Computer-based identification of relationships between medical concepts and cluster analysis in clinical notes

Clinical notes contain information about medical concepts or entities (such as diseases, treatments and drugs) that provide a comprehensive and overall impression of the patient’s health. The automatic extraction of these entities is relevant for health experts and researchers as they identify associations between the latter. However, automatically extracting information from clinical notes is challenging, due to their narrative format. This research describes a process to automatically extract and aggregate medical entities from clinical notes, as well as the process to identify clusters of patients and disease-treatment relationships. The i2b2 2008 Obesity dataset was used, and consists of 1237 discharge summaries of overweight and diabetic patients. Therefore, this thesis is focused on obesity diseases. For the automatic extraction of medical entities, MetaMap and cTAKES were used, and the automatic extraction capacity of both tools compared. Also, UMLS enabled the aggregation of the extracted entities. Two approaches were applied for cluster analysis. Firstly, a sparse K-means algorithm was used over a patient-disease matrix with 14 comorbidities related to obesity. Secondly, to visualize and analyze other diseases present in the clinical notes, 86 diseases were used to identify clusters of patients with a network-based approach. Furthermore, bipartite graphs were used to explore disease-treatment relationships among some of the clusters obtained. The result of the experiments we conducted show cTAKES slightly outperforming MetaMap, but this situation can change, considering other configuration options in the respective tools, including an abbreviation list. Moreover, concept aggregation (with similar and different semantic types) was shown to be a good strategy for improving medical entity extraction. The sparse K-means enabled identification of three types of clusters (high, medium and low), based on the number of comorbidities and the percentage of patients suffering from them. These results show that diabetes, hypercholesterolemia, atherosclerotic cardiovascular diseases, congestive heart failure, obstructive sleep apnea, and depression were the most prevalent diseases. With the network approach, it was possible to visualize and analyze patient information. In it, three sub-graphs or clusters were identified: obese patients with metabolic problems, obese patients with infection problems, and obese patients with a mechanical problem. Bipartite graphs for a disease-treatment relationship showed treatments for different types of diseases, which means that obese patients are suffering from multiple diseases. This work shows that clinical notes are a rich source of information, and they can be used to explore, visualize, and analyze patient’s information by applying different approaches. More work is needed to explore the relationship between the different medical entities from clinical notes and from different disease datasets. Also, considering that some medical documents express events in time, this characteristic should be considered in future works to form a personalized portrait of clusters, diseases and patients

Do UK based weight management programmes cause weight loss maintenance in adults? A systematic review

The aim of this dissertation was to examine whether UK based weight management programmes promote weight loss maintenance (follow up of 12 months to assess effectiveness of intervention in weight loss) in adults through the process of a systematic review. The World Health Organisation (WHO) has described obesity as a "global epidemic". Weight management comprises two phases; weight loss and weight loss maintenance. The latter phase is the true goal for obesity and the most difficult element of weight management to achieve. However much less is know about this as compared with the weight loss phase. There is little purpose in committing time and money to reducing obesity if the weight is regained. This is counter-productive and weight loss maintenance is essential to combat the obesity epidemic. Searches were made for relevant information from a variety of scientific online databases and journals,. Seven articles met the inclusion criteria and were analysed in the review. All studies incorporated a multi-component (diet, exercise, behaviur modification) intervention approach. All control and internvetion groups reported weight loss at 12 months when compared with baseline. All groups recieved an intervention. One study reported a significant difference (P<0.05) between groups. Four studies reported on at least one component (diet, physical activity, behaviour modification) however there was not enough information to conclude whether they complied with national guidelines (NICE CG43 and SIGN 115). High attrition rates and loss to follow up are problematic for each study except one. Analysis on an intention to treat basis was common however this is problematic and there are alternative methods which may be more suitable for dealing with missing data

Comorbidity burden in axial spondyloarthritis: a cluster analysis

Objectives To examine how comorbidities cluster in axial spondyloarthritis (axSpA) and whether these clusters are associated with quality of life, global health and other outcome measures. Methods We conducted a cross-sectional study of consecutive patients meeting ASAS criteria for axSpA in Liverpool, UK. Outcome measures included quality of life (EQ5D), global health and disease activity (BASDAI). We used hierarchical cluster analysis to group patients according to 38 pre-specified comorbidities. In multivariable linear models, the associations between distinct comorbidity clusters and each outcome measure were compared, using axSpA patients with no comorbidities as the reference group. Analyses were adjusted for age, gender, symptom duration, BMI, deprivation, NSAID-use and smoking. Results We studied 419 patients (69% male, mean age 46 years). 255 patients (61%) had at least one comorbidity, among whom the median number was 1 (range 1–6). Common comorbidities were hypertension (19%) and depression (16%). Of 15 clusters identified, the most prevalent clusters were hypertension-coronary heart disease and depression-anxiety. Compared with patients with no comorbidities, the fibromyalgia-irritable bowel syndrome cluster was associated with adverse patient-reported outcome measures; these patients reported 1.5-unit poorer global health (95%CI 0.01, 2.9), reduced quality of life (0.25-unit lower EQ5D; 95%CI −0.37, −0.12) and 1.8-unit higher BASDAI (95% CI 0.4, 3.3). Similar effect estimates were found for patients in the depression-anxiety cluster. Conclusion Comorbidity is common among axSpA patients. The two most common comorbidities were hypertension and depression. Patients in the depression-anxiety and fibromyalgia-IBS clusters reported poorer health and increased axSpA severity

Evaluating the role of COPD in patients with heart failure using multiple electronic health data sources

Heart failure (HF) and COPD frequently co-exist. Shared symptoms and risk factors make diagnosis and management difficult and current understanding of the relationship between the diseases is limited. I used several electronic healthcare record (EHR) data sources, from the United States (US) and the United Kingdom (UK) to evaluate the impact of COPD on outcomes in patients with HF. First, I aimed to demonstrate that comorbidity data from EHR can be used to derive meaningful clusters in patients with chronic HF, expecting COPD to be a main driver of this phenotyping endeavour. Second, I compared outcomes (hospitalisation, mortality, healthcare utilisation) in patients with COPD-HF, between left ventricular ejection fraction (LVEF) groups. Third, I pooled data from previously published studies to assess the overall effect of HF management (beta-blockers) on outcomes in COPD. In a fourth study I examined whether COPD was associated with in-hospital mortality and management of patients hospitalised for HF and assessed association with LVEF. Lastly, I investigated whether COPD affected readmission in a population of patients hospitalised for HF. This work provides evidence to suggest that while COPD may not play a major role in determining a HF classification system based on comorbidities only, it affects clinical outcomes in the long-term, particularly for chronic HFpEF patients. Conversely, HF management such as beta-blockers does not appear to worsen outcomes in COPD patients. In the acute setting, coexisting COPD is independently associated with increased in-hospital mortality and decreased HF medication prescription and access to healthcare services amongst patients who survived their first HF admission. Readmission risk is higher amongst those with HF and COPD compared with HF-alone, though the most frequent reason for returning to hospital is still due to a cardiovascular cause.Open Acces

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset