Abstract: This paper advances the state of the art in text understanding of medical guidelines by releasing two new annotated clinical guidelines datasets, and establishing baselines for using machine learning to extract condition-action pairs. In contrast to prior work that relies on manually created rules, we report experiment with several supervised machine learning techniques to classify sentences as to whether they express conditions and actions. We show the limitations and possible extensions of this work on text mining of medical guidelines.

Introduction: Clinical decision-support system (CDSS) is any computer system intended to provide decision support for healthcare professionals, and using clinical data or knowledge BIBREF0 . The classic problem of diagnosis is only one of the clinical decision problems. Deciding which questions to ask, tests to order, procedures to perform, treatment to indicate, or which alternative medical care to try, are other examples of clinical decisions. CDSSs generally fall into two categories BIBREF0  Most of the questions physicians need to consult about with CDSSs are from the latter category. Medical guidelines (also known as clinical guidelines, clinical protocols or clinical practice guidelines) are most useful at the point of care and answering to "what to do" questions. Medical guidelines are systematically developed statements to assist with practitioners' and patients' decisions. They establish criteria regarding diagnosis, management, and treatment in specific areas of healthcare. For example, a sentence such as "if the A1C is 7.0% and a repeat result is 6.8%, the diagnosis of diabetes is confirmed" in medical guidelines determines what is true about a patient. Sentences such as "Topical and oral decongestants and antihistamines should be avoided in patients with ABRS" guide what to do or not to do with a patient. These examples illustrate conditions, criteria applicable to patients, and consequences of the conditions. The consequences may refer to activities, effects, intentions, or events. If a guideline-based CDSS needs to answer "what to do" questions, it has to have access to condition-action statements describing under what circumstances an action can be performed. Medical guidelines contain many condition-action statements. Condition-action statements provide information about expected process flow. If a guideline-based CDSS could extract and formalize these statements, it could help practitioners in the decision-making process. For example, it could help automatically asses the relationship between therapies, guidelines and outcomes, and in particular could help the impact of changing guidelines. However, completely automated extraction of condition-action statements does not seem possible. This is due among other things to the variety of linguistic expressions used in condition-action sentences. For example, they are not always in form of "{if} condition {then} action”. In the sentence "Conditions that affect erythrocyte turnover and hemoglobin variants must be considered, particularly when the A1C result does not correlate with the patient's clinical situation”, we have a condition-action sentence without an "{if}" term. We propose a supervised machine learning model classifying sentences as to whether they express a condition or not. After we determine a sentence contain a condition, we use natural language processing and information extraction tools to extract conditions and resulting activities. With the help of a domain expert, we annotated three sets of guidelines to create gold standards to measure the performance of our condition-action extracting models. The sets of guidelines are: hypertension BIBREF1 , chapter4 of asthma BIBREF2 , and rhinosinusitis BIBREF3 . Chapter 4 of asthma guidelines was selected for comparison with prior work of Wenzina and Kaiser BIBREF4 . We have annotated the guidelines for the conditions, consequences, modifiers of conditions, and type of consequences. These annotate sets of guidelines are available for experiments https://www.dropbox.com/.

Question: How large is the released data set?
Answer: 316 sentences in Hypertension corpus, 877 sentences in Rhinosinusitis corpus