Machine Learning-Based Models for Prediction of Critical Illness at Community, Paramedic, and Hospital Stages.

Overcrowding of emergency department (ED) has put a strain on national healthcare systems and adversely affected the clinical outcomes of critically ill patients. Early identification of critically ill patients prior to ED visits can help induce optimal patient flow and allocate medical resources effectively. This study aims to develop ML-based models for predicting critical illness in the community, paramedic, and hospital stages using Korean National Emergency Department Information System (NEDIS) data. Random forest and light gradient boosting machine (LightGBM) were applied to develop predictive models. The predictive model performance based on AUROC in community stage, paramedic stage, and hospital stage was estimated to be 0.870 (95% CI: 0.869-0.871), 0.897 (95% CI: 0.896-0.898), and 0.950 (95% CI: 0.949-0.950) in random forest and 0.877 (95% CI: 0.876-0.878), 0.899 (95% CI: 0.898-0.900), and 0.950 (95% CI: 0.950-0.951) in LightGBM, respectively. The ML models showed high performance in predicting critical illness using variables available at each stage, which can be helpful in guiding patients to appropriate hospitals according to their severity of illness. Furthermore, a simulation model can be developed for proper allocation of limited medical resources.

Analysis of Fixed-Dose Combination Products Approved by the US Food and Drug Administration, 2010-2015: Implications for Designing a Regulatory Shortcut to New Drug Application.

BACKGROUND: Fixed-dose combination (FDC) drugs have been an attractive product in pharmaceutical markets because of their unique advantages, but general guidance directing the clinical development of FDC drugs is not yet available in the US. METHOD: All drug approval reports of FDC products approved by the US FDA from January 2010 to December 2015 were intensively analyzed to investigate the regulatory requirements of the US FDA. RESULT: Through analyzing 63 approved FDCs out of 655 New Drug Application (NDA) approvals, it was found that completion of the full phases of clinical trials was not always required for approval by the FDA, which indicates that some phases of clinical studies can be possibly exempted, if justified. CONCLUSION: The results imply that pharmaceutical companies can accelerate FDC development and enter the market earlier if scientific regulatory rationales are established.