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Expansion And Contraction Around Physiological Time-Series Trajectory For Current And Future Patient Condition Determination
Expansion And Contraction Around Physiological Time-Series Trajectory For Current And Future Patient Condition Determination
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机译:生理时间序列轨迹周围的伸缩,用于确定当前和将来的患者状况
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摘要
Systems for diagnostic decision support utilizing machine learning techniques are provided. A library of physiological data from prior patients can be utilized to train a classification component. Physiological data, including time parameterized data, can be mapped into finite discrete hyperdimensional space for classification. Dimensionality and resolution may be dynamically optimized. Classification mechanisms may incorporate recognition of quantitative interpretation information and exogenous effects. A radial expansion or contraction around a time-parameterized patient descriptor may be utilized to select library data for use in an analysis.
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