Representation and classification techniques for clinical data focused on obesity and its comorbidities

In this paper, we describe the three representation and classification techniques used in our submission for the I2B2 Shared-Task – Challenges in Natural Language Processing and Clinical Data. Our methods combine a simple bag-of-words representation, a UMLS (Unified Medical Language System) concept representation, and a noun-phrase representation with the AdaBoost classification algorithm. The results obtained for the two tracks of the competition, the textual and the intuitive track, show that the bag-of-words representation obtained better results than the other two. Introduction The I2B2 Shared-Task, Challenges in Natural Language Processing for Clinical Data is focused on obesity and its co-morbidities. The data released for the competition consists of discharge summaries of patients annotated with obesity and related diseases. Each patient record went under a process of deidentification. The procedure consisted in an automatic and two manual passes for every record. The disagreements were solved using a third manual annotator. The doctor and patient names were replaced by random names extracted from the US Census Bureau 1 names dictionary. The phone numbers, ID numbers, and ages were replaced by randomly drawn digits for each of the numbers. The dates were replaced by surrogate ones. The new locations and hospital names were obtained by permuting syllables of the original ones. Valid cities, state names, and zip codes were added to the new generated locations. The records released as data for the competition were document-level annotated with obesity information and its co-morbidities. Each document is a longitudinal record of a patient; the information that it contains is collected over time. The goal of the challenge is the development of systems that accurately identify obese patients and the co-morbidities correlated with the disease. 1 http://www.census. gov/ Task Description The task was focused on a data set of clinical patient records obtained as a result of a query-based retrieval on the stem “obes” extracted from the Research Patient Data Repository (RPDR). Two types of annotation were supplied for each record: textual annotation – judgments made only on texts; and intuitive annotation – judgments made on implicit information in the narrative text (e.g., computing the BMI (Body Mass Index) is considered part of an intuitive annotation process). Two doctors annotated in parallel the data, and the disagreements were resolved by the help of a third expert. For the textual judgments the possible classes are: "Y" that stands for "Yes, the patient has the comorbidity", "N" that stands for "No, the patient does not have the co-morbidity", "Q" that stands for "Questionable whether the patient has the comorbidity", "U" that stands for "the co-morbidity is not mentioned in the record". For the intuitive annotations, only the "Y", "N", and "Q" class labels are used, "U" is irrelevant as an intuitive judgment. The annotations made the challenge a multi-class, multi-label classification task focused on obesity and its co-morbidities. 15 most representative comorbidities of obesity were considered in this task. The challenge consisted in two tracks, one for the intuitive annotations, and one for the textual annotations. The participating teams had the choice to submit up to three runs for each of the annotations or for both. The following two tables present the training data sets for each type of annotation, textual and intuitive, for all 16 diseases, obesity with its 15 co-morbidities. The test data consisted in an additional 40% of annotated records. The evaluation metrics used in the challenge are: precision, recall, and F-measure. Due to high variations in the labels of the data, the macroaveraged F-measure is considered the primary evaluation measure, followed by the micro-averaged F-measure.