癌症或其他慢性疾病患者在治疗前后常常会出现不同的症状。这些症状可以是身体、胃肠道、心理或认知（记忆丧失）等不同类型。以往的研究集中于通过症状调查收集数据，并使用传统的统计方法（如主成分分析或因子分析）来分析症状以及症状的相关性。本研究提出了一个计算系统，名为SymptomGraph，用于在电子健康记录（EHR）的临床笔记中识别症状聚类。SymptomGraph开发了一套自然语言处理（NLP）和人工智能（AI）方法，首先从临床笔记中提取医务人员记录的症状，然后使用语义症状表达聚类方法发现一组典型症状。基于症状的共现关系构建了一个症状图。最后，开发了一个图聚类算法来发现症状聚类。虽然SymptomGraph适用于叙述性临床笔记，但它可以被适应用于分析症状调查数据。我们将SymptomGraph应用于一个结直肠癌患者同时患有和不患有二型糖尿病的数据集，以在化疗一年后检测患者的症状聚类。结果显示，SymptomGraph能够识别结直肠癌患者在化疗后的典型症状聚类。结果还表明，患有糖尿病的结直肠癌患者通常表现出更多的周围神经病变症状，年轻患者有酒精或烟草滥用的心理障碍，晚期癌症患者更容易出现记忆丧失症状。我们的系统还可以应用于提取和分析其他慢性疾病或急性疾病（如COVID-19）的症状聚类。

Patients with cancer or other chronic diseases often experience different symptoms before or after treatments. The symptoms could be physical, gastrointestinal, psychological, or cognitive (memory loss), or other types. Previous research focuses on understanding the individual symptoms or symptom correlations by collecting data through symptom surveys and using traditional statistical methods to analyze the symptoms, such as principal component analysis or factor analysis. This research proposes a computational system, SymptomGraph, to identify the symptom clusters in the narrative text of written clinical notes in electronic health records (EHR). SymptomGraph is developed to use a set of natural language processing (NLP) and artificial intelligence (AI) methods to first extract the clinician-documented symptoms from clinical notes. Then, a semantic symptom expression clustering method is used to discover a set of typical symptoms. A symptom graph is built based on the co-occurrences of the symptoms. Finally, a graph clustering algorithm is developed to discover the symptom clusters. Although SymptomGraph is applied to the narrative clinical notes, it can be adapted to analyze symptom survey data. We applied Symptom-Graph on a colorectal cancer patient with and without diabetes (Type 2) data set to detect the patient symptom clusters one year after the chemotherapy. Our results show that SymptomGraph can identify the typical symptom clusters of colorectal cancer patients' post-chemotherapy. The results also show that colorectal cancer patients with diabetes often show more symptoms of peripheral neuropathy, younger patients have mental dysfunctions of alcohol or tobacco abuse, and patients at later cancer stages show more memory loss symptoms. Our system can be generalized to extract and analyze symptom clusters of other chronic diseases or acute diseases like COVID-19.