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Review Article
Neurology
Recent trends of healthcare information and communication technologies in pediatrics: a systematic review
Se young Jung, Keehyuck Lee, Hee Hwang
Clin Exp Pediatr. 2022;65(6):291-299.   Published online December 15, 2021
· The innovation of healthcare information communication technology (ICT) was accelerated with the adoption of electronic health records (EHRs).
· Telemedicine currently has no technical barriers.
· EHRs and personal health records are being connected, and mobile/wearable technologies are being integrated into them.
· Conventional rule-based clinical decision support systems have already been implemented and used in EHRs and PHRs. Artificial intelligence/machine learning improves precision and accuracy.
Big data analysis and artificial intelligence in epilepsy – common data model analysis and machine learning-based seizure detection and forecasting
Yoon Gi Chung, Yonghoon Jeon, Sooyoung Yoo, Hunmin Kim, Hee Hwang
Clin Exp Pediatr. 2022;65(6):272-282.   Published online November 26, 2021
· Big data analysis, such as common data model and artificial intelligence, can solve relevant questions and improve clinical care.
· Recent deep learning studies achieved 0.887–0.996 areas under the receiver operating characteristic curve for automated interictal epileptiform discharge detection.
· Recent deep learning studies achieved 62.3%–99.0% accuracy for interictal-ictal classification in seizure detection and 75.0%– 87.8% sensitivity with a 0.06–0.21/hr false positive rate in seizure forecasting.
Other
Knowledge-guided artificial intelligence technologies for decoding complex multiomics interactions in cells
Dohoon Lee, Sun Kim
Clin Exp Pediatr. 2022;65(5):239-249.   Published online November 26, 2021
· The need for data-driven modeling of multiomics interactions was recently highlighted.
· Many artificial intelligence-driven models have been developed, but only a few have incorporated biological domain knowledge within model architectures or training procedures.
· Here we provide a comprehensive review of deep learning models to decipher complex multiomics interactions regarding the biological guidance imposed upon them to facilitate further development of biological knowledge-guided deep learning models.