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Modelling reassurances of clinicians with Hidden Markov models

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Modelling reassurances of clinicians with Hidden Markov models. / Popov, Valentin; Ellis-Robinson, Alesha; Humphris, Gerald.

In: BMC Medical Research Methodology, Vol. 19, 11, 09.01.2019.

Research output: Contribution to journalArticlepeer-review

Harvard

Popov, V, Ellis-Robinson, A & Humphris, G 2019, 'Modelling reassurances of clinicians with Hidden Markov models', BMC Medical Research Methodology, vol. 19, 11. https://doi.org/10.1186/s12874-018-0629-0

APA

Popov, V., Ellis-Robinson, A., & Humphris, G. (2019). Modelling reassurances of clinicians with Hidden Markov models. BMC Medical Research Methodology, 19, [11]. https://doi.org/10.1186/s12874-018-0629-0

Vancouver

Popov V, Ellis-Robinson A, Humphris G. Modelling reassurances of clinicians with Hidden Markov models. BMC Medical Research Methodology. 2019 Jan 9;19. 11. https://doi.org/10.1186/s12874-018-0629-0

Author

Popov, Valentin ; Ellis-Robinson, Alesha ; Humphris, Gerald. / Modelling reassurances of clinicians with Hidden Markov models. In: BMC Medical Research Methodology. 2019 ; Vol. 19.

Bibtex - Download

@article{315aa5bedd8441ccab1c0af00ed077dd,
title = "Modelling reassurances of clinicians with Hidden Markov models",
abstract = "Background: A key element in the interaction between clinicians and patients with cancer is reassurance giving. Learning about the stochastic nature of reassurances as well as making inferential statements about the influence of covariates such as patient response and time spent on previous reassurances are of particular importance. Methods: We fit Hidden Markov Models (HMMs) to reassurance type from multiple time series of clinicians' reassurances, decoded from audio files of review consultations between patients with breast cancer and their therapeutic radiographer. Assuming a latent state process driving the observations process, HMMs naturally accommodate serial dependence in the data. Extensions to the baseline model such as including covariates as well as allowing for fixed effects for the different clinicians are straightforward to implement. Results: We found that clinicians undergo different states, in which they are more or less inclined to provide a particular type of reassurance. The states are very persistent, however switches occasionally occur. The lengthier the previousreassurance, the more likely the clinician is to stay in the current state. Conclusions: HMMs prove to be a valuable tool and provide important insights for practitioners. Trial registration: Trial Registration number: ClinicalTrials.gov: NCT02599506. Prospectively registered on 11th March 2015. ",
keywords = "Reassurance, Hidden Markov models, Fixed effects",
author = "Valentin Popov and Alesha Ellis-Robinson and Gerald Humphris",
note = "Generous support was received from the charity Breast Cancer Now (grant number: 6873)",
year = "2019",
month = jan,
day = "9",
doi = "10.1186/s12874-018-0629-0",
language = "English",
volume = "19",
journal = "BMC Medical Research Methodology",
issn = "1471-2288",
publisher = "BioMed Central",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Modelling reassurances of clinicians with Hidden Markov models

AU - Popov, Valentin

AU - Ellis-Robinson, Alesha

AU - Humphris, Gerald

N1 - Generous support was received from the charity Breast Cancer Now (grant number: 6873)

PY - 2019/1/9

Y1 - 2019/1/9

N2 - Background: A key element in the interaction between clinicians and patients with cancer is reassurance giving. Learning about the stochastic nature of reassurances as well as making inferential statements about the influence of covariates such as patient response and time spent on previous reassurances are of particular importance. Methods: We fit Hidden Markov Models (HMMs) to reassurance type from multiple time series of clinicians' reassurances, decoded from audio files of review consultations between patients with breast cancer and their therapeutic radiographer. Assuming a latent state process driving the observations process, HMMs naturally accommodate serial dependence in the data. Extensions to the baseline model such as including covariates as well as allowing for fixed effects for the different clinicians are straightforward to implement. Results: We found that clinicians undergo different states, in which they are more or less inclined to provide a particular type of reassurance. The states are very persistent, however switches occasionally occur. The lengthier the previousreassurance, the more likely the clinician is to stay in the current state. Conclusions: HMMs prove to be a valuable tool and provide important insights for practitioners. Trial registration: Trial Registration number: ClinicalTrials.gov: NCT02599506. Prospectively registered on 11th March 2015.

AB - Background: A key element in the interaction between clinicians and patients with cancer is reassurance giving. Learning about the stochastic nature of reassurances as well as making inferential statements about the influence of covariates such as patient response and time spent on previous reassurances are of particular importance. Methods: We fit Hidden Markov Models (HMMs) to reassurance type from multiple time series of clinicians' reassurances, decoded from audio files of review consultations between patients with breast cancer and their therapeutic radiographer. Assuming a latent state process driving the observations process, HMMs naturally accommodate serial dependence in the data. Extensions to the baseline model such as including covariates as well as allowing for fixed effects for the different clinicians are straightforward to implement. Results: We found that clinicians undergo different states, in which they are more or less inclined to provide a particular type of reassurance. The states are very persistent, however switches occasionally occur. The lengthier the previousreassurance, the more likely the clinician is to stay in the current state. Conclusions: HMMs prove to be a valuable tool and provide important insights for practitioners. Trial registration: Trial Registration number: ClinicalTrials.gov: NCT02599506. Prospectively registered on 11th March 2015.

KW - Reassurance

KW - Hidden Markov models

KW - Fixed effects

U2 - 10.1186/s12874-018-0629-0

DO - 10.1186/s12874-018-0629-0

M3 - Article

VL - 19

JO - BMC Medical Research Methodology

JF - BMC Medical Research Methodology

SN - 1471-2288

M1 - 11

ER -

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