Machine Learning for Automatic Encoding of French Electronic Medical Records: Is More Data Better?
Stud Health Technol Inform
; 270: 312-316, 2020 Jun 16.
Article
in En
| MEDLINE
| ID: mdl-32570397
ABSTRACT
The encoding of Electronic Medical Records is a complex and time-consuming task. We report on a machine learning model for proposing diagnoses and procedures codes, from a large realistic dataset of 245 000 electronic medical records at the University Hospitals of Geneva. Our study particularly focuses on the impact of training data quantity on the model's performances. We show that the performances of the models do not increase while encoded instances from previous years are exploited for learning data. Furthermore, supervised models are shown to be highly perishable we show a potential drop in performances of around -10% per year. Consequently, great and constant care must be exercised for designing and updating the content of such knowledge bases exploited by machine learning.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Electronic Health Records
/
Machine Learning
Type of study:
Prognostic_studies
Language:
En
Journal:
Stud Health Technol Inform
Journal subject:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Year:
2020
Document type:
Article
Affiliation country:
Switzerland
Country of publication:
HOLANDA
/
HOLLAND
/
NETHERLANDS
/
NL
/
PAISES BAJOS
/
THE NETHERLANDS