Remember when I said they're hiding everything under microservices and metadata and biometadata?
They are. Here's how.
BIOSIGNAL DATA & MICROSERVICES
Part 1
Microservice-based architectures are increasingly used for biosignal data to enable scalable, modular acquisition, processing, and classification of physiological signals.
researchgate.net/publication…
These systems typically decouple components like sensing, data synchronization, and analysis, allowing for independent scaling and easier integration of diverse IoT devices.
researchgate.net/publication…
Key implementations include:
Event-Driven & Microservice Integration:
Recent frameworks use microservices and message brokers to synchronize data from multiple sources (e.g., ECG, electrodermal activity) at the edge, improving synchronization times and reducing latency compared to direct cloud delivery.
researchgate.net/publication…
Generic Multisource Architecture:
Proposed architectures focus on scalability and redundancy, using protocols like Lab Streaming Layer (LSL) for data streaming and synchronization, allowing for the integration of various sensors with different sampling frequencies.
labstreaminglayer.readthedoc…
Remote & Distributed Systems:
Distributed frameworks handle remote multimodal biosignal acquisition (e.g., via radars and cameras) using RESTful APIs and specialized time-series databases like InfluxDB for real-time storage and rapid read/write operations.
researchgate.net/publication…
Cloud-Based Aggregation:
Some models utilize a three-tier approach (sensors, gateways, cloud) where microservices manage the ingestion of biosignals, perform provisional analysis on mobile devices, and handle heavy-weight analysis and decision support in the cloud.
pmc.ncbi.nlm.nih.gov/article…
These approaches streamline the biosignal lifecycle, from raw data capture to clinical insight generation, by isolating concerns such as data ingestion, storage, and algorithmic processing into independent, manageable services.
ALL OF THIS CONNECTS TO INFERNO VIA FHIR
LabStreamingLayer (LSL) also ties into Fast Healthcare Interoperability Resources (FHIR)
This is where CDISC ADaM comes in.
cdisc.org/standards/foundati…
FHIR - Lab Streaming Layer
FHIR (Fast Healthcare Interoperability Resources) provides a framework for accessing and sharing laboratory data such as blood tests from a repository via its API.
For more detailed queries, such as finding all lab tests of a given type for a person, the process can become more complex due to the need to include more 'order-related' details about the result, such as the ordering clinician.
This involves navigating through resources like DiagnosticReport, ServiceRequest, and Practitioner, which may not be supported by all servers.
In clinical research, FHIR data can be mapped to the CDISC ADaM standard for analysis, facilitating the extraction of patient-level medical records including demographics, encounter information, medications, medical history, adverse events, laboratory, and vital signs.
Use of FHIR in Clinical Research: From Electronic Medical Records to Analysis
MAPPING FROM THE FHIR STANDARD TO ADaM
METHODS
MAPPING FROM THE FHIR STANDARD TO ADaM
Convert FHIR JSON to ADaM SAS Datasets
The client software created in Python for this project converted the JSON FHIR resource content to CSV files for easy conversion by SAS into ADaM SAS datasets capable of producing data displays for typical safety domains: labs, vital signs, and adverse events.
cdisc.org/kb/articles/use-fh…