Extraction Problem List from Medical Record

• Main Authors: Tsun-Yuan Cheng, 鄭存淵
• Other Authors: Jau-Min Wong
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/17496593375265584760

碩士 === 國立臺灣大學 === 醫學工程學研究所 === 97 === Problem Listing had become an essential component of current medical record. In spilt of its popularity, problem listing is still a cumbersome task. A computer aid system will be most welcome if it can automatically extract subjective problems from patients’ description and may to a limited list of assessment. In the development of such system, it is important to build a comprehensive corresponding set between these two variables of patient’s problem and possible diagnosis. We need a medical database while covering one year period of discharge notes of a teaching hospital, to develop this knowledge set. The problems extended from chief complain were map to the ontology of main diagnosis of each case. One twenty selected cases were needed to develop the knowledge set, after domain expert intervention. Those selected training data set had tested for internal and external validation, the result showed the precision and recall of internal validation around 0.7 under low ontological resolution, and around 0.6 under higher resolution. The result of external validation were 0.5409 in precision, 0.6651 in recall, F-measure of 0.5966 under low resolution and 0.3851 in precision, 0.5923 in recall, F-measure of 0.4667 under high resolution. And the result of a common unimproved nature language processing tool were 0.398 in precision, 0.775 in recall, 0.652 in F-measure and the result of expert reviewer were 0.912 in precision, 0.788 in recall, 0.845 in F-measure. The result indicated the corresponding set between patient’s problem and diagnosis in our training set is too complicated to be resolved. By a one twenty selected training set in a high resolution setting, more intensive domain expert’s inversion is necessary to have a better result.