bluuebunny/crossref_metadata_2025_split huggingface dataset:
What to expect when generating data through the use of a mobile health application
Authors: Schwab I , Ohnhäuser T , Scholten N 
Published: October 2024

Abstract

Background
Digitalization has significantly expanded the possibilities for collecting health data. Besides options such as online surveys, data can be collected through the use of digital applications (apps). Once users have given their consent, contact information from the app registration can be accessed to invite the relevant target group for further surveys. To generate a sufficient amount of data, a high level of willingness to participate and long-term involvement are required. This study aims to illustrate the willingness to share data and what can be expected from it by means of the Neo-MILK app, which supports mothers during lactation.

Methods
The willingness to share data and the data resulting from the use of the app is shown by the frequency of consent to the different data use options and the users’ participation.

Results
While over 68% of the n = 1079 registered app users consented to the use of their app data, 47% agreed to be contacted again for scientific purposes. Data from n = 421 active users were available for analysis. From n = 220 mothers, milk pumping data was available. In 36% of cases, little (&lt;10) or no information on pumping was provided. Of the users who agreed to be contacted again for scientific purposes, n = 300 were invited to participate in another survey and 23% (n = 69) participated.

Conclusions
In this example, the general willingness to share data and participate in further studies shows great potential for data collection. However, the data collection methods differ primarily in the motivation to participate. When collecting data through app usage, ensuring that the design of the app meets the needs of the user is essential to optimize the user experience and thereby generating complete data. If the use of contact information has been agreed to, incentives may be considered to increase the willingness to participate. Required ethics approvals and data protection regulations should also be ensured for app-based data collection.

Key messages
• Generating data via app usage as well as the collection of data through the use of users’ contact information shows great potential for health research.
• App-based data collection should be considered when planning future health research projects.
Record linkage as a vital key player for the COVID-19 syndemic
Authors: Doetsch J N , Kajantie E , Dias V , Indredavik M S , Devold R K , Teixeira R , Reittu J , Barros H 
Published: October 2024

Abstract

Chronicity, comorbidities, and social context influence COVID-19 risk and prognosis, drawing attention to a syndemic dimension. We discuss how linking routinely collected data and data collected through observational population-based cohorts can contribute to advance knowledge on the association between COVID-19 and chronic diseases and social indicators. Record linkage as a multidimensional tool may ultimately enable defining and optimizing of integrated strategies, which may create multilevel public health measures to foster solidarity on health in all policies when ethical and legal barriers under prerequisite data protection and privacy can be overcome. Legal discrepancies have been proving detrimental to research in member states, including those which already had established a margin for research for the common good without explicit consent. We hence call for further harmonization of data protection requirements for scientific research activities in the EU/EEA, focusing in particular on health-related research. A proposed framework demonstrates the role of record linkage as a trailblazing key player in research optimization due to its multidimensional possibilities calling for harmonization.

Key messages
• Further harmonization of data protection requirements for scientific research may create multilevel public health measures.
• As a multidimensional tool, it optimizes integrated strategies and fosters solidarity on Health in All Policies (HiAP).
Impact of AI on public health emergency communication in Europe and Central Asia: A Delphi study
Authors: Mahl D , Schäfer M S , Voinea S A , Adib K , Duncan B , Salvi C , Ortiz D 
Published: October 2024

Abstract

Background
Artificial Intelligence (AI) holds the potential to fundamentally transform public health communication - presenting promising opportunities alongside profound challenges and risks. This two-wave Delphi study therefore invited practitioners and scholars to assess the potential impact of AI on the risk communication, community engagement, and infodemic management (RCCE-IM) of public health authorities in European and Central Asian countries.

Methods
In two successive online surveys, 54 experts - both RCCE-IM practitioners and scholars with expertise in (digital) health communication, risk communication, and AI - from 28 countries provided their assessment of the following analytical dimensions: (1) opportunities, challenges, and risks of AI, (2) conditions for the responsible use of AI, (3) potential future scenarios, and (4) prioritized actions. The first Delphi wave followed an open and exploratory approach, while the second wave sought to prioritize and rank key findings from the previous round. The survey instruments included open-ended and closed-ended questions that were analyzed through statistical analysis, inductive and deductive coding.

Results
Results showed that AI holds various opportunities for the RCCE-IM of public health communicators. Challenges and risks, such as algorithmic bias or concerns about inclusivity, equally affect RCCE-IM efforts. Experts identified seven principles for the responsible use of AI (e.g., fair, human-centered) and predicted optimistic (e.g., optimized workflows) and pessimistic (e.g., flood of (mis)information) scenarios for the future impact of AI. Prioritized actions range from regulation, resource allocation, and feedback to capacity building, public trust, and education.

Conclusions
To responsibly navigate the opportunities, challenges, and risks of AI for public health emergency communication, clear guiding principles, ongoing critical evaluation, and multi-sectoral collaboration are needed.

Key messages
• AI is likely to transform public health emergency communication.
• A set of agreed-upon principles will support public health authorities in the responsible management of the opportunities, challenges, and risks of AI.
Usability of an AI-based independent pedestrian navigation device designed for blind people
Authors: Dascal M , Blaga O , Padure M , Amariei C 
Published: October 2024

Abstract

Background
2.2 billion people live with visual impairment (VI) worldwide and face orientation and mobility challenges in various aspects of their lives. Despite technological advancements, the most used mobility solutions for VI are still the walking cane and the guide dog. The objective of this research is to assess the system usability of an AI-based independent pedestrian navigation device (.lumen glasses), designed for blind people.

Methods
During 31st of October and 2nd of November 2023, 27 visually impaired people living in Bucharest, Romania, tested the .lumen glasses for 1-3 hours during an outdoor navigation task. The glasses use AI to recognize the user’s environment and haptic and audio feedback to guide them. The System Usability Scale (SUS), a 10-item instrument, was used to evaluate the level of usability the participants ascribe to the .lumen glasses. Qualitative feedback was collected as part of the post-test survey and analyzed using thematic analysis. Data were triangulated to identify usability issues.

Results
The mean age of the participants was 30.3 years old (SD = 12.07, min=21 max =56, range: 48), 55.6% (N = 15) of the participants were male, 96,3% (N = 26) were completely blind, and 81.5% were using a white cane for orientation and mobility. Our findings reveal a mean SUS score of 83.65 (SD = 15.23, range 47.5, min=52.5, max=100) indicating high usability. The thematic analysis revealed 5 themes: (1) auditive and (2) haptic feedback in navigation, (3) hardware (adjustment system & buttons), (4) tutorial & presentation of how the device works and (5) other aspects (usage in combination with other assistive technologies etc.). The participants declared that the glasses are intuitive to use and promote the feeling of safety and independence.

Conclusions
Data suggest that the .lumen glasses area promising assistive technology for the mobility of VI. Findings are currently used to steer the development and implementation of the AI-based system.

Key messages
• According to participants’ feedback, .lumen glasses are a scalable AI-based mobility solution that can make navigation and orientation easier for visually impaired individuals.
• .lumen glasses designed for the visually impaired individuals are one of the current and limited assistive technology improving the mobility and orientation.
Digital health and some thematic shifts in bioethics in academic publications after the pandemic
Authors: Mirchev M , Mircheva I 
Published: October 2024

Abstract

Background
Digital health (DH) presents vast opportunities, but also dynamic shift in viewpoints, which is reflected in academic works. Since DH concerns patient data, a major issue is complying with the bioethical principles, and ensuring patients’ privacy and security. The aim of this research is to explore avenues, in which ethics and privacy are reflected in DH, especially in terms of research, legislation, artificial intelligence (AI) and blockchain applications in medicine and public health.

Methods
Literature search for full text publications in PubMed, Scopus and ScienceDirect focused on ethics and privacy in DH in the period 2019-2023 found 325 articles in English authored by academicians. Six thematic categories were identified: ethics (fundamental principles; ethics in using novel DH tools); privacy (in use of apps/wearables; as a challenge/barrier to DH interventions/technologies); ethics/privacy in: DH research (data gathering, use, sharing); policy and legislation (for DH applications); AI and blockchains (in medicine and public health).

Results
Articles were from 32 countries: USA-24.3%; UK-13.5%; Germany-7.7%; Canada-7.4%; Australia-6.7%; Netherlands, Italy, Switzerland-4.7% each; China, India-3.0% each and 20.3% from 22 other countries; 86.5% in medical/DH journals. Only 10.2% were published in 2019; 17.8% - 2020; 19.4% - 2021; 27.1% - 2022 and 25.5% - 2023. Privacy was discussed in 26.5%; ethics-23.7%; research-19.4%; AI-15.1%; policy and legislation-9.5% and blockchains-5.5% of the articles. The publication activity of all six categories increased after the unfold of the pandemic in 2020. In 2023 publications for ethics (13%), privacy (5.8%) and research (6.3%) decreased; for legislation (12.9%), AI (14.3%) and blockchains in healthcare (16.8%) increased.

Conclusions
The pandemic and its aftermath present a change in academic interest. Traditional ethical fundaments in DH slightly lose position in favor of top notch technologies and apt legislation.

Key messages
• The pandemic and modern technologies enforced new digital health applications. Further research will reveal the needs of AI and blockchain applications respecting privacy and the ethical principles.
• Besides existing legislation and regulations, in order to catch up with the dynamic developments in DH, the legal base needs to become more flexible. Technologies change, privacy importance does not.
The effects of weather and mobility on respiratory viruses before and during the COVID-19 pandemic
Authors: Perfeito L , Varela-Lasheras I , Mesquita S , Gonçalves-Sá J 
Published: October 2024

Abstract

Background
The flu season is caused by a combination of different pathogens, including influenza viruses, which cause the flu, and non-influenza respiratory viruses, that cause common colds or influenza-like illness. These viruses exhibit similar dynamics and, given that outbreaks occur mostly in the winter and there is almost no circulation during the summer, in temperate regions, meteorological conditions have historically been regarded as a principal modulator of their epidemiology. However, after the emergence of SARS-CoV2, in late 2019, the dynamics of these respiratory viruses were strongly perturbed worldwide: some infections displayed near-eradication, while others experienced temporal shifts or occurred “off-season”. This disruption raised questions regarding the dominant role of weather while also providing an unique opportunity to investigate the roles of other determinants in their epidemiological dynamics.

Methods
Weather, mobility and epidemiological surveillance data was collected for Influenza, RSV, hCOV and hMPV, from Canada and the USA, from 2016 to 2023. Statistical analysis and modeling were employed to test the effects of weather and mobility on viral dynamics, before and during the COVID-19 pandemic.

Results
Using Beta Regressions, we found that whereas in the pre-COVID-19 pandemic period, weather had a strong effect, in the pandemic period, this effect was strongly reduced post-pandemic with mobility playing a more significant role.

Conclusions
These results, together with previous studies, dispute the general belief that respiratory viral dynamics are mostly dictated by weather and indicate that behavioral changes resulting from the non-pharmacological interventions implemented to control SARS-CoV2, played a key role. This disruption of past dynamical equilibrium raises important questions regarding the factors that modulate them, particularly in a context of climate change.

Key messages
• We took advantage of the disruption caused by COVID-19 to study dynamics of other respiratory viruses and to disentangle the effects of weather from those of human behavior.
• While before 2020 cold temperatures were highly correlated with incidence, afterwards cold weather was no longer a necessary condition and human mobility became central.
Video consultations among registered nurses in public sector and associated factors
Authors: Kainiemi E , Virtanen L , Heponiemi T , Vehko T , Kaihlanen A-M 
Published: October 2024

Abstract

Background
In digital health services, including services for older people, video consultations can enhance access to care while enabling interaction through audio and visual means. Registered nurses are pivotal in digital care delivery and there is a need for more understanding regarding the use of video consultations in nursing. This study aims to examine factors associated with nurses’ video consultations in the public sector.

Methods
The Finnish ‘Survey on information systems for registered nurses’ was delivered for registered nurses in March 2023. The respondents were asked whether their work had included video consultations in the past six months. Binary logistic regression was used to examine if career stage and digital skills (basic IT-skills, proficiency in data security, skills in digital working environments) were associated with nurses’ video consultations, adjusted for working unit and location of employment.

Results
The study included 2,525 public sector nurses (92.7% female, mean age 47.34, SD 10.47). Of them, twelve percent had used video consultations with patients. Most nurses used video consultations in outpatient units (63.3%), whereas they were less used among those working in acute care (3.3%), housing services (3.3%) and home-based services (8.2%). Early-career nurses had lower odds (OR .38, 95% CI .19-.73) of video consultations compared to mid-career nurses. Nurses who had good skills in working in digital environments (OR 2.07, 95% CI 1.26-3.39) had greater odds of video consultations than those with poorer skills.

Conclusions
The study suggests a limited adoption of video consultations among public sector nurses, particularly among early-career professionals. Increasing targeted support and training, and the availability of tools for various patient service needs in video consultations, are needed. By recognizing the importance of nurses’ digital skills, digital care delivery could be enhanced and potential public health benefits promoted.

Key messages
• Relatively few registered nurses used video consultations in the public sector. More research is needed to explore if their usage could be increased safely for patients, also in early nursing careers.
• Proficiency in digital work environments is imperative for ensuring the quality of care in video consultations. Therefore, collaborative efforts are required to enhance professionals’ digital skills.
Exploring Meditron-7b performance on a small-scale purpose made dataset in human nutrition queries
Authors: Scarsi N , Büsser B 
Published: October 2024

Abstract

The interest in Large Languages Models (LLMs) has been rapidly evolving over the last years, boosting concerns about their potential to improve the efficiency and effectiveness of clinical, educational and research work in medicine. In this study, we aimed to test the performance of MEDITRON, a recently released suite of open-source LLMs with 7B parameters adapted to the medical domain, on a small sample of human nutrition queries. To facilitate our assessment, we curated a specialized dataset comprising a diverse range of human nutrition-related queries, which have been manually collected from various medical databases and nutrition exams. The dataset was adapted for the scope of our analysis to encompass variations in language complexity, query types, and semantic nuances commonly encountered in real-world settings. Additionally, to ensure a standardized and clinically relevant context for our evaluations, we engineered a specialized prompt designed to mimic interactions with a highly esteemed physician specializing in nutrition, food science, and diet-related disorders. The prompt guided the generation of nutrition-related queries in a structured format, enabling MEDITRON to provide responses consistent with the latest advancements in medical research. Our preliminary findings revealed promising capabilities of MEDITRON in understanding medical language, providing contextually appropriate responses to several human nutrition related questions. Through this study, we add valuable insights to the ongoing discussion around the deployment of LLMs in public health, highlighting their potential to improve access to essential human nutrition literacy.

Key messages
• Design of LLMs tools holds the potential to improve healthcare in clinical, research and education applications.
• Further evidence is needed to foster their actual integration in real world settings.
Multi-objective Symbolic Regression for Clinician-in-the-loop Machine Learning algorithms
Authors: Ferrari D , Wang Y , Edgeworth J , Curcin V 
Published: October 2024

Abstract

Background
The power of machine learning (ML) in medicine is further enhanced by input from clinical experts with detailed knowledge of phenomena explored and the organization in which the ML models will be deployed. This input is needed not only in the problem conception and evaluation, but also in the training itself using the clinician-in-the-loop approach. We explore the applicability of such an approach to Multi-objective Symbolic Regression (MOSR) and compare it with state-of-the-art methodologies.

Methods
This is a retrospective study based on the admission data of 22576 patients routinely collected by the intensive care units (ICU) of Guy’s and St. Thomas’ NHS Foundation Trust in central London between April 1st, 2008, and December 31st, 2022. Patient-level data was used, including blood tests, urine tests, medical history, signs, symptoms, and common clinical scores. The outcome was modelled as a binary classification; four experiments were executed predicting mortality at 7 days (4.5% of patients), 30 days (10.2%), 6 months (17.4%) and 12 months (21.2%). A random train-test split of 80/20% ratio was performed. Clinician-in-the-loop approach used a Multi-objective Symbolic Regression algorithm bespoke designed to also optimize for the F1-score.

Results
For all four experiments, MOSR outperformed all other available ML algorithms providing significantly higher F1-scores and AUC. This makes MOSR the most competitive algorithm, particularly for imbalanced datasets such as typical ICU admission data.

Conclusions
We have demonstrated how ML algorithms designed for a clinician-in-the-loop approach outperform standard off-the-shelf alternatives and note the role of the application environment and clinical knowledge in the training itself enhancing the model performance. We therefore champion the adoption of approaches that, like MOSR, enable clinical input also in shaping the training behaviour.

Key messages
• The input of clinical knowledge and understanding of the application domain dynamics are crucial for ML development in healthcare.
• In Health Data Science we need ML algorithms like MOSR capable of receiving clinical input also in shaping the training behaviour.
GPT models classify free text from emergency department accesses in children with medical complexity
Authors: Brigiari G , Franzoi M , La Piana C , Zanin A , Benini F , Gregori D 
Published: October 2024

Abstract

Background
Emergency Department (ED) management software often collects clinical information as free text, but its unstructured nature limits routine use. While Machine Learning (ML) techniques have been employed to classify such information, they demand substantial data and computational resources. GPT-based models, using direct text prompts, offer a solution without requiring retraining, potentially simplifying clinical practice.

Aims
To assess OpenAI GPT’s performance in automating data extraction from ED medical records of complex pediatric patients.

Methods
Electronic admission records of children and young adults referring to the pediatric ED of Padova University Hospital (2007-2023) were analyzed. Principal variables of interest were discharge diagnosis, reason for ED access, required medical procedures, color code at triage, color code at discharge/hospitalization, outcome of the access. GPT-4 classification, via OpenAI API, was compared with manual classification by an expert pediatrician on sample of 791 records.

Results
Preliminary results of this study analyzed 107 records, showing a correct classification of discharge diagnosis in 95% of records. The reason for access to the ED was correctly identified in 97% of cases, while outcome of admission and color code, both at triage and discharge/hospitalization, were correctly classified in all given cases. Misclassification occurred mainly in cases where classification made by the model was incomplete (e.g., in the case of a trauma, abbreviated site of lesion was not identified by the model).

Conclusions
GPT-based models demonstrate feasibility, accuracy, and efficiency in transforming unstructured data into structured formats. Ultimately, GPT-based models could provide aid to the medical personnel in decision-making processes.

Key messages
• Emergency departments often collect clinical data in unstructured free text format, resulting in usability limitations.
• GPT-powered models efficiently convert unstructured data to structured formats, offering potential support for medical decision-making.