As discussed in Section 2.1 regarding clinical text embedding, there are some challenges in achieving accurate representation. Firstly, EHR is longitudinal and high-dimensional which poses challenges for AI models to learn statistical properties from complex data. Secondly, the medical field involves concepts of varying granularity, making it challenging to align medical concepts across different data modalities with heterogeneous levels of detail. For instance, a patient exhibiting pleuropericardial adhesion in their EHRs may be eligible for a clinical trial focused on broader cardiovascular conditions . Thirdly, detailed information such as numerical values or units is often overlooked in existing work. Information like ages, values of lab results and medication dosage could significantly impact the results. Therefore, building accurate feature requires not only professional data processing but also a well-designed network for information extraction. Fourthly, some eligibility criteria may have temporal aspects, such as a patient's medical history over a certain time frame. Modeling these temporal dependencies requires specialized techniques.