DCC Terminology Service—An Automated CI/CD Pipeline for Converting Clinical and Biomedical Terminologies in Graph Format for the Swiss Personalized Health Network

One goal of the Swiss Personalized Health Network (SPHN) is to provide an infrastructure for FAIR (Findable, Accessible, Interoperable and Reusable) health-related data for research purposes. Semantic web technology and biomedical terminologies are key to achieving semantic interoperability. To enable the integrative use of different terminologies, a terminology service is a important component of the SPHN Infrastructure for FAIR data. It provides both the current and historical versions of the terminologies in an SPHN-compliant graph format. To minimize the usually high maintenance effort of a terminology service, we developed an automated CI/CD pipeline for converting clinical and biomedical terminologies in an SPHN-compatible way. Hospitals, research infrastructure providers, as well as any other data providers, can download a terminology bundle (currently composed of SNOMED CT, LOINC, UCUM, ATC, ICD-10-GM, and CHOP) and deploy it in their local terminology service. The distributed service architecture allows each party to fulfill their local IT and security requirements, while still having an up-to-date interoperable stack of SPHN-compliant terminologies. In the future, more terminologies and mappings will be added to the terminology service according to the needs of the SPHN community.

Download Full-text

Clinical data for paediatric research: the Swiss approach

BMC Proceedings ◽

10.1186/s12919-021-00226-3 ◽

2021 ◽

Vol 15 (S13) ◽

Author(s):

Milenko Rakic ◽

Manon Jaboyedoff ◽

Sara Bachmann ◽

Christoph Berger ◽

Manuel Diezi ◽

...

Keyword(s):

Child Health ◽

Electronic Health Records ◽

Lessons Learned ◽

Infrastructure Development ◽

Health Records ◽

Health Network ◽

Related Data ◽

Core Dataset ◽

Health Related ◽

Electronic Health

Abstract Background and purpose Continuous improvement of health and healthcare system is hampered by inefficient processes of generating new evidence, particularly in the case of rare diseases and paediatrics. Currently, most evidence is generated through specific research projects, which typically require extra encounters with patients, are costly and entail long delays between the recognition of specific needs in healthcare and the generation of necessary evidence to address those needs. The Swiss Personalised Health Network (SPHN) aims to improve the use of data obtained during routine healthcare encounters by harmonizing data across Switzerland and facilitating accessibility for research. The project “Harmonising the collection of health-related data and biospecimens in paediatric hospitals throughout Switzerland (SwissPedData)” was an infrastructure development project funded by the SPHN, which aimed to identify and describe available data on child health in Switzerland and to agree on a standardised core dataset for electronic health records across all paediatric teaching hospitals. Here, we describe the results of a two-day symposium that aimed to summarise what had been achieved in the SwissPedData project, to put it in an international context, and to discuss the next steps for a sustainable future. The target audience included clinicians and researchers who produce and use health-related data on children in Switzerland. Key highlights The symposium consisted of state-of-the-art lectures from national and international keynote speakers, workshops and plenary discussions. This manuscript summarises the talks and discussions in four sections: (I) a description of the Swiss Personalized Health Network and the results of the SwissPedData project; (II) examples of similar initiatives from other countries; (III) an overview of existing health-related datasets and projects in Switzerland; and (IV) a summary of the lessons learned and future prospective from workshops and plenary discussions. Implications Streamlined processes linking initial collection of information during routine healthcare encounters, standardised recording of this information in electronic health records and fast accessibility for research are essential to accelerate research in child health and make it affordable. Ongoing projects prove that this is feasible in Switzerland and elsewhere. International collaboration is vital to success. The next steps include the implementation of the SwissPedData core dataset in the clinical information systems of Swiss hospitals, the use of this data to address priority research questions, and the acquisition of sustainable funding to support a slim central infrastructure and local support in each hospital. This will lay the foundation for a national paediatric learning health system in Switzerland.

Download Full-text

The SPHN Ecosystem Towards FAIR Data

10.20944/preprints202109.0505.v1 ◽

2021 ◽

Author(s):

Sabine Österle ◽

Vasundra Touré ◽

Katrin Crameri

Keyword(s):

Local Data ◽

Related Data ◽

Secondary Use ◽

Health Related ◽

Fair Principles ◽

Personalized Health ◽

Machine Readable ◽

Clinical Terminologies ◽

Readable Format ◽

Rdf Schema

Health-related data originating from diverse sources are commonly stored in manifold databases and formats, making it difficult to find, access and gather data for research purposes. In addition, so-called secondary use scenarios for health data are usually hindered by local data codes, missing dictionaries and the lack of metadata and context descriptions. Following the FAIR principles (Findable, Accessible, Interoperable and Reusable), we developed a decentralized infrastructure to overcome these hurdles and enable collaborative research by making the meaning of health-related data understandable to both, humans and machines. This infrastructure is currently being implemented in the realm of the Swiss Personalized Health Network (SPHN), a research infrastructure initiative for enabling the use and exchange of health-related data for research in Switzerland. The SPHN ecosystem for FAIR data consists of the SPHN Dataset (semantic definitions), the SPHN RDF Schema (linkage and transport of the semantics in a machine-readable format), a project RDF template, extensive guidelines and conventions on how to generate SPHN RDF schema, a Terminology Service (converter of clinical terminologies in RDF), and a Quality Assurance Framework (automated data validation with SHACLs and SPARQLs). The SPHN ecosystem has been built in a way that it can easily be adapted and extended by any SPHN project to fit individual needs. By providing such a national ecosystem, SPHN supports researchers in generating, processing and sharing FAIR data.

Download Full-text

Learning the Language of Healthcare Enabling Semantic Web Technology in CHCS

10.21236/ada601078 ◽

2013 ◽

Author(s):

Wesley Turner

Keyword(s):

Semantic Web ◽

Web Technology ◽

Semantic Web Technology

Download Full-text

Novel Visualization of Large Health Related Data Sets

10.21236/ada624744 ◽

2015 ◽

Author(s):

William E. Hammond ◽

Vivian L. West ◽

David Borland ◽

Igor Akushevich ◽

Eugenia M. Heinz

Keyword(s):

Data Sets ◽

Related Data ◽

Health Related

Download Full-text

Real-Time Health-related Data, Wearable Medical Sensor Devices, and Smart Cyber-Physical Systems

American Journal of Medical Research ◽

10.22381/ajmr6220193 ◽

2019 ◽

Vol 6 (2) ◽

pp. 25

Keyword(s):

Real Time ◽

Cyber Physical Systems ◽

Physical Systems ◽

Related Data ◽

Sensor Devices ◽

Health Related ◽

Wearable Medical

Download Full-text

Assessment of Dietary Habits, Physical Activity and Lifestyle in Medical University Students

Sustainability ◽

10.3390/su13063572 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3572

Author(s):

Lavinia-Maria Pop ◽

Magdalena Iorga ◽

Iulia-Diana Muraru ◽

Florin-Dumitru Petrariu

Keyword(s):

Physical Activity ◽

Medical Students ◽

Fast Food ◽

Dietary Habits ◽

Healthy Life ◽

Related Data ◽

The Third ◽

Health Related ◽

Medical University ◽

Busy Schedule

A busy schedule and demanding tasks challenge medical students to adjust their lifestyle and dietary habits. The aim of this study was to identify dietary habits and health-related behaviours among students. A number of 403 students (80.40% female, aged M = 21.21 ± 4.56) enrolled in a medical university provided answers to a questionnaire constructed especially for this research, which was divided into three parts: the first part collected socio-demographic, anthropometric, and medical data; the second part inquired about dietary habits, lifestyle, sleep, physical activity, water intake, and use of alcohol and cigarettes; and the third part collected information about nutrition-related data and the consumption of fruit, vegetables, meat, eggs, fish, and sweets. Data were analysed using SPSS v24. Students usually slept M = 6.71 ± 1.52 h/day, and one-third had self-imposed diet restrictions to control their weight. For both genders, the most important meal was lunch, and one-third of students had breakfast each morning. On average, the students consumed 1.64 ± 0.88 l of water per day and had 220 min of physical activity per week. Data about the consumption of fruit, vegetables, meat, eggs, fish, sweets, fast food, coffee, tea, alcohol, or carbohydrate drinks were presented. The results of our study proved that medical students have knowledge about how to maintain a healthy life and they practice it, which is important for their subsequent professional life.

Download Full-text

EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots

Electronics ◽

10.3390/electronics10060666 ◽

2021 ◽

Vol 10 (6) ◽

pp. 666

Author(s):

Davide Calvaresi ◽

Jean-Paul Calbimonte ◽

Enrico Siboni ◽

Stefan Eggenschwiler ◽

Gaetano Manzo ◽

...

Keyword(s):

Breast Cancer Survivors ◽

Physical Conditioning ◽

Privacy Management ◽

Web Interface ◽

Agent Based ◽

Multimodal Interactions ◽

Physical Exercises ◽

Health Related ◽

Health Assistant ◽

Personalized Health

Context. Asynchronous messaging is increasingly used to support human–machine interactions, generally implemented through chatbots. Such virtual entities assist the users in activities of different kinds (e.g., work, leisure, and health-related) and are becoming ingrained into humans’ habits due to factors including (i) the availability of mobile devices such as smartphones and tablets, (ii) the increasingly engaging nature of chatbot interactions, (iii) the release of dedicated APIs from messaging platforms, and (iv) increasingly complex AI-based mechanisms to power the bots’ behaviors. Nevertheless, most of the modern chatbots rely on state machines (implementing conversational rules) and one-fits-all approaches, neglecting personalization, data-stream privacy management, multi-topic management/interconnection, and multimodal interactions. Objective. This work addresses the challenges above through an agent-based framework for chatbot development named EREBOTS. Methods. The foundations of the framework are based on the implementation of (i) multi-front-end connectors and interfaces (i.e., Telegram, dedicated App, and web interface), (ii) enabling the configuration of multi-scenario behaviors (i.e., preventive physical conditioning, smoking cessation, and support for breast-cancer survivors), (iii) online learning, (iv) personalized conversations and recommendations (i.e., mood boost, anti-craving persuasion, and balance-preserving physical exercises), and (v) responsive multi-device monitoring interface (i.e., doctor and admin). Results. EREBOTS has been tested in the context of physical balance preservation in social confinement times (due to the ongoing pandemic). Thirteen individuals characterized by diverse age, gender, and country distribution have actively participated in the experimentation, reporting advancements in the physical balance and overall satisfaction of the interaction and exercises’ variety they have been proposed.

Download Full-text