Abstract: Continuous temperature monitors can be used to collect frequent temperature data over large periods of time. A machine learning system trained with collected temperature data can be used to predict future temperature data for patients. Such predictions can be clinically beneficial for disease states in which fever can be fatal, particularly for those fevers having fast onset. For example, patients undergoing chimeric antigen receptor T-cell (CAR-T) therapy may suffer fevers caused by cytokine release syndrome (CRS). Accordingly, a continuous temperature monitor is used to collect temperature data from CAR-T patients with high risk of CRS (or like patients with high fever risk), and the collected temperature data processed by a machine learning system to predict the patient's future temperature profiles. Results of analysis of the predicted temperature profiles may then be provided to the patient and/or clinician.

Abstract: A sentiment analysis scheduling method, system, and computer program product include analyzing prior sentiments based on past events, building a personalized data model with a categorized event type and a sentiment outcome based on the prior sentiments, analyzing an upcoming event content and predicting a potential sentiment outcome for the upcoming event based on the personalized data model, and rearranging an order of future events to achieve the predicted potential sentiment outcome for the upcoming event.

Abstract: Embodiments are directed to methodologies, systems and computer program products for generating, for each of a plurality of risk factors in a patient database containing information of a plurality of patients, an index of input values for the risk factor. For each patient P of the plurality of patients, a series of local impact scores is computed for the patient. Computing the series of local impact scores for the patient includes calculating a risk score for the patient with respect to each of the indexed input values for each of the plurality of risk factors. For at least one of the plurality of patients, at least some of the plurality of risk factors are ranked based at least partly on the computed local impact scores for each of the at least some risk factors, and an indication of the ranked risk factors for the at least one patient is provided.

Abstract: Embodiments are directed to methodologies, systems and computer program products for generating, for each of a plurality of risk factors in a patient database containing information of a plurality of patients, an index of input values for the risk factor. For each patient P of the plurality of patients, a series of local impact scores is computed for the patient. Computing the series of local impact scores for the patient includes calculating a risk score for the patient with respect to each of the indexed input values for each of the plurality of risk factors. For at least one of the plurality of patients, at least some of the plurality of risk factors are ranked based at least partly on the computed local impact scores for each of the at least some risk factors, and an indication of the ranked risk factors for the at least one patient is provided.

Abstract: Systems and methods for treating a patient for a medical condition using an interactive treatment pathway interface associated with the medical condition include receiving, through an interface, patient data representing a medical status of a patient. Treatment pathway data representing a sequence of actions for a particular treatment or diagnosis of a medical condition of the patient are retrieved. The interface guides the particular treatment or diagnosis of the medical condition by a traversal of the treatment pathway. A data trace is generated from additional patient data received during the traversal. The represents the instance of the particular treatment or diagnosis of the medical condition of the patient. The data are used to affect the particular treatment or diagnosis for the medical condition to the patient and inform machine learning operations for diagnosis or treatment of the medical condition.

Abstract: Aspects of the present invention include a method, system and computer program product. The method includes identifying, by a processor, a set of global risk factors for a target event using training patients, and providing, by the processor, a disease progression timeline with defined time stamps by aligning longitudinal data of the training patients based on the defined time stamp of risk targets. The method also includes positioning, by the processor, a target patient at one of the defined time stamps on the disease progression timeline, and identifying, by the processor, at least one of the training patients similar to the target patient with the same one of the defined time stamps on the disease progression timeline. The method further includes calculating, by the processor, a time-varying predictive pattern of at least a portion of the global set of risk factors for the target patient along the disease progression timeline.

Abstract: Continuous temperature monitors can be used to collect frequent temperature data over large periods of time. A machine learning system trained with collected temperature data can be used to predict future temperature data for patients. Such predictions can be clinically beneficial for disease states in which fever can be fatal, particularly for those fevers having fast onset. For example, patients undergoing chimeric antigen receptor T-cell (CAR-T) therapy may suffer fevers caused by cytokine release syndrome (CRS). Accordingly, a continuous temperature monitor is used to collect temperature data from CAR-T patients with high risk of CRS (or like patients with high fever risk), and the collected temperature data processed by a machine learning system to predict the patient's future temperature profiles. Results of analysis of the predicted temperature profiles may then be provided to the patient and/or clinician.

Abstract: A pathway for each entity in a cohort may be extracted from raw data comprising a plurality of events. A common graph structure may be created based the extracted pathways. For each cohort, a relative signature trait may be computed for each edge based on a number of appearances of the edge in the extracted pathways that are associated with the cohort. A relationship expression may be obtained. The relationship expression may be with respect to at least two cohorts for which relative signature traits were computed. The common graph structure may be processed based on the relationship expression to determine an output label for each edge. A visualization of the common graph structure may be displayed.

Abstract: There is provided a method of creating a cohort clinical pathway graph based on knowledge-driven manual user input and automated data-driven mining comprising: receiving via a graphical user interface (GUI), manual selections including: knowledge-driven variable(s) denoting clinically significant values representing elements of a clinical decision making process, and an anchoring location of each knowledge-driven node denoting a respective knowledge-drive variable within a directed acyclic graph (DAG), computing individual clinical pathways for each of the sampled population of patients by automatically computing data-driven nodes denoting the data-driven discovery of event types relative to the manual selections, and aggregating the individual clinical pathways to compute a cohort clinical pathway DAG, wherein the cohort clinical pathway DAG includes nodes comprising the knowledge-driven nodes, the data-driven nodes, and links connecting the nodes, each link denoting an automatically discovered sequence betw

Abstract: A pathway for each entity in a cohort may be extracted from raw data comprising a plurality of events. A common graph structure may be created based the extracted pathways. For each cohort, a relative signature trait may be computed for each edge based on a number of appearances of the edge in the extracted pathways that are associated with the cohort. A relationship expression may be obtained. The relationship expression may be with respect to at least two cohorts for which relative signature traits were computed. The common graph structure may be processed based on the relationship expression to determine an output label for each edge. A visualization of the common graph structure may be displayed.

Abstract: There is provided a method of creating a cohort clinical pathway graph based on knowledge-driven manual user input and automated data-driven mining comprising: receiving via a graphical user interface (GUI), manual selections including: knowledge-driven variable(s) denoting clinically significant values representing elements of a clinical decision making process, and an anchoring location of each knowledge-driven node denoting a respective knowledge-drive variable within a directed acyclic graph (DAG), computing individual clinical pathways for each of the sampled population of patients by automatically computing data-driven nodes denoting the data-driven discovery of event types relative to the manual selections, and aggregating the individual clinical pathways to compute a cohort clinical pathway DAG, wherein the cohort clinical pathway DAG includes nodes comprising the knowledge-driven nodes, the data-driven nodes, and links connecting the nodes, each link denoting an automatically discovered sequence betw

Abstract: Aspects of the present invention include a method, system and computer program product. The method includes identifying, by a processor, a set of global risk factors for a target event using training patients, and providing, by the processor, a disease progression timeline with defined time stamps by aligning longitudinal data of the training patients based on the defined time stamp of risk targets. The method also includes positioning, by the processor, a target patient at one of the defined time stamps on the disease progression timeline, and identifying, by the processor, at least one of the training patients similar to the target patient with the same one of the defined time stamps on the disease progression timeline. The method further includes calculating, by the processor, a time-varying predictive pattern of at least a portion of the global set of risk factors for the target patient along the disease progression timeline.

Abstract: Embodiments are directed to methodologies, systems and computer program products for generating, for each of a plurality of risk factors in a patient database containing information of a plurality of patients, an index of input values for the risk factor. For each patient P of the plurality of patients, a series of local impact scores is computed for the patient. Computing the series of local impact scores for the patient includes calculating a risk score for the patient with respect to each of the indexed input values for each of the plurality of risk factors. For at least one of the plurality of patients, at least some of the plurality of risk factors are ranked based at least partly on the computed local impact scores for each of the at least some risk factors, and an indication of the ranked risk factors for the at least one patient is provided.

Abstract: Embodiments are directed to methodologies, systems and computer program products for generating, for each of a plurality of risk factors in a patient database containing information of a plurality of patients, an index of input values for the risk factor. For each patient P of the plurality of patients, a series of local impact scores is computed for the patient. Computing the series of local impact scores for the patient includes calculating a risk score for the patient with respect to each of the indexed input values for each of the plurality of risk factors. For at least one of the plurality of patients, at least some of the plurality of risk factors are ranked based at least partly on the computed local impact scores for each of the at least some risk factors, and an indication of the ranked risk factors for the at least one patient is provided.

Abstract: Methods and apparatus are provided for iterative refinement of cohorts using visual exploration and data analytics. A cohort comprised of multiple data objects is defined by obtaining an initial cohort seeding; visualizing the initial cohort using a selected view to present a current cohort; reducing the current cohort using one or more visual filters; visualizing the current cohort using a selected view; expanding the current cohort using one or more selected analytics; and determining whether the current cohort should be further modified using one or more of additional reductions and additional expansions. Cohorts can be passed between views and analytics via drag-and-drop interactions as an analysis unfolds.

Abstract: Methods and apparatus are provided for iterative refinement of cohorts using visual exploration and data analytics. A cohort comprised of multiple data objects is defined by obtaining an initial cohort seeding; visualizing the initial cohort using a selected view to present a current cohort; reducing the current cohort using one or more visual filters; visualizing the current cohort using a selected view; expanding the current cohort using one or more selected analytics; and determining whether the current cohort should be further modified using one or more of additional reductions and additional expansions. Cohorts can be passed between views and analytics via drag-and-drop interactions as an analysis unfolds.

Abstract: Methods, systems, and articles of manufacture for exemplary visual analytics are provided herein. Visual analytics techniques are provided that combine pattern mining and temporal event visualization-based techniques. Visual episode query tools allow interactive specification of episode definitions and are combined with on-demand data analytics that perform pattern mining to help discover important intermediate events within an episode, and dynamic information visualization that allow interactive exploration and analysis of clinical event sequence data. The disclosed interactive visualization techniques identify events that impact outcome and how those associations change over time.

Abstract: Methods, systems, and articles of manufacture for exemplary visual analytics are provided herein. Visual analytics techniques are provided that combine pattern mining and temporal event visualization-based techniques. Visual episode query tools allow interactive specification of episode definitions and are combined with on-demand data analytics that perform pattern mining to help discover important intermediate events within an episode, and dynamic information visualization that allow interactive exploration and analysis of clinical event sequence data. The disclosed interactive visualization techniques identify events that impact outcome and how those associations change over time.

Abstract: Methods and apparatus are provided for iterative refinement of cohorts using visual exploration and data analytics. A cohort comprised of multiple data objects is defined by obtaining an initial cohort seeding; visualizing the initial cohort using a selected view to present a current cohort; reducing the current cohort using one or more visual filters; visualizing the current cohort using a selected view; expanding the current cohort using one or more selected analytics; and determining whether the current cohort should be further modified using one or more of additional reductions and additional expansions. Cohorts can be passed between views and analytics via drag-and-drop interactions as an analysis unfolds.

Abstract: Techniques are presented for determining a first set of display elements, each display element associated with a first display characteristic. An interesting element in the first set of display elements is determined. A second set of elements related to the interesting element is determined. A third set of elements based on the first and second set of elements is determined using intersection, union, subtraction addition and other logical operations. A third set of elements not yet added to the element collection is determined. The elements in the third set are associated with a second or ghosted display characteristic. The elements in the third set are inserted within the sorted context of the visualization based on spatial distortion rules which help to preserve spatial memory cues in the visualization of the element collection. The elements may be documents in an information repository, linked contact information, linked information records in a database or other types of information.