<-AI Modules Go to ToC Datasets->
| 1 Definition | 2 Functional Requirements | 3 Syntax | 4 Semantics |
1 Definition
Taxonomies of AIH Data.
2 Functional Requirements
Taxonomies cover:
- AIH Data Classes
- AIH Data Users
- AIH Data Statuses
- AIH Data Usages
- Anonymization/De-Identification
- Anomalies
3 Syntax
https://schemas.mpai.community/AIH1/V1.0/data/AIHTaxonomies.json
4 Semantics
| Label | Description | |
| Header | AIH Taxonomies Header | |
| – Standard -AIHTaxonomies | The characters AIH-AHT-V | |
| – Version | Major version – 1 or 2 characters | |
| – Dot-separator | The character “.” | |
| – Subversion | Minor version – 1 or 2 characters | |
| AIH Data Classes | AIH Data Classes are defined by MPAI-TFA Health Data Types | |
| AIH Data Users | Different profiles of third-party users can affect the licensing of AIH Data Processing. | |
| – End User | Individual who interacts with the AIH platform, primarily via a smartphone, providing personal health data and receiving personalised data. | |
| – Non-Profit Entity | Entity that is non-profit, e.g., a university. | |
| – Profit Entity | Entity that is for profit, e.g., a pharmaceutical company. | |
| – Clinical Entities | Entity that looks after the health of patients. | |
| – Authorised Entity | Entity that has been authorised by an End User to process some of their AIH Data. | |
| – Caregivers | Health providers that interact with the AIH-HSP to provide health and care services to specific End Users (nurses, caregivers, etc.). is folded into 2 intermediaries (back end and 3rd party). | |
| AIH Data Status | In terms of Anonymised, Pseudonymised, Identified | |
| – Anonymised | AIH Data may be used if Anonymised | |
| – Pseudonymised | AIH Data may be used if Pseudonymised | |
| – Identified | AIH Data may be used for Identified End User. | |
| AIH Data Usage | Types of authorised usage of AIH Data. | |
| – Unrestricted | The processed data is open to public or semi-public consultation. | |
| – Pseudonymised | The processed data may be published if End User identity are pseudonymised | |
| – Anonymised | The processed data may be published if End User identity are anonymised | |
| – Research | The processed data may be published if the publication is made on a journal to report research results. | |
| – Patient use | The processed data may only be used by the patient or by individual authorised by the patient. | |
| – Health care | The processed data may only be used by a Clinical Entity for health-related purpose in the Clinical Entity. | |
| AIH Data Process | The types of Processes applied to AIH Data | |
| – | ||
| AIH Data AnonymDeID | ||
| – Data Masking | ||
| – Data Aggregation | ||
| – Generalisation | ||
| – Perturbation | ||
| – Tokenization | ||
| – Hashing | ||
| – Removal of Identifiers | ||
| – K-Anonymity | ||
| – L-Diversity | ||
| – Differential privacy | ||
| – Synthetic Data Generation | ||
| – Homomorphic Encryption | ||
| AIH Data Anomalies | Classes of alert messages caused by anomalies in health | |
| Definition | Anomaly examples | |
| – Point Anomaly | Individual data points that deviate significantly from the rest of the dataset | Sudden spikes in heart rate or blood pressure readings. |
| – Contextual Anomaly | Anomalous data points that in a specific context – may be normal in another. | Elevated heart rate during sleep versus during exercise. |
| – Collective Anomaly | A set of related data points that collectively deviate from the expected pattern. | A series of abnormal ECG readings indicating a potential cardiac event) |
| – Medical Condition Anomaly | Abnormalities in patient data due to medical conditions. | Seizures, falls, arrhythmias, atrial fibrillation, ventricular tachycardia. |
| – Erroneous Data Anomaly | Data errors that may be due to faults or malicious attacks. | Anomaly in – Biorhythm signals (e.g., heartbeat anomalous patterns, respiratory anomalous patterns. – Multimodal patterns (diverse data sources show conflicting patterns) |
