Proceedings of the 3rd Workshop on Software Engineering in Health Care
Bibliographic record
Abstract
The computer science research community has now recognized to the importance of fundamental, use-inspired and use-directed research and development, in computer science and engineering, for human health, healthcare, and healthcare economics. To cite one example, the first priority stated in the December 2010 Report of the (US) President's Council of Advisors on Science and Technology (PCAST), to the President of the United States, entitled Designing a Digital Future, is that the US Government ...should invest in a national, long-term, multi-agency research initiative on [networking and information technology] for health that goes well beyond the current national program to adopt electronic health records. The initiative should include sponsorship of multi-disciplinary research on three themes: to make possible comprehensive lifelong multi-source health records for individuals; to enable both professionals and the public to obtain and act on health knowledge from diverse and varied sources as part of an interoperable health IT ecosystem; and to provide appropriate information, tools, and assistive technologies that empower individuals to take charge of their own health and healthcare and to reduce its cost. The urgent need for improvements in health, healthcare and healthcare economics has emerged as a strategic priority not only for society, in general, but for computer science and engineering, in particular. Software engineering research has a potentially pivotal role to play, because required advances in health and healthcare require fundamental advances in our ability to produce ultracomplex and critical software underlying future health cyber-infrastructure systems and devices. Yet the software engineering research community has yet to fully organize itself around a clear, ambitious, and promising research agenda for health and healthcare. The engagement of software engineering researchers in the healthcare domain remains light. Moreover, fostering involvement of top researchers might require changes the norms and habits of our own research community. The SEHC workshop series provides a high-visibility, high-impact venue for researchers in software engineering who are active or interested in this area, to assemble to understand and advance the state of the art. The SEHC 2011 workshop will focus specifically on the need to develop and articulate a research agenda and strategy. The product of the workshop will be a significant draft research agenda document. We encourage those interested in this area to attend and contribute to the formulation of this document. A day of talks will be followed by an agenda- setting keynote and a full day of collaboration to produce a draft Software Engineering Agenda for Use-Inspired Research in Health and Healthcare.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 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".