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BACKGROUND Recent advances in smartphones and wearable devices have led to an increasing number of mHealth mobile applications (mHealth apps), enabling consumers to obtain drug information without the need for hospital visits. Consequently, interest in these apps has increased. However, a comprehensive analysis or quality evaluation has yet to be conducted for a variety of drug-related mobile apps. Content analysis and quality evaluation of mHealth apps not only show their usefulness and utility, but also provide guidance for consumers in selecting and utilizing relevant content and for app developers to implement the necessary functions. OBJECTIVE The aim of this study was to identify and analyze drug-related mHealth apps that contain the content and functionality required by consumers for adverse drug reaction (ADR) reporting. METHODS We evaluated the usefulness and utility of various drug-related mobile apps, both domestic (Korean) and international, and examined the main functions and features of the selected apps. To increase the reliability of the evaluation, three established assessment tools—the Mobile Application Rating Scale, its user version, and the Korean Health-IT User Evaluation Scale—were utilized. Additionally, mixed research methods comprising both quantitative and qualitative evaluations were conducted through interviews to evaluate the needs of participants regarding drug-related mobile apps and their perceptions of existing apps. RESULTS The results of the app quality evaluation showed that the majority of apps focus primarily on providing drug information and lacked features for recording, searching, and reporting ADRs from consumers. Although some international apps have features for reporting 10drug side effects, no such features are available in domestic apps. CONCLUSIONS In the future, we intend to develop an mHealth app with features necessary for recording and reporting ADRs the results of this study.

Purpose: The present study sought to identify prognostic factors for breast cancer survival and recurrence using a machine learning approach and electronic medical record data.Methods: We used a machine learning technique called feature selection to identify factors influencing breast cancer prognosis, and factors affecting survival and recurrence in a Cox regression model. Results: History of relapse, type of surgery, diagnostic route, SEER stage, and hormone therapy all affected breast cancer survival. Recurrence of breast cancer was affected by age, history of diabetes, breast reconstruction, pain, breast lumps, nipple discharge, and the presence of other symptoms. According to the survival analysis based on feature selection, patients with diabetes had a significantly higher risk of early recurrence of breast cancer (hazard ratio, 4.8; 95% confidence interval, 2.04–11.2, p < 0.05). Conclusions: The present study identified several factors associated with breast cancer prognosis. While survival was affected by the diagnostic route, recurrence was primarily influenced by breast cancer symptoms and other underlying health conditions. A more accurate and standardized model considering time-to-event data could be developed in the future to evaluate prognostic factors and predict prognoses, and for clinical validation