Abstract: A graph-based clinical concept mapping algorithm maps ICD-9 (International Classification of Disease, Revision 9) and ICD-10 (International Classification of Disease, Revision 10) codes to unified Systematized Nomenclature of Medicine (SNOMED) clinical concepts to normalize longitudinal healthcare data to thereby improve tracking and the use of such data for research and commercial purposes. The graph-based clinical concept mapping algorithm advantageously combines a novel graph-based search algorithm and natural language processing to map orphan ICD codes (those without equivalents across codebases) by finding optimally relevant shared SNOMED concepts. The graph-based clinical concept mapping algorithm is further advantageously utilized to group ICD-9/10 codes into higher order, more prevalent SNOMED concepts to support clinical interpretation.

Abstract: A graph-based clinical concept mapping algorithm maps ICD-9 (International Classification of Disease, Revision 9) and ICD-10 (International Classification of Disease, Revision 10) codes to unified Systematized Nomenclature of Medicine (SNOMED) clinical concepts to normalize longitudinal healthcare data to thereby improve tracking and the use of such data for research and commercial purposes. The graph-based clinical concept mapping algorithm advantageously combines a novel graph-based search algorithm and natural language processing to map orphan ICD codes (those without equivalents across codebases) by finding optimally relevant shared SNOMED concepts. The graph-based clinical concept mapping algorithm is further advantageously utilized to group ICD-9/10 codes into higher order, more prevalent SNOMED concepts to support clinical interpretation.

Abstract: An unbiased ETL (extract, transform, load) system for timed medical event prediction utilizes a rolling series of time-bound cross-sections of patient healthcare data. Patients may be labelled as belonging to one or more classes (e.g. positive or negative) for each cross-section in the series depending on current healthcare status. Rather than using a single snapshot, the unbiased ETL system employs multiple snapshots of patient medical histories to provide a capability to classify a patient at different points in time, as appropriate. Supervised learning for the system is thereby enabled over multiple different periods of a patient's medical journey which advantageously supports a more statistically robust medical event prediction model and eliminates several classes of bias. Additionally, the unbiased ETL system enables customization of a prediction window to account for lags in data collection, data processing, and length of use of the medical event predictions.

Abstract: A system for rendering visual images that combines sophisticated anti-aliasing and pixel blending techniques with control pipelining in hardware embodiment. A highly-parallel rendering pipeline performs sophisticated polygon edge interpolation, pixel blending and anti-aliasing rendering operations in hardware. Primitive polygons are transformed to subpixel coordinates and then sliced and diced to create "pixlink" elements mapped to each pixel. An oversized frame buffer memory allows the storage of many pixlinks for each pixel. Z-sorting is avoided through the use of a linked-list data object for each pixlink vector in a pixel stack. Because all image data values for X, Y, Z, R, G, B and pixel coverage A are maintained in the pixlink data object, sophisticated blending operations are possible for anti-aliasing and transparency. Data parallelism in the rendering pipeline overcomes the processor efficiency problem arising from the computation-intensive rendering algorithms used in the system of this invention.