Heterogeneous Biomedical Intelligence Monitoring
Description
HBIMS — Heterogeneous Biomedical Intelligence Monitoring System Primary evaluation corpus for Chapter 1 · FBI-Architecture (Foundations of Intelligent Biomedical Applications). Description HBIMS is a multi-system healthcare monitoring corpus designed for intelligent biomedical application research. Modern care is not captured by any single sensor or record system: radiology archives, ICU monitors, laboratory analyzers, genomic sequencers, EHR feeds, consumer wearables, and remote patient monitoring platforms each produce partial views of the same patient journey. HBIMS models that reality by binding seven parallel monitoring streams into one heterogeneous biomedical case per patient encounter. Each HBIMS case represents a patient monitored across hospital, laboratory, genomic, clinical-record, wearable, and remote-care infrastructure. Stream-specific feature vectors are aligned to the Stage 3 ecosystem modalities of the FBI-Architecture (FoundationalBiomedicalPipeline): imaging from PACS/RIS, continuous physiology from ICU monitoring, structured laboratory outputs from LIS, variant-level signals from genomic platforms, longitudinal documentation from EHR hubs, ambulatory telemetry from wearables, and time-series alerts from RPM platforms. Cases are labeled by future-application intent (Diagnosis through Autonomous Care) so Stage 6 can learn which collaborative intelligence outcome the integrated monitoring picture supports. The corpus contains 3,584 cases from 1,024 patients, with 512 cases per class across seven future-application categories (C0–C6). Cases span outpatient, inpatient, and remote-monitoring care settings and include evolution-pathway and AI-healthcare pathway features for Stages 1–2. HBIMS is the primary data source for training, validation, Streamlit demos, and evaluation plots in Chapter 1; reported FBI-Architecture performance is 99.04% validation accuracy @ epoch 301 (batch size 64, NVIDIA H100 GPU).