Drug Repositioning Using Temporal Trajectories of Accompanying Comorbidities in Diabetes Mellitus.

BACKGROUND: Most studies of systematic drug repositioning have used drug-oriented data such as chemical structures, gene expression patterns, and adverse effect profiles. As it is often difficult to prove repositioning candidates' effectiveness in real-world clinical settings, we used patient-centered real-world data for screening repositioning candidate drugs for multiple diseases simultaneously, especially for diabetic complications. METHODS: Using the National Health Insurance Service-National Sample Cohort (2002 to 2013), we analyzed claims data of 43,048 patients with type 2 diabetes mellitus (age ≥40 years). To find repositioning candidate disease-drug pairs, a nested case-control study was used for 29 pairs of diabetic complications and the drugs that met our criteria. To validate this study design, we conducted an external validation for a selected candidate pair using electronic health records. RESULTS: We found 24 repositioning candidate disease-drug pairs. In the external validation study for the candidate pair cerebral infarction and glycopyrrolate, we found that glycopyrrolate was associated with decreased risk of cerebral infarction (hazard ratio, 0.10; 95% confidence interval, 0.02 to 0.44). CONCLUSION: To reduce risks of diabetic complications, it would be possible to consider these candidate drugs instead of other drugs, given the same indications. Moreover, this methodology could be applied to diseases other than diabetes to discover their repositioning candidates, thereby offering a new approach to drug repositioning.

Machine Learning Approach for Active Vaccine Safety Monitoring.

BACKGROUND: Vaccine safety surveillance is important because it is related to vaccine hesitancy, which affects vaccination rate. To increase confidence in vaccination, the active monitoring of vaccine adverse events is important. For effective active surveillance, we developed and verified a machine learning-based active surveillance system using national claim data. METHODS: We used two databases, one from the Korea Disease Control and Prevention Agency, which contains flu vaccination records for the elderly, and another from the National Health Insurance Service, which contains the claim data of vaccinated people. We developed a case-crossover design based machine learning model to predict the health outcome of interest events (anaphylaxis and agranulocytosis) using a random forest. Feature importance values were evaluated to determine candidate associations with each outcome. We investigated the relationship of the features to each event via a literature review, comparison with the Side Effect Resource, and using the Local Interpretable Model-agnostic Explanation method. RESULTS: The trained model predicted each health outcome of interest with a high accuracy (approximately 70%). We found literature supporting our results, and most of the important drug-related features were listed in the Side Effect Resource database as inducing the health outcome of interest. For anaphylaxis, flu vaccination ranked high in our feature importance analysis and had a positive association in Local Interpretable Model-Agnostic Explanation analysis. Although the feature importance of vaccination was lower for agranulocytosis, it also had a positive relationship in the Local Interpretable Model-Agnostic Explanation analysis. CONCLUSION: We developed a machine learning-based active surveillance system for detecting possible factors that can induce adverse events using health claim and vaccination databases. The results of the study demonstrated a potentially useful application of two linked national health record databases. Our model can contribute to the establishment of a system for conducting active surveillance on vaccination.

Temporal trajectories of accompanying comorbidities in patients with type 2 diabetes: a Korean nationwide observational study.

Type 2 diabetes mellitus is a major concern globally and well known for increasing risk of complications. However, diabetes complications often remain undiagnosed and untreated in a large number of high-risk patients. In this study based on claims data collected in South Korea, we aimed to explore the diagnostic progression and sex- and age-related differences among patients with type 2 diabetes using time-considered patterns of the incidence of comorbidities that evolved after a diagnosis of type 2 diabetes. This study compared 164,593 patients who met the full criteria for type 2 diabetes with age group-, sex-, encounter type-, and diagnosis date-matched controls who had not been diagnosed with type 2 diabetes. We identified 76,423 significant trajectories of four diagnoses from the dataset. The top 30 trajectories with the highest average relative risks comprised microvascular, macrovascular, and miscellaneous complications. Compared with the trajectories of male groups, those of female groups included relatively fewer second-order nodes and contained hubs. Moreover, the trajectories of male groups contained diagnoses belonging to various categories. Our trajectories provide additional information about sex- and age-related differences in the risks of complications and identifying sequential relationships between type 2 diabetes and potentially complications.

Correction: Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals.

[This corrects the article DOI: 10.1371/journal.pone.0207749.].

Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals.

BACKGROUND: The importance of identifying and evaluating adverse drug reactions (ADRs) has been widely recognized. Many studies have developed algorithms for ADR signal detection using electronic health record (EHR) data. In this study, we propose a machine learning (ML) model that enables accurate ADR signal detection by integrating features from existing algorithms based on inpatient EHR laboratory results. MATERIALS AND METHODS: To construct an ADR reference dataset, we extracted known drug-laboratory event pairs represented by a laboratory test from the EU-SPC and SIDER databases. All possible drug-laboratory event pairs, except known ones, are considered unknown. To detect a known drug-laboratory event pair, three existing algorithms-CERT, CLEAR, and PACE-were applied to 21-year inpatient EHR data. We also constructed ML models (based on random forest, L1 regularized logistic regression, support vector machine, and a neural network) that use the intermediate products of the CERT, CLEAR, and PACE algorithms as inputs and determine whether a drug-laboratory event pair is associated. For performance comparison, we evaluated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-measure, and area under receiver operating characteristic (AUROC). RESULTS: All measures of ML models outperformed those of existing algorithms with sensitivity of 0.593-0.793, specificity of 0.619-0.796, NPV of 0.645-0.727, PPV of 0.680-0.777, F1-measure of 0.629-0.709, and AUROC of 0.737-0.816. Features related to change or distribution of shape were considered important for detecting ADR signals. CONCLUSIONS: Improved performance of ML models indicated that applying our model to EHR data is feasible and promising for detecting more accurate and comprehensive ADR signals.