Interdisciplinary development and application of computational methods in informatics for clinical applications
Notice bibliographique
Résumé
This focus issue serves to highlight the challenging but highly valuable work of interdisciplinary teams collaborating across traditional scientific silos.1–25 There were several points of origin that motivated our choice to highlight interdisciplinary work. One point of origin that initially motivated us as guest associate editors of this focus issue was our shared personal experiences on high-impact clinical informatics projects.26–30 A second point of origin comes from talking to others who are engaged in similarly scoped efforts like the ICU Cockpit.31,32 For example, Dr Keller who led the ICU Cockpit work has spoken of similar roadblocks, shared experiences, need for time spent communicating and listening, and of how few people understand how difficult and time-consuming these efforts are—often 10-15 years from beginning to deployment. The projects we have been part of, and projects of similar scope whose leaders we have commiserated with, took years of collaborative effort from large interdisciplinary teams drawing members across the research and deployment pipelines. The collaborative clinical informatics projects that motivated this focus issue included highly engaged experts spanning a range of diverse teams of practicing clinicians, computational scientists, human–computer interaction and implementation science researchers, informaticians, and operational engineers who run the day-to-day electronic health record (EHR) systems. Interestingly, we observed that experts from diverse teams who presented components of these collaborative projects outside of their direct field of application were met with misunderstandings which manifested in dismissal of ideas, underestimation of the difficulty of another field’s problems, underestimation of the deep innovation required to translate and assemble the science, and general undervaluation of translating research to operations within the clinical informatics space. It is well known that open communication across highly distinct scientific domains is required to solve complex real-world problems and is the reason why, for instance, Oppenheimer fought so hard for open dialog between all scientists working on the Manhattan project.33 A third point of origin was our belief that clinical informatics communities could increase their engagement in interdisciplinary work and that missed opportunities for impact abound when collaborative interdisciplinary expertise is lacking. The goal of increasing dialog between distinct fields to drive increased interdisciplinary work served as a motivation for the Banff International Research Station titled: Dynamics and Data Assimilation, Physiology and Bioinformatics: Mathematics at the Interface of Theory and Clinical Applications in 2022 comprising researchers from a wide variety of interdisciplinary fields including several of the focus issue guest associate editors who were motivated to move these ideas forward through a journal focus issue. Despite some high-profile examples of interdisciplinary work, our anecdotal experience has been that examples of cross-field communication and interdisciplinary teams are hard to identify in peer-reviewed literature. We would like to see this change.
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 | 0,014 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».