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2.
Water Res ; 171: 115454, 2020 Mar 15.
Artigo em Inglês | MEDLINE | ID: mdl-31918388

RESUMO

The water quality prediction performance of machine learning models may be not only dependent on the models, but also dependent on the parameters in data set chosen for training the learning models. Moreover, the key water parameters should also be identified by the learning models, in order to further reduce prediction costs and improve prediction efficiency. Here we endeavored for the first time to compare the water quality prediction performance of 10 learning models (7 traditional and 3 ensemble models) using big data (33,612 observations) from the major rivers and lakes in China from 2012 to 2018, based on the precision, recall, F1-score, weighted F1-score, and explore the potential key water parameters for future model prediction. Our results showed that the bigger data could improve the performance of learning models in prediction of water quality. Compared to other 7 models, decision tree (DT), random forest (RF) and deep cascade forest (DCF) trained by data sets of pH, DO, CODMn, and NH3-N had significantly better performance in prediction of all 6 Levels of water quality recommended by Chinese government. Moreover, two key water parameter sets (DO, CODMn, and NH3-N; CODMn, and NH3-N) were identified and validated by DT, RF and DCF to be high specificities for perdition water quality. Therefore, DT, RF and DCF with selected key water parameters could be prioritized for future water quality monitoring and providing timely water quality warning.


Assuntos
Qualidade da Água , Água , Big Data , China , Aprendizado de Máquina
4.
Z Kinder Jugendpsychiatr Psychother ; 48(1): 47-56, 2020 Jan.
Artigo em Alemão | MEDLINE | ID: mdl-30375920

RESUMO

Progress and challenges in the analysis of big data in social media of adolescents Abstract. Social media are ubiquitous today, and adolescents use them to express their thoughts, feelings, and behaviours. New interdisciplinary methods allow the automatic analysis of the massive amounts of data (big data) available on social networking websites using machine-learning tools to detect indicators of mental-health problems and disorders by identifying differences with common activity and communication patterns. This review first introduces the concept and potential fields of applications of big data in social media. It then discusses the first studies that used big data analyses and detected mental-health problems by identifying differences in the structure of social networks, in the use of certain words, and in the communication of opinions and sentiments. Future studies employing several assessment points could use longitudinal mediation analysis to model intraindividual changes in order to understand when and through which mechanisms social media use has an impact on mental health. Furthermore, future studies should include additional mental disorders, various sources of information, a broader age range, and additional social-networking websites to develop more precise models for the early detection of mental disorders. This would enable the development of personalised intervention programs to promote mental health and resilience in adolescents.


Assuntos
Big Data , Saúde Mental/estatística & dados numéricos , Mídias Sociais/estatística & dados numéricos , Adolescente , Comportamento do Adolescente , Comunicação , Humanos , Transtornos Mentais/epidemiologia
7.
World Neurosurg ; 133: e842-e849, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31562965

RESUMO

BACKGROUND: Modern science and healthcare generate vast amounts of data, and, coupled with the increasingly inexpensive and accessible computing, a tremendous opportunity exists to use these data to improve care. A better understanding of data science and its relationship to neurosurgical practice will be increasingly important as we transition into this modern "big data" era. METHODS: A review of the literature was performed for key articles referencing big data for neurosurgical care or related topics. RESULTS: In the present report, we first defined the nature and scope of data science from a technical perspective. We then discussed its relationship to the modern neurosurgical practice, highlighting key references, which might form a useful introductory reading list. CONCLUSIONS: Numerous challenges exist going forward; however, organized neurosurgery has an important role in fostering and facilitating these efforts to merge data science with neurosurgical practice.


Assuntos
Big Data , Neurocirurgia , Procedimentos Neurocirúrgicos , Humanos , Neurocirurgiões
8.
Int J Biometeorol ; 64(1): 95-104, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31478106

RESUMO

This study aims to use big data (climate data, internet query data and school calendar patterns (SCP)) to improve pertussis surveillance and prediction, and develop an early warning model for pertussis epidemics. We collected weekly pertussis notifications, SCP, climate and internet search query data (Baidu index (BI)) in Jinan, China between 2013 and 2017. Time series decomposition and temporal risk assessment were used for examining the epidemic features in pertussis infections. A seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to predict pertussis occurrence using identified predictors. Our study demonstrates clear seasonal patterns in pertussis epidemics, and pertussis activity was most significantly associated with BI at 2-week lag (rBI = 0.73, p < 0.05), temperature at 1-week lag (rtemp = 0.19, p < 0.05) and rainfall at 2-week lag (rrainfall = 0.27, p < 0.05). No obvious relationship between pertussis peaks and school attendance was found in the study. Pertussis cases were more likely to be temporally concentrated throughout the epidemics during the study period. SARIMA models with 2-week-lagged BI and 1-week-lagged temperature had better predictive performance (ßsearch query = 0.06, p = 0.02; ßtemp = 0.16, p = 0.03) with large correlation coefficients (r = 0.67, p < 0.01) and low root mean squared error (RMSE) value (r = 3.59). The regression tree model identified threshold values of potential predictors (search query, climate and SCP) for pertussis epidemics. Our results showed that internet query in conjunction with social and climatic data can predict pertussis epidemics, which is a foundation of using such data to develop early warning systems.


Assuntos
Epidemias , Coqueluche , Big Data , China , Cidades , Humanos , Incidência
10.
Nat Biotechnol ; 37(12): 1482-1492, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31796933

RESUMO

The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data points. We compare PHATE to other tools on a variety of artificial and biological datasets, and find that it consistently preserves a range of patterns in data, including continual progressions, branches and clusters, better than other tools. We define a manifold preservation metric, which we call denoised embedding manifold preservation (DEMaP), and show that PHATE produces lower-dimensional embeddings that are quantitatively better denoised as compared to existing visualization methods. An analysis of a newly generated single-cell RNA sequencing dataset on human germ-layer differentiation demonstrates how PHATE reveals unique biological insight into the main developmental branches, including identification of three previously undescribed subpopulations. We also show that PHATE is applicable to a wide variety of data types, including mass cytometry, single-cell RNA sequencing, Hi-C and gut microbiome data.


Assuntos
Genômica/métodos , Ensaios de Triagem em Larga Escala/métodos , Processamento de Imagem Assistida por Computador/métodos , Algoritmos , Animais , Big Data , Diferenciação Celular , Células Cultivadas , Simulação por Computador , Bases de Dados Genéticas , Microbioma Gastrointestinal , Humanos , Camundongos , Análise de Sequência de RNA , Análise de Célula Única
11.
BMC Bioinformatics ; 20(Suppl 9): 366, 2019 Nov 22.
Artigo em Inglês | MEDLINE | ID: mdl-31757212

RESUMO

BACKGROUND: Several large public repositories of microarray datasets and RNA-seq data are available. Two prominent examples include ArrayExpress and NCBI GEO. Unfortunately, there is no easy way to import and manipulate data from such resources, because the data is stored in large files, requiring large bandwidth to download and special purpose data manipulation tools to extract subsets relevant for the specific analysis. RESULTS: TACITuS is a web-based system that supports rapid query access to high-throughput microarray and NGS repositories. The system is equipped with modules capable of managing large files, storing them in a cloud environment and extracting subsets of data in an easy and efficient way. The system also supports the ability to import data into Galaxy for further analysis. CONCLUSIONS: TACITuS automates most of the pre-processing needed to analyze high-throughput microarray and NGS data from large publicly-available repositories. The system implements several modules to manage large files in an easy and efficient way. Furthermore, it is capable deal with Galaxy environment allowing users to analyze data through a user-friendly interface.


Assuntos
Big Data , Coleta de Dados , Software , Transcriptoma/genética , Linhagem Celular Tumoral , Bases de Dados Genéticas , Humanos , Interface Usuário-Computador
12.
Klin Monbl Augenheilkd ; 236(12): 1418-1422, 2019 Dec.
Artigo em Alemão | MEDLINE | ID: mdl-31671463

RESUMO

Age-related macular degeneration (AMD) is the leading cause of blindness in the western world. Intravitreal injection of anti-vascular endothelial growth factor (anti-VEGF) is an effective therapy of the neovascular form of this condition. Multimodal imaging and standardised electronic patient documentation have helped to improve the diagnosis and management of AMD patients recent years. With the advent of artificial intelligence and big data, there are many opportunities for the future. This article is intended to give an overview of possible applications.


Assuntos
Inteligência Artificial , Big Data , Neovascularização de Coroide , Degeneração Macular , Inibidores da Angiogênese , Humanos , Injeções Intravítreas , Degeneração Macular/terapia , Ranibizumab , Fator A de Crescimento do Endotélio Vascular
13.
Adv Exp Med Biol ; 1192: 3-15, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31705487

RESUMO

The modern society is a so-called era of big data. Whereas nearly everybody recognizes the "era of big data", no one can exactly define how big the data is a "big data". The reason for the ambiguity of the term big data mainly arises from the widespread of using that term. Along the widespread application of the digital technology in the everyday life, a large amount of data is generated every second in relation with every human behavior (i.e., measuring body movements through sensors, texts sent and received via social networking services). In addition, nonhuman data such as weather and Global Positioning System signals has been cumulated and analyzed in perspectives of big data (Kan et al. in Int J Environ Res Public Health 15(4), 2018 [1]). The big data has also influenced the medical science, which includes the field of psychiatry (Monteith et al. in Int J Bipolar Disord 3(1):21, 2015 [2]). In this chapter, we first introduce the definition of the term "big data". Then, we discuss researches which apply big data to solve problems in the clinical practice of psychiatry.


Assuntos
Big Data , Psiquiatria , Humanos , Pesquisa
15.
Medicine (Baltimore) ; 98(43): e17630, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31651877

RESUMO

BACKGROUND: The randomized controlled trial (RCT) is the gold-standard research design in biomedicine. However, practical concerns often limit the sample size, n, the number of patients in a RCT. We aim to show that the power of a RCT can be increased by increasing p, the number of baseline covariates (sex, age, socio-demographic, genomic, and clinical profiles et al, of the patients) collected in the RCT (referred to as the 'dimension'). METHODS: The conventional test for treatment effects is based on testing the 'crude null' that the outcomes of the subjects are of no difference between the two arms of a RCT. We propose a 'high-dimensional test' which is based on testing the 'sharp null' that the experimental intervention has no treatment effect whatsoever, for patients of any covariate profile. RESULTS: Using computer simulations, we show that the high-dimensional test can become very powerful in detecting treatment effects for very large p, but not so for small or moderate p. Using a real dataset, we demonstrate that the P value of the high-dimensional test decreases as the number of baseline covariates increases, though it is still not significant. CONCLUSION: In this big-data era, pushing p of a RCT to the millions, billions, or even trillions may someday become feasible. And the high-dimensional test proposed in this study can become very powerful in detecting treatment effects.


Assuntos
Big Data , Ensaios Clínicos Controlados Aleatórios como Assunto , Simulação por Computador , Interpretação Estatística de Dados , Humanos , Tamanho da Amostra
17.
Clin Exp Rheumatol ; 37 Suppl 120(5): 64-72, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31621574

RESUMO

In the most recent years, an extraordinary research effort has emerged to disentangle osteoarthritis heterogeneity, opening new avenues for progressing with therapeutic development and unravelling the pathogenesis of this complex condition. Several phenotypes and endotypes have been proposed albeit none has been sufficiently validated for clinical or research use as yet. This review discusses the latest advances in OA phenotyping including how new modern statistical strategies based on machine learning and big data can help advance this field of research.


Assuntos
Osteoartrite , Medicina de Precisão , Big Data , Previsões , Humanos , Osteoartrite/classificação , Fenótipo
18.
Hist Philos Life Sci ; 41(4): 41, 2019 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-31591649

RESUMO

Dominant forms of contemporary big-data based digital citizen science do not question the institutional divide between qualified experts and lay-persons. In our paper, we turn to the historical case of a large-scale amateur project on biogeographical birdwatching in the late nineteenth and early twentieth century to show that networked amateur research (that produces a large set of data) can operate in a more autonomous mode. This mode depends on certain cultural values, the constitution of specific knowledge objects, and the design of self-governed infrastructures. We conclude by arguing that the contemporary quest for autonomous citizen science is part of a broader discourse on the autonomy of scientific research in general. Just as the actors in our historical case positioned themselves against the elitism of gentlemen scientists, avant-garde groups of the twenty first century like biohackers and civic tech enthusiasts position themselves against the system of professional science-while "digital citizen science" remains to oscillate between claims for autonomy and realities of heteronomy, constantly reaffirming the classic lay-expert divide.


Assuntos
Distribuição Animal , Big Data , Aves , Animais , /métodos , Alemanha , História do Século XIX , História do Século XX
19.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 36(5): 818-826, 2019 Oct 25.
Artigo em Chinês | MEDLINE | ID: mdl-31631631

RESUMO

The analysis of big data in medical field cannot be isolated from the high quality clinical database, and the construction of first aid database in our country is still in the early stage of exploration. This paper introduces the idea and key technology of the construction of multi-parameter first aid database. By combining emergency business flow with information flow, an emergency data integration model was designed with reference to the architecture of the Medical Information Mart for Intensive Care III (MIMIC-III), created by Computational Physiology Laboratory of Massachusetts Institute of Technology (MIT), and a high-quality first-aid database was built. The database currently covers 22 941 medical records for 19 814 different patients from May 2015 to October 2017, including relatively complete information on physiology, biochemistry, treatment, examination, nursing, etc. And based on the database, the first First-Aid Big Data Datathon event, which 13 teams from all over the country participated in, was launched. The First-Aid database provides a reference for the construction and application of clinical database in China. And it could provide powerful data support for scientific research, clinical decision making and the improvement of medical quality, which will further promote secondary analysis of clinical data in our country.


Assuntos
Big Data , Cuidados Críticos , Bases de Dados Factuais , Informática Médica , Humanos
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