Construction of Perioperative Medical Data Platform and Its Typical Practice to Predict Postoperative Acute Moderate to Severe Pain With Machine Learning Models

Guangdong Second Provincial General Hospital

Status: Enrolling

Conditions: Risk Reduction; Acute Pain; Anesthesia

Treatments: Other: No intervention

Study type: Observational

Funder types: Other

Identifiers

NCT05569460

Guangdong2nd

Details and patient eligibility

About

Data intelligence platform was widely used to facilitate the process of clinical research. However, a platform that integrates natural language processing (NLP) and machine learning (ML) algorithms has not been reported in perioperative medical management.

Enrollment: 6,500 estimated patients

Sex: All

Ages: 18 to 80 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• Patients were included if they were above 18 years old, undergoing non-local anesthesia surgery.

Exclusion criteria

• The basic information such gender, age, height, weight, and body mass index (BMI) were missing.
• Patients undergoing day surgery, with a history of multiple operations, or entering ICU after surgery, and losing the NRS score during movement at 24h after surgery.

Trial design

6,500 participants in 2 patient groups

Numeric Rating Scale (NRS) score after surgery <4

Treatment:

Other: No intervention

Numeric Rating Scale (NRS) score after surgery ≥ 4

Treatment:

Other: No intervention