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1.
Ann Med ; 56(1): 2362869, 2024 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-38853633

RESUMO

Infectious diseases are a major threat for human and animal health worldwide. Artificial Intelligence (AI) combined algorithms including Machine Learning and Big Data analytics have emerged as a potential solution to analyse diverse datasets and face challenges posed by infectious diseases. In this commentary we explore the potential applications and limitations of ML to management of infectious disease. It explores challenges in key areas such as outbreak prediction, pathogen identification, drug discovery, and personalized medicine. We propose potential solutions to mitigate these hurdles and applications of ML to identify biomolecules for effective treatment and prevention of infectious diseases. In addition to use of ML for management of infectious diseases, potential applications are based on catastrophic evolution events for the identification of biomolecular targets to reduce risks for infectious diseases and vaccinomics for discovery and characterization of vaccine protective antigens using intelligent Big Data analytics techniques. These considerations set a foundation for developing effective strategies for managing infectious diseases in the future.


Infectious diseases are a major challenge worldwideArtificial Intelligence (AI) combined algorithms have emerged as a potential solution to analyse diverse datasets and face challenges posed by infectious diseasesFuture directions include applications of ML to identify biomolecules for effective treatment and prevention of infectious diseases.


Assuntos
Doenças Transmissíveis , Aprendizado de Máquina , Humanos , Doenças Transmissíveis/epidemiologia , Medicina de Precisão/métodos , Descoberta de Drogas/métodos , Big Data , Inteligência Artificial , Algoritmos
2.
PLoS One ; 19(5): e0294481, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38776299

RESUMO

The COVID-19 pandemic has triggered a global public health crisis, affecting hundreds of countries. With the increasing number of infected cases, developing automated COVID-19 identification tools based on CT images can effectively assist clinical diagnosis and reduce the tedious workload of image interpretation. To expand the dataset for machine learning methods, it is necessary to aggregate cases from different medical systems to learn robust and generalizable models. This paper proposes a novel deep learning joint framework that can effectively handle heterogeneous datasets with distribution discrepancies for accurate COVID-19 identification. We address the cross-site domain shift by redesigning the COVID-Net's network architecture and learning strategy, and independent feature normalization in latent space to improve prediction accuracy and learning efficiency. Additionally, we propose using a contrastive training objective to enhance the domain invariance of semantic embeddings and boost classification performance on each dataset. We develop and evaluate our method with two large-scale public COVID-19 diagnosis datasets containing CT images. Extensive experiments show that our method consistently improves the performance both datasets, outperforming the original COVID-Net trained on each dataset by 13.27% and 15.15% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods.


Assuntos
Big Data , COVID-19 , Humanos , COVID-19/epidemiologia , Tomografia Computadorizada por Raios X/métodos , SARS-CoV-2/isolamento & purificação , Aprendizado Profundo , Hospitais , Pandemias , Aprendizado de Máquina , Sistemas de Informação
3.
PLoS One ; 19(5): e0303297, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38768218

RESUMO

The planning of human resources and the management of enterprises consider the organization's size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations' poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses' operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs.


Assuntos
Big Data , Alocação de Recursos , Humanos , Alocação de Recursos/métodos , Eficiência Organizacional
4.
PLoS One ; 19(5): e0299726, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38787862

RESUMO

The layout, scale and spatial form of urban employment centers are important guidelines for the rational layout of public service facilities such as urban transportation, medical care, and education. In this paper, we use Internet cell phone positioning data to identify the workplace and residence of users in the Beijing city area and obtain commuting data of the employed to measure the employment center system in Beijing. Firstly, the employment density distribution is generated using the data of the working places of the employed persons, and the employment centers are identified based on the employment density of Beijing. Then, we use the business registration data of employment centers to measure the industrial diversity within the employment centers by using the ecological Shannon Wiener Diversity Index, and combine the commuting links between employment centers and places of residence to measure the energy level of each employment center, analyze the hinterland and sphere of influence of each center, and finally using the industrial diversity index of employment centers and the average commuting time of employed persons, combined with the K-Means clustering algorithm, to classify the employment centers in Beijing. The employment center identification and classification method based on big data constructed in this study can help solve the limitations of the previous employment center system research in terms of center identification and commuting linkage measurement due to large spatial units and lack of commuting data to a certain extent. The study can provide reference for the regular understanding and technical analysis of employment centers and provide help for the employment multi-center system in Beijing in terms of quantifying the employment spatial structure, guiding the construction of multi-center system, and adjusting the land use rules.


Assuntos
Emprego , Meios de Transporte , Pequim , Humanos , Emprego/estatística & dados numéricos , Meios de Transporte/estatística & dados numéricos , Big Data , Local de Trabalho , População Urbana
5.
Sci Rep ; 14(1): 11887, 2024 05 24.
Artigo em Inglês | MEDLINE | ID: mdl-38789442

RESUMO

Translational data is of paramount importance for medical research and clinical innovation. It has the potential to benefit individuals and organizations, however, the protection of personal data must be guaranteed. Collecting diverse omics data and electronic health records (EHR), re-using the minimized data, as well as providing a reliable data transfer between different institutions are mandatory steps for the development of the promising field of big data and artificial intelligence in medical research. This is made possible within the proposed data platform in this research project. The established data platform enables the collaboration between public and commercial organizations by data transfer from various clinical systems into a cloud for supporting multi-site research while ensuring compliant data governance.


Assuntos
Segurança Computacional , Registros Eletrônicos de Saúde , Humanos , Big Data , Pesquisa Biomédica , Comportamento Cooperativo
6.
Qual Life Res ; 33(7): 1975-1983, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38771557

RESUMO

PURPOSE: Individuals with health conditions often use online patient forums to share their experiences. These patient data are freely available and have rarely been used in patient-reported outcomes (PRO) research. Web scraping, the automated identification and coding of webpage data, can be employed to collect patient experiences for PRO research. The objective of this study was to assess the feasibility of using web scraping to support the development of a new PRO measure for breast implant illness (BII). METHODS: Nine publicly available BII-specific web forums were chosen post-consultation with two prominent BII advocacy leaders. The Python Selenium and Pandas packages were used to automate extraction of de-identified text from the individual posts/comments into a spreadsheet. Data were coded using a line-by-line approach and constant comparison was used to create top-level domains and sub-domains. RESULTS: 6362 unique codes were identified and organized into four top-level domains of information needs, symptom experiences, life impact of BII, and care experiences. Information needs of women included seeking/sharing information pre-breast implant surgery, post-breast implant surgery, while contemplating explant surgery, and post-explant surgery. Symptoms commonly described by women included fatigue, brain fog, and musculoskeletal symptoms. Many comments described BII's impact on daily activities and psychosocial wellbeing. Lastly, some comments described negative care experiences and experiences related to advocating for themselves to providers. CONCLUSION: This proof-of-concept study demonstrated the feasibility of employing web scraping as a cost-effective, efficient method to understand the experiences of women with BII. These data will be used to inform the development of a BII-specific PROM.


Assuntos
Implantes de Mama , Internet , Medidas de Resultados Relatados pelo Paciente , Humanos , Feminino , Implantes de Mama/psicologia , Big Data , Estudo de Prova de Conceito , Qualidade de Vida , Estudos de Viabilidade
7.
Sci Adv ; 10(22): eadj0266, 2024 May 31.
Artigo em Inglês | MEDLINE | ID: mdl-38820165

RESUMO

Selection bias poses a substantial challenge to valid statistical inference in nonprobability samples. This study compared estimates of the first-dose COVID-19 vaccination rates among Indian adults in 2021 from a large nonprobability sample, the COVID-19 Trends and Impact Survey (CTIS), and a small probability survey, the Center for Voting Options and Trends in Election Research (CVoter), against national benchmark data from the COVID Vaccine Intelligence Network. Notably, CTIS exhibits a larger estimation error on average (0.37) compared to CVoter (0.14). Additionally, we explored the accuracy (regarding mean squared error) of CTIS in estimating successive differences (over time) and subgroup differences (for females versus males) in mean vaccine uptakes. Compared to the overall vaccination rates, targeting these alternative estimands comparing differences or relative differences in two means increased the effective sample size. These results suggest that the Big Data Paradox can manifest in countries beyond the United States and may not apply equally to every estimand of interest.


Assuntos
Big Data , Vacinas contra COVID-19 , COVID-19 , SARS-CoV-2 , Vacinação , Humanos , COVID-19/epidemiologia , COVID-19/prevenção & controle , Vacinas contra COVID-19/administração & dosagem , Feminino , Vacinação/estatística & dados numéricos , Masculino , SARS-CoV-2/imunologia , Adulto , Inquéritos e Questionários , Índia/epidemiologia , Pessoa de Meia-Idade
8.
Comput Biol Med ; 176: 108577, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38739981

RESUMO

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.


Assuntos
Big Data , Neoplasias , Humanos , Neoplasias/terapia , Aprendizado de Máquina , Inteligência Artificial
9.
Brief Bioinform ; 25(3)2024 Mar 27.
Artigo em Inglês | MEDLINE | ID: mdl-38711370

RESUMO

Across many scientific disciplines, the development of computational models and algorithms for generating artificial or synthetic data is gaining momentum. In biology, there is a great opportunity to explore this further as more and more big data at multi-omics level are generated recently. In this opinion, we discuss the latest trends in biological applications based on process-driven and data-driven aspects. Moving ahead, we believe these methodologies can help shape novel multi-omics-scale cellular inferences.


Assuntos
Algoritmos , Biologia Computacional , Biologia Computacional/métodos , Genômica/métodos , Humanos , Big Data , Proteômica/métodos , Multiômica
10.
BMC Public Health ; 24(1): 1254, 2024 May 07.
Artigo em Inglês | MEDLINE | ID: mdl-38714982

RESUMO

BACKGROUND: Depression is a global burden with profound personal and economic consequences. Previous studies have reported that the amount of physical activity is associated with depression. However, the relationship between the temporal patterns of physical activity and depressive symptoms is poorly understood. In this exploratory study, we hypothesize that a particular temporal pattern of daily physical activity could be associated with depressive symptoms and might be a better marker than the total amount of physical activity. METHODS: To address the hypothesis, we investigated the association between depressive symptoms and daily dominant activity behaviors based on 24-h temporal patterns of physical activity. We conducted a cross-sectional study on NHANES 2011-2012 data collected from the noninstitutionalized civilian resident population of the United States. The number of participants that had the whole set of physical activity data collected by the accelerometer is 6613. Among 6613 participants, 4242 participants had complete demography and Patient Health Questionnaire-9 (PHQ-9) questionnaire, a tool to quantify depressive symptoms. The association between activity-count behaviors and depressive symptoms was analyzed using multivariable logistic regression to adjust for confounding factors in sequential models. RESULTS: We identified four physical activity-count behaviors based on five physical activity-counting patterns classified by unsupervised machine learning. Regarding PHQ-9 scores, we found that evening dominant behavior was positively associated with depressive symptoms compared to morning dominant behavior as the control group. CONCLUSIONS: Our results might contribute to monitoring and identifying individuals with latent depressive symptoms, emphasizing the importance of nuanced activity patterns and their probability of assessing depressive symptoms effectively.


Assuntos
Depressão , Exercício Físico , Aprendizado de Máquina , Humanos , Estudos Transversais , Masculino , Feminino , Exercício Físico/psicologia , Depressão/epidemiologia , Pessoa de Meia-Idade , Adulto , Estados Unidos/epidemiologia , Big Data , Inquéritos Nutricionais , Fatores de Tempo , Acelerometria , Idoso
11.
Rev Saude Publica ; 58: 17, 2024.
Artigo em Inglês, Português | MEDLINE | ID: mdl-38716929

RESUMO

OBJECTIVE: This study aims to integrate the concepts of planetary health and big data into the Donabedian model to evaluate the Brazilian dengue control program in the state of São Paulo. METHODS: Data science methods were used to integrate and analyze dengue-related data, adding context to the structure and outcome components of the Donabedian model. This data, considering the period from 2010 to 2019, was collected from sources such as Department of Informatics of the Unified Health System (DATASUS), the Brazilian Institute of Geography and Statistics (IBGE), WorldClim, and MapBiomas. These data were integrated into a Data Warehouse. K-means algorithm was used to identify groups with similar contexts. Then, statistical analyses and spatial visualizations of the groups were performed, considering socioeconomic and demographic variables, soil, health structure, and dengue cases. OUTCOMES: Using climate variables, the K-means algorithm identified four groups of municipalities with similar characteristics. The comparison of their indicators revealed certain patterns in the municipalities with the worst performance in terms of dengue case outcomes. Although presenting better economic conditions, these municipalities held a lower average number of community healthcare agents and basic health units per inhabitant. Thus, economic conditions did not reflect better health structure among the three studied indicators. Another characteristic of these municipalities is urbanization. The worst performing municipalities presented a higher rate of urban population and human activity related to urbanization. CONCLUSIONS: This methodology identified important deficiencies in the implementation of the dengue control program in the state of São Paulo. The integration of several databases and the use of Data Science methods allowed the evaluation of the program on a large scale, considering the context in which activities are conducted. These data can be used by the public administration to plan actions and invest according to the deficiencies of each location.


Assuntos
Big Data , Dengue , Humanos , Dengue/prevenção & controle , Dengue/epidemiologia , Brasil/epidemiologia , Avaliação de Programas e Projetos de Saúde , Fatores Socioeconômicos , Programas Nacionais de Saúde , Algoritmos
12.
J Am Board Fam Med ; 37(2): 161-164, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38740469

RESUMO

This issue highlights changes in medical care delivery since the start of the COVID-19 pandemic and features research to advance the delivery of primary care. Several articles report on the effectiveness of telehealth, including its use for hospital follow-up, medication abortion, management of diabetes, and as a potential tool for reducing health disparities. Other articles detail innovations in clinical practice, from the use of artificial intelligence and machine learning to a validated simple risk score that can support outpatient triage decisions for patients with COVID-19. Notably one article reports the impact of a voluntary program using scribes in a large health system on physician documentation behaviors and performance. One article addresses the wage gap between early-career female and male family physicians. Several articles report on inappropriate testing for common health problems; are you following recommendations for ordering Pulmonary Function Tests, mt-sDNA for colon cancer screening, and HIV testing?


Assuntos
Inteligência Artificial , Big Data , COVID-19 , Medicina de Família e Comunidade , Telemedicina , Humanos , Medicina de Família e Comunidade/métodos , Medicina de Família e Comunidade/organização & administração , COVID-19/epidemiologia , Telemedicina/organização & administração , Telemedicina/métodos , SARS-CoV-2 , Melhoria de Qualidade , Atenção Primária à Saúde/organização & administração , Atenção Primária à Saúde/métodos , Pandemias
13.
PLoS One ; 19(5): e0298236, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38728314

RESUMO

Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal-spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution.


Assuntos
Big Data , Terremotos , China , Humanos , Smartphone , Desastres
14.
J Med Internet Res ; 26: e48572, 2024 May 03.
Artigo em Inglês | MEDLINE | ID: mdl-38700923

RESUMO

BACKGROUND: Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates. OBJECTIVE: In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety. METHODS: In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades. RESULTS: The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages. CONCLUSIONS: In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation.


Assuntos
Big Data , Mineração de Dados , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Humanos , Mineração de Dados/métodos , Farmacovigilância , Modelos Teóricos , Sistemas de Notificação de Reações Adversas a Medicamentos/estatística & dados numéricos
15.
J Neurol ; 271(6): 3616-3624, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38561543

RESUMO

BACKGROUND: The Big Multiple Sclerosis Data (BMSD) network ( https://bigmsdata.org ) was initiated in 2014 and includes the national multiple sclerosis (MS) registries of the Czech Republic, Denmark, France, Italy, and Sweden as well as the international MSBase registry. BMSD has addressed the ethical, legal, technical, and governance-related challenges for data sharing and so far, published three scientific papers on pooled datasets as proof of concept for its collaborative design. DATA COLLECTION: Although BMSD registries operate independently on different platforms, similarities in variables, definitions and data structure allow joint analysis of data. Certain coordinated modifications in how the registries collect adverse event data have been implemented after BMSD consensus decisions, showing the ability to develop together. DATA MANAGEMENT: Scientific projects can be proposed by external sponsors via the coordinating centre and each registry decides independently on participation, respecting its governance structure. Research datasets are established in a project-to-project fashion and a project-specific data model is developed, based on a unifying core data model. To overcome challenges in data sharing, BMSD has developed procedures for federated data analysis. FUTURE PERSPECTIVES: Presently, BMSD is seeking a qualification opinion from the European Medicines Agency (EMA) to conduct post-authorization safety studies (PASS) and aims to pursue a qualification opinion also for post-authorization effectiveness studies (PAES). BMSD aspires to promote the advancement of real-world evidence research in the MS field.


Assuntos
Esclerose Múltipla , Sistema de Registros , Humanos , Big Data , Disseminação de Informação , Cooperação Internacional , Esclerose Múltipla/epidemiologia , Esclerose Múltipla/terapia
17.
Med Klin Intensivmed Notfmed ; 119(5): 352-357, 2024 Jun.
Artigo em Alemão | MEDLINE | ID: mdl-38668882

RESUMO

Intensive care units provide a data-rich environment with the potential to generate datasets in the realm of big data, which could be utilized to train powerful machine learning (ML) models. However, the currently available datasets are too small and exhibit too little diversity due to their limitation to individual hospitals. This lack of extensive and varied datasets is a primary reason for the limited generalizability and resulting low clinical utility of current ML models. Often, these models are based on data from single centers and suffer from poor external validity. There is an urgent need for the development of large-scale, multicentric, and multinational datasets. Ensuring data protection and minimizing re-identification risks pose central challenges in this process. The "Amsterdam University Medical Center database (AmsterdamUMCdb)" and the "Salzburg Intensive Care database (SICdb)" demonstrate that open access datasets are possible in Europe while complying with the data protection regulations of the General Data Protection Regulation (GDPR). Another challenge in building intensive care datasets is the absence of semantic definitions in the source data and the heterogeneity of data formats. Establishing binding industry standards for the semantic definition is crucial to ensure seamless semantic interoperability between datasets.


Assuntos
Cuidados Críticos , Unidades de Terapia Intensiva , Aprendizado de Máquina , Humanos , Cuidados Críticos/normas , Alemanha , Segurança Computacional , Europa (Continente) , Bases de Dados Factuais , Conjuntos de Dados como Assunto , Big Data , Confidencialidade
18.
PLoS One ; 19(4): e0297028, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38557742

RESUMO

Machine learning techniques that rely on textual features or sentiment lexicons can lead to erroneous sentiment analysis. These techniques are especially vulnerable to domain-related difficulties, especially when dealing in Big data. In addition, labeling is time-consuming and supervised machine learning algorithms often lack labeled data. Transfer learning can help save time and obtain high performance with fewer datasets in this field. To cope this, we used a transfer learning-based Multi-Domain Sentiment Classification (MDSC) technique. We are able to identify the sentiment polarity of text in a target domain that is unlabeled by looking at reviews in a labelled source domain. This research aims to evaluate the impact of domain adaptation and measure the extent to which transfer learning enhances sentiment analysis outcomes. We employed transfer learning models BERT, RoBERTa, ELECTRA, and ULMFiT to improve the performance in sentiment analysis. We analyzed sentiment through various transformer models and compared the performance of LSTM and CNN. The experiments are carried on five publicly available sentiment analysis datasets, namely Hotel Reviews (HR), Movie Reviews (MR), Sentiment140 Tweets (ST), Citation Sentiment Corpus (CSC), and Bioinformatics Citation Corpus (BCC), to adapt multi-target domains. The performance of numerous models employing transfer learning from diverse datasets demonstrating how various factors influence the outputs.


Assuntos
Big Data , Briozoários , Animais , Análise de Sentimentos , Algoritmos , Biologia Computacional
20.
BMC Med Inform Decis Mak ; 24(1): 92, 2024 Apr 05.
Artigo em Inglês | MEDLINE | ID: mdl-38575951

RESUMO

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today.


Assuntos
Big Data , Tecnologia , Humanos , Biologia Computacional , Instalações de Saúde , Redes Neurais de Computação
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