Meta-lasso: new insight on infection prediction after minimally invasive surgery.

Surgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients. In the realm of predictive healthcare, machine learning algorithms have been instrumental in anticipating various surgical outcomes, including SSI. However, accurately predicting infection after minimally invasive surgery remains a clinical challenge due to the multitude of physiological and surgical factors associated with it. Furthermore, clinical patient data, in addition to being high-dimensional, often exists the long-tail problem, posing difficulties for traditional machine learning algorithms in effectively processing such data. Based on this insight, we propose a novel approach called meta-lasso for infection prediction following minimally invasive surgery. Our approach leverages the sparse learning algorithm lasso regression to select informative features and introduces a meta-learning framework to mitigate bias towards the dominant class. We conducted a retrospective cohort study on patients who had undergone minimally invasive surgery for lung cancer at Shanghai Chest Hospital between 2018 and 2020. The evaluation encompassed key performance metrics, including sensitivity, specificity, precision (PPV), negative predictive value (NPV), and accuracy. Our approach has surpassed the performance of logistic regression, random forest, Naive Bayes classifier, gradient boosting decision tree, ANN, and lasso regression, with sensitivity at 0.798, specificity at 0.779, precision at 0.789, NPV at 0.798, and accuracy at 0.788 and has greatly improved the classification performance of the inferior class.

Explainable and efficient deep early warning system for cardiac arrest prediction from electronic health records.

Cardiac arrest (CA) is a fatal acute event. The development of new CA early warning system based on time series of vital signs from electronic health records (EHR) has great potential to reduce CA damage. In this process, recursive architecture-based deep learning, as a powerful tool for time series data processing, enables automatically extract features from various monitoring clinical parameters and to further improve the performance for acute critical illness prediction. However, the unexplainable nature and excessive time caused by black box structure with poor parallelism are the limitations of its development, especially in the CA clinical application with strict requirement of emergency treatment and low hidden dangers. In this study, we present an explainable and efficient deep early warning system for CA prediction, which features are captured by an efficient temporal convolutional network (TCN) on EHR clinical parameters sequence and explained by deep Taylor decomposition (DTD) theoretical framework. To demonstrate the feasibility of our method and further evaluate its performance, prediction and explanation experiments were performed. Experimental results show that our method achieves superior CA prediction accuracy compared with standard national early warning score (NEWS), in terms of overall AUROC (0.850 Vs. 0.476) and F1-Score (0.750 Vs. 0.450). Furthermore, our method improves the interpretability and efficiency of deep learning-based CA early warning system. It provides the relevance of prediction results for each clinical parameter and about 1.7 times speed enhancement for system calculation compared with the long short-term memory network.

Contextual embedding bootstrapped neural network for medical information extraction of coronary artery disease records.

Coronary artery disease (CAD) is the major cause of human death worldwide. The development of new CAD early diagnosis methods based on medical big data has a great potential to reduce the risk of CAD death. In this process, neural network (NN), as a powerful tool for electronic medical record (EMR) processing, enables extract structured data accurately to unlock medical information and to further improve CAD diagnosis. However, the excessive time and labor caused by dataset's annotation is the main limitation of its application, especially on the CAD records situation with large natural language text and biomedical professional content. In this study, we present an annotation cost saving NN approach for CAD records, which is bootstrapped by deep language model with contextual embedding pre-trained on large unannotated CAD corpus. To demonstrate the feasibility and to further evaluate the performance of our approach, we performed pre-training experiment and term classification experiment, by using the unannotated and annotated CAD records, respectively. The results showed that our contextual embedding bootstrapped NN for CAD records has better performance under the condition of annotations reduction.