Antonia Quinn, Jason Lazar, Riccardo Bianchi
Development of a Transition to Residency Course Using a Design Thinking Framework
Academic medicine : journal of the Association of American Medical Colleges
icd10 widely used statistical analysis mortality rates medical reimbursement automatic icd10 coding desperately needed manually assigning codes expensive timeconsuming laborintensive diagnoses described medical records differ significantly used icd10 classification making impossible existing automatic coding techniques perform well enough support medical billing resource allocation research requirements meanwhile current automatic coding approaches oriented toward english icd10 method automatically assigning icd10 codes diagnoses extracted chinese discharge records provided paper first bert creates word representations two texts second context representation layer incorporates contextual information representation time step word representations using bidirectional long shortterm memory third matching layer compares contextual embedding uncoded diagnosis record weighted version contextual character embeddings manually coded diagnosis record matching strategy elementwise subtraction elementwise multiplication neural network layer fourth matching vectors combined using onelayer convolutional neural network sigmoid used output matching results evaluate proposed method 1003558 manually coded primary diagnoses gathered homepage discharge medical records experimental results showed proposed method outperformed popular deep semantic matching algorithms dssm convnet esim abcnn demonstrated stateoftheart results single text matching accuracy 0986 precision 0979 recall 0983 f1score 0981 automatic icd10 coding chinese diagnoses successful using proposed deep semantic matching approach based analogical reasoning