Dataset Overview
Definition：7 consecutive admissions above core field diseases Exampleis provided byChangsha Langhui Information Technology Co., Ltd.Medical data set provided. Covers complete longitudinal tracking data for persons with chronic illnesses who have been hospitalized seven times in a row and more, containing a core structured field for each hospitalization, providing valuable time series data for disease progress prediction and slow disease management AI.
| Dataset Name | Data set on cases in the core field and above for seven consecutive visits |
| Total Data Volume | 1 million cases of chronic disease vertically tracked |
| Data Type | Structured data (time series) |
| Core Features | Full tracking of hospitalizations 7 times and more per patient |
| Application value | Disease progress forecast, re-hospital risk assessment, slow disease management AI |
Core Data Fields
Each piece of data contains complete structured fields, covering all requirements from basic metadata to professional analysis.
| Field Category | Field Count | Typical Fields |
|---|---|---|
| Basic information on hospitalization | 8 | Date of admission, date of discharge, number of days of hospitalization, admission/dependance, admission, discharge |
| 10 | Main, secondary, ICD code, diagnostic trajectory per hospitalization | |
| Treatment information | 6 | Major operations/operations, critical drugs, changes in treatment programme |
| Test Indicators | 10 | Time series of key biochemical indicators (blood routine, liver and kidney function, blood sugar, blood resin, tumor markers, etc.) |
| Outcome indicators | 4 | Discharge status, interval between hospitalizations, occurrence of complications, death (if any) |
Data Sample Showcase
The following is a de-identified structured JSON sample illustrating the typical data format.
Other Organiser "Patient id": "CHR-2023-000123", "chronic diseases": "admissions": "VIIT," "Date": "2019-03", "dx primary": "Hba1c": 9.2, "los days":5}, ♪ "visit": 2, "date": "2019-08", "dx primary": "Unstable Heart Strangulation", "hba1c": 7.8, "procedure": "Cronary Artery Embracation", "los days": 7} "VIIT": 3, "Date": "2020-02", "dx primary": "heart failure", "hba1c": 8.5, "los days": 12. I don't know. "outcome": ♪ I'm sorry ♪
AI Application Scenarios
Forecasts of disease progress
Training time series model for longitudinal tracking data, predicting disease progression and acute risk of increased risk for slow patients.
Re-hospital risk assessment
Multi-cycle hospital data training for re-hospital risk prediction models, supporting discharge decision-making and follow-up planning.
AI for Chronic Disease Management
Multiple hospital data training in personalized slow disease management AI, providing recommendations for the optimization of treatment programmes.
Treatment impact assessment
Time series analysis models for pre- and post-treatment indicators to assess the effectiveness of different treatment programmes.
Early warning of complications
Test indicators for time trends to train early warning models for complications.
Medical resource planning
The hospitalization of slow patients is used for medical resource allocation and bed management AI.
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