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Browsing by Subject "Symptom clusters"
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Item Examining the associations between PTSD symptom clusters and alcohol-related problems in a sample of low-SES treatment-seeking Black/African American adults(Elsevier, 2022) Fischer, Ian C.; Bennett, Melanie E.; Pietrzak, Robert H.; Kok, Brian C.; Roche, Daniel J.O.; Psychiatry, School of MedicinePosttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) often co-occur. This comorbidity negatively influences treatment outcomes, functioning, and quality of life. To better understand the relation between PTSD and AUD, research has begun to examine the influence of PTSD symptom clusters on alcohol-related problems. The current study is the first to analyze the associations between PTSD symptom clusters and alcohol consumption and AUD symptom severity in a treatment-seeking sample of Black/African American (AA) adults with co-occurring AUD and PTSD symptoms. Examination of these associations may help to facilitate greater recovery in this underserved population by identifying more precise targets for treatment. PTSD symptom clusters were identified from both the current 4-factor model identified in the DSM-5 and from a recently proposed 7-factor model. Participants were Black/AA adults (50.6% male) who endorsed trauma exposure and were seeking treatment for alcohol misuse. The majority (66%) were unemployed and almost half (45%) reported an income at or lower than $20,000. In the 4-factor model, results showed Cluster D symptoms of PTSD (i.e., negative alterations in cognitions and mood) were independently associated with alcohol consequences. Use of the 7-factor model, which divides Cluster D into symptoms of negative affect and anhedonia, further demonstrated that only anhedonic symptoms were independently associated with alcohol consequences. No symptom clusters were uniquely associated with alcohol consumption. Results suggest the absence of positive emotions, rather than the presence of negative emotions, are primarily associated with alcohol-related problems in a sample of trauma-exposed, Black/AA adults seeking treatment for alcohol misuse.Item Symptom clusters in breast cancer survivors with and without type 2 diabetes over the cancer trajectory(Elsevier, 2023-11-14) Storey, Susan; Luo, Xiao; Ren, Jie; Huang, Kun; Von Ah, Diane; School of NursingObjective: This study aimed to investigate symptoms and symptom clusters in breast cancer survivors (BCS) with and without type 2 diabetes across three crucial periods during the cancer trajectory (0-6 months, 12-18 months, and 24-30 months) post-initial chemotherapy. Methods: Eight common symptoms in both BCS and individuals with diabetes were identified through natural language processing of electronic health records from January 2007 to December 2018. Exploratory factor analysis was employed to discern symptom clusters, evaluating their stability, consistency, and clinical relevance. Results: Among the 4601 BCS in the study, 20% (n = 905) had a diabetes diagnosis. Gastrointestinal symptoms and fatigue were prevalent in both groups. While BCS in both groups exhibited an equal number of clusters, the composition of these clusters differed. Symptom clusters varied over time between BCS with and without diabetes. BCS with diabetes demonstrated less stability (repeated clusters) and consistency (same individual symptoms comprising clusters) than their counterparts without diabetes. This suggests that BCS with diabetes may experience distinct symptom clusters at pivotal points in the cancer treatment trajectory. Conclusions: Healthcare providers must be attentive to BCS with diabetes throughout the cancer trajectory, considering intensified and/or unique profiles of symptoms and symptom clusters. Interdisciplinary cancer survivorship models are essential for effective diabetes management in BCS. Implementing a comprehensive diabetes management program throughout the cancer trajectory could alleviate symptoms and symptom clusters, ultimately enhancing health outcomes and potentially reducing healthcare resource utilization.Item SymptomGraph: Identifying Symptom Clusters from Narrative Clinical Notes using Graph Clustering(ACM, 2023) Tahabi, Fattah Muhammad; Storey, Susan; Luo, Xiao; Electrical and Computer Engineering, School of Engineering and TechnologyPatients 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.