Adaptive Symptom Monitoring Using Hidden Markov Models - An Application in Ecological Momentary Assessment

Wearable and mobile technology provides new opportunities to manage health conditions remotely and unobtrusively. For example, healthcare providers can repeatedly sample a person's condition to monitor progression of symptoms and intervene if necessary. There is usually a utility-tolerability t...

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Bibliographic Details
Main Authors:	Bucci, S. (Author), Casson, A.J (Author), Hulme, W.J (Author), Lewis, S. (Author), Martin, G.P (Author), Peek, N. (Author), Sperrin, M. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	adaptive behavior adaptive sampling Adaptive sampling algorithm anxiety Article Bayes theorem behavior disorder Continuous time systems delusion depression digital phenotyping ecological momentary assessment Ecological Momentary Assessment ecological momentary intervention Economic and social effects fatigue finite element analysis hallucination Health care providers Health condition hidden Markov model hidden Markov models Hidden Markov models High frequency HF High sampling frequencies human human experiment Humans machine learning mathematical analysis mental health Mental Health mHealth mobile application Mobile Applications Mobile Technology Model parameters monitoring Monitoring, Physiologic phenotype physiologic monitoring Positive and Negative Syndrome Scale prediction probability risk prediction schizophrenia Schizophrenia simulation sleep quality State transitions Wearable technology
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Adaptive Symptom Monitoring Using Hidden Markov Models - An Application in Ecological Momentary Assessment

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