Natural Language Processing and Machine Learning to Enable Clinical Decision Support for Treatment of Pediatric Pneumonia.
AMIA Annu Symp Proc
; 2020: 1130-1139, 2020.
Article
em En
| MEDLINE
| ID: mdl-33936489
ABSTRACT
Pneumonia is the most frequent cause of infectious disease-related deaths in children worldwide. Clinical decision support (CDS) applications can guide appropriate treatment, but the system must first recognize the appropriate diagnosis. To enable CDS for pediatric pneumonia, we developed an algorithm integrating natural language processing (NLP) and random forest classifiers to identify potential pediatric pneumonia from radiology reports. We deployed the algorithm in the EHR of a large children's hospital using real-time NLP. We describe the development and deployment of the algorithm, and evaluate our approach using 9-months of data gathered while the system was in use. Our model, trained on individual radiology reports, had an AUC of 0.954. The intervention, evaluated on patient encounters that could include multiple radiology reports, achieved a sensitivity, specificity, and positive predictive value of0.899, 0.949, and 0.781, respectively.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Pediatria
/
Pneumonia
/
Processamento de Linguagem Natural
/
Sistemas de Apoio a Decisões Clínicas
/
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
Limite:
Child
/
Humans
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article