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A multi-machine learning-based predictive model for inpatient prognosis: A single-center study integrating test indicators, derived variables and demographic characteristics

A multi-machine learning-based predictive model for inpatient prognosis: A single-center study integrating test indicators, derived variables and demographic characteristics

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500112136
Enrollment
Unknown
Registered
2025-11-11
Start date
2025-11-15
Completion date
Unknown
Last updated
2025-11-17

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

Conditions

Sepsis

Interventions

Observation group:None

Sponsors

Taizhou Hospital of Zhejiang Province
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 90 Years

Inclusion criteria

Inclusion criteria: 1.The patient was hospitalized for over 24 hours, with complete baseline admission information and essential laboratory test results available for analysis.

Exclusion criteria

Exclusion criteria: 1.Outpatients with severely incomplete medical records (more than 30% of relevant indicators missing).

Design outcomes

Primary

MeasureTime frame
Activate partial thromboplastin time;Total_bilirubin;Aspartic transaminase;Thrombin time;International normalized ratio;RDW;Albumin;Lactic acid;Procalcitonin;Prothrombin time;Creatinine;—

Secondary

MeasureTime frame
Glucose;Chloride_Ion;Alanine aminotransferase;D-dimer;Potassium;Erythrocyte count;White blood cell count;Power of hydrogen;Chloride;Sodium_Ion;Urea;Platelet_Count;Fibrinogen;Sodium;Potassium_Ion;—

Countries

China

Contacts

Public ContactSu Zhengxian

Taizhou Hospital of Zhejiang Province

suzx@enzemed.com+86 675 85199342

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026