Building a research partnership between computer scientists and health service researchers for access and analysis of population-level health datasets: what are we learning?
Bibliographic record
Abstract
Background and rationale There is widespread enthusiasm to improve health through the application of artificial intelligence and machine learning (AI/ML) methods to large population-level health datasets. Achieving this may require successful collaboration between institutions as well as between computer scientists (CS), machine learning researchers (MLR) and health service researchers (HSR). Main Aim Describe lessons learned in creating the Health Artificial Data and Analysis Platform (HAIDAP) in Ontario, Canada. Methods/Approach A partnership between a HSR institute (ICES), an AI/ML institute (Vector) and a high-performance computing center (HPC4H) was initiated in 2017 to enable the application of AI/ML methods to population-level health data for the province of Ontario (population 14M). We describe lessons learned (and being learned) following the HAIDAP’s launch. Results The HAIDAP was launched in 2019. Major learnings include: 1) importance of institutional partnerships and alignment with institutional strategies; 2) potential of joint institutional risk-sharing models; 3) need for scientific collaborations bridging disciplines around joint research projects; 4) sensitivity to different scientific cultures (e.g., academic prestige of conference proceedings for MLR vs journal publications for HSR; traditional statistical vs. ML model assumptions); 5) differences in research timeline expectations; 6) different experience with and expectations for access to de-identified routinely collected data (e.g., need for research ethics committee project approvals and privacy impact assessments); 7) developing data access models that enable greater flexibility (e.g., importing code or using open source tools); 8) broadening data access models to allow modern high-dimensional exploratory data analysis; 9) obtaining support of information/privacy regulator; 10) the hardware is the (relatively) easy part compared to other success factors. Conclusion The HAIDAP has enabled multi-disciplinary collaborations and novel AI/ML research of Ontario’s population-level health data. Collectively we have learned that additional effort is required to develop systems and processes enabling more efficient access to data and analytic tools for the analysis of administrative health data.
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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 | 0.300 | 0.295 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.039 | 0.051 |
| Open science | 0.011 | 0.045 |
| Research integrity | 0.019 | 0.031 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".