Traditional Approaches Sample Clauses

Traditional Approaches. For large datasets, manual extraction and classification are tedious, time-consuming and error-prone. Automated tools, on the other hand, are typically tuned for particular domains, and precisely annotated training datasets need to be developed and learned to establish decision models through which subsequent reports can be processed. Once trained, the decision models of extraction and classification are difficult or impossible to modify and improve. Therefore, these approaches lack the capability of taking user feedbacks, to adapt and improve the extraction or classification algorithm in real time. In addition, automatically extracted data suffers from inaccuracies as a result of natural limits on statistical machine learning techniques. For healthcare research that requires completely accurate data, the above discussion points to one constant: human involvement is necessary, whether to perform the actual extraction or for post-extraction verification. In this thesis, we consider an online machine learning approach.