Abstract B071: Automated extraction and provision of electronic health record data from children with cancer to National Childhood Cancer Center (NCCR) cancer registries
Notice bibliographique
Résumé
Abstract Introduction: The National Childhood Cancer Registry (NCCR) is a central infrastructure that integrates childhood cancer data from central cancer registries and other sources to enhance access to and utilization of pediatric cancer data. The NCCR currently represents >70% of the United States pediatric population. Registries collect and publish cancer incidence and survival data for public health surveillance. Electronic health records (EHRs) are one data source. EHR data are currently obtained by manual collection at individual hospitals that then transfer data to population-based cancer registries. This study aimed to use automated data collection from hospital EHRs to broaden and standardize data reported to registries. Methods: ExtractEHR is an R based software tool that extracts and formats data derived from EHRs from multiple institutions and vendors for research initiatives. ExtractEHR was installed by the hospital technical team to extract the following EHR components: addresses; demographics; inpatient and clinic visits; laboratory, microbiology, pathology, genomic, and radiology test results; medications orders and administrations; procedures; height and weight; and oncology clinician notes. Patients were identified by the central cancer registry and case lists provided to the hospital team. ExtractEHR extracted data for all patients from time of diagnosis through most recent encounter in the EHR. Data elements were extracted as comma-separated values (csv) files. The hospital clinical lead checked data for completeness and worked with the hospital technical and ExtractEHR teams to iteratively adjust ExtractEHR mapping to ensure complete data extraction. Once considered complete, csv files were transferred to the registry team using a secure file transfer system. The initial pilot was completed for patients with 5 diagnosis types over a 2-year period at Children’s Healthcare of Atlanta (CHOA) and transferred to the Georgia Cancer Registry. Results: Data from 306 patients with astrocytoma, Hodgkin Lymphoma, neuroblastoma, Ewing or osteosarcoma, or leukemia diagnosed at CHOA in 2019 or 2020 were extracted using ExtractEHR. There were 3,015,822 data elements extracted across all EHR components, including 832,006 laboratory test results, 574,462 medication administrations, and 34,756 oncology notes. Both structured data (e.g. laboratory results) and unstructured free text data (e.g. notes) were successfully extracted. Data stored in PDFs in the EHR were not extracted due to storage parameters in the EHR data warehouse. All extracted data were successfully transferred to the registry. Conclusions: ExtractEHR can extract and transfer a broad range of clinical data on children treated for cancer to cancer registries. The data granularity obtained using ExtractEHR is impossible to obtain manually. Work is ongoing to expand to all cancers and implement ExtractEHR at 4 additional hospitals. These data will be deidentified by the registry teams and submitted to the NCCR to expand the comprehensiveness of clinical data included in the NCCR. Citation Format: Tamara P. Miller, Johanna Goderre Jones, Elizabeth Hsu, Edward Krause, Yun Gun Jo, Judy Lee, Kevin Ward, Lynne Penberthy, Richard Aplenc. Automated extraction and provision of electronic health record data from children with cancer to National Childhood Cancer Center (NCCR) cancer registries [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B071.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,062 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,008 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,009 |
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 source (Gemma direct ou Codex distillé), 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 ».