Objectives Current mortality prediction models used in the intensive care unit (ICU) have a limited role for specific diseases such as influenza, and we aimed to establish an explainable machine learning (ML) model for predicting mortality in critically ill influenza patients using a real-world severe influenza data set. Study design A cross-sectional retrospective multicentre study in Taiwan Setting Eight medical centres in Taiwan. Participants A total of 336 patients requiring ICU-admission for virology-proven influenza at eight hospitals during an influenza epidemic between October 2015 and March 2016. Primary and secondary outcome measures We employed extreme gradient boosting (XGBoost) to establish the prediction model, compared the performance with logistic regression (LR) and random forest (RF), demonstrated the feature importance categorised by clinical domains, and used SHapley Additive exPlanations (SHAP) for visualised interpretation. Results The data set contained 76 features of the 336 patients with severe influenza. The severity was apparently high, as shown by the high Acute Physiology and Chronic Health Evaluation II score (22, 17 to 29) and pneumonia severity index score (118, 88 to 151). XGBoost model (area under the curve (AUC): 0.842; 95% CI 0.749 to 0.928) outperformed RF (AUC: 0.809; 95% CI 0.629 to 0.891) and LR (AUC: 0.701; 95% CI 0.573 to 0.825) for predicting 30-day mortality. To give clinicians an intuitive understanding of feature exploitation, we stratified features by the clinical domain. The cumulative feature importance in the fluid balance domain, ventilation domain, laboratory data domain, demographic and symptom domain, management domain and severity score domain was 0.253, 0.113, 0.177, 0.140, 0.152 and 0.165, respectively. We further used SHAP plots to illustrate associations between features and 30-day mortality in critically ill influenza patients. Conclusions We used a real-world data set and applied an ML approach, mainly XGBoost, to establish a practical and explainable mortality prediction model in critically ill influenza patients.

Sequential patterns in customer transactional databases are commonly mined for E-Commerce recommendations. In many practical applications, the absence of certain item-sets and sequences could have important implications. Mining frequent sequences comprising not only the occurrence but also the absence of certain sequences will increase the accuracy of product recommendations. A sequential pattern containing at least one absent item set is called a negative sequential pattern. In this paper, we formulate the problem of negative sequential pattern mining by introducing practical constraints and propose an algorithm called PNSP for the mining. The discovered patterns can then be more interesting and effective to use. The experimental results show that PNSP may discover negative sequential patterns for practical E-commerce applications.

The importance of the data stored in the smart phones is increased as more applications are deployed and executed. Once the smart phone is damaged or lost, the valuable information treasured in the device is lost altogether. If cloud storage can be integrated with cloud services for periodical data backup of a mobile client, the risk of data lost can be minimized. However, the important data might be uncovered by a malicious third party during retrieval or transmission of information using wireless cloud storage without proper authentication and protection. Therefore, in this paper, we design an archive mechanism that integrates cloud storage, hybrid cryptography, and digital signatures to provide security requirements for data storage of mobile phones. Our mechanism not only can avoid malicious attackers from illegal access but also can share desired information with targeted friends by distinct access rights.