Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare

The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data. We also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. A secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.

[1]  Tong Liu,et al.  Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals , 2016, Sci. Program..

[2]  A. Boonstra,et al.  Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions , 2010, BMC health services research.

[3]  Maria D. Pasic,et al.  Proteomics and peptidomics: moving toward precision medicine in urological malignancies , 2016, Oncotarget.

[4]  Jimeng Sun,et al.  Using recurrent neural network models for early detection of heart failure onset , 2016, J. Am. Medical Informatics Assoc..

[5]  Chenkai Wu,et al.  A Modified Healthy Aging Index and Its Association with Mortality: The National Health and Nutrition Examination Survey, 1999–2002 , 2017, The journals of gerontology. Series A, Biological sciences and medical sciences.

[6]  Anandita Rajpurohit,et al.  Functional analysis of non-hotspot AKT1 mutants found in human breast cancers identifies novel driver mutations: implications for personalized medicine , 2012, Oncotarget.

[7]  Yvo Desmedt,et al.  Threshold Cryptosystems , 1989, CRYPTO.

[8]  Elias Strehle Public Versus Private Blockchains , 2020 .

[9]  Polina Mamoshina,et al.  Design of efficient computational workflows for in silico drug repurposing. , 2017, Drug discovery today.

[10]  Ahto Buldas,et al.  Optimally Efficient Accountable Time-Stamping , 2000, Public Key Cryptography.

[11]  Dorothy E. Denning,et al.  Cryptography and Data Security , 1982 .

[12]  On Blockchain Auditability , 2016 .

[13]  Richard W Grant,et al.  Identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records , 2009, BMC health services research.

[14]  Taravat Ghafourian,et al.  Machine learning for predicting lifespan-extending chemical compounds , 2017, Aging.

[15]  Yurij Ionov,et al.  A high throughput method for identifying personalized tumor-associated antigens , 2010, Oncotarget.

[16]  Michael J Levy,et al.  Assessment of pancreatic neuroendocrine tumor cytologic genotype diversity to guide personalized medicine using a custom gastroenteropancreatic next-generation sequencing panel. , 2017, Oncotarget.

[17]  Vijay S. Pande,et al.  Low Data Drug Discovery with One-Shot Learning , 2016, ACS central science.

[18]  Elizabeth Brunk,et al.  Antigen receptor repertoire profiling from RNA-seq data , 2022 .

[19]  Lawrence O. Hall,et al.  Fine-tuning convolutional deep features for MRI based brain tumor classification , 2017, Medical Imaging.

[20]  Kenneth Rockwood,et al.  Accumulation of Deficits as a Proxy Measure of Aging , 2001, TheScientificWorldJournal.

[21]  Alfred Menezes,et al.  Handbook of Applied Cryptography , 2018 .

[22]  Yuji Ikegaya,et al.  Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds. , 2017, Journal of pharmacological sciences.

[23]  Tamara B Harris,et al.  A physiologic index of comorbidity: relationship to mortality and disability. , 2008, The journals of gerontology. Series A, Biological sciences and medical sciences.

[24]  E. Pretorius,et al.  Viscoelasticity as a measurement of clot structure in poorly controlled type 2 diabetes patients: towards a precision and personalized medicine approach , 2016, Oncotarget.

[25]  Joann G Elmore,et al.  Do Patients Who Access Clinical Information on Patient Internet Portals Have More Primary Care Visits? , 2016, Medical care.

[26]  Qunyuan Zhang,et al.  Heritability of and mortality prediction with a longevity phenotype: the healthy aging index. , 2014, The journals of gerontology. Series A, Biological sciences and medical sciences.

[27]  M. Schatz,et al.  Big Data: Astronomical or Genomical? , 2015, PLoS biology.

[28]  Tang Ming . Wei Lian. Si Tuo Lin Si,et al.  Cryptography and Network Security - Principles and Practice , 2015 .

[29]  Michel E. Vandenberghe,et al.  Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer , 2017, Scientific Reports.

[30]  Zaki Nossair,et al.  A Recurrent Neural Network Approach for Predicting Glucose Concentration in Type-1 Diabetic Patients , 2011, EANN/AIAI.

[31]  D. Stone,et al.  Regulatory and policy barriers to effective clinical data exchange: lessons learned from MedsInfo-ED. , 2005, Health affairs.

[32]  Hyeon-Eui Kim,et al.  Blockchain distributed ledger technologies for biomedical and health care applications , 2017, J. Am. Medical Informatics Assoc..

[33]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[34]  Gail P. Jarvik,et al.  Impact of HIPAA’s Minimum Necessary Standard on Genomic Data Sharing , 2018, Genetics in Medicine.

[35]  Alexander M Aliper,et al.  Pathway activation strength is a novel independent prognostic biomarker for cetuximab sensitivity in colorectal cancer patients , 2015, Human Genome Variation.

[36]  Paola Sebastiani,et al.  Biomarker signatures of aging , 2017, Aging cell.

[37]  Lovekesh Vig,et al.  Anomaly detection in ECG time signals via deep long short-term memory networks , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[38]  Shih-Neng Yang,et al.  Identification of Breast Cancer Using Integrated Information from MRI and Mammography , 2015, PloS one.

[39]  M. Kayaalp Patient Privacy in the Era of Big Data , 2017, Balkan medical journal.

[40]  Sergey Nikolenko,et al.  druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico. , 2017, Molecular pharmaceutics.

[41]  Daniel Roggen,et al.  Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition , 2016, Sensors.

[42]  Evgeny Putin,et al.  Deep biomarkers of human aging: Application of deep neural networks to biomarker development , 2016, Aging.

[43]  A. Deal,et al.  Frailty and inflammatory markers in older adults with cancer , 2017, Aging.

[44]  G. Berchem,et al.  Cell-free DNA and next-generation sequencing in the service of personalized medicine for lung cancer , 2016, Oncotarget.

[45]  Leslie Lamport,et al.  Reaching Agreement in the Presence of Faults , 1980, JACM.

[46]  Nancy A. Lynch,et al.  Consensus in the presence of partial synchrony , 1988, JACM.

[47]  M. Rubin,et al.  Molecular alterations in prostate cancer and association with MRI features , 2015, Prostate Cancer and Prostatic Diseases.

[48]  Christina Kluge,et al.  Service-Oriented Architecture: Concepts, Technology, and Design , 2005 .

[49]  Leslie Lamport,et al.  The Byzantine Generals Problem , 1982, TOPL.

[50]  Nicolas Borisov,et al.  A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation , 2015, Oncotarget.

[51]  Michael P. Lisanti,et al.  Mitochondrial biomarkers predict tumor progression and poor overall survival in gastric cancers: Companion diagnostics for personalized medicine , 2017, Oncotarget.

[52]  Meysam Asgari,et al.  Predicting severity of Parkinson's disease from speech , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[53]  Bolin Liu,et al.  Integrative analysis of novel hypomethylation and gene expression signatures in glioblastomas , 2017, Oncotarget.

[54]  Frederick L. Tyson,et al.  Environmental Epigenomics in Health and Disease , 2013, Epigenetics and Human Health.

[55]  Hugo Y. K. Lam,et al.  Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes , 2012, Cell.

[56]  Andrey Kazennov,et al.  The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology , 2016, Oncotarget.

[57]  Digital Assets on Public Blockchains , 2016 .

[58]  Aedín C. Culhane,et al.  Dimension reduction techniques for the integrative analysis of multi-omics data , 2016, Briefings Bioinform..

[59]  Jens Nielsen,et al.  Systems Biology of Metabolism: A Driver for Developing Personalized and Precision Medicine. , 2017, Cell metabolism.

[60]  Zenon Mariak,et al.  Systematic biobanking, novel imaging techniques, and advanced molecular analysis for precise tumor diagnosis and therapy: The Polish MOBIT project. , 2017, Advances in medical sciences.

[61]  Nicholas Ayache,et al.  Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition , 2017, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[62]  G. Passos Transcriptomics in Health and Disease , 2014, Springer International Publishing.

[63]  Satoshi Nakamoto Bitcoin : A Peer-to-Peer Electronic Cash System , 2009 .

[64]  D. Upton,et al.  Improving data transparency in clinical trials using blockchain smart contracts , 2016, F1000Research.

[65]  S. Thacker,et al.  HIPAA privacy rule and public health. Guidance from CDC and the U.S. Department of Health and Human Services. , 2003, MMWR supplements.

[66]  Alex Zhavoronkov,et al.  Applications of Deep Learning in Biomedicine. , 2016, Molecular pharmaceutics.

[67]  Dhananjay Kumar,et al.  An efficient system for anomaly detection using deep learning classifier , 2017, Signal Image Video Process..

[68]  Gustavo Henrique Goulart Trossini,et al.  Use of machine learning approaches for novel drug discovery , 2016, Expert opinion on drug discovery.

[69]  Matt Kaeberlein,et al.  A review of the biomedical innovations for healthy longevity , 2017, Aging.

[70]  V. Marx Biology: The big challenges of big data , 2013, Nature.

[71]  Sergey Plis,et al.  Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data. , 2016, Molecular pharmaceutics.

[72]  Chen-Tan Lin,et al.  Research Paper: Use of a Patient-Accessible Electronic Medical Record in a Practice for Congestive Heart Failure: Patient and Physician Experiences , 2004, J. Am. Medical Informatics Assoc..

[73]  Melanie Swan,et al.  Blockchain: Blueprint for a New Economy , 2015 .

[74]  H. Krumholz,et al.  Blockchain Technology: Applications in Health Care , 2017, Circulation. Cardiovascular quality and outcomes.

[75]  Alex Zhavoronkov,et al.  From personalized medicine to personalized science: uniting science and medicine for patient-driven, goal-oriented research. , 2013, Rejuvenation Research.

[76]  Richard B. Schwab,et al.  Molecular inimitability amongst tumors: implications for precision cancer medicine in the age of personalized oncology , 2015, Oncotarget.

[77]  Milan Radovich,et al.  Clinical benefit of a precision medicine based approach for guiding treatment of refractory cancers , 2016, Oncotarget.

[78]  J. Piette,et al.  Mobile Health Devices as Tools for Worldwide Cardiovascular Risk Reduction and Disease Management , 2015, Circulation.

[79]  D. Levy,et al.  Prediction of coronary heart disease using risk factor categories. , 1998, Circulation.

[80]  Xuerong Chen,et al.  Nucleic Acid Amplification Testing and Sequencing Combined with Acid-Fast Staining in Needle Biopsy Lung Tissues for the Diagnosis of Smear-Negative Pulmonary Tuberculosis , 2016, PloS one.

[81]  Angelita Habr-Gama,et al.  The use of personalized biomarkers and liquid biopsies to monitor treatment response and disease recurrence in locally advanced rectal cancer after neoadjuvant chemoradiation , 2015, Oncotarget.

[82]  Ancha Baranova,et al.  Markers of arterial health could serve as accurate non-invasive predictors of human biological and chronological age , 2017, Aging.

[83]  Dmitry Vengertsev,et al.  Anomaly Detection in Graph : Unsupervised Learning , Graph-based Features and Deep Architecture , 2015 .

[84]  Yunfeng Wu,et al.  Dysphonic Voice Pattern Analysis of Patients in Parkinson's Disease Using Minimum Interclass Probability Risk Feature Selection and Bagging Ensemble Learning Methods , 2017, Comput. Math. Methods Medicine.

[85]  William Stafford Noble,et al.  Machine learning applications in genetics and genomics , 2015, Nature Reviews Genetics.

[86]  Adi Shamir,et al.  How to share a secret , 1979, CACM.

[87]  Ming Wen,et al.  Deep-Learning-Based Drug-Target Interaction Prediction. , 2017, Journal of proteome research.

[88]  Nigel Cory Cross-Border Data Flows: Where Are the Barriers, and What Do They Cost? , 2017 .

[89]  Alex Zhavoronkov,et al.  Biomedical Progress Rates as New Parameters for Models of Economic Growth in Developed Countries , 2013, International journal of environmental research and public health.

[90]  Jae Kwon,et al.  Tendermint : Consensus without Mining , 2014 .

[91]  Yoshua Bengio,et al.  Generative Adversarial Networks , 2014, ArXiv.

[92]  Ian D. Wilson,et al.  Gut microbiota modulation of chemotherapy efficacy and toxicity , 2017, Nature Reviews Gastroenterology &Hepatology.