Abstract: Systems and methods for automatically notifying a caregiver that a patient is in need of clinical intervention are disclosed. The systems and methods include the utilization of a machine learning model to automatically notify the caregiver when a patient is statistically likely to need clinical intervention within a predetermined time period.

Abstract: Systems and methods for automatically selecting or identifying a candidate patient for enrollment in a clinical trial are disclosed. The systems and methods include the utilization of a machine learning model to automatically identify or select a candidate patient who satisfies clinical trial inclusion criteria and is statistically likely to meet one or more clinical trial endpoints.

Abstract: Methods for determining the time at which a drug should be administered, based on patient electronic health record data. Machine learning techniques are used to correlate trends in health record data with successful drug treatment, ultimately anticipating the optimal time of drug administration. Multiple types of data, including demographic, physiological, treatment, and clinical notes data, can be used to train the classification component. Multiple patient populations can be used as sources of patient data for training classification component. Data input requirements, dimensionality, and performance metrics may be optimized.

Abstract: Methods for identifying to which patients a drug should be administered, based on underlying drug mechanism of action, are provided. Multiple types of data, including demographic, physiological, treatment, and clinical notes data, can be used to train a classification component. Multiple patient populations can be used as sources of patient data for training classification component. Data input requirements, dimensionality, and performance metrics may be optimized.

Abstract: Methods for identifying to which patients a drug should be administered, based on underlying drug mechanism of action, are provided. Machine learning techniques are used to determine that patient's underlying disease pathway includes drug mechanism of action target; the drug is then administered, based on this determination. Multiple types of data, including demographic, physiological, treatment, and clinical notes data, can be used to train a classification component. Multiple patient populations can be used as sources of patient data for training classification component. Data input requirements, dimensionality, and performance metrics may be optimized.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.