Abstract: A classification method and system for medical conditions based on the concept of subtypes, which are classes of patients whose medical fact patterns as analyzed in an N-dimensional space places them closer to other patients belonging to the same subtype than to patients who belong to different subtypes and, who share similar likelihood of certain specified outcomes. A computer system processes patient data for a plurality of patients from a set of patients called a cohort. The computer system processes the patient data for the cohort to group patients into sub-cohorts of similar patients, i.e., each sub-cohort includes patients who have similar medical fact patterns in their patient data. Patients in different sub-cohorts generally, but not necessarily, have significant differences in their patient data. The computer system generates quantitative definitions, describing the patients in the sub-cohorts.

Abstract: Health care information for multiple patients is processed to classify patients into categories. Additional data fields related to a category in which a patient is classified are added to the patient record. These data fields are populated in part by automatically processing the existing patient data. Such automatic processing can result in a probability that the underlying data supports having a particular value stored in one of the added data fields, and this probability also can be stored. Over time, additional data can be obtained from patients, caregivers and other sources, for structured data fields based on data entry forms for patient reported outcomes, caregiver reported outcomes, events of interest, survival and resource utilization. A set of factor scores is computed for each patient, for each category in which the patient is classified.

Abstract: Health care information for multiple patients is processed to classify patients into categories. Additional data fields related to a category in which a patient is classified are added to the patient record. These data fields are populated in part by automatically processing the existing patient data. Such automatic processing can result in a probability that the underlying data supports having a particular value stored in one of the added data fields, and this probability also can be stored. Over time, additional data can be obtained from patients, caregivers and other sources, for structured data fields based on data entry forms for patient reported outcomes, caregiver reported outcomes, events of interest, survival and resource utilization. A set of factor scores is computed for each patient, for each category in which the patient is classified.

Abstract: A predictive model can be applied to data representing a history of events for an entity to compute a value indicative of an outcome related to a reference time for that entity. The effect of an event from an entity's history of events on an outcome for the entity at a reference time can vary based on the type of event and relative time of that event with respect to the reference time. The effect of an event from an entity's history of events on an outcome for the entity also can vary due to other characteristics of the entity in combination with the event. These effects are captured as weights. For an entity, functions of sets of events from the history of events are computed for the entity and a set of weights for events. The computed results are inputs to the predictive model.

Abstract: A classification method and system for medical conditions based on the concept of subtypes, which are classes of patients whose medical fact patterns as analyzed in an N-dimensional space places them closer to other patients belonging to the same subtype than to patients who belong to different subtypes and, who share similar likelihood of certain specified outcomes. A computer system processes patient data for a plurality of patients from a set of patients called a cohort. The computer system processes the patient data for the cohort to group patients into sub-cohorts of similar patients, i.e., each sub-cohort includes patients who have similar medical fact patterns in their patient data. Patients in different sub-cohorts generally, but not necessarily, have significant differences in their patient data. The computer system generates quantitative definitions, describing the patients in the sub-cohorts.

Abstract: Health care information for multiple patients is processed to classify patients into categories. Additional data fields related to a category in which a patient is classified are added to the patient record. These data fields are populated in part by automatically processing the existing patient data. Such automatic processing can result in a probability that the underlying data supports having a particular value stored in one of the added data fields, and this probability also can be stored. Over time, additional data can be obtained from patients, caregivers and other sources, for structured data fields based on data entry forms for patient reported outcomes, caregiver reported outcomes, events of interest, survival and resource utilization. A set of factor scores is computed for each patient, for each category in which the patient is classified.

Abstract: Health care information for multiple patients is processed to classify patients into categories. Additional data fields related to a category in which a patient is classified are added to the patient record. These data fields are populated in part by automatically processing the existing patient data. Such automatic processing can result in a probability that the underlying data supports having a particular value stored in one of the added data fields, and this probability also can be stored. Over time, additional data can be obtained from patients, caregivers and other sources, for structured data fields based on data entry forms for patient reported outcomes, caregiver reported outcomes, events of interest, survival and resource utilization. A set of factor scores is computed for each patient, for each category in which the patient is classified.