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使用机器学习在现实世界数据集上融合瘤记录

Clarissa Krämer^1,2, Susanne Schmitt^1,2, Franz Rothlauf¹

¹Johannes Gutenberg University, Mainz, Germany.

Studies in health technology and informatics

|May 17, 2025

概括

人工神经网络 (ANN) 通过比基于规则的方法更有效地合并重复的瘤记录来改善癌症数据融合. 这提高了有价值的癌症登记数据的可用性.

科学领域:

在瘤学瘤学.
数据科学数据科学数据科学
生物信息学是一种生物信息学.

背景情况:

癌症登记册收集了同一个瘤的众多报告,导致数据冗余和不一致.
这些数据质量问题阻碍了对关键癌症信息的有效利用.

研究的目的:

为了比较人工神经网络 (ANN) 与癌症数据融合的确定性基于规则的方法的有效性.
加强将多个瘤记录整合到一个单一的,准确的表示中.

主要方法:

利用来自德国莱兰-柏林州癌症登记处的现实世界表格数据集.
应用了人工神经网络 (ANN) 和基于规则的确定性数据融合方法.
使用F1宏分数对结直肠癌,乳腺癌和前列腺癌数据进行绩效评估.

主要成果:

人工神经网络与基于规则的确定性方法相比,表现优越.
数据融合性能受到特征数量和数据分布的影响.
观察到宏观F1得分增加,每个变量类别较少,数据集更平衡.

结论:

与传统的基于规则的系统相比,ANN为癌症注册表中的数据融合提供了更有效的解决方案.
优化特征特征和数据集平衡可以进一步提高数据融合准确性.
增强的数据融合促进了更可靠的癌症数据分析和应用.

关键词:

数据融合数据融合癌症登记册癌症登记册电子健康记录电子健康记录机器学习是机器学习.医疗记录医疗记录合并多个记录.神经网络的神经网络的神经网络

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