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 Hospital6,500 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

6,500

Conditions

Anesthesia Acute Pain Risk Reduction

Interventions

No interventionOTHER

No intervention

Eligibility

Sex: ALLMin age: 18 YearsMax age: 80 YearsHealthy volunteers:

Medical Language ↔ Plain English

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.

Locations (1)

Guangdong Second Provincial General Hospital

Guangzhou, China

RECRUITING

Outcomes

Primary Outcomes

Area under the curve

It measures the prediction effect of the algorithm model

Time frame: April 2020 to May 2021

Data from ClinicalTrials.gov

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