Proceedings of the 6th International Conference on Digital Health Conference
Bibliographic record
Abstract
Welcome to the 6th International Conference on Digital Health (www.acm-digitalhealth.org), held in conjunction with the 25th International World Wide Web Conference (WWW 2016) and incooperation with ACM Special Interest Group on Management of Data (SIGMOD) and Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) in Montreal, Canada from 11th April 2016 until 13th April 2016. Following a great success of eHealth 2008 in London, 2009 in Istanbul, 2010 in Casablanca and ehealth 2011 in Malaga with high profile presence from WHO and ECDC, and following on International Workshop on Public Health in the Digital Age (1st PHDA 20113 and 2nd PHDA 2014) building a community of public health informatics professionals, the 5th DH 2015 provided a major re-launch of this prime international interdisciplinary event for the first time co-located with WWW 2015 in Florence, Italy bringing together frontline public health professionals and computer science researches in data mining, crowdsourcing and Big Data analysis for public health surveillance. Following the successful model from 2015 we organized the programme into more independent Tracks and arranged the proceedings to be printed by ACM Digital Library. Building on the successful collocation with WWW 2015, this year DH 2016 promises to attract computer scientists attending WWW 2016 to public health data management and analytics challenges, and we are also inviting a wider industry, start-ups and medical audience. We have a great academic programme including 8 full research papers, 15 short papers, 4 extended abstracts, 23 posters and a line-up of industry and healthcare speakers confirmed. The DH 2016 conference is following its predecessors on social media. You can follow our Twitter account (@eHealthconf) for the latest updates. We welcome online discussion and feedback - the official hashtag for the conference is #DH2016. We also have a Facebook page at http://www.facebook.com/eHealthConf. And please take a look at our Flickr page for the poster presentations at https://www.flickr.com/groups/digitalhealth2016/. This year we are repeating a very popular start-up event to bring together the academic, industry, start up and medical audiences in an effective and enjoyable way. We are also including a special PhD Track for the first time to provide feedback and mentoring advice to PhD students as well as students-aimed "Health challenge" to get hands-on experience with health data and intervention design in interdisciplinary groups.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.290 | 0.167 |
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".