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Scaling behaviours ...
Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity
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- Bourached, Anthony (författare)
- Harvard Medical School,University College London
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- Bonkhoff, Anna K. (författare)
- Harvard Medical School
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- Schirmer, Markus D. (författare)
- Harvard Medical School
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- Regenhardt, Robert W. (författare)
- Harvard Medical School
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- Bretzner, Martin (författare)
- Harvard Medical School
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- Hong, Sungmin (författare)
- Harvard Medical School
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- Dalca, Adrian (författare)
- Massachusetts Institute of Technology,Massachusetts General Hospital
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- Giese, Anne-Katrin (författare)
- University Medical Center Hamburg-Eppendorf
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- Winzeck, Stefan (författare)
- Imperial College London,Massachusetts General Hospital
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- Jern, Christina, 1962 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för biomedicin, avdelningen för laboratoriemedicin,Department of Laboratory Medicine,Sahlgrenska Academy,Sahlgrenska University Hospital
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- Lindgren, Arne G. (författare)
- Lund University,Lunds universitet,Neurologi, Lund,Sektion IV,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Klinisk strokeforskning,Forskargrupper vid Lunds universitet,Neurology, Lund,Section IV,Department of Clinical Sciences, Lund,Faculty of Medicine,Clinical Stroke Research Group,Lund University Research Groups,Skåne University Hospital
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- Maguire, Jane (författare)
- University of Technology Sydney
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- Wu, Ona (författare)
- Massachusetts General Hospital
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- Rhee, John (författare)
- Massachusetts General Hospital
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- Kimchi, Eyal Y. (författare)
- Northwestern University Feinberg School of Medicine
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- Rost, Natalia S. (författare)
- Harvard Medical School
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(creator_code:org_t)
- 2023
- 2023
- Engelska.
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Ingår i: BRAIN COMMUNICATIONS. - 2632-1297. ; 6:1
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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https://lup.lub.lu.s...
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Abstract
Ämnesord
Stäng
- Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 105-107 examples. It is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity. Our analyses relied on a total of 1430 patients assembled from the MRI-Genetics Interface Exploration collaboration and a Massachusetts General Hospital-based study. The outcome of interest was National Institutes of Health Stroke Scale-based stroke severity in the acute phase after ischaemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation and test set to investigate the effects of sample size; we subsampled the train set to 100, 300 and 900 and trained the algorithms to predict the stroke severity score for each sample size with regularized linear regression and an eight-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R2) in the test set. While linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by similar to 20% when increasing the sample size 9x [maximum for 100 patients: 0.279 +/- 0.005 (R2, 95% confidence interval), 900 patients: 0.337 +/- 0.006]. In summary, for sample sizes of 900 patients, deep learning showed a higher prediction performance than typically employed linear methods. These findings suggest the existence of non-linear relationships between lesion location and stroke severity that can be utilized for an improved prediction performance for larger sample sizes. Bourached et al. contrast linear and deep learning-based algorithms in their prediction performances of stroke severity depending on the training set sample sizes. They find that linear regression outperforms deep learning-based algorithms for smaller training samples comprising lesion location information of 100 patients, while deep learning excels in the case of larger samples (N = 900).
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Neurosciences (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Neurologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Neurology (hsv//eng)
Nyckelord
- ischaemic stroke
- stroke severity
- prediction
- deep learning
- scaling behaviour
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Bourached, Antho ...
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Bonkhoff, Anna K ...
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Schirmer, Markus ...
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Regenhardt, Robe ...
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Bretzner, Martin
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Hong, Sungmin
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visa fler...
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Dalca, Adrian
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Giese, Anne-Katr ...
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Winzeck, Stefan
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Jern, Christina, ...
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Lindgren, Arne G ...
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Maguire, Jane
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Wu, Ona
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Rhee, John
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Kimchi, Eyal Y.
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Rost, Natalia S.
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- Om ämnet
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Medicinska och f ...
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och Neurovetenskaper
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Neurologi
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BRAIN COMMUNICAT ...
- Av lärosätet
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Göteborgs universitet
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Lunds universitet