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Ability to Predict ...
Ability to Predict Melanoma Within 5 Years Using Registry Data and a Convolutional Neural Network: A Proof of Concept Study
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- Gillstedt, Martin, 1977 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för dermatologi och venereologi,Institute of Clinical Sciences, Department of Dermatology and Venereology
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- Polesie, Sam (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för dermatologi och venereologi,Institute of Clinical Sciences, Department of Dermatology and Venereology
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(creator_code:org_t)
- 2022-07-13
- 2022
- Engelska.
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Ingår i: Acta Dermato-Venereologica. - : Medical Journals Sweden AB. - 0001-5555 .- 1651-2057. ; 102
- Relaterad länk:
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https://gup.ub.gu.se...
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https://doi.org/10.2...
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Abstract
Ämnesord
Stäng
- Research relating to machine learning algorithms, including convolutional neural networks, has increased during the past 5 years. The aim of this pilot study was to investigate how accurately a convolutional neural network, trained on Swedish registry data, could perform in predicting cutaneous invasive and in situ melanoma (CMM) within 5 years. A cohort of 1,208,393 individuals was used. Registry data ranged from 4 July 2005 to 31 December 2011, predicting CMM between 1 January 2012 and 31 December 2016. A convolutional neural network with one-dimensional convolutions with respect to time was trained using healthcare databases and registers. The algorithm was trained on 23,886 individuals. Validation was performed on a hold out validation set including 6,000 individuals. After training and validation, the convolutional neural network was evaluated on a test set (1,000 individuals with an CMM occurring within 5 years and 5,000 without). The area under the receiver-operating characteristic curve was 0.59 (95% confidence interval (95% CI) 0.57???0.61). The point on the receiver-operating characteristic curve where sensitivity equalled specificity had a value of 56% (sensitivity 95% CI 53???60% and specificity 95% CI 55???58%). Albeit at an early stage, this pilot investigation demonstrates potential usefulness for machine learning algorithms in predicting melanoma risk.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Dermatologi och venereologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Dermatology and Venereal Diseases (hsv//eng)
Nyckelord
- area under curve
- deep learning
- epidemiological methods
- machine
- learning
- melanoma
- receiver-operating characteristic curve
- sensitivity
- and specificity
- Dermatology
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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