Utilizing Big Data analytics and electronic health record data in HIV prevention, treatment, and care research: a literature review

Qiao, Shan; Li, Xiaoming; Olatosi, Bankole; Young, Sean D.

doi:10.1080/09540121.2021.1948499

Cited by 7 publications

(3 citation statements)

References 68 publications

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“…The application of Big Data Science in the field of HIV has expanded significantly over the last decade alongside machine learning algorithms [26], but often with limited attention to how these approaches may amplify or mitigate existing health inequities if results are used to guide the application of interventions and resources. At its core, Big Data Science comprises high-dimensional data, characterized by the "volume, variety, and velocity" of big data [27], along with the application of advanced statistical techniques and modeling approaches that capitalize on computational capacity for data storage and analytic speed [28]. Several frameworks have been proposed to manage challenges related to the social, ethical, and legal considerations of big data [29][30][31].…”

Section: Increasing the Specificity Of The Hiv Response Means Explici...mentioning

confidence: 99%

“…The promise of Big Data Science in guiding the HIV response lies in its potential to help identify gaps in previously undiscovered or understudied pathways to HIV acquisition and onward transmission, including the consequences for health outcomes and associated comorbidities. Integral to Big Data Science for HIV is the concept that large and diverse data sources [28,36,37], can be leveraged to better understand HIV epidemics and the pandemic as a whole. These data sources consist of routinely collected data such as electronic health records and program/clinic registers [38,39], surveillance data, and auxiliary data sources such as social media and digital data along with traditional research data collected through trials or observational studies.…”

Section: The Promise Of Big Data Sciencementioning

confidence: 99%

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Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response

Rucinski,

Knight,

Willis

et al. 2024

Curr HIV/AIDS Rep

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Purpose of Review Big Data Science can be used to pragmatically guide the allocation of resources within the context of national HIV programs and inform priorities for intervention. In this review, we discuss the importance of grounding Big Data Science in the principles of equity and social justice to optimize the efficiency and effectiveness of the global HIV response. Recent Findings Social, ethical, and legal considerations of Big Data Science have been identified in the context of HIV research. However, efforts to mitigate these challenges have been limited. Consequences include disciplinary silos within the field of HIV, a lack of meaningful engagement and ownership with and by communities, and potential misinterpretation or misappropriation of analyses that could further exacerbate health inequities. Summary Big Data Science can support the HIV response by helping to identify gaps in previously undiscovered or understudied pathways to HIV acquisition and onward transmission, including the consequences for health outcomes and associated comorbidities. However, in the absence of a guiding framework for equity, alongside meaningful collaboration with communities through balanced partnerships, a reliance on big data could continue to reinforce inequities within and across marginalized populations.

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Section: Increasing the Specificity Of The Hiv Response Means Explici...mentioning

confidence: 99%

Section: The Promise Of Big Data Sciencementioning

confidence: 99%

Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response

Rucinski,

Knight,

Willis

et al. 2024

Curr HIV/AIDS Rep

View full text Add to dashboard Cite

show abstract

“…Even in drug discovery research, the amount of data that can be collected and processed is exploding. This is driven by various advantages, such as the proliferation of comprehensive biomolecular measurement devices, including next-generation sequences, or the digitization of medical information [51][52][53][54][55]. In contrast, the pharmaceutical industry faces challenges that complicate the development of new drugs, such as rising research and development costs and the depletion of drug targets that can be developed using conventional approaches.…”

Section: Introductionmentioning

confidence: 99%

Applications of Deep Learning for Drug Discovery Systems with BigData

Matsuzaka

Yashiro

2022

BioMedInformatics

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The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process of pharmaceutical research and development, is progressing. By using the ability to process large amounts of data, which is a characteristic of AI, and achieving advanced data analysis and inference, there are benefits such as shortening development time, reducing costs, and reducing the workload of researchers. There are various problems in drug development, but the following two issues are particularly problematic: (1) the yearly increases in development time and cost of drugs and (2) the difficulty in finding highly accurate target genes. Therefore, screening and simulation using AI are expected. Researchers have high demands for data collection and the utilization of infrastructure for AI analysis. In the field of drug discovery, for example, interest in data use increases with the amount of chemical or biological data available. The application of AI in drug discovery is becoming more active due to improvement in computer processing power and the development and spread of machine-learning frameworks, including deep learning. To evaluate performance, various statistical indices have been introduced. However, the factors affected in performance have not been revealed completely. In this study, we summarized and reviewed the applications of deep learning for drug discovery with BigData.

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Ethical Perspectives in Using Technology-Enabled Research for Key HIV Populations in Rights-Constrained Settings

Flores

Young

2023

Curr HIV/AIDS Rep

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Utilizing Big Data analytics and electronic health record data in HIV prevention, treatment, and care research: a literature review

Cited by 7 publications

References 68 publications

Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response

Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response

Applications of Deep Learning for Drug Discovery Systems with BigData

Ethical Perspectives in Using Technology-Enabled Research for Key HIV Populations in Rights-Constrained Settings

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