Editing Cohort study (section)

===AI for cohort study===
Conventionally, cohort studies require manual definitions of the common characteristics, which are time-consuming and labor-intensive, demanding extensive domain expertise. To address these limitations, researchers<ref>{{Cite journal|year=2024|title=CohortNet: Empowering Cohort Discovery for Interpretable Healthcare Analytics.|journal=Proc. VLDB Endow.|volume=17|issue=10|pages=22487–2500 |last1=Cai |first1=Qingpeng |last2=Zheng |first2=Kaiping |last3=Jagadish |first3=H. V. |last4=Ooi |first4=Beng Chin |last5=Yip |first5=James |doi=10.14778/3675034.3675041 |arxiv=2406.14015 }}.</ref> have increasingly explored the integration of AI technologies (e.g., CohortNet.<ref>{{cite web|title=GitHub - CohortNet|website=[[GitHub]] |url=https://github.com/KimballCai/CohortNet |access-date=2024-01-30}}</ref> COOL<ref>{{cite web|title=Introduction to COOL |url=https://www.comp.nus.edu.sg/~dbsystem/cool/ |access-date=2023-06-21}}</ref>) to automate the identification of cohorts with their definitions. For example, in healthcare, we can identify patients with a certain combination of feature conditions as a specific cohort, typically resulting in a similar outcome or end-point. Once the cohort is identified, we further learn the commonalities among the associated patients and obtain meaningful cohort representations. These AI-derived cohorts not only enhance the ability to evaluate new patients but also hold significant potential to accelerate medical research and discovery.