classification of risk【保险】 - PMC

A. Kuendgen, M. Nomdedeu, H. Tuechler, G. Garcia-Manero, R. S. Komrokji, M. A. Sekeres, M. G. Della Porta, M. Cazzola, A. E. DeZern, G. J. Roboz, D. P. Steensma, A. A. Van de Loosdrecht, R. F. Schlenk, J. Grau, X. Calvo, S. Blum, A. Pereira, P. Valent, D. Costa, A. Giagounidis, B. Xicoy, H. Döhner, U. Platzbecker, C. Pedro, M. Lübbert, I. Oiartzabal, M. Díez-Campelo, M. T. Cedena, S. Machherndl-Spandl, M. López-Pavía, C. D. Baldus, M. Martinez-de-Sola, R. Stauder, B. Merchan, A. List, C. Ganster, T. Schroeder, M. T. Voso, M. Pfeilstöcker, H. Sill, B. Hildebrandt, J. Esteve, B. Nomdedeu, F. Cobo, R. Haas, F. Sole, U. Germing, P. L. Greenberg, D. Haase, G. Sanz

Leukemia. 2021; 35(3): 835–849. Published online 2020 Jun 29. doi: 10.1038/s41375-020-0917-7

PMCID:: PMC7932916

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Assessment of 2022 European LeukemiaNet risk classification system in real‐world cohort from China

Enbo Chen, Changqing Jiao, Jian Yu, Yu Gong, Duo Jin, Xiaoyu Ma, Jianling Cui, Zhonghui Wu, Junjie Zhou, Haixia Wang, Bobing Su, Jian Ge

Cancer Med. 2023 Dec; 12(24): 21615–21626. Published online 2023 Dec 14. doi: 10.1002/cam4.6696

PMCID:: PMC10757130

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A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages

Saima Rathore, Mohamad Habes, Muhammad Aksam Iftikhar, Amanda Shacklett, Christos Davatzikos

Neuroimage. Author manuscript; available in PMC 2018 Jul 15.

Published in final edited form as: Neuroimage. 2017 Jul 15; 155: 530–548. Published online 2017 Apr 13. doi: 10.1016/j.neuroimage.2017.03.057

PMCID:: PMC5511557

Abstract Article PDF–701K

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Estimating Classification Consistency of Screening Measures and Quantifying the Impact of Measurement Bias

Oscar Gonzalez, A. R. Georgeson, William E. Pelham, III, Rachel T. Fouladi

Psychol Assess. Author manuscript; available in PMC 2022 Jul 1.

Published in final edited form as: Psychol Assess. 2021 Jul; 33(7): 596–609. Published online 2021 May 17. doi: 10.1037/pas0000938

PMCID:: PMC8412438

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Cognitive and interpersonal vulnerabilities to adolescent depression: Classification of risk profiles and implications for personalized prevention

Benjamin L. Hankin, Jami F. Young, Robert Gallop, Judy Garber

J Abnorm Child Psychol. Author manuscript; available in PMC 2019 Oct 1.

Published in final edited form as: J Abnorm Child Psychol. 2018 Oct; 46(7): 1521–1533. doi: 10.1007/s10802-018-0401-2

PMCID:: PMC6060038

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Endoscopic Kyoto classification of Helicobacter pylori infection and gastric cancer risk diagnosis

Osamu Toyoshima, Toshihiro Nishizawa, Kazuhiko Koike

World J Gastroenterol. 2020 Feb 7; 26(5): 466–477. Published online 2020 Feb 7. doi: 10.3748/wjg.v26.i5.466

PMCID:: PMC7015719

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A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Jasjit S. Suri, Mrinalini Bhagawati, Sudip Paul, Athanasios D. Protogerou, Petros P. Sfikakis, George D. Kitas, Narendra N. Khanna, Zoltan Ruzsa, Aditya M. Sharma, Sanjay Saxena, Gavino Faa, John R. Laird, Amer M. Johri, Manudeep K. Kalra, Kosmas I. Paraskevas, Luca Saba

Diagnostics (Basel) 2022 Mar; 12(3): 722. Published online 2022 Mar 16. doi: 10.3390/diagnostics12030722

PMCID:: PMC8947682

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Use of Different Food Classification Systems to Assess the Association between Ultra-Processed Food Consumption and Cardiometabolic Health in an Elderly Population with Metabolic Syndrome (PREDIMED-Plus Cohort)

Celia Martinez-Perez, Rodrigo San-Cristobal, Pilar Guallar-Castillon, Miguel Ángel Martínez-González, Jordi Salas-Salvadó, Dolores Corella, Olga Castañer, Jose Alfredo Martinez, Ángel M. Alonso-Gómez, Julia Wärnberg, Jesús Vioque, Dora Romaguera, José López-Miranda, Ramon Estruch, Francisco J. Tinahones, José Lapetra, Lluis Serra-Majem, Aurora Bueno-Cavanillas, Josep A. Tur, Vicente Martín Sánchez, Xavier Pintó, José J. Gaforio, Pilar Matía-Martín, Josep Vidal, Clotilde Vázquez, Emilio Ros, Maira Bes-Rastrollo, Nancy Babio, Jose V. Sorlí, Camille Lassale, Beatriz Pérez-Sanz, Jessica Vaquero-Luna, María Julia Ajejas Bazán, María Concepción Barceló-Iglesias, Jadwiga Konieczna, Antonio García Ríos, María Rosa Bernal-López, José Manuel Santos-Lozano, Estefanía Toledo, Nerea Becerra-Tomás, Olga Portoles, María Dolores Zomeño, Itziar Abete, Anai Moreno-Rodriguez, Oscar Lecea-Juarez, Stephanie K. Nishi, Júlia Muñoz-Martínez, José M. Ordovás, Lidia Daimiel

Nutrients. 2021 Jul; 13(7): 2471. Published online 2021 Jul 20. doi: 10.3390/nu13072471

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A new phenotypic classification system for dyslipidemias based on the standard lipid panel

Maureen Sampson, Rami A. Ballout, Daniel Soffer, Anna Wolska, Sierra Wilson, Jeff Meeusen, Leslie J. Donato, Erica Fatica, James D. Otvos, Eliot A. Brinton, Robert S. Rosenson, Peter Wilson, Marcelo Amar, Robert Shamburek, Sotirios K. Karathanasis, Alan T. Remaley

Lipids Health Dis. 2021; 20: 170. Published online 2021 Nov 27. doi: 10.1186/s12944-021-01585-8

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Risk stratification based on components of the complete blood count in patients with acute coronary syndrome: A classification and regression tree analysis

Xiaowei Niu, Guoyong Liu, Lichao Huo, Jingjing Zhang, Ming Bai, Yu Peng, Zheng Zhang

Sci Rep. 2018; 8: 2838. Published online 2018 Feb 12. doi: 10.1038/s41598-018-21139-w

PMCID:: PMC5809451

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Divergent Classification Methods of Post-Concussion Syndrome after Mild Traumatic Brain Injury: Prevalence Rates, Risk Factors, and Functional Outcome

Daphne C. Voormolen, Maryse C. Cnossen, Suzanne Polinder, Nicole von Steinbuechel, Pieter E. Vos, Juanita A. Haagsma

J Neurotrauma. 2018 Jun 1; 35(11): 1233–1241. Published online 2018 Jun 1. doi: 10.1089/neu.2017.5257

PMCID:: PMC6909759

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An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

Xinqi Wang, Keming Mao, Lizhe Wang, Peiyi Yang, Duo Lu, Ping He

Sensors (Basel) 2019 Jan; 19(1): 194. Published online 2019 Jan 7. doi: 10.3390/s19010194

PMCID:: PMC6338921

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Classification using Ensemble Learning under Weighted Misclassification Loss

Yizhen Xu, Tao Liu, Michael J. Daniels, Rami Kantor, Ann Mwangi, Joseph W Hogan

Stat Med. Author manuscript; available in PMC 2020 Feb 27.

Published in final edited form as: Stat Med. 2019 May 20; 38(11): 2002–2012. Published online 2019 Jan 4. doi: 10.1002/sim.8082

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Tailoring Therapy for Children With Neuroblastoma on the Basis of Risk Group Classification: Past, Present, and Future

Wayne H. Liang, Sara M. Federico, Wendy B. London, Arlene Naranjo, Meredith S. Irwin, Samuel L. Volchenboum, Susan L. Cohn

JCO Clin Cancer Inform. 2020; 4: CCI.20.00074. Published online 2020 Oct 15. doi: 10.1200/CCI.20.00074

PMCID:: PMC7608590

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Cardiovascular risk assessment in patients with antiphospholipid syndrome: a cross-sectional performance analysis of nine clinical risk prediction tools

George C Drosos, George Konstantonis, Petros P Sfikakis, Maria G Tektonidou

RMD Open. 2023; 9(4): e003601. Published online 2023 Nov 27. doi: 10.1136/rmdopen-2023-003601

PMCID:: PMC10685980

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Risk- and response-based classification of childhood B-precursor acute lymphoblastic leukemia: a combined analysis of prognostic markers from the Pediatric Oncology Group (POG) and Children's Cancer Group (CCG)

Kirk R. Schultz, D. Jeanette Pullen, Harland N. Sather, Jonathan J. Shuster, Meenakshi Devidas, Michael J. Borowitz, Andrew J. Carroll, Nyla A. Heerema, Jeffrey E. Rubnitz, Mignon L. Loh, Elizabeth A. Raetz, Naomi J. Winick, Stephen P. Hunger, William L. Carroll, Paul S. Gaynon, Bruce M. Camitta

Blood. 2007 Feb 1; 109(3): 926–935. Prepublished online 2006 Sep 26. doi: 10.1182/blood-2006-01-024729

PMCID:: PMC1785141

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Select item 530608119.

Cosmetics Europe compilation of historical serious eye damage/eye irritation in vivo data analysed by drivers of classification to support the selection of chemicals for development and evaluation of alternative methods/strategies: the Draize eye test Reference Database (DRD)

João Barroso, Uwe Pfannenbecker, Els Adriaens, Nathalie Alépée, Magalie Cluzel, Ann De Smedt, Jalila Hibatallah, Martina Klaric, Karsten R. Mewes, Marion Millet, Marie Templier, Pauline McNamee

Arch Toxicol. 2017; 91(2): 521–547. Published online 2016 Mar 21. doi: 10.1007/s00204-016-1679-x

PMCID:: PMC5306081

Abstract Article PDF–814K

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Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations

Chang Ming, Valeria Viassolo, Nicole Probst-Hensch, Ivo D. Dinov, Pierre O. Chappuis, Maria C. Katapodi

Br J Cancer. 2020 Sep 1; 123(5): 860–867. Published online 2020 Jun 22. doi: 10.1038/s41416-020-0937-0

PMCID:: PMC7463251

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Figure 4

Discriminant function analysis based on radiomics features extracted from three MRI sequences for the GISTs risk classification. (A) T1WI, 61.0% of the three originally grouped cases (three GISTs risk classification) were correctly classified; (B) T2WI, 70.7% of the three originally grouped cases (three GISTs risk classification) were correctly classified; (C) ADC, 83.3% of the three originally grouped cases (three GISTs risk classification) were correctly classified; (D) combined three MR sequences, 78.9% of the three originally grouped cases (three GISTs risk classification) were correctly classified.

MRI-Based Radiomics Models for Predicting Risk Classification of Gastrointestinal Stromal Tumors

Front Oncol. 2021;11:631927.
Figure 4Risk classification of cases with histiocytic and non-histiocytic sarcomas

(A) Classification of 44 cases with histiocytic sarcoma into 3 risk groups. There are significant differences between groups next to each other: Low risk group vs. Intermediate risk group, p =.04; Intermediate risk group vs. High risk group, p <.0001. (B) Classification of 36 cases with non-histiocytic sarcoma into 3 risk groups. There are significant differences between groups next to each other: Low risk group vs. Intermediate risk group, p =.0002; Intermediate risk group vs. High risk group, p <.0001.

Prognostic factors for histiocytic and dendritic cell neoplasms

Oncotarget. 2017 Nov 17;8(58):98723-98732.
Figure 3

Outcome after classification by the COG risk classification algorithm. (A) CCG 1950s/1960s B-precursor ALL event-free survival outcome by COG risk classification algorithm. The P value for the log-rank test was less than .001. Hazard ratios (with low risk being the baseline) were 1.53 for standard risk, 2.73 for high risk, and 8.82 for very high risk. (B) POG ALinC 16 B-precursor ALL event-free survival outcome by COG risk classification algorithm (does not include rapidity of response because those data were not collected for these studies). The P value for the log-rank test was less than .001. Hazard ratios (with the low-risk group being the baseline) were 2.05 for standard risk, 3.34 for high risk, and 15.02 for very high risk.

Risk- and response-based classification of childhood B-precursor acute lymphoblastic leukemia: a combined analysis of prognostic markers from the Pediatric Oncology Group (POG) and Children's Cancer Group (CCG)

Blood. 2007 Feb 1;109(3):926-935.
Fig 1Correlation between the MammaPrint risk classification and the EP score (left) and EPclin score (right).

Threshold values for EP- and EPclin-based risk classification are indicated as dashed lines.

MammaPrint versus EndoPredict: Poor correlation in disease recurrence risk classification of hormone receptor positive breast cancer

PLoS One. 2017;12(8):e0183458.