more general term, Fahr's syndrome, is used for primary and secondary basal ganglia calcification, regardless of the etiology, but the term Fahr's disease is used to describe primary, idiopathic cases. Fahr's disease may present with neurological symptoms, such as parkinsonism and extrapyramidal symptoms, dysarthria, paresis, convulsion, and syncope. Psychiatric disorders, including behavioral disorders, psychosis, and mood disorders, as well as cognitive disorders can occur. CT is useful for the diagnosis of Fahr's disease. Herein we present a patient diagnosed as Fahr's disease that presented with symptoms of depression, delusions, and auditory hallucinations. The 47-year-old male patient was hospitalized in a forensic psychiatry inpatient clinic due to aggressive behavior and was subsequently diagnosed with major depressive disorder with psychotic features. While hospitalized he was treated with antidepressant and antipsychotic drugs, as well as electroconvulsive therapy, resulting in significant improvement in his symptoms. As bilateral basal ganglia calcification was observed via CT, the patient was diagnosed as Fahr's disease. This case report emphasizes the importance of cranial imaging and detailed laboratory examination when evaluating patients with psychosis and affective symptoms. Pathologies such as Fahr's disease must be included in the differential diagnosis, especially in cases with neurological symptoms and cranial imaging findings.

Ultrasound-guided injection for MR arthrography of the hip: comparison of two different techniques.

OBJECTIVE: The purpose of this study was to prospectively evaluate the two different ultrasound-guided injection techniques for MR arthrography of the hip. MATERIALS AND METHODS: Fifty-nine consecutive patients (21 men, 38 women) referred for MR arthrographies of the hip were prospectively included in the study. Three patients underwent bilateral MR arthrography. The two injection techniques were quantitatively and qualitatively compared. Quantitative analysis was performed by the comparison of injected contrast material volume into the hip joint. Qualitative analysis was performed with regard to extraarticular leakage of contrast material into the soft tissues. Extraarticular leakage of contrast material was graded as none, minimal, moderate, or severe according to the MR images. Each patient rated discomfort after the procedure using a visual analogue scale (VAS). RESULTS: The injected contrast material volume was less in femoral head puncture technique (mean 8.9 ± 3.4 ml) when compared to femoral neck puncture technique (mean 11.2 ± 2.9 ml) (p < 0.05). The chi-squared test showed significantly more contrast leakage by femoral head puncture technique (p < 0.05). Statistical analysis showed no difference between the head and neck puncture groups in terms of feeling of pain (p = 0.744) or in the body mass index (p = 0.658) of the patients. CONCLUSION: The femoral neck injection technique provides high intraarticular contrast volume and produces less extraarticular contrast leakage than the femoral head injection technique when US guidance is used for MR arthrography of the hip.