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. In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drug-event pair in texts, and then, we use patterns for characterizing ADR relation. The multiple-instance learning with expectation-maximization method is employed to estimate model parameters. The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time. By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data. Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively.

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. METHODS: A large amount of articles on ADRs in the last twenty years is collected. These articles are grouped by recent data types used to study ADRs: omics, social media and electronic medical records (EMRs), and reviewed in terms of the problem addressed, the datasets used and methods. RESULTS: Corresponding three tables are established providing brief information on the research for ADRs detection and prediction. CONCLUSION: The data-driven approach has shown to be powerful in ADRs detection and prediction. The review helps researchers and pharmacists to have a quick overview on the current status of ADRs detection and prediction.