Abstract 4087: Developing a standardized framework for curating oncology datasets generated by manual abstraction and artificial intelligence
Notice bibliographique
Résumé
Abstract Background: The widespread uptake of electronic health records (EHRs) has made the creation of custom, real-world datasets for research more feasible. As a result, multiple research datasets with overlapping populations are often generated, using different methodologies, and frequently siloed within and between research groups, limiting the scope of the data’s use. Currently, there is no standard for collating and evaluating such data. Using existing lung oncology datasets, we developed an approach to determine optimal methods of combining and curating clinical data from different sources. Methods: Two separate study datasets containing data for lung cancer patients diagnosed and/or treated within Princess Margaret Cancer Centre (PM, Toronto) were investigated. Study 1 manually abstracted clinical data for 1,990 patients, first seen at PM between 2014-2016; Study 2 leveraged the artificial intelligence engine, DARWEN™, to extract clinical data directly from EHRs for 4,466 patients, diagnosed between 2014-2018. Each dataset was individually assessed for internal consistency before comparing the overlapping population (Test Group, n=1892) to identify, investigate, and resolve differences. Patterns of data extraction performance were evaluated to define optimal methods for combining datasets and informing future data collection. Herein, epidermal growth factor receptor (EGFR) mutation status is used as an illustrative example. Results: Study 1 and 2 had similar distributions of clinicodemographic data and frequency of EGFR mutations. The Test Group had 100% agreement for date of birth, and >99% agreement for sex, with all discrepancies resulting from human error in Study 1. The Test Group had a 98% agreement for EGFR positivity and 98-99% agreement for specific exon mutations. Of the 106 disagreements for specific mutations, 50% (n=53) were due to Study 1 human error. Study 2 prioritized specificity over sensitivity for biomarker extraction, resulting in more false negatives (25% of errors, n=26). As DARWEN™ only extracted EGFR data from pathology reports, 18% (n=19) of discrepancies were due to lack of access to relevant information captured elsewhere in patients’ EHRs. Adjudicators could not resolve the remaining 7% of disagreements (n=8). Conclusions: By comparing overlapping datasets, the strengths and weaknesses of each study design and extraction methodology were identified. This process demonstrated the effectiveness of artificial intelligence for extracting accurate patient-level clinicodemographic and mutation status data from EHRs, and the value of targeted manual chart review. Our approach provides a roadmap for leveraging existing clinical datasets to their fullest potential, which is relevant across diverse data extraction methods and study designs. Citation Format: Benjamin M. Grant, Aein Zarrin, Luna Zhan, Rami Ajaj, Lina Darwish, Khaleeq Khan, Devalben Patel, Kaitlyn Chiasson, Karmugi Balaratnam, Maisha T. Chowdhury, Amir-Arsalan Sabouhanian, Joshua Herman, Preet Walia, Evan Strom, Catherine Brown, Miguel Garcia-Pardo, Sabine Schmid, Christopher Pettengell, Erin L. Stewart, Geoffrey Liu. Developing a standardized framework for curating oncology datasets generated by manual abstraction and artificial intelligence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4087.
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 | 0,050 | 0,076 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,006 |
| Bibliométrie | 0,013 | 0,008 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,010 | 0,008 |
| Science ouverte | 0,006 | 0,014 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,005 |
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 ».