Artificial intelligence method to classify ophthalmic emergency severity based on symptoms: a validation study.

ABSTRACT: OBJECTIVES:We investigated the usefulness of machine learning artificial intelligence (AI) in classifying the severity of ophthalmic emergency for timely hospital visits. STUDY DESIGN:This retrospective study analysed the patients who first visited the Armed Forces Daegu Hospital between May and December 2019. General patient information, events and symptoms were input variables. Events, symptoms, diagnoses and treatments were output variables. The output variables were classified into four classes (red, orange, yellow and green, indicating immediate to no emergency cases). About 200 cases of the class-balanced validation data set were randomly selected before all training procedures. An ensemble AI model using combinations of fully connected neural networks with the synthetic minority oversampling technique algorithm was adopted. PARTICIPANTS:A total of 1681 patients were included. MAJOR OUTCOMES:Model performance was evaluated using accuracy, precision, recall and F1 scores. RESULTS:The accuracy of the model was 99.05%. The precision of each class (red, orange, yellow and green) was 100%, 98.10%, 92.73% and 100%. The recalls of each class were 100%, 100%, 98.08% and 95.33%. The F1 scores of each class were 100%, 99.04%, 95.33% and 96.00%. CONCLUSIONS:We provided support for an AI method to classify ophthalmic emergency severity based on symptoms.

SUBMITTER: Ahn H 

PROVIDER: S-EPMC7337880 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature