Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.
J Am Med Inform Assoc
; 31(7): 1578-1582, 2024 Jun 20.
Article
in En
| MEDLINE
| ID: mdl-38700253
ABSTRACT
OBJECTIVE:
Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients. MATERIALS ANDMETHODS:
We trained logistic regression models using note metadata and a Term Frequency Inverse Document Frequency (TF-IDF) text representation. We evaluated performance with precision, recall, F1, AUC, and a clinical qualitative assessment.RESULTS:
The metadata only model achieved an AUC 0.930 and the metadata and TF-IDF model an AUC 0.937. Qualitative assessment revealed a need for better text representation and to further customize predictions for the user.DISCUSSION:
Our model effectively surfaces the top 10 notes a clinician wants to review when seeing an oncology patient. Further studies can characterize different types of clinician users and better tailor the task for different care settings.CONCLUSION:
EHR audit logs can provide important relevance data for training ML models that assist with note-writing in the oncology setting.Key words
Full text:
1
Database:
MEDLINE
Main subject:
Electronic Health Records
/
Machine Learning
/
Medical Oncology
Limits:
Humans
Language:
En
Journal:
J Am Med Inform Assoc
Journal subject:
INFORMATICA MEDICA
Year:
2024
Type:
Article
Affiliation country:
United States