EXPLOITING HIDDEN STRUCTURES IN EPIDEMIOLOGICAL DATA

提高分类回归建模的准确性和范围 性能将是非常宝贵的酒精相关的研究和 整个医疗保健社区。 流行病学模型提供了更好的 统计显著性和预测性能增强了 研究人员识别酒精相关症状新模式的能力 以及改善对医疗和精神状况的分析， 现有数据库。 理论研究和经验证据， 对酒精相关数据库的分类数据分析表明， 可以通过优化来实现建模结果的实质性改进 选定预测因子的数据表示（重新编码）方案 模型中的（解释性）变量。 Martingale Research将开发一个 利用模式组合技术的高级数据记录算法 识别、随机优化和遗传算法， 预测变量（连续的、分类的）和 以原则性的方式分类结果变量。 本研究开发 一个自动重新编码优化算法，并演示了 算法使用代表先前存在的NIAAA赞助的数据库 数据库。 该项目将表明，一种先进的重新编码算法 提高了大类分类数据的可靠性和有效性 回归模型 这些结果将提供必要的第一步 对于第二阶段数据集重新编码的其他研究， 开发商业可用数据分析的基础 软件包。 建议的商业应用：Martingale Research Corporation打算 将数据库重新编码算法开发成商业软件 包. 这些算法旨在提高整体性能 分类模型，旨在解释数据经常遇到的， 医疗保健领域。 这种方法同样适用于其他行业 利用分类模型进行财务预测， 分析、信息解释和管理。

Improving the accuracy and scope of categorical regression modeling performance would be invaluable to alcohol-related research and the overall health care community. Epidemiological models that provide better statistical significance and predictive performance enhance the researcher's ability to identify new patterns of alcohol-related symptoms as well as improving analysis of medical and psychiatric conditions in existing databases. Theoretical research and empirical evidence based on categorical data analyses for alcohol-related databases shows that substantial improvement in modeling results can be achieved by optimizing the data representation (recoding) scheme for selected predictor (explanatory) variables in a model. Martingale Research will develop an advanced data recoding algorithm utilizing techniques combining pattern recognition, stochastic optimization, and genetic algorithms to exploit structural relationships between predictor (continuous, categorical) and categorical outcome variables in a principled manner. This study develops an automated recoding optimization algorithm and demonstrates the algorithm using a database representative of pre-existing NIAAA sponsored databases. The project will show that an advanced recoding algorithm improves reliability and validity for a large class of categorical data regression models. These results will provide the essential first steps for additional investigations of dataset recoding for Phase II and form the foundation for developing a commercially available data analysis software package. PROPOSED COMMERCIAL APPLICATION: Martingale Research Corporation intends to develop database recoding algorithms into a commercial software package. These algorithms are intended to improve the overall performance of categorical models designed to explain data frequently encountered in the health care field. This approach applies as well to other industries that utilize categorical modeling to do financial prediction, risk analysis, and information interpretation and management.