TY - JOUR
AU1 - Weng, Wei-Hung
AU2 - Wagholikar, Kavishwar
AU3 - McCray, Alexa
AU4 - Szolovits, Peter
AU5 - Chueh, Henry
AB - Background: The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constructed a machine learning-based natural language processing (NLP) pipeline and developed medical subdomain classifiers based on the content of the note. Methods: We constructed the pipeline using the clinical NLP system, clinical Text Analysis and Knowledge Extraction System (cTAKES), the Unified Medical Language System (UMLS) Metathesaurus, Semantic Network, and learning algorithms to extract features from two datasets — clinical notes from Integrating Data for Analysis, Anonymization, and Sharing (iDASH) data repository (n = 431) and Massachusetts General Hospital (MGH) (n = 91,237), and built medical subdomain classifiers with different combinations of data representation methods and supervised learning algorithms. We evaluated the performance of classifiers and their portability across the two datasets. Results: The convolutional recurrent neural network with neural word embeddings trained-medical subdomain classifier yielded the best performance measurement on iDASH and MGH datasets with area under receiver operating characteristic curve (AUC) of 0.975 and 0.991, and F1 scores of 0.845 and 0.870, respectively. Considering better clinical interpretability, linear support vector machine-trained medical subdomain classifier 
TI - Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach
JF - BMC Medical Informatics and Decision Making
DO - 10.1186/s12911-017-0556-8
DA - 2017-12-01
UR - https://www.deepdyve.com/lp/springer-journals/medical-subdomain-classification-of-clinical-notes-using-a-machine-910GF1Peap
SP - 1
EP - 13
VL - 17
IS - 1
DP - DeepDyve
ER -