Implementation of a machine learning model in acute coronary syndrome and stroke risk assessment for patients with lower urinary tract symptoms.
Taiwan J Obstet Gynecol
; 63(4): 518-526, 2024 Jul.
Article
en En
| MEDLINE
| ID: mdl-39004479
ABSTRACT
OBJECTIVE:
The global population is aging and the burden of lower urinary tract symptoms (LUTS) is expected to increase. According to the National Health Insurance Research Database, our previous studies have showed LUTS may predispose patients to cardiovascular disease. However, it is difficult to provide a personalized risk assessment in the context of "having acute coronary syndrome (ACS) and stroke." This study aimed to develop an artificial intelligence (AI)-based prediction model for patients with LUTS. MATERIAL ANDMETHODS:
We retrospectively reviewed the electronic medical records of 1799 patients with LUTS at Chi Mei Medical Center between January 1, 2001 and December, 31, 2018. Features with >10 cases and high correlations with outcomes were imported into six machine learning algorithms. The study outcomes included ACS and stroke. Model performances was evaluated using the area under the receiver operating characteristic curve (AUC). The model with the highest AUC was used to implement the clinical risk prediction application.RESULTS:
Age, systemic blood pressure, diastolic blood pressure, creatinine, glycated hemoglobin, hypertension, diabetes mellitus and hyperlipidemia were the most relevant features that affect the outcomes. Based on the AUC, our optimal model was built using multilayer perception (AUC = 0.803) to predict ACS and stroke events within 3 years.CONCLUSION:
We successfully built an AI-based prediction system that can be used as a prediction model to achieve time-saving, precise, personalized risk evaluation; it can also be used to offer warning, enhance patient adherence, early intervention and better health care outcomes.Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Accidente Cerebrovascular
/
Síndrome Coronario Agudo
/
Síntomas del Sistema Urinario Inferior
/
Aprendizaje Automático
Idioma:
En
Revista:
Taiwan J Obstet Gynecol
Asunto de la revista:
GINECOLOGIA
/
OBSTETRICIA
Año:
2024
Tipo del documento:
Article