Abstract: A computer-based system and method to assign patients to providers. In one embodiment, the invention predicts the performance of provider-patient pairs in terms of different outcome-, financial- and satisfaction-related metrics across providers using advanced machine learning methodologies to develop distinct models for each of these metrics across each of the providers using a historical database. In another embodiment, patients are assigned to providers in a batch or online manner using this information through an optimization framework that looks to maximize or minimize arbitrary combinations of the outcome-, financial-, and satisfaction-related metrics subject to practical operational constraints.

Abstract: A computer-based system to determine whether patients should be treated as inpatients or outpatients. The invention makes personalized predictions about the risk and timing of adverse outcomes for the patient, and further assesses how this risk and timing may vary if the patients are treated as inpatients or outpatients. This information informs how patients are assigned to an appropriate therapy. The invention includes logic relevant to predicting patient risk, decoupling patient risk into components inherent to the patient as well as additions/subtractions associated with the choice of treatment, and predicting the timing of adverse outcomes given censored data. The invention can be extended to use in a broad range of other application domains (e.g., matching learners to courses either offered in-classroom or online for education).

Abstract: A method for assessing whether a patient is at risk of developing a clinical condition includes receiving training data representing a set of patient-related variables for each of a plurality of patients; generating model data based on the received training data; receiving target data representing the set of patient-related variables for a target patient; determining a risk level for the target patient of developing the clinical condition; and indicating the risk level of the target patient, where the set of patient-related variables consists of a first set of variables when the clinical condition is a mortality condition and a second set of variables when the clinical condition is a morbidity condition.

Abstract: A method for providing a personalized health risk of a patient includes receiving training data corresponding to a plurality of patients and target data corresponding to a target patient; generating model data based on the training data according to an anomaly detection method; either determining whether the target data is anomalous with respect to the training data, or determining the extent to which the target data is anomalous with respect to the training data; and either indicating whether the target patient is at risk of the adverse outcome, or indicating the extent to which the target patient is at risk of the adverse outcome.

Abstract: A method and apparatus for predicting patient outcome from a physiological segmentable signal of a patient. In one embodiment, the method comprises the steps of obtaining the physiological segmentable signal of the patient; segmenting the physiological segmentable signal into a plurality of separate segmentable components; calculating a time series of the morphological distance between adjacent separate segmentable components of the plurality of separate segmentable components; and predicting patient outcome in response to the time series of the morphological distance.

Abstract: A method and apparatus for predicting patient outcome from a physiological segmentable signal of a patient. In one embodiment, the method comprises the steps of obtaining the physiological segmentable signal of the patient; segmenting the physiological segmentable signal into a plurality of separate segmentable components; calculating a time series of the morphological distance between adjacent separate segmentable components of the plurality of separate segmentable components; and predicting patient outcome in response to the time series of the morphological distance.

Abstract: A method and apparatus for predicting patient outcome from a physiological segmentable signal of a patient. In one embodiment, the method comprises the steps of obtaining the physiological segmentable signal of the patient; segmenting the physiological segmentable signal into a plurality of separate segmentable components; calculating a time series of the morphological distance between adjacent separate segmentable components of the plurality of separate segmentable components; and predicting patient outcome in response to the time series of the morphological distance.

Abstract: The application relates a methodology and apparatus for identifying predictive patterns for acute clinical events in the absence of prior knowledge. Principles of conservation are used to identify activity that consistently precedes an outcome in patients, and describe a two-stage process that allows us to more efficiently search for such patterns in large datasets. This is achieved by first transforming continuous physiological signals from multiple patients into symbolic sequences, and by then searching for patterns in these reduced representations that are strongly associated with an outcome.

Abstract: A method for assessing whether a patient is at risk of developing a clinical condition includes receiving training data representing a set of patient-related variables for each of a plurality of patients; generating model data based on the received training data; receiving target data representing the set of patient-related variables for a target patient; determining a risk level for the target patient of developing the clinical condition; and indicating the risk level of the target patient, where the set of patient-related variables consists of a first set of variables when the clinical condition is a mortality condition and a second set of variables when the clinical condition is a morbidity condition.

Abstract: A method for providing a personalized health risk of a patient includes receiving training data corresponding to a plurality of patients and target data corresponding to a target patient; generating model data based on the training data according to an anomaly detection method; either determining whether the target data is anomalous with respect to the training data, or determining the extent to which the target data is anomalous with respect to the training data; and either indicating whether the target patient is at risk of the adverse outcome, or indicating the extent to which the target patient is at risk of the adverse outcome.

Abstract: The invention relates in part to methods for partitioning a plurality of patients into risk profile groups comprising the steps of: recording a physiological signal from each patient of a plurality of patients; segmenting the physiological signal into a plurality of components for each patient of a plurality of patients; grouping the components into a plurality of information classes for each patient of a plurality of patients; assigning a representation to each information class for each patient of a plurality of patients; and grouping the patients in response to the representations of their respective information classes.

Abstract: The application relates a methodology and apparatus for identifying predictive patterns for acute clinical events in the absence of prior knowledge. Principles of conservation are used to identify activity that consistently precedes an outcome in patients, and describe a two-stage process that allows us to more efficiently search for such patterns in large datasets. This is achieved by first transforming continuous physiological signals from multiple patients into symbolic sequences, and by then searching for patterns in these reduced representations that are strongly associated with an outcome.

Abstract: A method and apparatus for predicting patient outcome from a physiological segmentable signal of a patient. In one embodiment, the method comprises the steps of obtaining the physiological segmentable signal of the patient; segmenting the physiological segmentable signal into a plurality of separate segmentable components; calculating a time series of the morphological distance between adjacent separate segmentable components of the plurality of separate segmentable components; and predicting patient outcome in response to the time series of the morphological distance.