| 1 Definition | 2 Functional Requirements | 3 Syntax | 4 Semantics |
1 Definition
Taxonomies of various aspects related to IH Data.
2 Functional Requirements
Taxonomies cover:
- AIH Data Classes
- AIH Data Users
- AIH Data Statuses
- AIH Data Usages
- Anonymisation/De-Identification Algorithms
- Anomaly Types
3 Syntax
https://schemas.mpai.community/AIH1/V1.0/data/AIHTaxonomies.json
4 Semantics
| Label | Description | |||
| Header | AIH Taxonomies Header – Standard “AIH-HLT-V” | |||
| AIH Data Classes | The classes of Health data. | |||
| BehaviouralSignal | Observable human behavioural activity captured through sensors or digital interaction systems. | |||
| ClinicalRecord | Symbolic health information such as diagnoses, observations, procedures, medications, and laboratory results. | |||
| MedicalImaging | Spatially organised visual representations of anatomical or functional structures. | |||
| NeurophysiologicalSignal | Biosignals captured from neural or neuro‑cognitive processes. | |||
| Omics | Molecular‑level biological information derived from assays such as whole‑genome sequencing, whole‑exome sequencing, etc. | |||
| PhysiologicalSignal | Biosignals captured from physiological processes whose primary structure is a sampled temporal sequence representing time‑series measurements acquired from sensors. | |||
| 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 personal device, 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 Entity | 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. | |||
| – Caregiver | 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. | |||
| – Neurophysiological Signal Object | Spatially organised visual representations of anatomical or functional structures. | |||
| – Physiological Signal Object | Biosignals captured from physiological processes whose primary structure is a sampled temporal sequence representing time‑series measurements | |||
| DeID & Anonym Algorithms | DeID&Anonymisation Algorithm. | |||
| – Data Masking | Replaces sensitive data with altered values while preserving the original data structure and format. | |||
| – Data Aggregation | Combines multiple data records into summary values to reduce individual data exposure. | |||
| – Generalisation | Substitutes specific data values with broader categories to reduce identifiability. | |||
| – Perturbation | Introduces controlled modifications or noise into data to prevent accurate inference of original values. | |||
| – Tokenisation | Replaces sensitive data with surrogate tokens, with original values stored in a secure mapping. | |||
| – Hashing | Applies a one‑way cryptographic transformation that prevents recovery of the original data. | |||
| – Removal of Identifiers | Deletes direct identifiers from a dataset to reduce the likelihood of re‑identification. | |||
| – K‑Anonymity | Ensures each record is indistinguishable from at least k–1 others based on quasi‑identifiers. | |||
| – L‑Diversity | Ensures that multiple distinct sensitive values exist within each k‑anonymous group. | |||
| – Differential Privacy | Provides formal guarantees by limiting the influence of any single data subject through calibrated noise. | |||
| – Synthetic Data Generation | Produces artificial data exhibiting statistical similarity to real data without representing real individuals. | |||
| – Homomorphic Encryption | Enables computation on encrypted data and returns encrypted results without plaintext exposure. | |||
| Risk | Risks classified according to Manchester Protocol. | |||
| – Red | Emergency. Indicates critical situations that require immediate attention. | |||
| – Orange | Very urgent. Patients who need quick attention but whose condition is not immediately life-threatening. | |||
| – Yellow | Urgent. Indicates that the patient needs care, but the condition is not serious. | |||
| – Green | Less urgent. Patients with less severe conditions that can wait a bit longer for care. | |||
| – Blue | Non-urgent. Patients whose conditions are not urgent and can wait for care. | |||
| 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). |
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