Doctor of Philosophy

dissertationElectronic Health Records (EHRs) provide a wealth of information for secondary uses. Methods are developed to improve usefulness of free text query and text processing and demonstrate advantages to using these methods for clinical research, specifically cohort identification and enhancement. Cohort identification is a critical early step in clinical research. Problems may arise when too few patients are identified, or the cohort consists of a nonrepresentative sample. Methods of improving query formation through query expansion are described. Inclusion of free text search in addition to structured data search is investigated to determine the incremental improvement of adding unstructured text search over structured data search alone. Query expansion using topic- and synonym-based expansion improved information retrieval performance. An ensemble method was not successful. The addition of free text search compared to structured data search alone demonstrated increased cohort size in all cases, with dramatic increases in some. Representation of patients in subpopulations that may have been underrepresented otherwise is also shown. We demonstrate clinical impact by showing that a serious clinical condition, scleroderma renal crisis, can be predicted by adding free text search. A novel information extraction algorithm is developed and evaluated (Regular Expression Discovery for Extraction, or REDEx) for cohort enrichment. The REDEx algorithm is demonstrated to accurately extract information from free text clinical iv narratives. Temporal expressions as well as bodyweight-related measures are extracted. Additional patients and additional measurement occurrences are identified using these extracted values that were not identifiable through structured data alone. The REDEx algorithm transfers the burden of machine learning training from annotators to domain experts. We developed automated query expansion methods that greatly improve performance of keyword-based information retrieval. We also developed NLP methods for unstructured data and demonstrate that cohort size can be greatly increased, a more complete population can be identified, and important clinical conditions can be detected that are often missed otherwise. We found a much more complete representation of patients can be obtained. We also developed a novel machine learning algorithm for information extraction, REDEx, that efficiently extracts clinical values from unstructured clinical text, adding additional information and observations over what is available in structured text alone

Table-to-Text: Generating Descriptive Text for Scientific Tables from Randomized Controlled Trials

Unprecedented amounts of data have been generated in the biomedical domain, and the bottleneck for biomedical research has shifted from data generation to data management, interpretation, and communication. Therefore, it is highly desirable to develop systems to assist in text generation from biomedical data, which will greatly improve the dissemination of scientific findings. However, very few studies have investigated issues of data-to-text generation in the biomedical domain. Here I present a systematic study for generating descriptive text from tables in randomized clinical trials (RCT) articles, which includes: (1) an information model for representing RCT tables; (2) annotated corpora containing pairs of RCT table and descriptive text, and labeled structural and semantic information of RCT tables; (3) methods for recognizing structural and semantic information of RCT tables; (4) methods for generating text from RCT tables, evaluated by a user study on three aspects: relevance, grammatical quality, and matching. The proposed hybrid text generation method achieved a low bilingual evaluation understudy (BLEU) score of 5.69; but human review achieved scores of 9.3, 9.9 and 9.3 for relevance, grammatical quality and matching, respectively, which are comparable to review of original human-written text. To the best of our knowledge, this is the first study to generate text from scientific tables in the biomedical domain. The proposed information model, labeled corpora and developed methods for recognizing tables and generating descriptive text could also facilitate other biomedical and informatics research and applications

Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining

[EN] Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients¿ dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients¿ unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.This research was partially funded by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement no. 727560.Valero Ramon, Z.; Fernández Llatas, C.; Valdivieso, B.; Traver Salcedo, V. (2020). Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining. Sensors. 20(18):1-25. https://doi.org/10.3390/s20185330S1252018Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. 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Evaluation of Various Inspiratory Times and Inflation Pressures During Airway Pressure Release Ventilation

There are few recommendations on how best to apply certain modes of mechanical ventilation. The application of Airway Pressure Release Ventilation (APRV) includes strategic implementation of specific inspiratory times (I-times) and particular mean airway pressures (MAWP) neither of which is standardized. This study utilized a retrospective analysis of archived electronic health record data to evaluate the clinical outcomes of adult patients that had been placed on APRV for at least 8 hours. 68 adult subjects were evaluated as part of a convenient purposive sample. All outcomes of interest (surrogates) for short-term clinical outcomes to include the PaO2/FiO2 (P/F) ratio, Oxygen Index and Oxygen Saturation Index (OI; OSI), and Modified Sequential Organ Failure Assessment (MSOFA) scores showed improvement after at least 8 hours on APRV. Most notably, there was significant improvement in P/F ratio (p = .012) and OSI (p = .000). Results of regression analysis showed P low as a statistically significant negative predictor of pre-APRV P/F ratio with a higher initial P low coinciding with a lower P/F ratio. The regression analysis also showed MAWP as a significant positive predictor of post-APRV OSI and P high and P low as significant negative predictors of post-APRV MSOFA scores. In summary, it was found that settings for P high, Plow, and T low in addition to overall MAWP and Body Mass Index (BMI) had significant correlation to impact at least one of the short-term clinical outcomes measured

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