Dr. Rabiner Replies

Rabiner, Charles J.

doi:10.1176/ajp.1974.131.12.1410a

Cited by 4 publications

(8 citation statements)

References 2 publications

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“…Following recruitment, individuals were labeled as engaged if they reported any of the ten symptoms on a given day. A first-order hidden Markov model [ 25 , 26 ] was then used to estimate the levels of engagement of participants, using the depmixS4 R package (I Visser, Netherlands)[ 27 ] ( Multimedia Appendix 2 ). The model assumed three latent engagement states: high, low, and disengaged.…”

Section: Methodsmentioning

confidence: 99%

Recruitment and Ongoing Engagement in a UK Smartphone Study Examining the Association Between Weather and Pain: Cohort Study

Druce

McBeth²,

Veer

et al. 2017

JMIR Mhealth Uhealth

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BackgroundThe huge increase in smartphone use heralds an enormous opportunity for epidemiology research, but there is limited evidence regarding long-term engagement and attrition in mobile health (mHealth) studies.ObjectiveThe objective of this study was to examine how representative the Cloudy with a Chance of Pain study population is of wider chronic-pain populations and to explore patterns of engagement among participants during the first 6 months of the study.MethodsParticipants in the United Kingdom who had chronic pain (≥3 months) and enrolled between January 20, 2016 and January 29, 2016 were eligible if they were aged ≥17 years and used the study app to report any of 10 pain-related symptoms during the study period. Participant characteristics were compared with data from the Health Survey for England (HSE) 2011. Distinct clusters of engagement over time were determined using first-order hidden Markov models, and participant characteristics were compared between the clusters.ResultsCompared with the data from the HSE, our sample comprised a higher proportion of women (80.51%, 5129/6370 vs 55.61%, 4782/8599) and fewer persons at the extremes of age (16-34 and 75+). Four clusters of engagement were identified: high (13.60%, 865/6370), moderate (21.76%, 1384/6370), low (39.35%, 2503/6370), and tourists (25.44%, 1618/6370), between which median days of data entry ranged from 1 (interquartile range; IQR: 1-1; tourist) to 149 (124-163; high). Those in the high-engagement cluster were typically older, whereas those in the tourist cluster were mostly male. Few other differences distinguished the clusters.ConclusionsCloudy with a Chance of Pain demonstrates a rapid and successful recruitment of a large, representative, and engaged sample of people with chronic pain and provides strong evidence to suggest that smartphones could provide a viable alternative to traditional data collection methods.

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Section: Methodsmentioning

confidence: 99%

Recruitment and Ongoing Engagement in a UK Smartphone Study Examining the Association Between Weather and Pain: Cohort Study

Druce

McBeth²,

Veer

et al. 2017

JMIR Mhealth Uhealth

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“…Los modelos ocultos de Markov describen procesos estocásticos doblemente anidados, compuestos de una capa oculta que controla la evolución temporal de las características espectrales de una capa observable (Rabiner, 1989).…”

Section: Materiales Y Métodosunclassified

“…(2) Cuya optimización se alcanza ajustando los parámetros de cada modelo por separado, con los datos de entrenamiento de la clase correspondiente (Bilmes, 1998;Rabiner, 1989).…”

Section: Criterio Mleunclassified

“…es verdadero y 0 de otra forma. Para realizar el cálculo de log(P(φ r nφ r |λ 1 )) se emplea el algoritmo de Viterbi (Rabiner, 1989).…”

Section: Criterio De Entrenamiento Propuestounclassified

“…En este trabajo se aborda el problema de entrenamiento en Modelos Ocultos de Markov (Hidden Markov Models -HMM). Modelos ampliamente utilizados en sistemas de reconocimiento de voz, ubicándose como una herramienta estándar para modelar las variaciones estocásticas presentes en este tipo de bioseñales (Rabiner, 1989). En este caso específico se estudia el problema de entrenamiento en una aplicación de detección de patologías en señales de voz, que comprende uno de los problemas mas importantes en aplicaciones biomédicas de las tecnologías de reconocimiento de voz (Wang & Jo, 2007).…”

Section: Introductionunclassified

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Entrenamiento Discriminativo Maximizando una Distancia entre Modelos de Clases

Sarria-Paja

Castellanos-Domínguez

2010

TecnoL.

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En este trabajo se presenta una técnica de entrenamiento discriminativo para modelos ocultos de Markov, orientado a la detección de patologías en señales de voz. La técnica busca maximizar el área que encierra la curva ROC (Receiver Operating Characteristic curve) ajustando los parámetros de modelo, empleando como función objetivo la distancia entre las medias de las funciones de densidad de probabilidad subyacentes asociadas a cada clase. Como resultado se obtiene una mejora en el desempeño del sistema de clasificación comparada con diferentes criterios de entrenamiento.

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Συστημα Συνθεσης Ομιλιας Στην Ελληνικη Γλωσσα Με Απεριοριστο Λεξιλογιο

Σταθοπουλου¹

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Dr. Rabiner Replies

Cited by 4 publications

References 2 publications

Recruitment and Ongoing Engagement in a UK Smartphone Study Examining the Association Between Weather and Pain: Cohort Study

Recruitment and Ongoing Engagement in a UK Smartphone Study Examining the Association Between Weather and Pain: Cohort Study

Entrenamiento Discriminativo Maximizando una Distancia entre Modelos de Clases

Συστημα Συνθεσης Ομιλιας Στην Ελληνικη Γλωσσα Με Απεριοριστο Λεξιλογιο

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