Database‐driven research and big data analytic approaches in transfusion medicine

Ning, Shuoyan; Li, Na; Barty, Rebecca; Arnold, Donald M.; Heddle, Nancy M.

doi:10.1111/trf.16939

Cited by 5 publications

(3 citation statements)

References 50 publications

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“…One way to conceptualize the application of big data to transfusion medicine is to combine various types of health data, including electronic health records [5, 6], electronic medical records [7], personal health records [8], laboratory information systems [9], medical practice management [10] software, and hemovigilance data [11]. This combination creates a large database that healthcare professionals can use to identify patterns and trends, leading to improved practices in blood product usage, inventory management, and more, ultimately, improving patient outcomes [4, 12]. By including hemovigilance data in the discussion of big data in transfusion medicine, it highlights the importance of monitoring and ensuring the safety and quality of blood products, which is a critical aspect of transfusion medicine.…”

Section: Introductionmentioning

confidence: 99%

Big Data in Transfusion Medicine and Artificial Intelligence Analysis for Red Blood Cell Quality Control

Lopes¹,

Recktenwald²,

Simionato³

et al. 2023

Transfus Med Hemother

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Background: “Artificial intelligence” and “big data” increasingly take the step from just being interesting concepts to being relevant or even part of our lives. This general statement holds also true for transfusion medicine. Besides all advancements in transfusion medicine, there is not yet an established red blood cell quality measure, which is generally applied. Summary: We highlight the usefulness of big data in transfusion medicine. Furthermore, we emphasize in the example of quality control of red blood cell units the application of artificial intelligence. Key Messages: A variety of concepts making use of big data and artificial intelligence are readily available but still await to be implemented into any clinical routine. For the quality control of red blood cell units, clinical validation is still required.

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Section: Introductionmentioning

confidence: 99%

Big Data in Transfusion Medicine and Artificial Intelligence Analysis for Red Blood Cell Quality Control

Lopes¹,

Recktenwald²,

Simionato³

et al. 2023

Transfus Med Hemother

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show abstract

“…This data-intensive (i.e., big data) approach to assessing stored RBC quality necessitates using computational statistical techniques to objectively extract meaningful patterns from these datasets ( 84 ). Such combination of big data and computational statistical tools is critical to biological interpretation and aiding the data-driven selection of stored RBCs for precision transfusion medicine ( 85 ).…”

Section: Current Practices In Rbc Storage and Quality Assessmentmentioning

confidence: 99%

Assessment of stored red blood cells through lab-on-a-chip technologies for precision transfusion medicine

Işıksaçan

D’Alessandro

Wolf

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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Transfusion of red blood cells (RBCs) is one of the most valuable and widespread treatments in modern medicine. Lifesaving RBC transfusions are facilitated by the cold storage of RBC units in blood banks worldwide. Currently, RBC storage and subsequent transfusion practices are performed using simplistic workflows. More specifically, most blood banks follow the “first-in-first-out” principle to avoid wastage, whereas most healthcare providers prefer the “last-in-first-out” approach simply favoring chronologically younger RBCs. Neither approach addresses recent advances through -omics showing that stored RBC quality is highly variable depending on donor-, time-, and processing-specific factors. Thus, it is time to rethink our workflows in transfusion medicine taking advantage of novel technologies to perform RBC quality assessment. We imagine a future where lab-on-a-chip technologies utilize novel predictive markers of RBC quality identified by -omics and machine learning to usher in a new era of safer and precise transfusion medicine.

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“…In this research, we propose a computer infrastructure based on decentralised data warehouses. More data can be processed with ease and efficiency [16] thanks to this system's smart design and inexpensive cost. With this article.…”

Section: Related Workmentioning

confidence: 99%

Real-time COPD-based Clinical Data Management using Machine Learning

Kawadkar,

Jeeva Karunya,

B.Rebecca

2023

IJSMIEN

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Our research focuses on using big data technology with a structured and personalized nursing care approach to improve treatment for patients with COPD and respiratory arrest. This study evaluates a continuous care strategy for COPD and RF therapies using big data. The approach reduces COPD and RF severity and flare-up frequency, duration, and number to improve clients’ quality of life. A prominent university hospital’s lung health system included 100 COPD and RF patients. They were randomly assigned to the control or experimental group. A department called control group patients first. After the consultation, patients received answers to their issues. Trial participants received 24/7 care through the internet and big data analytics. The unit’s first nursing care anchored these activities. Health, fitness, function evaluation, and rehabilitation goals can be customized for each patient. Individualized therapy using big data technologies improves outcomes for stable COPD and RF patients, including lung capacity, frequency of acute episodes, readmission rates, self-management, and quality of life. This strategy may be useful in a nursing home where the patient receives constant care.

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Database‐driven research and big data analytic approaches in transfusion medicine

Cited by 5 publications

References 50 publications

Big Data in Transfusion Medicine and Artificial Intelligence Analysis for Red Blood Cell Quality Control

Big Data in Transfusion Medicine and Artificial Intelligence Analysis for Red Blood Cell Quality Control

Assessment of stored red blood cells through lab-on-a-chip technologies for precision transfusion medicine

Real-time COPD-based Clinical Data Management using Machine Learning

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