Abstract：
We developed a scoring system for assessing glaucoma risk using demographic and laboratory factors by employing a no-code approach (automated coding) using ChatGPT-4. Comprehensive health checkup data were collected from the Korea National Health and Nutrition Examination Survey. Using ChatGPT-4, logistic regression was conducted to predict glaucoma without coding or manual numerical processes, and the scoring system was developed based on the odds ratios (ORs). ChatGPT-4 also facilitated the no-code creation of an easy-to-use risk calculator for glaucoma. The ORs for the high-risk groups were calculated to measure performance. ChatGPT-4 automatically developed a scoring system based on demographic and laboratory factors, and successfully implemented a risk calculator tool. The predictive ability of the scoring system was comparable to that of traditional machine learning approaches. For high-risk groups with 1-2, 3-4, and 5 + points, the calculated ORs for glaucoma were 1.87, 2.72, and 15.36 in the validation set, respectively, compared with the group with 0 or fewer points. This study presented a novel no-code approach for developing a glaucoma risk assessment tool using ChatGPT-4, highlighting its potential for democratizing advanced predictive analytics, making them readily available for clinical use in glaucoma detection.
摘要：
我们通过使用ChatGPT-4采用无代码方法（自动编码），开发了一种使用人口统计学和实验室因素评估青光眼风险的评分系统。从韩国国家健康和营养检查调查中收集了全面的健康检查数据。使用ChatGPT-4，进行逻辑回归来预测青光眼，而无需编码或手动数值过程。评分系统是基于比值比(ORs)开发的。ChatGPT-4还促进了无代码创建易于使用的青光眼风险计算器。计算高危人群的OR来衡量绩效。ChatGPT-4自动开发了基于人口统计和实验室因素的评分系统，并成功实施了风险计算器工具。评分系统的预测能力与传统机器学习方法相当。对于1-2分、3-4分和5分以上的高危人群，在验证集中，青光眼的计算OR分别为1.87、2.72和15.36，分别,与0分或更少的组相比。这项研究提出了一种使用ChatGPT-4开发青光眼风险评估工具的新型无代码方法，强调了其使高级预测分析民主化的潜力。使它们容易用于青光眼检测的临床使用。
