Identifying Race/Ethnicity Data via Natural Language Processing Among Women in a Uterine Fibroid Cohort Study
Notice bibliographique
Résumé
Background/Aims: Uterine fibroids are associated with morbidity including abnormal bleeding, anemia, pelvic/bladder symptoms and adverse reproductive outcomes. Symptomatic fibroids may affect 25% of women in their late 40s. Race is among the most consistent risk factors known. The Uterine Fibroid Study aims to use automated data to estimate fibroid incidence rates/trends during 2005–2014 in a retrospective cohort of women at Group Health. Race/ethnicity captured from automated structured data has improved yet remains incomplete, particularly with use of retrospective data. Methods: The study included women 18–65 years old without hysterectomy, continuously enrolled with evidence of encounter in 3 years before study entry. Incidence estimates required absence of fibroid history. We collected fibroid diagnoses, demographics and other data from the Virtual Date Warehouse (VDW). VDW demographic race/ethnicity data is sourced from data collected from Group Practice patients, at time of encounter, via entry in the electronic health record. Additionally, Group Health collects race/ethnicity data from breast cancer screening program and tumor registry data. To complement traditional structured race/ethnicity data from VDW, we augmented with race/ethnicity extracted from free-text clinical notes via natural language processing (NLP). Our NLP system used a rule-based dictionary look-up approach to identify common terms used to describe patient race/ethnicity and custom rules to disambiguate race/ethnicity terms that also have other clinical meanings (e.g. the term “white” in “54-year-old white female” as opposed to “Her white blood cell count improved”). We conducted a partial validation of the NLP system in a sample of patients with known structured race/ethnicity data. Results: Prior to amending race/ethnicity data with NLP, in the cohort of 277,821 women, 37.4% had race/ethnicity unknown. Fibroid incidence rates (per 10,000 person-years) were 156 for Hispanics, 133 for whites, 265 for African-Americans, 152 for Asian/Pacific Islanders and 108 for unknown race. NLP work on identifying race/ethnicity in the unknown race group is ongoing and results are pending. Conclusion: Race/ethnicity is an important risk factor for a number of conditions, including uterine fibroids. Improving capture of race/ethnicity from available automated data sources potentially could improve accuracy of research findings and enhance patient care by providing a better understanding of the burden of disease in subgroups of affected patients.
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 | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».