BJAS & JAIR (Journals)
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Predicting Dengue in the Philippines Using an Artificial Neural Network
Get PDFDengue fever is an infectious disease caused by Flavivirus transmitted by Aedes mosquito. This disease predominantly occurs in the tropical and subtropical regions. With no specific treatment, the most effective way to prevent dengue is vector control. The dependence of Aedes mosquito population on meteorological variables make prediction of dengue infection possible using conventional statistical and epidemiologic models. However, with increasing average global temperature, the predictability of these models may be lessened employing the need for artificial neural network. This study uses artificial neural network to predict dengue incidence in the entire Philippines with humidity, rainfall, and temperature as independent variables. All generated predictive models have mean squared logarithmic error of less than 0.04
Telemedicine for Older Adults During COVID-19: A Literature Review
Get PDFWhen intended for the older population, Telehealth has the potential to be beneficial, especially during a crisis like COVID-19 pandemic, when older people were the most vulnerable group.
This paper gives an overview of the growing use of Telehealth among people 65 years and older during COVID-19 pandemic by reviewing relevant studies published from the beginning of the pandemic until the time of starting the data extraction process, our review found that telemedicine use, in general, has been beneficial for healthcare providers, caregivers, and patients as it is in the management of the epidemic.
Most telemedicine interventions or services were available for the management and control of chronic diseases, telemedicine has provided the possibility of immediate assessment and counseling of patients infected with COVID-19, multimedia treatment remotely, patient education and training, facilitating real-time data exchange, leading the collection, processing, and storage of medical information for patients, advising on strategic planning and drug use for patients with COVID-19, dealing with the worries of patients, digital assessment tools, helped in implementing social distancing, reduced reliance on public transportation, minimized the virus infection risk that may happen because of in-person contacts. It has promising use in the management of mental health crises associated and non-associated with COVID19.
The paper also reviews the possible limitation that can hinder better use of telemedicine by older adults such as the limited ability to perform a physical examination, concern about the quality, not getting personally connected to the medical provider, hearing or visual problems and privacy concerns
Navigating the Divide: Informatics as Science and Digitalization as Practice
Get PDFThe dialogue session “Navigating the Divide: Informatics as Science and Digitalisation as Practice” on the first day of the DigiHealthDayS 2024 proposed a discussion around the intersection of informatics—the science—and digitalization—the practice—and how to navigate these (uncharted) waters safely.
To explore future directions for informatics professionals in bridging the gap between science and practice, the keynote speakers and panelists explored strategies for integrating data-driven insights with digital technologies to enhance patient care and create new knowledge
True Innovation is an Idea Turned into Value
Get PDFThe session “El Futuro Es Nuestro”, held at the DigiHealthDays 2024 in Pfarrkirchen, Germany, explored the transformative power of true innovation in healthcare. Hosted by Lemonmint in collaboration with i-xpand, the session featured a keynote by Hal Wolf, CEO and President of HIMSS, and highlighted the importance of turning ideas into tangible value. Student-led presentations showcased innovative approaches to addressing local healthcare challenges, emphasizing the balance between purpose and practical application. Participants discussed how meaningful innovation can impact healthcare systems, patient outcomes, and future industry leaders
Moonshot Dialogue: The Future of AI in Healthcare and Cutting Edge Digital Health Technologies
Get PDFAs societies around the world are grappling with the effects of aging populations, rising rates of chronic diseases, and overstretched health systems, the deployment of artificial intelligence (AI) in healthcare has emerged as both the beacon of hope but also as subject of close scrutiny. The promise of AI-powered digital health technologies to transform care delivery, enhance diagnostic precision, and improve outcomes is compelling, particularly in a context where progressive neurological disorders, cardiovascular diseases and cancer are expected to rise in prevalence. Yet, this transformation is unfolding against a backdrop of the need for a rigorous regulatory oversight, ethical deliberation, and a determined push to set global standards for responsible innovation
The Future of Medicine and the Logic of Data Management: Data Discrimination Problems
Get PDFConsidered problems of medical data management. It is emphasized that this process has the greatest potential to ensure accurate and cost-effective patient care, as well as knowledge transfer during medical education. Analyzing the main areas of professional application of data: integration of big data, working with data of various types (from batch to stream) and their transformation for further use, we note that all the listed areas do not have a clear interpretation and recommendations for their use.
The purpose of the study was to substantiate the strategy of a safe and effective medical data management system and the logic of creating completely open data systems. This strategy will allow streamlining the data flows of instrumental and laboratory studies and ensure the delivery of their big data directly to medical institutions or medical research centers.
Conclusions: 1. There is a need for a comprehensive real-time medical data management system that will allow physicians, patients, and external users to enter their medical and lifestyle data into the system. 2. The inclusion of big data analytics will help to better predict or diagnose diseases, and accordingly help in the development of an effective plan for the prevention of complications and treatment of the disease. 3. Scaling communication with each patient in real time is possible only with the help of artificial intelligence. Manual intervention simply cannot serve thousands of users at the same time, each in their own way, addressing each individual directly. This conclusion should be especially emphasized when creating a perfect model of family medicine organization. 4. Transferring information is at the heart of developmental biology, and thus it is imperative that we can form a logical and structured approach to the healthcare language. If, as it appears, information theory has much offer to biology, further advances will depend on its integration. Given that these fields share many terms with developmental biology, effective collaboration may necessitate redefining the meaning attached to signaling, communication and information, in the context of the biology and medicine. Biosemiotics is inherently concerned with the language and rules of signals and codes in biological systems. It combines many ideas from diverse areas including systems theory, information theory and linguistics and may offer us a new perspective on the classification and meaning of biological and medical signaling
Alumni Voice Session at the DigiHealthDayS-2024
Get PDFThe Alumni Voice session during the DigiHealthDayS 2024 highlighted the importance of digital health literacy as well as the academic and professional trajectories of the Master of Digital Health graduates. Three brilliant alumni shared their personal and diverse experiences, offering insights that may shape students\u27 perspectives on career development and industry challenges. The session concluded with a forward-looking dialogue about the future of healthcare
AI Needs High-Quality Health Data at Scale - Will the EHDS Deliver?
Get PDFA panel discussion was held during the DigiHealthDayS-2024 Scientific Congress on 15th November 2025. It explored the potential for the European Health Data Space (EHDS) to become a high-value resource of good quality data for the development of AI innovations to support better and safer healthcare. Panel members discussed whether the measures presently intended for the provision of secondary used data sets within the EHDS and AI Act will be suitable for AI development. At present, a standardized data quality label will be defined, but its use will be optional, interoperability standards for data sets sharing for research are not mandated. However, AI innovators need access to large-scale, high-fidelity data sets that have well-documented provenance and quality, and which are accurately representative of the populations on whom the innovators wish to target their solutions. The EHDS has the potential to accelerate the availability of high-quality data sets, but the adoption of the data quality and utility label to assess the data sets must be strongly promoted and accompanied by measures and incentives for health systems to actively improve the quality of the data they routinely collect within EHR systems
Implementation of a Novel Research Platform to Research New Academic Healthcare Professions at the Deggendorf Institute of Technology
Get PDFThe academization of healthcare professions such as academically qualified nurse practitioners (NPs) and physician assistants (PAs) are interesting options to resolve the rapidly increasing staff shortage in the German healthcare system and to stabilize patient care. In other countries academically qualified NPs and PAs have already been demonstrated to play a critical role in improving access to healthcare as well as quality of patient care. However, it still needs to be shown if this can be transferred one-to-one to the German healthcare system. Thus, a research platform is necessary to assess and evaluate these new academically qualified healthcare professionals regarding their effects on efficiency, cost-effectiveness and quality of patient care. Following thorough evaluation of various research tools regarding data safety, feasibility and power, REDCap has been implemented at the Deggendorf Institute of Technology (DIT) as part of an extensive platform to research these new academic healthcare professions. Particularly, this new research platform and REDCap are currently being used in two nation-wide registry studies on new healthcare professions: PA-Reg investigates the role of PAs and AkaP-Reg the role of academically qualified NPs within the German healthcare system.More data on the academization of healthcare professions such as academically qualified nurse practitioners (NPs) and physician assistants (PAs) in Germany are urgently needed. Thus, a research platform that enables the assessment and evaluation of these new academically qualified healthcare professions regarding their effects on efficiency, cost-effectiveness and quality of patient care nationwide is necessary and depicted in this manuscript.
We now describe a novel research platform, which was implemented at the Deggendorf Institute of Technology (DIT) for nationwide research of novel healthcare professions. Following thorough evaluation of various research tools regarding data safety, feasibility and power, REDCap has been selected as part of this extensive platform for data acquisition and management. In addition, the platform is based on modules encompassing project-internal as well as external stakeholders and infrastructure, data management including ethical approval and statistics, as well as enrollment of study participants.
Hence, this new research platform including REDCap aims to characterize new healthcare professions regarding their effects and performance in the German healthcare system. In addition, this platform can be used as a role model for nationwide research also in natural and social sciences.
In Deutschland werden dringend mehr Daten zur Akademisierung von Gesundheitsberufen wie akademisch qualifizierten Nurse Practitioners (NPs) und Physician Assistants (PAs) benötigt. Daher ist eine Forschungsplattform erforderlich, die eine bundesweite Erhebung und Bewertung dieser neuen akademisch qualifizierten Gesundheitsberufe bezüglich ihrer Auswirkungen auf Effizienz, Kosteneffektivität, und Qualität der Patientenversorgung ermöglicht. Eine solche Plattform wird in diesem Manuskript dargestellt.
Wir beschreiben hier eine neue Forschungsplattform, die an der Technischen Hochschule Deggendorf zur bundesweiten Erforschung neuer Gesundheitsberufe implementiert wurde. Nach einer gründlichen Bewertung verschiedener Forschungssoftwaretools hinsichtlich Datensicherheit, Praktikabilität und Leistungsfähigkeit wurde schlussendlich REDCap als Bestandteil dieser umfassenden Plattform für die Datenerhebung und -verwaltung ausgewählt. Darüber hinaus basiert die Plattform auf Modulen, die projektinterne sowie externe Partner und Infrastruktur, Datenmanagement inklusive Ethikvotum und Statistik sowie die Rekrutierung von Studienteilnehmenden umfassen.
Daher zielt diese neue Forschungsplattform mit REDCap integriert darauf ab, neue Gesundheitsberufe hinsichtlich ihrer Auswirkungen und Leistungsfähigkeit im deutschen Gesundheitssystem zu charakterisieren. Zudem kann die Plattform als Modell für bundesweite Forschung auch in den Natur- und Sozialwissenschaften dienen
Effective data recording in agriculture: A robust system for mobile machines
No full textDie präzise Erfassung und Verarbeitung von Sensordaten stellt eine Schlüsselvoraussetzung für die Entwicklung KI-gestützter Anwendungen in der Landwirtschaft dar. In dieser Studie werden die Konzeption und Implementierung eines robusten Datenaufzeichnungssystems für mobile landwirtschaftliche Maschinen vorgestellt, das auch unter extremen Einsatzbedingungen zuverlässig arbeitet. Das System erfasst und speichert die Bild- und Signaldatenströme einer multispektralen Kamera im sichtbaren und nahinfraroten Spektralbereich sowie zweier maschinenspezifischer CAN-Bussysteme. Die Hardware basiert auf einem Rechnersystem mit stoß- und vibrationsresistentem Gehäuse sowie austauschbaren SSD-Speichern, die eine langfristige Sicherung großer Datenmengen ermöglichen. Die Softwarearchitektur erlaubt eine autarke, ereignisgesteuerte Datenerfassung und gewährleistet durch das Precision Time Protocol eine präzise Synchronisation aller Quellen. Validierungen im Labor und unter realen Einsatzbedingungen auf einer Überlademaschine für Zuckerrüben belegen die hohe Verfügbarkeit, die konsistente Datenqualität sowie die zeitlich determinierte Erfassung und zeitnahe Verarbeitung der Sensordaten. Die Bildqualität wurde anhand des Signal-Rausch-Verhältnisses bewertet und als gut eingestuft; zudem konnte eine korrekte Ausleuchtung in 99,97 % der Aufnahmen nachgewiesen werden. Die Ergebnisse bestätigen die Praxistauglichkeit des Systems und seine Eignung als Aufzeichnungsgerät dieser Datenströme. Das entwickelte Konzept bietet darüber hinaus eine flexibel anpassbare Lösung für zukünftige Anwendungen zur Datenakquise in der Präzisionslandwirtschaft.
The precise acquisition and processing of sensor data is a key prerequisite for the development of AI-supported applications in agriculture. This study presents the design and implementation of a robust data recording system for mobile agricultural machinery that operates reliably even under extreme operating conditions. The system acquires and stores the image and signal data streams from a multispectral camera in the visible and near-infrared spectral range, as well as from two machine-specific CAN bus systems. The hardware is based on a computer system with a shock- and vibration-resistant housing and replaceable SSD storages, enabling long-term archiving of large data volumes. The software architecture allows for autonomous, event-driven data acquisition and ensures precise synchronization of all sources through the Precision Time Protocol. Validations in laboratory and under real-world operating conditions on a sugar beet overloading machine demonstrate the high availability, consistent data quality, and the time-determined acquisition and real-time processing of sensor data. Image quality was assessed based on the signal-to-noise ratio and rated as good. Furthermore, correct illumination was confirmed in 99.97% of the images. The results confirm the system\u27s practicality and its suitability as a recording device for these data streams. The developed concept also offers a flexibly adaptable solution for future applications usable for data acquisition in precision agriculture.The development of a robust data recording system for mobile machines is crucial for the precise collection and analysis of data in precision agriculture. This study develops a recording system that operates reliably under harsh conditions such as vibration, dust, and changing climatic conditions. It is adapted for this application, but can also be designed as a recording system for other agricultural recording scenarios, such as crop cultivation or harvesting. The system integrates a multispectral camera that captures images in the visible and near-infrared range, as well as two CAN bus systems for acquiring machine data. The camera is protected by a robust housing and operates with an illumination system in the visible and infrared spectral range to ensure consistent, high-quality images. The data is retrieved in a time-synchronized manner and stored on a rugged computer specifically designed for use in demanding environments. The system\u27s software enables automatic and time-controlled data acquisition, supported by precise time synchronization. Initial tests and validations in the laboratory and on-site on a sugar beet transfer machine have demonstrated the system\u27s ability to collect and process accurate and consistent data. The results of this project provide a solid foundation for future developments in data recording and processing using artificial intelligence in agriculture and contribute to increasing the efficiency of agricultural processes