Search Results - "machine-learning algorithm"

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports by Kessler, R C, van Loo, H M, Wardenaar, K J, Bossarte, R M, Brenner, L A, Cai, T, Ebert, D D, Hwang, I, Li, J, de Jonge, P, Nierenberg, A A, Petukhova, M V, Rosellini, A J, Sampson, N A, Schoevers, R A, Wilcox, M A, Zaslavsky, A M

“…Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to…”
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Evolution and impact of bias in human and machine learning algorithm interaction by Sun, Wenlong, Nasraoui, Olfa, Shafto, Patrick

“…Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions…”
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Detecting suicidal risk using MMPI-2 based on machine learning algorithm by Kim, Sunhae, Lee, Hye-Kyung, Lee, Kounseok

“…Minnesota Multiphasic Personality Inventory-2 (MMPI-2) is a widely used tool for early detection of psychological maladjustment and assessing the level of…”
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Traditional Ceramic Sculpture Feature Recognition Based on the Machine Learning Algorithm by Zhu, XiaoLei

“… designed a traditional ceramic sculpture modeling feature recognition method based on the machine learning algorithm…”
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Machine Learning Algorithm Detection of Confluent B-Lines by Baloescu, Cristiana, Rucki, Agnieszka A, Chen, Alvin, Zahiri, Mohsen, Ghoshal, Goutam, Wang, Jing, Chew, Rita, Kessler, David, Chan, Daniela K I, Hicks, Bryson, Schnittke, Nikolai, Shupp, Jeffrey, Gregory, Kenton, Raju, Balasundar, Moore, Christopher

“… The objective of this study was to test a machine learning algorithm for confluent B-line identification…”
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PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease by Kim, Nam Heon, Park, Ukeob, Yang, Dong Won, Choi, Seong Hye, Youn, Young Chul, Kang, Seung Wan

“…) population, and validated it with Aβ PET. 19-channel resting-state EEG and Aβ PET were collected from 311 subjects: 196 SCD(36 Aβ +, 160 Aβ −), 115…”
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Satellite-based PM2.5 estimation directly from reflectance at the top of the atmosphere using a machine learning algorithm by Liu, Jianjun, Weng, Fuzhong, Li, Zhanqing

“… (AOD). The latter is subject to numerous sources of errors, making the satellite retrievals of PM2.5 highly uncertain…”
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The feasibility of Ki parametric imaging in clinical dynamic 18F‐FDG total‐body PET using a simulated‐data‐driven machine learning algorithm by Gu, Wenjian, Liu, Weiping, Yang, Wentong, Wang, Yihan, Liu, Ze, Zhu, Zhanshi, Li, Yanxiao, Xu, Tianyi, Shi, Hongcheng, Wang, Kuanquan, Luo, Gongning, Chen, Xiaojun, Zhou, Yun

“… Previous research has employed machine learning algorithms to generate reliable Ki images from 18F…”
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Patients With Femoral Neck Fractures Are at Risk for Conversion to Arthroplasty After Internal Fixation: A Machine-learning Algorithm by van de Kuit, Anouk, Oosterhoff, Jacobien H F, Dijkstra, Hidde, Sprague, Sheila, Bzovsky, Sofia, Bhandari, Mohit, Swiontkowski, Marc, Schemitsch, Emil H, IJpma, Frank F A, Poolman, Rudolf W, Doornberg, Job N, Hendrickx, Laurent A M

“…Femoral neck fractures are common and are frequently treated with internal fixation. A major disadvantage of internal fixation is the substantially high number…”
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The performance of VCS(volume, conductivity, light scatter) parameters in distinguishing latent tuberculosis and active tuberculosis by using machine learning algorithm by Chen, Lijiao, Yuan, Lingke, Sun, Tingting, Liu, Ruiqing, Huang, Qing, Deng, Shaoli

“… (volume, conductivity, light scatter) of neutrophils(NE), monocytes(MO), and lymphocytes(LY) were collected and was constructed classification model by four machine learning algorithms…”
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The feasibility of K i parametric imaging in clinical dynamic 18 F‐FDG total‐body PET using a simulated‐data‐driven machine learning algorithm by Gu, Wenjian, Liu, Weiping, Yang, Wentong, Wang, Yihan, Liu, Ze, Zhu, Zhanshi, Li, Yanxiao, Xu, Tianyi, Shi, Hongcheng, Wang, Kuanquan, Luo, Gongning, Chen, Xiaojun, Zhou, Yun

“… Previous research has employed machine learning algorithms to generate reliable K images from F-FDG PET scans with a duration of 10…”
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Prediction of findings at screening colonoscopy using a machine learning algorithm based on complete blood counts (ColonFlag) by Hilsden, Robert J., Heitman, Steven J., Mizrahi, Barak, Narod, Steven A., Goshen, Ran

“… All subjects had at least one CBC result within the year prior to colonoscopy. Based on age, sex, red blood cell parameters, inflammatory…”
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A Clinically-Translatable Machine Learning Algorithm for the Prediction of Alzheimer's Disease Conversion in Individuals with Mild and Premild Cognitive Impairment by Grassi, Massimiliano, Perna, Giampaolo, Caldirola, Daniela, Schruers, Koen, Duara, Ranjan, Loewenstein, David A

“… Currently available machine learning algorithms achieve only limited predictive accuracy or they are based on expensive and hard-to-collect information…”
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Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA by De Francesco, Silvia, Crema, Claudio, Archetti, Damiano, Muscio, Cristina, Reid, Robert I., Nigri, Anna, Bruzzone, Maria Grazia, Tagliavini, Fabrizio, Lodi, Raffaele, D’Angelo, Egidio, Boeve, Brad, Kantarci, Kejal, Firbank, Michael, Taylor, John-Paul, Tiraboschi, Pietro, Redolfi, Alberto

“… Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables…”
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A Novel Machine Learning Algorithm Predicts Dementia With Lewy Bodies Versus Parkinson's Disease Dementia Based on Clinical and Neuropsychological Scores by Bougea, Anastasia, Efthymiopoulou, Efthymia, Spanou, Ioanna, Zikos, Panagiotis

“…Our aim was to develop a machine learning algorithm based only on non-invasively clinic collectable predictors, for the accurate diagnosis of these disorders…”
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Predicting the Risk of Chronic Kidney Disease (CKD) Using Machine Learning Algorithm by Wang, Weilun, Chakraborty, Goutam, Chakraborty, Basabi

“…Background: Creatinine is a type of metabolite of blood that is strongly correlated to glomerular filtration rate (GFR). As measuring GFR is difficult,…”
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A prospective multicenter validation study of a machine learning algorithm classifier on quantitative electroencephalogram for differentiating between dementia with Lewy bodies and Alzheimer’s dementia by Suzuki, Yukiko, Suzuki, Maki, Shigenobu, Kazue, Shinosaki, Kazuhiro, Aoki, Yasunori, Kikuchi, Hirokazu, Baba, Toru, Hashimoto, Mamoru, Araki, Toshihiko, Johnsen, Kristinn, Ikeda, Manabu, Mori, Etsuro

“…). This study was carried out in Japan to validate an Electroencephalography (EEG)-derived machine learning algorithm for discriminating DLB from AD which developed based on a database of EEG records from two different European countries…”
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Validation of ‘Somnivore’, a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data by Allocca, Giancarlo, Ma, Sherie, Martelli, Davide, Cerri, Matteo, Del Vecchio, Flavia, Bastianini, Stefano, Zoccoli, Giovanna, Amici, Roberto, Morairty, Stephen R., Aulsebrook, Anne E., Blackburn, Shaun, Lesku, John A., Rattenborg, Niels C., Vyssotski, Alexei L., Wams, Emma, Porcheret, Kate, Wulff, Katharina, Foster, Russell, Chan, Julia K. M., Nicholas, Christian L., Freestone, Dean R., Johnston, Leigh A., Gundlach, Andrew L.

“… Therefore, we evaluated a supervised machine learning algorithm, Somnivore , for automated wake-sleep stage classification…”
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A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography by Gatos, Ilias, Tsantis, Stavros, Spiliopoulos, Stavros, Karnabatidis, Dimitris, Theotokas, Ioannis, Zoumpoulis, Pavlos, Loupas, Thanasis, Hazle, John D, Kagadis, George C

“…) using ultrasound shear wave elastography (SWE) imaging, with a stiffness value-clustering and machine-learning algorithm…”
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A machine-learning algorithm for predicting brain age using Rey-Osterrieth complex figure tests of healthy participants by Simfukwe, Chanda, Youn, Young Chul, Jeong, Ho Tae

“… RCFT drawings from 1,970 healthy subjects (ages 45-90 years) were collected from the Korean elderly community…”
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