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Utilizing a deep reinforcement learning model for facilitating decision-making in optimizing drug regimens among heart failure patients

Utilizing a deep reinforcement learning model for facilitating decision-making in optimizing drug regimens among heart failure patients

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400092290
Enrollment
Unknown
Registered
2024-11-13
Start date
2024-11-15
Completion date
Unknown
Last updated
2024-11-18

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

Conditions

Heart Failure

Interventions

Observation group:None

Sponsors

Guangdong Second Provincial Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 75 Years

Inclusion criteria

Inclusion criteria: a. Basic information: age 18-75 years old, both sexes; b. Primary diagnosis: acute heart failure, acute exacerbation of chronic heart failure, NYHA class II-IV, Killip class II-IV; c. Patients agreed to sign the informed consent for the clinical study to be included in the observation.

Exclusion criteria

Exclusion criteria: a. Hospital stay more than 14 days; b. Patients who died or left the hospital against medical advice; c. Patients with severe liver, kidney and other multiple organ failure; d. Severely infected patients; e. patients with peripheral circulation disorders; d. Patients who underwent surgery during hospitalization; e. Patients with confirmed cancer; f. pregnant or lactating women; g. Patients with any psychiatric or psychological illness.

Design outcomes

Primary

MeasureTime frame
Rate of referral;—

Secondary

MeasureTime frame
Safety rate;—

Countries

China

Contacts

Public ContactYu Lao

Guangdong Second Provincial Hospital

1141077275@qq.com+86 137 5180 1043

Outcome results

None listed

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