Abstract: Provided herein are pharmaceutical compositions and tissue/organ preservation solutions comprising itaconate and/or derivatives thereof and methods of use thereof. In particular, provided herein are compositions comprising itaconate and methods of use thereof for the preservation of tissues/organs for transplantation and treatment/prevention/reduction of conditions such as myocardial infarction, sudden cardiac arrest, stroke, senescence, cardiomyopathies, and cardiotoxicity.

Abstract: Provided herein are agents that modulate histone acetylation and pathways downstream thereof, and methods for the treatment and prevention of organ injury therewith. In particular, provided herein are combinations of histone deacetylase (HDAC) inhibitors, bromodomain and extraterminal-containing protein family (BET) inhibitors, promoters of histone acetyl transferase (HAT) activity, mineralocorticoid receptor (MR) antagonists, nuclear factor erythroid 2-related factor 2 (NRF2) activators, and/or aldehyde dehydrogenase (ALDH) agonists, and methods of use thereof for the treatment and prevention of heart injury.

Abstract: A plurality of events are extracted from a plurality of electronic health records associated with a first patient. The extracted plurality of events are analyzed to identify a plurality of stimulus events and a plurality of response events. An association between a first stimulus event and a first response event is determined. A stimulus-response (SR) variable is generated for the first patient based at least in part on the determined association, and the generated SR variable is integrated into one or more predictive cognitive models.

Abstract: A plurality of attributes are extracted from a plurality of electronic health records, where each electronic health record is associated with a patient in a plurality of patients. Additionally, a training data set and a scoring data set are generated based on the plurality of attributes, and a patient similarity model is trained based on the training data set. A precision cohort is identified, where the precision cohort includes patients in the plurality of patients from the scoring data set that are similar to a first patient based on an electronic health record of the first patient and the similarity model. At least one result statistic for each of a plurality of treatments given to patients in the precision cohort is determined, and a first treatment of the plurality of treatments is selected for the first patient based at least in part on the determined result statistics.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a question prediction and answering engine for predicting questions a medical professional is attempting to answer. An interaction monitoring component monitors interaction of a medical professional with a patient electronic medical record (EMR). A question selection component selects a set of questions the medical professional is attempting to obtain an answer to from the patient EMR. The question prediction and answering engine analyzes the patient EMR to generate a set of answers to the set of questions from at least a portion of the patient EMR and outputs a report correlating the set of questions and the set of answers to the medical professional.

Abstract: Provided herein are organ preservation solutions and methods of use thereof for preserving organs. In particular, provided herein are preservation solutions comprising a mineralocorticoid receptor antagonist, aldehyde dehydrogenase agonist, and/or histone deacetylase inhibitor, and methods of perfusing and storing an organ while awaiting transplantation or non-transplant surgery.

Abstract: A notation assistant system has a machine learning system, a notation processing system, a scoring system, and a suggestion system. The machine learning system trains a classifier for rating a factor related to a clinical narrative note that describes a patient's health status. The notation processing system processes a clinical narrative note and the scoring system determines a factor rating, such as a completion score or a clarity score. The scoring system provides the factor rating to an end-user device to display to a user. The notation assistant system is configured to perform a method as a user is entering a clinical narrative note to provide real-time feedback, such as the factor rating. The suggestion system is configured to provide suggestions for modifying the clinical narrative note to improve the rating factor. The notation assistant system applies to patient health conditions such as a health status of a patient's heart for a patient receiving care for congestive heart failure.

Abstract: A system and associated method for assessing level of completeness of healthcare data storage based on collected data. The system includes a collection system, a measurement system, an expectation system, and an alerting system. In a method of assessing the health of stored data, the system collects real-time data from at least one data source, determines measurements for a plurality of parameters based on the collected data, and generates expectations for a future period of time for the plurality of parameters based on data analysis technique. The system also compares the expectations for the future period of time to subsequent measurements collected for that period of time to determine whether the subsequent measurements satisfy an expectation threshold and provide an alert to a client terminal. The alert is a result of the comparison of the expectations and the subsequent measurements and provides an assessment of data storage quality and alerts on anticipated deficiencies.

Abstract: A method, a system, and a computer program product are provided. Speech signals from a medical conversation between a medical provider and a patient are converted to text based on a first domain model associated with a medical scenario. The first domain model is selected from multiple domain models associated with a workflow of the medical provider. One or more triggers are detected, each of which indicates a respective change in the medical scenario. A corresponding second domain model is applied to the medical conversation to more accurately convert the speech signals to text in response to each of the detected one or more triggers. The corresponding second domain model is associated with a respective change in the medical scenario of the workflow of the medical provider. A clinical note is provided based on the text produced by converting the speech signals.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a relevant information graphical presentation engine for providing a graphical user interface (GUI) that presents information from a patient electronic medical record (EMR). The relevant information graphical presentation engine generates a GUI presenting patient EMR data pertinent to a condition of the patient. The relevant information graphical presentation engine identifies a change in treatment recorded in the patient EMR, submits a question to a healthcare cognitive system to query the patient EMR for a reason for the change in treatment, and receives at least one answer from the healthcare cognitive system identifying of a reason portion of the patient EMR that provides a reason for the change in treatment.

Abstract: Provided is a method, a computer program product, and a system for generating personalized treatment options and associated outcome estimates for patients. The method includes retrieving information from an electronic health records database relating to a patient cohort, detecting decision points from the information retrieved, and computing actual treatment options from the decision points. The method also includes computing precision cohort treatment options from the decision points using precision cohort analytics on a patient group and analyzing the decision points by comparing the actual treatment options with the precision cohort treatment options for each of the decision points to determine recommended measures. The method further includes generating a precision population analysis report based on the recommended measure.

Abstract: A mechanism is provided for presenting contextually relevant patient data in relation to other patients in a graphical user interface. Using a medical condition associated with a patient and a current treatment being followed by the patient, a cohort of similar patients with the same medical condition are identified. For each of the patients in the cohort, a set of next treatments are identified for those patients that stopped the current treatment and followed a next treatment. For each next treatment followed by a patent in the cohort of similar patients, a determination is made as to whether the next treatment controlled or failed to control the same medical condition for the patient. Then a presentation is provided indicating the set of next treatments, a number of patients following each next treatment, whether each treatment controlled or failed to control the same medical condition, and a statistical significance.

Abstract: A system and associated method provides an assessment of patient's heart health status based on past medical data. The system has a collection system, a processing system, a profiling system, and a user tools system. The collection system collects data from at least one data source having the past medical data of a plurality of patients. The processing system processes unstructured data to extract information related to patient health at different points in time. The profiling system develops a classifier based on the extracted information and other collected data. The profiling system further categorizes the extracted information into one of a plurality of health statuses at the different points in time. The user tools system generates a user interface comprising the plurality of health statuses at the different points in time. The health statuses relate to a compensated or decompensated status of the patient's heart.

Abstract: A notation assistant system has a machine learning system, a notation processing system, a scoring system, and a suggestion system. The machine learning system trains a classifier for rating a factor related to a clinical narrative note that describes a patient's health status. The notation processing system processes a clinical narrative note and the scoring system determines a factor rating, such as a completion score or a clarity score. The scoring system provides the factor rating to an end-user device to display to a user. The notation assistant system is configured to perform a method as a user is entering a clinical narrative note to provide real-time feedback, such as the factor rating. The suggestion system is configured to provide suggestions for modifying the clinical narrative note to improve the rating factor. The notation assistant system applies to patient health conditions such as a health status of a patient's heart for a patient receiving care for congestive heart failure.

Abstract: A system and associated method for assessing level of completeness of healthcare data storage based on collected data. The system includes a collection system, a measurement system, an expectation system, and an alerting system. In a method of assessing the health of stored data, the system collects real-time data from at least one data source, determines measurements for a plurality of parameters based on the collected data, and generates expectations for a future period of time for the plurality of parameters based on data analysis technique. The system also compares the expectations for the future period of time to subsequent measurements collected for that period of time to determine whether the subsequent measurements satisfy an expectation threshold and provide an alert to a client terminal. The alert is a result of the comparison of the expectations and the subsequent measurements and provides an assessment of data storage quality and alerts on anticipated deficiencies.

Abstract: A method, a system, and a computer program product are provided. Speech signals from a medical conversation between a medical provider and a patient are converted to text based on a first domain model associated with a medical scenario. The first domain model is selected from multiple domain models associated with a workflow of the medical provider. One or more triggers are detected, each of which indicates a respective change in the medical scenario. A corresponding second domain model is applied to the medical conversation to more accurately convert the speech signals to text in response to each of the detected one or more triggers. The corresponding second domain model is associated with a respective change in the medical scenario of the workflow of the medical provider. A clinical note is provided based on the text produced by converting the speech signals.

Abstract: A method, computer system, and a computer program product for optimizing speech recognition in a smart medical room. The present invention may include selecting, from a database, one or more speech domain models based on a plurality of signals from a plurality of biometric sensors associated with a plurality of medical equipment, wherein the one or more speech domain models are trained with one or more feedback from a clinician based on a medical encounter and from a continuous feedback display in the smart medical room, wherein the one or more feedback from the clinician is based on an optional notification to the clinician to confirm the one or more speech models in use.

Abstract: A method, computer system, and a computer program product for optimizing speech recognition in a smart medical room. The present invention may include selecting, from a database, one or more speech domain models based on a plurality of signals from a plurality of biometric sensors associated with a plurality of medical equipment, wherein the one or more speech domain models are trained with one or more feedback from a clinician based on a medical encounter and from a continuous feedback display in the smart medical room, wherein the one or more feedback from the clinician is based on an optional notification to the clinician to confirm the one or more speech models in use.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a cognitive analysis engine for analysis and disambiguation of electronic medical records for presentation of pertinent information for a medical treatment plan. The cognitive analysis engine receives a medical condition for a current or upcoming interaction with a patient. The cognitive analysis engine receives a medical mental model that emulates the thinking of a medical professional with regard to reviewing a patient electronic medical record (EMR) to identify pertinent information for a medical treatment plan to treat the medical condition.

Abstract: A method, computer system, and a computer program product for optimizing speech recognition in a smart medical room. The present invention may include receiving a piece of verbal data associated with a medical encounter from one or more audio recording devices. The present invention may also include accessing a plurality of signals from a plurality of biometric sensors associated with a plurality of medical equipment associated with the smart medical room based on the received piece of verbal data associated with the medical encounter. The present invention may further include selecting, from a database, one or more speech domain models based on the accessed plurality of signals from the plurality of biometric sensors associated with the plurality of medical equipment, wherein the one or more speech domain models are utilized to optimize a transcription of speech during the medical encounter in the smart medical room.