{"id":166775,"date":"2012-04-01T00:00:00","date_gmt":"2012-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/pneumonia-identification-using-statistical-feature-selection\/"},"modified":"2018-10-16T21:13:43","modified_gmt":"2018-10-17T04:13:43","slug":"pneumonia-identification-using-statistical-feature-selection","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/pneumonia-identification-using-statistical-feature-selection\/","title":{"rendered":"Pneumonia identification using statistical feature selection"},"content":{"rendered":"
\n

Objective
\nThis paper describes a natural language processing system for the task of pneumonia identification. Based on the information extracted from the narrative reports associated with a patient, the task is to identify whether or not the patient is positive for pneumonia.<\/p>\n

Design
\nA binary classifier was employed to identify pneumonia from a dataset of multiple types of clinical notes created for 426 patients during their stay in the intensive care unit. For this purpose, three types of features were considered: (1) word n-grams, (2) Unified Medical Language System (UMLS) concepts, and (3) assertion values associated with pneumonia expressions. System performance was greatly increased by a feature selection approach which uses statistical significance testing to rank features based on their association with the two categories of pneumonia identification.<\/p>\n

Results
\nBesides testing our system on the entire cohort of 426 patients (unrestricted dataset), we also used a smaller subset of 236 patients (restricted dataset). The performance of the system was compared with the results of a baseline previously proposed for these two datasets. The best results achieved by the system (85.71 and 81.67 F1-measure) are significantly better than the baseline results (50.70 and 49.10 F1-measure) on the restricted and unrestricted datasets, respectively.<\/p>\n

Conclusion
\nUsing a statistical feature selection approach that allows the feature extractor to consider only the most informative features from the feature space significantly improves the performance over a baseline that uses all the features from the same feature space. Extracting the assertion value for pneumonia expressions further improves the system performance.<\/p>\n<\/div>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Objective This paper describes a natural language processing system for the task of pneumonia identification. Based on the information extracted from the narrative reports associated with a patient, the task is to identify whether or not the patient is positive for pneumonia. 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