Overview
Acute non-traumatic chest pain is one of the common causes of presentation in emergency patients, but the causes of acute non-traumatic chest pain are complex, the severity of the condition varies greatly, and the specificity of symptoms is not high. Machine learning and intelligent auxiliary models can greatly shorten the time of clinical decision-making, and improve the accuracy of etiological diagnosis in patients with chest pain, reduce the rate of misdiagnosis and missed diagnosis, and provide a clear direction for further treatment.
Description
Prospective observational studies used outpatient and follow-up information to construct an auxiliary early warning model of acute non-traumatic chest pain based on federated learning, and optimized the accuracy of early warning models through retrospective and prospective studies of large cohort data, and established an efficient and stable early warning and classification model for acute non-traumatic chest pain.
Eligibility
Inclusion Criteria:
- older than 18 years old;
- provided written informed consent;
- Outpatient visits in the pilot hospitals from June 2022 to December 2022
Exclusion Criteria:
- did not provide written informed consent and were unwilling to be followed up;
- traumatic chest pain;
- systemic pain caused by malignant tumors or rheumatic diseases involving the chest;
- transferred patients;
- sudden death or death during hospital treatment;
- women who are known to be pregnant or lactating;
- have participated in other clinical trials within 3 months before enrolling in this trial or are currently participating in other clinical trials The lender;
- According to the investigator's judgment, the patient was unable to complete the study or comply with the requirements of the study;
- Patients were lost to follow-up.