Biomedical data analytics for better patient outcomes

被引:0
|
作者
Ghofrani, Alireza [1 ]
Taherdoost, Hamed [1 ,2 ,3 ,4 ]
机构
[1] Hamta Business Corp, Vancouver, BC, Canada
[2] Univ Canada West, Vancouver, BC, Canada
[3] Westcliff Univ, Irvine, CA 92614 USA
[4] Global Univ Syst, GUS Inst, London, England
关键词
clinical practice; patient care enhancement; data sources; health-care innovation; medical decision-making; QUALITY-OF-LIFE; BIG DATA; HEALTH; CARE; DISEASE; CLASSIFICATION; FRAMEWORK; PATTERNS; MODELS;
D O I
10.1016/j.drudis.2024.104280
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Medical professionals today have access to immense amounts of data, which enables them to make decisions that enhance patient care and treatment efficacy. This innovative strategy can improve global health care by bridging the divide between clinical practice and medical research. This paper reviews biomedical developments aimed at improving patient outcomes by addressing three main questions regarding techniques, data sources and challenges. The review includes peer-reviewed articles from 2018 to 2023, found via systematic searches in PubMed, Scopus and Google Scholar. The results show diverse disease-specific applications. Challenges such as data quality and ethics are discussed, underscoring data analytics' potential for patient-focused health care. The review concludes that successful implementation requires addressing gaps, collaboration and innovation in biomedical science and data analytics.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] Comparison of an Accelerated Garble Embedding Methodology for Privacy Preserving in Biomedical Data Analytics
    Hristov-Kalamov, Nikola
    Fernandez-Ruiz, Raul
    Alvarez-Marquina, Agustin
    Nunez-Vidal, Esther
    Dominguez-Mateos, Francisco
    Palacios-Alonso, Daniel
    ARTIFICIAL INTELLIGENCE FOR NEUROSCIENCE AND EMOTIONAL SYSTEMS, PT I, IWINAC 2024, 2024, 14674 : 282 - 299
  • [32] Data Science and Predictive Analytics: Biomedical and Health Applications Using R.
    Saracco, Benjamin H.
    JOURNAL OF THE MEDICAL LIBRARY ASSOCIATION, 2020, 108 (02) : 334 - 334
  • [33] ENABLING AN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING INFRASTRUCTURE TO IMPROVE PATIENT CARE AND OUTCOMES BY IMPROVING HEALTHCARE DATA ANALYTICS
    Walter, Andreas
    Drake, Douglas T.
    MEDICINE, 2021, 100 (33)
  • [34] Understanding patient needs and predicting outcomes in IgA nephropathy using data analytics and artificial intelligence: a narrative review
    Schena, Francesco Paolo
    Manno, Carlo
    Strippoli, Giovanni
    CLINICAL KIDNEY JOURNAL, 2023, 16 : ii55 - ii61
  • [35] Promises and pitfalls of the newly emerging outcomes databases - Better data makes better potential outcomes
    Steinwachs, DM
    BEHAVIORAL HEALTHCARE TOMORROW, 1997, 6 (02): : 48 - +
  • [36] PATIENT - FAMILY - MEDICAL - LAWYER: WORKING TOGETHER FOR BETTER OUTCOMES FOR THE PATIENT
    Gianoutsos, P.
    JOURNAL OF THORACIC ONCOLOGY, 2011, 6 (03) : S2 - S3
  • [37] Sharing Health Data for Better Outcomes on PatientsLikeMe
    Wicks, Paul
    Massagli, Michael
    Frost, Jeana
    Brownstein, Catherine
    Okun, Sally
    Vaughan, Timothy
    Bradley, Richard
    Heywood, James
    JOURNAL OF MEDICAL INTERNET RESEARCH, 2010, 12 (02)
  • [38] The Pathway to Patient Data Ownership and Better Health
    Mikk, Katherine A.
    Sleeper, Harry A.
    Topol, Eric J.
    JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2017, 318 (15): : 1433 - 1434
  • [39] Is Higher Patient Satisfaction Associated With Better Stroke Outcomes?
    Xiang, Xiao
    Xu, Wendy Yi
    Foraker, Randi E.
    AMERICAN JOURNAL OF MANAGED CARE, 2017, 23 (10): : E316 - +
  • [40] TRANSLATION OF PGCS MOLECULAR FINDINGS INTO BETTER PATIENT OUTCOMES
    Austin, Jehannine
    EUROPEAN NEUROPSYCHOPHARMACOLOGY, 2019, 29 : S7 - S8