From smartphone supported Citizen Health Science to Cooperative Citizen Test Lab
Bibliographic record
Abstract
The wide dissemination of smartphones provides new opportunities for Citizen Health Science, based on citizen value creation of P4 Health (Participatory, Preventive, Predictive and Personalized). In the best case this may lead to better prediction and prevention of diseases and personalised support and treatment of citizen and patient to great clinical benefit and cost reduction. However, there is at the same time a risk of ‘O4 medicine’ (overtesting, overdiagnosis, overtreatment, overcharging). In this workshop we contribute with our experiences from three projects that aim to bridge personal health and population health by combining 24/7 data from smartphones with other kinds of health data: HealthD360 - Health data that creates value for the citizen In HealthD360 we investigate the possibilities for creating better health for the citizens by gathering data from the public healthcare system and linking them with data from the citizens' smartphones and wearables. The ambition is to promote more personal, secure, and coherent treatment in collaboration with patients and healthcare professionals. We will share experiences with developing solutions to monitor diabetic foot ulcers and to support mental health in schoolchildren. FEMaLe – Finding Endometriosis using Machine Learning The EU-funded FEMaLe project is working on a machine-learning multi-omics platform that can analyse omics data sets and feed the information into a personalised predictive model. The focus of the project is to improve intervention for individuals with endometriosis, a condition where tissue normally lining the uterus grows outside the uterus. A combination of tools, such as a mobile application and augmented reality surgery software, will be co-created, facilitating improved disease management and the delivery of precision medicine. We will share experiences with co-creating a consensus study survey as well as the Lucy Application, which is your personal gynecological virtual assistant, helping to take care of your female health. CoronaLytics: A 360 degree mobile/wearable data household approach to guide shared precision health and decision-making during the COVID-19 epidemic In the research project CoronaLytics citizen could contribute to with personal data gathered on a smartphone. Both automatic data collection of activity and heartrate data and data bases on questionnaires was used. The project worked with the citizen perspective and focused on daily impact on everyday life during the pandemic. We will share our gained experiences in how to engage patients and citizens in the design, development, and implementation processes. Workshop focus Based on these experiences and those of the workshop participants, we will discuss how governance, design and analytic methods can support the development of the citizen health science concept to secure citizen value creation of P4 Health. We will discuss the idea of a cooperative governance model to secure data solidarity. And how Citizen Health Science in combination with Denmark's unique potential can secure a democratic health model, where the partnership between citizens and researchers forms a population test lab. This may be the framework for the next generation of population-based research, including development and testing of personal health solutions based on motivated consent and participation from Danish citizens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".