About this Research Topic
Currently, sensors used for remote health monitoring are available in many commercial devices sold on the market, allowing a widespread use of this type of solution. Smartphones, smartwatches, and other devices may collect different types of data and enable communication between patients and their healthcare providers. Personalizing diagnosis and treatment for individual patients through the information provided by such devices, may be the key to better population health by making medicine more preventive, predictive, precise, and participatory. As such, it is crucial for the research community to explore how emerging technologies, such as mobile and wearable devices, artificial intelligence, big data, data fusion, data analysis, and data imputation, can be used to optimize personalized medical treatments.
This Research Topic focuses on data analysis methodologies used to optimize the predictive power and precision of health tracking devices with the goal to enable more personalized and predictive medicine.
We welcome submissions from the fields of physics, statistics, telemedicine, biomedical engineering, digital signal processing, artificial intelligence, personalized/precision medicine, system engineering, and health privacy and security. Possible themes can include but are not limited to:
- Artificial intelligence in Personalized, Predictive, Preventive, Participatory, and Precision Medicine
- Big Data in Personalized, Predictive, Preventive, Participatory, and Precision Medicine
- Innovative data analysis methodologies in wearable and mobile sensors for healthcare treatments
- Privacy and security of healthcare systems
- Estimation of events with different kinds of data
- Healthcare technologies to general well-being, novel methods to enhance the medicine
All manuscript types will be considered. However, the highest priority will be given to original research, all types of reviews, and methods articles.
Keywords: personalized medicine, precision medicine, wearable devices, health tracking, data analysis, methodology, big data, data fusion, data imputation
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.