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Venous Thromboembolism Risk Prediction Using Kernel Extreme Learning Machine Enhanced by an Information-Guided and Triangular-Geometry- Based Gradient Optimizer

Venous Thromboembolism Risk Prediction Using Kernel Extreme Learning Machine Enhanced by an Information-Guided and Triangular-Geometry- Based Gradient Optimizer

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600116476
Enrollment
Unknown
Registered
2026-01-11
Start date
2025-07-01
Completion date
Unknown
Last updated
2026-01-27

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

Conditions

Venous thromboembolism,VTE

Interventions

inpatients newly diagnosed with VTE,:None
hospitalized individuals without VTE:None

Sponsors

Ningbo Medical Centre Lihuili Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
16 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.The diagnosis of VTE was confirmed based on at least one of the following criteria: (1) DVT verified by lower extremity vascular ultrasonography; 2.or (2) PTE confirmed by computed tomography pulmonary angiography (CTPA).

Exclusion criteria

Exclusion criteria: Under 16 years old;

Design outcomes

Primary

MeasureTime frame
Laboratory assessments, relevant imaging data;—

Secondary

MeasureTime frame
Caprini and Padua Thrombosis Risk Assessment Scales;—

Countries

China

Contacts

Public ContactMengming Xia

Ningbo Medical Centre Lihuili Hospital

501649062@qq.com+86 574 5583 5883

Outcome results

None listed

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