Notice bibliographique
Résumé
There is a growing interest in using public data for open government policy involving health informatics and healthcare systems. This paper investigated the characteristics of publically available data sets in health informatics that were derived from electronic health records (EHRs), healthcare systems, and a variety of open-government libraries, data marts, or data catalogues.Data used in this study consisted of public data sets that did not require any registration to access online. In total, nine web-based platforms on the Internet were used that included: British Columbia (BC) Data Catalogue, Canadian Institute for Health Information (CIHI), Harvard Dataverse, MIMIC-eICU, FigShare, GitHub, Google Dataset, UCI Machine Learning Repository, and Zenodo. Our initial search across these platforms found over 10,000 public use files that had data sets related to health informatics.We found 558 data sets that matched search criterion that ranged from years 2013-2022. The data source types were mostly found using the health informatics search filters followed by the combination of health informatics and healthcare systems, but fewer data sets were found when using EHR as the criterion. Almost 85% of the total data sets were from 2020-2022. The range of data sizes were 11KB to 7.8MB. The eICU (hosted by MIT’s MIMIC data mart) platform had the largest data set followed by Zenodo, and GitHub. Additionally, any bioinformatics in the 558 data sets were excluded and further classification on the content and usability, and dashboard visualization towards experiential learning resulted in 117 data sets.Of these 117 data sets, we further tested their usability to graph and create a dashboard within 2-5 minutes of loading the data to Tableau© that then used a Data Usability Scale (DUS) scoring developed from the industry standard of System Usability Scale (SUS). Data were deemed usable and useful for >60% average DUS scoring. Finally, 25 sets of data could be used effectively in classroom exercises dealing with electronic records and decision support for health care. Best data for dashboard usability were from MIMIC-eICU, and other websites like Zenodo produced low to high usability. The data sets with low to poor usability were from FigShare, Dataverse, CIHI, and BC Data Catalogue, respectively.Overall, 25 data sets with high usability of data related health informatics and healthcare systems showed 60-85% usability. Moreover, all nine platforms showed ease-of-use search patterns to establish the criteria in a short amount of time. However, more investigation is needed to compare data-to-dashboard visualization for single to multiple files for experiential learning in health informatics.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,039 | 0,035 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».