Imaging screening of catastrophic neurological events using a software tool: preliminary results

Automated screening of potential organ donors using a temporal machine learning model

Organ donation is not meeting demand, and yet 30-60% of potential donors are potentially not identified. Current systems rely on manual identification and referral to an Organ Donation Organization (ODO). We hypothesized that developing an automated screening system based on machine learning could reduce the proportion of missed potentially eligible organ donors. Using routine clinical data and laboratory time-series, we retrospectively developed and tested a neural network model to automatically identify potential organ donors. We first trained a convolutive autoencoder that learned from the longitudinal changes of over 100 types of laboratory results. We then added a deep neural network classifier. This model was compared to a simpler logistic regression model. We observed an AUROC of 0.966 (CI 0.949-0.981) for the neural network and 0.940 (0.908-0.969) for the logistic regression model. At a prespecified cutoff, sensitivity and specificity were similar between both models at 84% and 93%. Accuracy of the neural network model was robust across donor subgroups and remained stable in a prospective simulation, while the logistic regression model performance declined when applied to rarer subgroups and in the prospective simulation. Our findings support using machine learning models to help with the identification of potential organ donors using routinely collected clinical and laboratory data.

Using clinical signs of neglect to identify elder neglect cases

Elder neglect is the one of the most pervasive forms of mistreatment, and often the only place outside of the individual's residence to identify and assist neglected individuals is in a medical setting. However, elder neglect cases treated in hospitals do not present with a single diagnosis or clinical sign, but rather involve a complex constellation of clinical signs. Currently, there is a lack of comprehensive guidelines on which clinical signs to use in screening tools for neglect among patients treated in hospitals. Using the DELPHI method, a group of experts developed and tested a scale to be used as a pre-screener that conceptually could be integrated into electronic health record systems so that it could identify potential neglect cases in an automated manner. By applying the scale as a pre-screener for neglect, the tool would reduce the pool of at-risk patients who would benefit from in-depth screening for elder neglect by 95%.