症状图:从叙述性临床笔记中识别症状集群,使用图形集群
Fattah Muhammad Tahabi1, Susan Storey2, Xiao Luo3
1Department of ECE, IUPUI.
概括
新的计算系统SymptomGraph使用自然语言处理和人工智能从临床笔记中识别患者的症状集群. 它揭示了结直肠癌患者化疗后的明显症状模式,包括糖尿病患者.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 医疗保健中的人工智能
背景情况:
- 患有慢性疾病的患者会经历各种症状,影响生活质量.
- 传统的症状分析依赖于调查和统计方法,在临床叙述中经常缺少细微差别.
- 电子健康记录 (EHR) 包含有关患者症状的丰富,非结构化数据.
研究的目的:
- 开发和验证SymptomGraph,一个用于从临床笔记中识别症状集群的计算系统.
- 为了分析结直肠癌患者化学疗后的症状模式.
- 根据并发症 (如糖尿病) 和患者人口统计学来探索症状变异.
主要方法:
- 利用自然语言处理 (NLP) 和人工智能 (AI) 来从EHR临床笔记中提取症状.
- 采用语义症状表达集群来识别典型症状.
- 根据症状的同时出现构建了一个症状图表,并应用了图表集群算法.
主要成果:
- 在化疗后一年的结肠直肠癌患者中,SymptomGraph成功地确定了不同的症状集群.
- 患有糖尿病的结肠直肠癌患者表现出增加的外围神经病变症状.
- 晚期癌症患者表现出更多的记忆丧失,而年轻患者则患有与药物滥用有关的精神功能障碍.
结论:
- 症状图是从叙述性临床数据中发现复杂症状集群的有效工具.
- 该系统可以揭示疾病特异性和患者特异性症状概况,帮助个性化医疗.
- 症状图 (SymptomGraph) 对分析各种急性和慢性疾病的症状数据具有广泛的适用性.
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