aion-clinical

v2.0.4 suspicious
7.0
High Risk

FM-3 formal knowledge representation for clinical trajectories — Allen algebra, causal inference, Shapley attribution, HL7 v2, FHIR R4, differential privacy. AGPL-3.0 / commercial dual license.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high credential risk and metadata inconsistencies, suggesting potential unauthorized access attempts and questionable maintenance practices.

  • High credential risk due to potential API key harvesting
  • Metadata indicating possible new or inactive maintainer and non-existent repository
Per-check LLM notes
  • Network: The network call to fetch jwks suggests the package is likely handling JWT authentication, which is not inherently malicious but should be reviewed for context.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected in the provided code snippet.
  • Credentials: The observed pattern suggests potential credential harvesting as it prompts for an API key and attempts authentication.
  • Metadata: The package shows signs of potential new or inactive maintainer activity and a non-existent repository, raising concerns about its legitimacy.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3798 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 238 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • t/jwks" try: with urllib.request.urlopen(url, timeout=5) as resp: _jwks_cache = j
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • dus (kein Echo) key = getpass.getpass("API-Key: ") try: principal = backend.authentic
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: iscad-it.de>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aion-clinical 
Create a mini-application that helps healthcare professionals analyze patient clinical trajectories using the 'aion-clinical' Python package. This tool will allow users to input patient data from various sources such as HL7 v2 messages and FHIR R4 resources, and then apply formal knowledge representations based on FM-3 to understand the temporal relationships, causality, and attributions within the patient's medical history. The application should also include functionality to ensure differential privacy of the patient data during analysis.

**Steps to Build the Application:**
1. **Setup Environment**: Install Python and the necessary libraries including 'aion-clinical'. Ensure your environment supports asynchronous operations due to the nature of the package.
2. **Data Input Module**: Develop a module that allows users to upload patient data in formats supported by 'aion-clinical', specifically HL7 v2 and FHIR R4. The module should parse these files into a format suitable for further analysis.
3. **Analysis Engine**: Utilize 'aion-clinical' to perform the following analyses:
   - Temporal Relationships: Use Allen Algebra to determine the temporal relationships between different events in the patient's clinical trajectory.
   - Causal Inference: Apply causal inference methods provided by 'aion-clinical' to understand which factors might have caused certain health outcomes.
   - Attribution Analysis: Implement Shapley value calculations to attribute outcomes to specific interventions or conditions.
4. **Privacy Assurance**: Incorporate differential privacy techniques offered by 'aion-clinical' to protect patient data during the analysis phase.
5. **Visualization and Reporting**: Create a user-friendly interface where the results of the analysis can be visualized and reported back to the user. Include options to export these reports in common file formats like PDF or CSV.
6. **Testing and Validation**: Rigorously test the application with real-world datasets to ensure accuracy and reliability of the analyses performed.

**Features**:
- Support for multiple data formats (HL7 v2, FHIR R4)
- Comprehensive analysis tools (temporal relationships, causal inference, attribution analysis)
- Differential privacy implementation
- User-friendly interface for input/output
- Exportable reports

By leveraging 'aion-clinical', this application aims to provide valuable insights into patient care pathways while ensuring patient data remains secure and private.

💬 Discussion Feed

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