Prediction of intensive care unit admission using machine learning in patients with odontogenic infection

• 발행기관: 대한구강악안면외과학회
• 저자명: Joo-Ha Yoon Sung Min Park
• 간행물 정보: 『대한구강악안면외과학회지』제50권 제4호, 216~221쪽, 전체 6쪽
• 주제분류: 의약학 > 일반외과학
• 파일형태: PDF
• 발행일자: 2024.08.31

국문 초록

Objectives: This study aimed to develop and validate a model to predict the need for intensive care unit (ICU) admission in patients with dental infections using an automated machine learning (ML) program called H2O-AutoML. Materials and Methods: Two models were created using only the information available at the initial examination. Model 1 was parameterized with only clinical symptoms and blood tests, excluding contrast-enhanced multi-detector computed tomography (MDCT) images available at the initial visit, whereas model 2 was created with the addition of the MDCT information to the model 1 parameters. Although model 2 was expected to be superior to model 1, we wanted to independently determine this conclusion. A total of 210 patients who visited the Department of Oral and Maxillofacial Surgery at the Dankook University Dental Hospital from March 2013 to August 2023 was included in this study. The patients’ demographic characteristics (sex, age, and place of residence), systemic factors (hypertension, diabetes mellitus [DM], kidney disease, liver disease, heart disease, anticoagulation therapy, and osteoporosis), local factors (smoking status, site of infection, postoperative wound infection, dysphagia, odynophagia, and trismus), and factors known from initial blood tests were obtained from their medical charts and retrospectively reviewed. Results: The generalized linear model algorithm provided the best diagnostic accuracy, with an area under the receiver operating characteristic values of 0.8289 in model 1 and 0.8415 in model 2. In both models, the C-reactive protein level was the most important variable, followed by DM. Conclusion: This study provides unprecedented data on the use of ML for successful prediction of ICU admission based on initial examination results. These findings will considerably contribute to the development of the field of dentistry, especially oral and maxillofacial surgery.

목차

Ⅰ. Introduction

Ⅱ. Materials and Methods

Ⅲ. Results

Ⅳ. Discussion

Ⅴ. Conclusion

ORCID

Authors’ Contributions

Funding

Conflict of Interest

References

키워드

Intensive care units Infections C-reactive protein Machine learning Algorithms