The practice of evidence-based medicine (EBM) encourages health professionals to make informed treatment decisions based on a careful analysis... Show moreThe practice of evidence-based medicine (EBM) encourages health professionals to make informed treatment decisions based on a careful analysis of current research. However, after caring for their patients, medical practitioners have little time to spend reading even a small fraction of the rapidly growing body of medical research literature. As a result, physicians must often rely on potentially outdated knowledge acquired in medical school. Systematic reviews of the literature exist for speci c clinical questions, but these must be manually created and updated as new research is published. Abstracts from well-written clinical research papers contain key information regarding the design and results of clinical trials. Unfortunately, the free text nature of abstracts makes it di cult for computer systems to use and time consuming for humans to read. I present a software system that reads abstracts from randomized controlled trials, extracts key clinical entities, computes the e ectiveness of the proposed interventions and compiles this information into machine readable and human readable summaries. This system uses machine learning and natural language processing techniques to extract the key clinical information describing the trial and its results. It extracts the names and sizes of treatment groups, population demographics, outcome measured in the trial and outcome results for each treatment group. Using the extracted outcome measurements, the system calculates key summary measures used by physicians when evaluating the e ectiveness of treatments. It computes absolute risk reduction (ARR) and number needed to treat (NNT) values complete with con dence intervals. The extracted information and computed statistics are automatically compiled into XML and HTML summaries that describe the details and results of the trial. xiii Extracting the necessary information needed to calculate these measures is not trivial. While there have been various approaches to generating summaries of medical research, this work has mostly focused on extracting trial characteristics (e.g. population demographics, intervention/outcome information). No one has attempted to extract all of the information needed, nor has anyone attempted to solve many of the tasks needed to reliably calculate the summary statistics. PH.D in Computer Science, December 2013 Show less
