Corrigendum to "Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records".

[This corrects the article DOI: 10.1155/2017/7575280.].

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records.

Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts. Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language.

Data-driven Approach to Detect and Predict Adverse Drug Reactions.

Background: Many factors that directly or indirectly cause adverse drug reaction (ADRs) varying from pharmacological, immunological and genetic factors to ethnic, age, gender, social factors as well as drug and disease related ones. On the other hand, advanced methods of statistics, machine learning and data mining allow the users to more effectively analyze the data for descriptive and predictive purposes. The fast changes in this field make it difficult to follow the research progress and context on ADR detection and prediction.