Prediction Model

The goal of this clinical trial is to develop and evaluate an Artificial Intelligence Assisted Prehabilitation Program (AI APP) for returning to work and cost-effectiveness analysis in patients with oral cancer (OC). The main questions it aims to answer are: * What kinds of needs are related to returning to work (RTW) in patients with OC from diagnosis to survival that we can incorporate into the development of AI APP to assist this population ? * How is the effect of the AI APP that based on findings from the first question for patients with OC on physical and psychological distress, fear of recurrence, self-efficacy in coping with cancer, communication, motor function, quality of life, and RTW? * How is the effect of the RTW AI prediction model to identify high-risk groups ? And how is the comprehensive cost effectiveness of benefits and quality of life of the AI APP for OC population? Researchers will compare patients without using AI APP to see if the AI APP works to assist with coping physical and psychological distress, communication, motor function, quality of life, and RTW issues for individuals with OC? Participants will: * Be asked to fulfill a structural questionnaire, or engage in a semi-structured one-by-one interview or a focus group to assess their physical, psychological, and social support needs in the first stage. * Be invited to participant the pilot testing of AI APP in the second stage. * Be provided and trained by 3-month AI APP for 3 months or cared as usual in the third stage. * Complete a structural questionnaire and follow up one year, including the baseline (before using the AI app) and at 1-2 weeks, 3 months, 6 months, 9 months, and 12 months after the baseline. * Engage in one-by-one interview or a focus group to assess user experiences of the AI APP.

Aplastic anemia (AA) is a rare haematologic disorder characterized by bone marrow failure and pancytopenia. Its occurrence during pregnancy is exceedingly rare but poses significant risks, including maternal hemorrhage, infections, and perinatal complications such as preterm birth and fetal growth restriction. Consequently, pregnancy with AA is highly perilous for both mothers and infants. Early management is critical to ensure the health of both the mother and the baby. However, there are currently no predictive tools available to assess adverse outcomes in pregnant women with AA. Our center plans to conduct a multicenter, combined retrospective and prospective cohort study.

Establish an artificial intelligence clinical decision support system for patients with carotid/vertebral artery cerebrovascular stenosis, early identification of patients who may have cerebral infarction. With the support of this project, it is expected that a secondary prevention clinical decision support system for chronic stroke will be established, which is likely to become an important auxiliary tool for the management of cerebrovascular diseases in the future.

Early prediction of acute kidney injury (AKI) may provide a crucial opportunity for AKI prevention. To date, no prediction model targeting AKI among general hospitalized patients in developing countries has been published. We developed a simple, real-time, interpretable AKI prediction model for general hospitalized patients from a large tertiary hospital in China, and validated it across five independent, geographically distinct, different tiered hospitals.

To design and validate a predictive model for malignant brain edema after endovascular thrombectomy.