| dc.description.abstract | Abstract
Reviewing the medical diagnosis records of a health care provider allows the meticulous analysis for generating invaluable insights into logistics management, quality control, and cost effectiveness for both small clinics and large health organizations. However, the majority of physicians still use paper medical records and the existing electronic medical records such as radiology reports are scripted mainly in an unstructured format. It is a time-consuming process as the medical examiner with domain knowledge has to read entire document in order to be able to fully understand the unstructured, free-form texts, which are in no matter hand-written or digital formats. It is an impossible task to non-medical examiners since the medical reports nowadays tend to record the situation in detail with many technical notes and terms in shorthand. In addition, the ambiguous terms “rule out”, “suspected diagnosis”, or “differential diagnosis” are more commonly used to denote the conditions of the organs or the diseases. But surgical outcome data are generally reported as raw morbidity and mortality, which do not necessarily reflect the level of effectiveness of surgical treatment. A tool, which can distinguish normal from abnormal cases and identify the key sentences supporting the conclusion, can possibly reduce the time cost in reviewing medical records. Furthermore, it can also help common examiners who have not enough medical knowledge to understand the reports more precisely and correctly.
NLP, which stands for natural language processing, is a novel technology for automatic document classification. It is the work horse to transform unstructured documents into a structured format to enable numerical analysis for information extraction. Thus, our goals are two folds: 1) to use NLP for mining the messages from diagnosis reports to distinguish the report cases are normal or abnormal 2) to find which sentences may lead to the consequences. There have been plenty of state-of-art methods to practice NLP such as Naïve-Bayes, Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformer (BERT) etc.
By using BERT classifier model, the identifying the case is abnormal or not task on Medical Subject Headings (MeSH) dataset (n = 3,955 annotated reports) we got validate (n = 792 labeled manually, f1-score of Normal cases = 0.97, f1-score of Abnormal cases = 0.94) and test score (n = 395 labeled manually, f1-score of Normal case= 0.98, f1-score of Abnormal cases = 0.96). And with sentence-BERT, we can get the similarity between each sentence and whole reports. It would help finding which sentence may belong to normal or abnormal. | en_US |