Predicting Postoperative Mortality With Deep Neural Networks and Natural Language Processing: Model Development and Validation.

Background: Machine learning (ML) achieves better predictions of postoperative mortality than previous prediction tools. Free-text descriptions of the preoperative diagnosis and the planned procedure are available preoperatively. Because reading these descriptions helps anesthesiologists evaluate the risk of the surgery, we hypothesized that deep learning (DL) models with unstructured text could improve postoperative mortality prediction.

Using Text Content From Coronary Catheterization Reports to Predict 5-Year Mortality Among Patients Undergoing Coronary Angiography: A Deep Learning Approach.

Background: Current predictive models for patients undergoing coronary angiography have complex parameters which limit their clinical application. Coronary catheterization reports that describe coronary lesions and the corresponding interventions provide information of the severity of the coronary artery disease and the completeness of the revascularization. This information is relevant for predicting patient prognosis.

Electrochemical SERS on 2D Mapping for Metabolites Detection.

Surface-enhanced Raman scattering (SERS) has been widely used for bioanalysis because it provides a high sensitivity for detecting analytes of ultralow concentrations. However, the clinical application of a 2D SERS-active substrate remains challenging because of the difficulty of obtaining accurate quantification, especially at low concentration. In this study, we proposed an analytical method that integrates an optimized sample mapping strategy with an electrochemical SERS (EC-SERS) technique to resolve this problem.

Electrochemical SERS for in Situ Monitoring the Redox States of PEDOT and Its Potential Application in Oxidant Detection.

In response to recent developments for applying conducting polymers on various biomedical applications, the development of characterization techniques for evaluating the states of conducting polymers in liquids is beneficial to the applications of these materials. In this study, we propose a platform using electrochemical surface-enhanced Raman scattering (EC-SERS) technology, which allows a direct measurement of the redox states of conducing polymers in liquids. A thiophene-based conducting polymer, hydroxymethyl poly(3,4-ethylenedioxythiophene) or poly(EDOT-OH), was used to demonstrate this concept.