Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learningbased (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases.

With the advent of the big data era, the interest of the international community is focusing on increasing the utilization of medical big data. Many hospitals are attempting to increase the efficiency of their operations and patient management by adopting artificial intelligence (AI) technology that enables the use of electronic medical record (EMR) data. EMR includes information about a patient's health history, such as diagnoses, medicines, tests, allergies, immunizations, treatment plans, personalized medical care, and improvement of medical quality and safety. EMR data can also be used for AI-based new drug development. In particular, it is effective to develop AI that can predict the occurrence of specific diseases or provide individualized customized treatments by classifying the individualized characteristics of patients. In order to improve performance of artificial intelligence research using EMR data, standardization and refinement of data are essential. In addition, since EMR data deal with sensitive personal information of patients, it is also vital to protect the patient's privacy.There are already various supports for the use of EMR data in the Korean government, and researchers are encouraged to be proactive.

Real-world evidence (RWE) and randomized control trial (RCT) data are considered mutually complementary. However, compared with RCT, the outcomes of RWE continue to be assigned lower credibility. It must be emphasized that RWE research is a real-world practice that does not need to be executed as RCT research for it to be reliable. The advantages and disadvantages of RWE must be discerned clearly, and then the proper protocol can be planned from the beginning of the research to secure as many samples as possible. Attention must be paid to privacy protection. Moreover, bias can be reduced meaningfully by reducing the number of dropouts through detailed and meticulous data quality management. RCT research, characterized as having the highest reliability, and RWE research, which reflects the actual clinical aspects, can have a mutually supplementary relationship. Indeed, once this is proven, the two could comprise the most powerful evidence-based research method in medicine.
Bias (Epidemiology) ; Data Accuracy ; Electronic Health Records* ; Methods ; Privacy

OBJECTIVES: Through telehealth, medical services have expanded beyond spatial boundaries and are now available in living spaces outside of hospitals. It can also contribute to patient medical knowledge improvement because patients can access their hospital records and data from home. However, concepts of telehealth are rather vague in Korea. METHODS: We refer to several clinical reports to determine the current clinical status of and obstacles to telehealth in Korea. RESULTS: Patients' health conditions are now reported regularly to doctors remotely, and patients can receive varied assistance. Self-improvement based on minute details that are beyond medical staff's reach is another possible benefit that may be realized with the help of a variety of medical equipment (sensors). The feasibility, clinical effect, and cost-benefit of telehealth have been verified by scientific evidence. CONCLUSIONS: Patients will be able to improve their treatment adherence by receiving help from various professionals, such as doctors, nurses, nutritionists, and sports therapists. This means that the actual treatment time per patient will increase as well. Ultimately, this will increase the quality of patients' self-administration of care to impede disease progression and prevent complications.
Disease Progression ; Hospital Records ; Humans ; Korea* ; Nutritionists ; Remote Consultation ; Sports ; Telecommunications ; Telemedicine*

Objectives: The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. Methods: A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable timeseries model. Results: The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. Conclusions Implementing a multicenter-based timeseries classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies.