Clinical Validation of a Pre-trained and Fine-tuned Model for Analyzing Time-Series Electronic Health Records in Critical Care

Clinical Validation of a Pre-trained and Fine-tuned Model for Analyzing Time-Series Electronic Health Records in Critical Care

The goal of this observational study is to validate the generalization capability and predictive performance of the "Time-Series Electronic Health Record Foundation Model (EHR Foundation Model)", which was pre-trained on a large-scale critical care dataset, within the real-world clinical environment of the Intensive Care Unit (ICU) at Peking University People's Hospital. The main question it aims to answer is:

· Based on the "pre-training + fine-tuning" paradigm, whether the EHR foundation model can effectively provide accurate and dynamic prognostic support information in real-world clinical scenarios.

Participants who agree to take part in the research, in addition to completing the signed informed consent form, if the patient's hospital stay is too short, will be obtained information on prognostic outcomes (e.g., 28-day mortality) through telephone follow-up.

The clinical validation design combines a retrospective cohort study with a prospective cohort study.

Inclusion Criteria:

• Age ≥ 18 years, no gender restriction;
• ICU length of stay ≥ 24 hours (to ensure sufficient time-series monitoring data for model generation);
• Complete electronic health record data, including baseline demographic characteristics and at least one complete laboratory test record after ICU admission.

Exclusion Criteria:

• Patients who are transferred out or die within 24 hours of ICU admission;
• Duplicate admission records for non-initial ICU admissions (only the initial ICU admission record is retained to ensure independence);
• Severe deficiency in core data (e.g., absence of major vital sign recordings or > 50% missing key laboratory test results);
• Patients with abandonment of treatment or discharge against medical advice, leading to inability to ascertain the definitive clinical outcome.