Data Integration Challenges for Machine Learning in Precision Medicine

被引:40
|
作者
Martinez-Garcia, Mireya [1 ]
Hernandez-Lemus, Enrique [2 ,3 ]
机构
[1] Natl Inst Cardiol Ignacio Chavez, Clin Res Div, Mexico City, DF, Mexico
[2] Natl Inst Gen Med INMEGEN, Computat Gen Div, Mexico City, DF, Mexico
[3] Univ Nacl Autnoma Mexico, Ctr Complex Sci, Mexico City, DF, Mexico
关键词
precision medicine; machine learning; data integration; meta-data mining; computational intelligence; BIG DATA ANALYTICS; MICROARRAY EXPERIMENT MIAME; HEALTH-CARE; UK BIOBANK; PERSONALIZED MEDICINE; GENE ONTOLOGY; ARTIFICIAL-INTELLIGENCE; MINIMUM INFORMATION; METADATA CHECKLIST; VARIABLE SELECTION;
D O I
10.3389/fmed.2021.784455
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.
引用
收藏
页数:21
相关论文
共 50 条
  • [31] The Gender of Biomedical Data: Challenges for Personalised and Precision Medicine
    Pot, Mirjam
    Spahl, Wanda
    Prainsack, Barbara
    SOMATECHNICS, 2019, 9 (02) : 170 - 187
  • [32] Data Integration using Machine Learning
    Birgersson, Marcus
    Hansson, Gustav
    Franke, Ulrik
    2016 IEEE 20TH INTERNATIONAL ENTERPRISE DISTRIBUTED OBJECT COMPUTING WORKSHOP (EDOCW), 2016, : 313 - 322
  • [33] Machine Learning for Medical Data Integration
    Mueller, Armin
    Christmann, Lara-Sophie
    Kohler, Severin
    Eils, Roland
    Prasser, Fabian
    CARING IS SHARING-EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION-PROCEEDINGS OF MIE 2023, 2023, 302 : 691 - 695
  • [34] Machine learning for data mining in medicine
    Lavrac, N
    ARTIFICIAL INTELLIGENCE IN MEDICINE, 1999, 1620 : 47 - 62
  • [35] Machine learning and multi-omics data in chronic lymphocytic leukemia: the future of precision medicine?
    Tsagiopoulou, Maria
    Gut, Ivo G.
    FRONTIERS IN GENETICS, 2024, 14
  • [36] Machine Learning (ML) in Medicine: Review, Applications, and Challenges
    Rahmani, Amir Masoud
    Yousefpoor, Efat
    Yousefpoor, Mohammad Sadegh
    Mehmood, Zahid
    Haider, Amir
    Hosseinzadeh, Mehdi
    Ali Naqvi, Rizwan
    MATHEMATICS, 2021, 9 (22)
  • [37] Machine learning in precision medicine to preserve privacy via encryption
    Briguglio, William
    Moghaddam, Parisa
    Yousef, Waleed A.
    Traore, Issa
    Mamun, Mohammad
    PATTERN RECOGNITION LETTERS, 2021, 151 : 148 - 154
  • [38] Machine learning and genomics: precision medicine versus patient privacy
    Azencott, C. -A.
    PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2018, 376 (2128):
  • [39] Equitable machine learning counteracts ancestral bias in precision medicine
    Smith, Leslie A.
    Cahill, James A.
    Lee, Ji-Hyun
    Graim, Kiley
    NATURE COMMUNICATIONS, 2025, 16 (01)
  • [40] Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases
    Fan, Shijie
    Chen, Yu
    Luo, Cheng
    Meng, Fanwang
    CURRENT PHARMACEUTICAL DESIGN, 2018, 24 (34) : 3998 - 4006