Dascena, Inc. — Department of Health and Human Services SBIR Phase I: 600

Description

Significance In this SBIR project we propose to improve the utility of AutoTriage a machine learning based clinical decision support CDS system by integrating clinician intervention medical information into its predictions Despite identified needs for CDS systems in patient transfer and discharge decisions existing tools do not meet high standards for sensitivity and specificity This is because current CDS methods are unable to distinguish changes in patient health due to clinician intervention from those arising due to an internal homeostatic mechanism Thus for example existing tools may erroneously suggest discharge for a patient currently undergoing a life sustaining treatment Research Question Can machine learning principles be used to create a classifier which incorporates signs of clinical intervention to inform transfer and discharge decision support ultimately leading to higher quality predictions In addition will such a tool be able to maintain its performance when tested on a different patient population or one for which the data quality is much poorer Prior Work We have developed AutoTriage a machine learning based CDSS for hour mortality prediction On the publicly available MIMIC III retrospective data set this system attains an area under the receiver operating characteristic curve AUROC of which is superior to commonly used triage scores MEWS AUROC SOFA and SAPS II on the same data set Specific Aims To integrate clinician intervention information into existing AutoTriage software Aim and to test the robustness of this modified tool to changes in patient population and data quality Aim Methods We will create gold standards for periods of clinician intervention using chart events and keywords from clinician notes Then we will train a binary classifier for identifying these periods and ultimately use the classifier to modify AutoTriage scores Robustness studies will be performed on the retrospective UC ReX and sparse MIMIC III databases Successful completion of Aim will be demonstrated if of all hours of clinician intervention are correctly classified if the test set area under the ROC curve improves by over its current value and if day readmission predictions are more accurate for patients treated within the last hour Aim will be completed if AutoTriage ROC area performance is within of its original value for both UC ReX and sparse MIMIC III sets Future Directions Following the proposed work the AutoTriage system will be deployed at the sites of our ongoing clinical implementations During this study we project that AutoTriage will assess mortality risk for ICU patients per year helping clinicians more effectively allocate interventions totaling $ million Clinical decision support CDS systems aid medical professionals by presenting them with information needed to make better decisions about patient care often in the form of alerts To be of practical benefit CDS tools must provide recommendations which accurately reflect the ongoing treatment provided by the clinician We propose to incorporate knowledge of a clinician s actions like the administration of fluids or of antibiotics into a CDS tool in order to improve the assessment of a patient s ability to be discharged or transferred safely