人工智能在临床试验中的匹配测验

当您参加测验时，就会出现答案和解释。

1. What task can clinical trial matching software support?
   • Compare documented patient information with trial eligibility criteria
   • Guarantee enrollment by a deadline
   • Replace all clinical judgment
   • Prove a treatment is effective before a trial
2. 为什么读取临床记录对于匹配软件来说是一个难题？
   • 记录总是结构完美
   • 许多关键信息都位于非结构化的自由文本注释中
   • 患者从不分享他们的记录
   • 所有医院都使用一种相同的格式
3. 资格“排除标准”有什么作用？
   • 保证患者被登记
   • 设置试验的资助金额
   • 列出使患者失去试验资格的条件
   • 任命该试验的首席研究员
4. 人工智能可以使用哪些标准化词汇来标准化记录中的实验室测试概念？
   • 超文本标记语言
   • JPEG
   • HTTP协议
   • 洛因克
5. Why might a team evaluate trial-matching results across patient subgroups?
   • To check whether candidate rankings or eligibility extraction differ across groups
   • To guarantee that every subgroup has equal trial access
   • To remove eligibility criteria from the protocol
   • To prove a trial has no recruitment bias
6. Which is a reasonable human-review option in a trial-matching workflow?
   • Automatically enroll every high-scoring candidate
   • Allow a qualified clinician or study team to review candidate matches before action
   • Let a similarity score override protocol criteria
   • Treat a model match as confirmation of eligibility
7. When an eligibility criterion is vague, what can embedding similarity contribute to a patient-trial match?
   • A final enrollment decision without a clinician
   • A new laboratory result not present in the chart
   • A semantic similarity signal to help rank possible matches for clinician review
   • A guarantee that the patient meets every inclusion criterion
8. In the guide's matching pipeline, what is a rule engine used for after patient and trial information are normalized?
   • Generating diagnoses that are absent from the chart
   • Replacing clinical review for every uncertain match
   • Ranking hospitals by their billing totals
   • Checking hard eligibility constraints such as age or lab thresholds