Dataset Overview
Definition：Symptoms and multidisciplinary consultations Exampleis provided byChangsha Langhui Information Technology Co., Ltd.A detailed textual record of the MDT multidisciplinary consultation covering suspected diseases, including specialist discussion, diagnosis and final treatment programmes in each section.
| Dataset Name | Dataset of suspected and multidisciplinary cases |
| Total Data Volume | 5 million MDT consultations |
| Data Type | Text (MDT consultation records) |
| Data Source | Resale |
| Core Value | Data on difficult cases is scarce and rare/complex diseases are of high value for AI training |
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 cases | 6 | Principal, current medical history, past history, preliminary diagnosis, admission to hospital, request for consultation |
| MDT discussion | 10 | Participation (name/doctor level), summaries of statements, identification diagnostic list, points of contention |
| Diagnostic reasoning | 6 | Identification of diagnostic sequencing, key identification basis, exclusion criteria, final diagnosis |
| Treatment programme | 8 | Treatment (surgery/drugs/rehabilitation, etc.), treatment rationale, risk assessment, alternative |
Data Sample Showcase
The following is a de-identified structured JSON sample illustrating the typical data format.
{
"case_id": "MDT-2023-0000456",
"patient": {"age_group": "45-55","gender": "male"},
"chief_complaint": "sample、sample、sample3sample",
"participants": ["sample","sample","sample","sample","sample"],
"discussion": [
{"dept": "sample value","opinion": "ANACN、CNdsDNACN，sample"},
{"dept": "sample value","opinion": "sample、sample"}
],
"final_dx": "sample（SLE），sample","plan": "sample+sample，sample"
}AI Application Scenarios
A. Auxiliary diagnosis of rare diseases
The identification diagnostic process and the reasoning path of the suspected cases are used to train the diagnosis of rare diseases.
MDT Smart-Assisted Decision-Making
MDT-assisted decision-making model for multidisciplinary consultation text training.
Auto-generated identification diagnosis
Training to identify diagnostic automatic generation models based on MDT discussions.
Clinical reasoning studies
The reasoning process for difficult cases can be used for medical AI teaching and clinical thinking training.
Rare disease knowledge mapping
Information on rare diseases in difficult cases is used to enrich the knowledge of rare diseases.
Complex case management system
Training of AAI assistants in complex case management for integrated MDT information.
Frequently asked questions
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