Abstract: The present system is configured to provide patient information as input to a prediction model to train the prediction model for generating predictions related to a need for clinical intervention for individual patients. The system is configured to receive vital signs information for one or more vital signs of an individual. The system is configured to process, via the trained prediction model, the vital signs information to generate (i) a first prediction related a clinical intervention need for the individual, (ii) sub-predictions contributing the first prediction or to at least another one of the sub-predictions, and (iii) relatedness information indicating how the first prediction and the contributing sub-predictions are related, the contributing sub-predictions corresponding to respective ones of the weighted predictive parameter features. The system is configured to cause a presentation related to a clinical intervention need for the individual based on the output of the model.

Abstract: A method for automated analysis of medical records which executes a medical analysis algorithm to analyze patient data to generate an electronic narrative document. The electronic narrative document is stored in a patient database, which may be retrieved for viewing and editing by a user. As changes are made by the user, the medical analysis algorithm will predict and make suggestions in real-time. If the user selects to manually modify the electronic narrative document, the changes are stored for subsequent use.

Abstract: A system (16) assesses the readiness of a patient to be discharged from an intensive care unit (ICU), hospital or other monitored clinical care setting to a less intensively monitored location. The system (16) includes one or more processor (46). The processors (46) are programmed to receive patient data for patients. Risks of death from discharge for the patients are calculated using a first predictive model of risk of death. Further, risks of readmission from discharge for the patients are calculated using a second predictive model of risk of readmission. Risks of death and/or risks of readmission for one or more of the patients are presented to a clinician or clinicians in different groups of risk to supplement discharge decisions by clinicians.