RESULTS: We introduce an interpretable and flexible solution (LungDWM) for Lung cancer subtype Diagnosis using Weakly paired Multiomics data. LungDWM first builds an attention-based encoder for each omics to pick out important diagnostic features and extract shared and complementary information across omics. Next, it proposes an individual loss to jointly extract the specific information of each omics and performs generative adversarial learning to impute missing omics of samples using extracted features. After that, it fuses the extracted and imputed features to diagnose cancer subtypes. Experiments on benchmark datasets show that LungDWM achieves a better performance than recent competitive methods, and has a high authenticity and good interpretability.

AVAILABILITY AND IMPLEMENTATION: The code is available at http://www.sdu-idea.cn/codes.php?name=LungDWM.

OBJECTIVE: This study aims to design and develop a smartphone app called OASapp to improve medication adherence among older adult stroke survivors and evaluate its usability.

METHODS: OASapp was developed in a three-phase development process. Phase 1 is the exploration phase (including a cross-sectional survey, a systematic review, a search for stroke apps on the app stores of Apple App Store and Google Play Store, and a nominal group technique). In phase 2, a prototype was designed based on the Health Belief Model and Technology Acceptance Model. In phase 3, Alpha and Beta testing was conducted to validate the app.

RESULTS: Twenty-five features for inclusion in the app were collected in round one, and 14 features remained and were ranked by the participants during nominal group technique. OASapp included five core components (medication management, risk factor management, health information, communication, and stroke map). Users of OASapp were satisfied based on reports from Alpha and Beta testing. The mean Usability Metric for User Experience (UMUX) score was 71.4 points (SD 14.6 points).