Base de dados : MEDLINE
Pesquisa : E05.318.308.056 [Categoria DeCS]
Referências encontradas : 1721 [refinar]
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  1 / 1721 MEDLINE  
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[PMID]:29437562
[Au] Autor:Zhang L; Wang H; Li Q; Zhao MH; Zhan QM
[Ad] Endereço:Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China zhanglx@bjmu.edu.cn.
[Ti] Título:Big data and medical research in China.
[So] Source:BMJ;360:j5910, 2018 02 05.
[Is] ISSN:1756-1833
[Cp] País de publicação:England
[La] Idioma:eng
[Mh] Termos MeSH primário: Pesquisa Biomédica
Conjuntos de Dados como Assunto
[Mh] Termos MeSH secundário: China
Tomada de Decisões
Seres Humanos
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180309
[Lr] Data última revisão:
180309
[Sb] Subgrupo de revista:AIM; IM
[Da] Data de entrada para processamento:180214
[St] Status:MEDLINE
[do] DOI:10.1136/bmj.j5910


  2 / 1721 MEDLINE  
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[PMID]:29408272
[Au] Autor:Saif I; Kasmi Y; Allali K; Ennaji MM
[Ad] Endereço:Team of Virology, Oncology and Medical Biotechnologies, Laboratory of Virology, Microbiology, Quality and Biotechnologies/ETB, Faculty of Science sand Technologies-Mohammedia, Hassan II University of Casablanca, Morocco.
[Ti] Título:Prediction of DNA methylation in the promoter of gene suppressor tumor.
[So] Source:Gene;651:166-173, 2018 Apr 20.
[Is] ISSN:1879-0038
[Cp] País de publicação:Netherlands
[La] Idioma:eng
[Ab] Resumo:The epigenetics methylation of cytosine is the most common epigenetic form in DNA sequences. It is highly concentrated in the promoter regions of the genes, leading to an inactivation of tumor suppressors regardless of their initial function. In this work, we aim to identify the highly methylated regions; the cytosine-phosphate-guanine (CpG) island located on the promoters and/or the first exon gene known for their key roles in the cell cycle, hence the need to study gene-gene interactions. The Frommer and hidden Markov model algorithms are used as computational methods to identify CpG islands with specificity and sensitivity up to 76% and 80%, respectively. The results obtained show, on the one hand, that the genes studied are suspected of developing hypermethylation in the promoter region of the gene involved in the case of a cancer. We then showed that the relative richness in CG results from a high level of methylation. On the other hand, we observe that the gene-gene interaction exhibits co-expression between the chosen genes. This let us to conclude that the hidden Markov model algorithm predicts more specific and valuable information about the hypermethylation in gene as a preventive and diagnostics tools for the personalized medicine; as that the tumor-suppresser-genes have relative co-expression and complementary relations which the hypermethylation affect in the samples studied in our work.
[Mh] Termos MeSH primário: Biologia Computacional/métodos
Ilhas de CpG
Metilação de DNA
Genes Supressores de Tumor
Regiões Promotoras Genéticas
[Mh] Termos MeSH secundário: Algoritmos
DNA de Neoplasias
Conjuntos de Dados como Assunto
Epistasia Genética
Seres Humanos
Cadeias de Markov
Modelos Genéticos
Neoplasias/genética
[Pt] Tipo de publicação:EVALUATION STUDIES; JOURNAL ARTICLE
[Nm] Nome de substância:
0 (DNA, Neoplasm)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180309
[Lr] Data última revisão:
180309
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180207
[St] Status:MEDLINE


  3 / 1721 MEDLINE  
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[PMID]:27772646
[Au] Autor:Lee CS; Lee AY; Holland GN; Van Gelder RN; Tufail A
[Ad] Endereço:Seattle, Washington. Electronic address: leecs2@uw.edu.
[Ti] Título:Big Data and Uveitis.
[So] Source:Ophthalmology;123(11):2273-2275, 2016 11.
[Is] ISSN:1549-4713
[Cp] País de publicação:United States
[La] Idioma:eng
[Mh] Termos MeSH primário: Conjuntos de Dados como Assunto
Uveíte/complicações
[Mh] Termos MeSH secundário: Bases de Dados Factuais
Assistência à Saúde/normas
Seres Humanos
[Pt] Tipo de publicação:EDITORIAL; RESEARCH SUPPORT, N.I.H., EXTRAMURAL; RESEARCH SUPPORT, NON-U.S. GOV'T
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180309
[Lr] Data última revisão:
180309
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:161025
[St] Status:MEDLINE


  4 / 1721 MEDLINE  
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[PMID]:29288362
[Au] Autor:Goonesekere NCW; Andersen W; Smith A; Wang X
[Ad] Endereço:Department of Chemistry and Biochemistry, University of Northern Iowa, 1227 W. 27th Street, Cedar Falls, IA, 50613-0423, USA. nalin.goonesekere@uni.edu.
[Ti] Título:Identification of genes highly downregulated in pancreatic cancer through a meta-analysis of microarray datasets: implications for discovery of novel tumor-suppressor genes and therapeutic targets.
[So] Source:J Cancer Res Clin Oncol;144(2):309-320, 2018 Feb.
[Is] ISSN:1432-1335
[Cp] País de publicação:Germany
[La] Idioma:eng
[Ab] Resumo:PURPOSE: The lack of specific symptoms at early tumor stages, together with a high biological aggressiveness of the tumor contribute to the high mortality rate for pancreatic cancer (PC), which has a 5-year survival rate of about 7%. Recent failures of targeted therapies inhibiting kinase activity in clinical trials have highlighted the need for new approaches towards combating this deadly disease. METHODS: In this study, we have identified genes that are significantly downregulated in PC, through a meta-analysis of large number of microarray datasets. We have used qRT-PCR to confirm the downregulation of selected genes in a panel of PC cell lines. RESULTS: This study has yielded several novel candidate tumor-suppressor genes (TSGs) including GNMT, CEL, PLA2G1B and SERPINI2. We highlight the role of GNMT, a methyl transferase associated with the methylation potential of the cell, and CEL, a lipase, as potential therapeutic targets. We have uncovered genetic links to risk factors associated with PC such as smoking and obesity. Genes important for patient survival and prognosis are also discussed, and we confirm the dysregulation of metabolic pathways previously observed in PC. CONCLUSIONS: While many of the genes downregulated in our dataset are associated with protein products normally produced by the pancreas for excretion, we have uncovered some genes whose downregulation appear to play a more causal role in PC. These genes will assist in providing a better understanding of the disease etiology of PC, and in the search for new therapeutic targets and biomarkers.
[Mh] Termos MeSH primário: Regulação Neoplásica da Expressão Gênica
Genes Supressores de Tumor
Neoplasias Pancreáticas/genética
[Mh] Termos MeSH secundário: Linhagem Celular Tumoral
Conjuntos de Dados como Assunto
Regulação para Baixo
Seres Humanos
Terapia de Alvo Molecular
Análise de Sequência com Séries de Oligonucleotídeos
Reação em Cadeia da Polimerase em Tempo Real
[Pt] Tipo de publicação:JOURNAL ARTICLE; META-ANALYSIS
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180308
[Lr] Data última revisão:
180308
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171231
[St] Status:MEDLINE
[do] DOI:10.1007/s00432-017-2558-4


  5 / 1721 MEDLINE  
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[PMID]:29273569
[Au] Autor:Zeinstra CG; Meuwly D; Ruifrok AC; Veldhuis RN; Spreeuwers LJ
[Ad] Endereço:Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands.
[Ti] Título:Forensic face recognition as a means to determine strength of evidence: A survey.
[So] Source:Forensic Sci Rev;30(1):21-32, 2018 Jan.
[Is] ISSN:1042-7201
[Cp] País de publicação:China (Republic : 1949- )
[La] Idioma:eng
[Ab] Resumo:This paper surveys the literature on forensic face recognition (FFR), with a particular focus on the strength of evidence as used in a court of law. FFR is the use of biometric face recognition for several applications in forensic science. It includes scenarios of ID verification and open-set identification, investigation and intelligence, and evaluation of the strength of evidence. We present FFR from operational, tactical, and strategic perspectives. We discuss criticism of FFR and we provide an overview of research efforts from multiple perspectives that relate to the domain of FFR. Finally, we sketch possible future directions for FFR.
[Mh] Termos MeSH primário: Identificação Biométrica
Face/anatomia & histologia
[Mh] Termos MeSH secundário: Conjuntos de Dados como Assunto
Prova Pericial
Ciências Forenses
Seres Humanos
Processamento de Imagem Assistida por Computador
Redes Neurais (Computação)
Competência Profissional
Pesquisa/tendências
[Pt] Tipo de publicação:JOURNAL ARTICLE; REVIEW
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180308
[Lr] Data última revisão:
180308
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171224
[St] Status:MEDLINE


  6 / 1721 MEDLINE  
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[PMID]:28453637
[Au] Autor:Daymont C; Ross ME; Russell Localio A; Fiks AG; Wasserman RC; Grundmeier RW
[Ad] Endereço:Departments of Pediatrics and Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA.
[Ti] Título:Automated identification of implausible values in growth data from pediatric electronic health records.
[So] Source:J Am Med Inform Assoc;24(6):1080-1087, 2017 Nov 01.
[Is] ISSN:1527-974X
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:Objective: Large electronic health record (EHR) datasets are increasingly used to facilitate research on growth, but measurement and recording errors can lead to biased results. We developed and tested an automated method for identifying implausible values in pediatric EHR growth data. Materials and Methods: Using deidentified data from 46 primary care sites, we developed an algorithm to identify weight and height values that should be excluded from analysis, including implausible values and values that were recorded repeatedly without remeasurement. The foundation of the algorithm is a comparison of each measurement, expressed as a standard deviation score, with a weighted moving average of a child's other measurements. We evaluated the performance of the algorithm by (1) comparing its results with the judgment of physician reviewers for a stratified random selection of 400 measurements and (2) evaluating its accuracy in a dataset with simulated errors. Results: Of 2 000 595 growth measurements from 280 610 patients 1 to 21 years old, 3.8% of weight and 4.5% of height values were identified as implausible or excluded for other reasons. The proportion excluded varied widely by primary care site. The automated method had a sensitivity of 97% (95% confidence interval [CI], 94-99%) and a specificity of 90% (95% CI, 85-94%) for identifying implausible values compared to physician judgment, and identified 95% (weight) and 98% (height) of simulated errors. Discussion and Conclusion: This automated, flexible, and validated method for preparing large datasets will facilitate the use of pediatric EHR growth datasets for research.
[Mh] Termos MeSH primário: Algoritmos
Registros Eletrônicos de Saúde
Gráficos de Crescimento
Crescimento
[Mh] Termos MeSH secundário: Adolescente
Estatura
Peso Corporal
Criança
Pré-Escolar
Conjuntos de Dados como Assunto
Feminino
Seres Humanos
Lactente
Masculino
Atenção Primária à Saúde
Adulto Jovem
[Pt] Tipo de publicação:JOURNAL ARTICLE; VALIDATION STUDIES
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180308
[Lr] Data última revisão:
180308
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170429
[St] Status:MEDLINE
[do] DOI:10.1093/jamia/ocx037


  7 / 1721 MEDLINE  
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[PMID]:29298978
[Au] Autor:Lee SI; Celik S; Logsdon BA; Lundberg SM; Martins TJ; Oehler VG; Estey EH; Miller CP; Chien S; Dai J; Saxena A; Blau CA; Becker PS
[Ad] Endereço:Paul G. Allen School of Computer Science and Engineering, University of Washington, 185 E Stevens Way NE, Seattle, WA, 98195, USA. suinlee@cs.washington.edu.
[Ti] Título:A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.
[So] Source:Nat Commun;9(1):42, 2018 01 03.
[Is] ISSN:2041-1723
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia (AML) by introducing: data from 30 AML patients including genome-wide gene expression profiles and in vitro sensitivity to 160 chemotherapy drugs, a computational method to identify reliable gene expression markers for drug sensitivity by incorporating multi-omic prior information relevant to each gene's potential to drive cancer. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone, and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents.
[Mh] Termos MeSH primário: DNA Helicases/genética
Resistência a Medicamentos Antineoplásicos/genética
Leucemia Mieloide Aguda/genética
Aprendizado de Máquina
Proteínas Nucleares/genética
Medicina de Precisão/métodos
Fatores de Transcrição/genética
[Mh] Termos MeSH secundário: Algoritmos
Antineoplásicos/farmacologia
Antineoplásicos/uso terapêutico
Biomarcadores Tumorais/metabolismo
Linhagem Celular
Conjuntos de Dados como Assunto
Etoposídeo/farmacologia
Etoposídeo/uso terapêutico
Seres Humanos
Leucemia Mieloide Aguda/tratamento farmacológico
Inibidores da Topoisomerase II/farmacologia
Inibidores da Topoisomerase II/uso terapêutico
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, N.I.H., EXTRAMURAL; RESEARCH SUPPORT, NON-U.S. GOV'T; RESEARCH SUPPORT, U.S. GOV'T, NON-P.H.S.
[Nm] Nome de substância:
0 (Antineoplastic Agents); 0 (Biomarkers, Tumor); 0 (Nuclear Proteins); 0 (Topoisomerase II Inhibitors); 0 (Transcription Factors); 6PLQ3CP4P3 (Etoposide); EC 3.6.1.- (SMARCA4 protein, human); EC 3.6.4.- (DNA Helicases)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180305
[Lr] Data última revisão:
180305
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180105
[St] Status:MEDLINE
[do] DOI:10.1038/s41467-017-02465-5


  8 / 1721 MEDLINE  
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[PMID]:29419978
[Au] Autor:Wiedemann LA
[Ti] Título:In Data Analytics and Informatics, One is the Loneliest Number.
[So] Source:J AHIMA;88(5):44-7, 2017 05.
[Is] ISSN:1060-5487
[Cp] País de publicação:United States
[La] Idioma:eng
[Mh] Termos MeSH primário: Conjuntos de Dados como Assunto/estatística & dados numéricos
Informática Médica/estatística & dados numéricos
Estatística como Assunto
[Mh] Termos MeSH secundário: Benchmarking/estatística & dados numéricos
Custos e Análise de Custo/estatística & dados numéricos
Fraude/estatística & dados numéricos
Gastos em Saúde/estatística & dados numéricos
Seres Humanos
Estados Unidos
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180301
[Lr] Data última revisão:
180301
[Sb] Subgrupo de revista:H
[Da] Data de entrada para processamento:180209
[St] Status:MEDLINE


  9 / 1721 MEDLINE  
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[PMID]:29386436
[Au] Autor:Komada F
[Ad] Endereço:Faculty of Pharmaceutical Sciences, Himeji Dokkyo University.
[Ti] Título:[Analysis of Time-to-onset of Interstitial Lung Disease after the Administration of Small Molecule Molecularly-targeted Drugs].
[So] Source:Yakugaku Zasshi;138(2):229-235, 2018.
[Is] ISSN:1347-5231
[Cp] País de publicação:Japan
[La] Idioma:jpn
[Ab] Resumo: The aim of this study was to investigate the time-to-onset of drug-induced interstitial lung disease (DILD) following the administration of small molecule molecularly-targeted drugs via the use of the spontaneous adverse reaction reporting system of the Japanese Adverse Drug Event Report database. DILD datasets for afatinib, alectinib, bortezomib, crizotinib, dasatinib, erlotinib, everolimus, gefitinib, imatinib, lapatinib, nilotinib, osimertinib, sorafenib, sunitinib, temsirolimus, and tofacitinib were used to calculate the median onset times of DILD and the Weibull distribution parameters, and to perform the hierarchical cluster analysis. The median onset times of DILD for afatinib, bortezomib, crizotinib, erlotinib, gefitinib, and nilotinib were within one month. The median onset times of DILD for dasatinib, everolimus, lapatinib, osimertinib, and temsirolimus ranged from 1 to 2 months. The median onset times of the DILD for alectinib, imatinib, and tofacitinib ranged from 2 to 3 months. The median onset times of the DILD for sunitinib and sorafenib ranged from 8 to 9 months. Weibull distributions for these drugs when using the cluster analysis showed that there were 4 clusters. Cluster 1 described a subgroup with early to later onset DILD and early failure type profiles or a random failure type profile. Cluster 2 exhibited early failure type profiles or a random failure type profile with early onset DILD. Cluster 3 exhibited a random failure type profile or wear out failure type profiles with later onset DILD. Cluster 4 exhibited an early failure type profile or a random failure type profile with the latest onset DILD.
[Mh] Termos MeSH primário: Sistemas de Notificação de Reações Adversas a Medicamentos
Bortezomib/efeitos adversos
Carbazóis/efeitos adversos
Bases de Dados como Assunto
Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/epidemiologia
Doenças Pulmonares Intersticiais/induzido quimicamente
Piperidinas/efeitos adversos
Quinazolinas/efeitos adversos
[Mh] Termos MeSH secundário: Análise por Conglomerados
Dasatinibe/efeitos adversos
Conjuntos de Dados como Assunto
Seres Humanos
Japão/epidemiologia
Doenças Pulmonares Intersticiais/epidemiologia
Terapia de Alvo Molecular/efeitos adversos
Tamanho da Partícula
Pirazóis/efeitos adversos
Piridinas/efeitos adversos
Fatores de Tempo
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (CH5424802); 0 (Carbazoles); 0 (Piperidines); 0 (Pyrazoles); 0 (Pyridines); 0 (Quinazolines); 41UD74L59M (afatinib); 53AH36668S (crizotinib); 69G8BD63PP (Bortezomib); RBZ1571X5H (Dasatinib)
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180228
[Lr] Data última revisão:
180228
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180202
[St] Status:MEDLINE
[do] DOI:10.1248/yakushi.17-00194


  10 / 1721 MEDLINE  
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[PMID]:29234807
[Au] Autor:Ting DSW; Cheung CY; Lim G; Tan GSW; Quang ND; Gan A; Hamzah H; Garcia-Franco R; San Yeo IY; Lee SY; Wong EYM; Sabanayagam C; Baskaran M; Ibrahim F; Tan NC; Finkelstein EA; Lamoureux EL; Wong IY; Bressler NM; Sivaprasad S; Varma R; Jonas JB; He MG; Cheng CY; Cheung GCM; Aung T; Hsu W; Lee ML; Wong TY
[Ad] Endereço:Singapore Eye Research Institute, Singapore National Eye Center, Singapore.
[Ti] Título:Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.
[So] Source:JAMA;318(22):2211-2223, 2017 12 12.
[Is] ISSN:1538-3598
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Importance: A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective: To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants: Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes. Exposures: Use of a deep learning system. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard. Results: In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images). Conclusions and Relevance: In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.
[Mh] Termos MeSH primário: Retinopatia Diabética/diagnóstico
Oftalmopatias/diagnóstico
Aprendizado de Máquina
Retina/patologia
[Mh] Termos MeSH secundário: Área Sob a Curva
Conjuntos de Dados como Assunto
Diabetes Mellitus/etnologia
Retinopatia Diabética/etnologia
Oftalmopatias/etnologia
Feminino
Glaucoma/diagnóstico
Seres Humanos
Masculino
Meia-Idade
Curva ROC
Retina/diagnóstico por imagem
Sensibilidade e Especificidade
[Pt] Tipo de publicação:COMPARATIVE STUDY; JOURNAL ARTICLE; VALIDATION STUDIES
[Em] Mês de entrada:1712
[Cu] Atualização por classe:180228
[Lr] Data última revisão:
180228
[Sb] Subgrupo de revista:AIM; IM
[Da] Data de entrada para processamento:171214
[St] Status:MEDLINE
[do] DOI:10.1001/jama.2017.18152



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