ClinicalTrials.gov
public full text
Public Domain
Establishment of Clinical Prediction Model for Postoperative Symptomatic Spinal Epidural Hematoma
The goal of this observational study is to establish a clinical prediction model based on artificial intelligence algorithms for patients with postoperative symptomatic spinal epidural hematoma(PSSEH) after spinal surgery. The investigators will collect the features of 2000 patients as the training set to train the models using AI algorithms, then recruit a prospective cohort of 2000 patients to test if the trained model is precise and consistent to predict the occurrence of PSSEH as well as to find out whether there is any new risk factor of PSSEH.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ASA Prediction Using Health Data and Medication Use#831713
- Applications of Artificial Intelligence and Machine Learning for Diagnosis, Prediction, and Smart Health Care#10767
- A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis#686411
- THE ROLE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE: A SYSTEMATIC REVIEW OF APPLICATIONS AND CHALLENGES#976438
- Predicting precision-based treatment plans using artificial intelligence and machine learning in complex medical scenarios#82429
Record · ID 1395737
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive