Mobile Health
Mobile health (mHealth) leverages mobile devices and digital technologies to improve healthcare delivery and patient engagement, primarily aiming to enhance accessibility, personalization, and efficiency of care. Current research heavily emphasizes the use of machine learning, particularly reinforcement learning and contextual bandit algorithms, to optimize personalized interventions, improve diagnostic accuracy (e.g., using audio or image data), and enhance user engagement with mHealth applications. This field is significant for its potential to improve healthcare outcomes, particularly in resource-constrained settings, by enabling remote monitoring, data-driven decision-making, and the development of novel digital biomarkers.
Papers
Your smartphone could act as a pulse-oximeter and as a single-lead ECG
Ahsan Mehmood, Asma Sarauji, M. Mahboob Ur Rahman, Tareq Y. Al-Naffouri
Towards robust paralinguistic assessment for real-world mobile health (mHealth) monitoring: an initial study of reverberation effects on speech
Judith Dineley, Ewan Carr, Faith Matcham, Johnny Downs, Richard Dobson, Thomas F Quatieri, Nicholas Cummins
Adaptive Interventions for Global Health: A Case Study of Malaria
África Periáñez, Andrew Trister, Madhav Nekkar, Ana Fernández del Río, Pedro L. Alonso
Synthetic Data Generator for Adaptive Interventions in Global Health
Aditya Rastogi, Juan Francisco Garamendi, Ana Fernández del Río, Anna Guitart, Moiz Hassan Khan, Dexian Tang, África Periáñez