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

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05569460

Enrollment

6500

Registered

2022-10-06

Start date

2022-10-31

Completion date

2023-12-31

Last updated

2022-10-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Acute Pain, Anesthesia, Risk Reduction

Keywords

Data Platform, Machine Learning, Postoperative Acute Pain

Brief summary

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.

Interventions

OTHERNo intervention

No intervention

Study design

Observational model

CASE_ONLY

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 80 Years

Healthy volunteers

Yes

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.

Design outcomes

Primary

Measure	Time frame	Description
Area under the curve	April 2020 to May 2021	It measures the prediction effect of the algorithm model

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026