AI model to help doctors predict patient outcomes

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This study aims to develop and implement an artificial intelligence (AI) model that assists healthcare providers in predicting patient diagnoses and outcomes, such as survival rates and length of hospital stays. By utilizing real-time, multimodal health data from electronic health records—including lab results, vital signs, medical history, and imaging—the study will track patients across various hospital departments. The performance of the AI model will be compared to standard care practices to evaluate its effectiveness in improving diagnostic and treatment accuracy in real-world settings.

Why it matters

Who can join: the full rules

Inclusion Criteria:

1. Patients admitted to any department of the hospital (e.g., ICU, general wards, emergency, outpatient services) during the study period.
2. Patients with available real-time electronic health record (EHR) data, including at least two of the following: laboratory results, vital signs, medical history, and imaging data.

Exclusion Criteria:

Patients currently enrolled in another clinical trial that could interfere with data collection or outcomes of this study.

Where it's running

• First Affiliated Hospital of Wenzhou Medical University
  Wenzhou, Zhejiang, China Recruiting
• Second Affiliated Hospital of Wenzhou Medical University
  Wenzhou, Zhejiang, China Recruiting

Study contacts

• Study coordinator: Fei Liu, MD
• Email: liufei_2359@163.com
• Phone: +86 13810512704

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

• Real-world use of LC-OCT to help diagnose basal cell carcinoma

  This study tests how well LC-OCT works in everyday dermatology practice to help diagnose basal cell carcinoma without always needing a biopsy.

  Recruiting Maastricht University Medical Center · Maastricht, Limburg · NCT07501780

• Iruplinalkib for ALK-positive advanced lung adenocarcinoma after lorlatinib

  We will try iruplinalkib in adults with advanced ALK-positive lung adenocarcinoma who progressed on or couldn't tolerate lorlatinib to see how well it works and how safe it is in routine care.

  Recruiting Peking University Shenzhen Hospital · Shenzhen, Guangdong · NCT07374614

• Real-world first-line atezolizumab for stage IV NSCLC with PD-L1 ≥50% in Türkiye

  This project will see how well first-line atezolizumab works and how safe it is for adults with stage IV non-small cell lung cancer whose tumors have PD-L1 ≥50% and no targetable mutations in routine care in Türkiye.

  Recruiting at 15 sites Antalya Training and Research Hospital · Adana and 24 other locations · NCT07279402

• Real-world look at severe respiratory failure (ARDS)

  This project will collect real-world data on adults at high risk for ARDS to see which clinical factors predict worse outcomes.

  Recruiting Shanghai Zhongshan Hospital · Shanghai, Shanghai Municipality · NCT07165717

• A kidney cancer prediction model to guide treatment decisions, including for patients with tumor thrombus

  This project will test whether a new prediction model can guide treatment choices to improve survival for adults with renal cell carcinoma, including those with venous tumor thrombus.

  Recruiting Peking University Third Hospital · Beijing, Beijing Municipality · NCT07117227

• Observational study of lorlatinib for advanced ALK-positive lung cancer patients

  This study looks at how well lorlatinib works and how safe it is for people with advanced ALK-positive lung cancer who are receiving treatment.

  Recruiting Sichuan Cancer Hospital and Research Institute · Chengdu, Sichuan · NCT06361589

How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.