Big Data Analytics In Medicine 大数据分析 公开课课件

Big Data in the Alzheimer's Disease Pilot
• EHRs in English • MRI Brain Images • Genomic Data
• Pharmacological knowledge extracted from publicly available datasets
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Big Data: Clinical Data
Electronic Health Records (EHR) from Big Data Point of View
Large volumes of Clinical Data that needs to be stored, retrieved, and aggregated Scalable Methods for collecting, compressing, sharing, and anonymizing medical data Scalable Methods for signal processing and for developing Big Data based clinical decision support systems (CDSSs)
• Genomic Data/Liquid Biopsy Samples
• Pharmacological knowledge extracted from publicly available datasets
• Biomedical ontologies and taxonomies • terminology standardization • semantically describing the EHRs
Targeted Therapy
P4 Medicine: Personalized, Predictive, Preventive, Participatory
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Pilot 1: Lung Cancer
Motivation:
• Lung cancer among the most • common and deadly diseases • costly cancers
iASiS will enable:
• Discovery of patterns associated with prognosis, outcomes, and response to treatments
• Association of medical and lifestyle advice to Alzheimer’s risk and stages of severity
Alzheimer’s Disease Evolution
Big Data Analytics study has found that changes in blood flow are the earliest known warning sign of Alzheimer's.
https:///releases/2016/07/160712130229.htm
Knowledge Graph
Open Big Data Analytics
•Heterogeneous open data
•Semantic indexing of the data via ontologies and thesauri
•Knowledge extraction from the data
Pilot 2: Alzheimer's Disease
Motivation:
• Approximately, 10% of people over 65 suffer from Alzheimer’s
• Heterogeneity of the symptoms impedes accurate diagnosis and treatments
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Precision Medicine
Stratified Medicine
Personalized Medicine
Medical model that proposes the
customization of healthcare, with medical decisions, practices, or products being tailored to the individual patient
Heart Attack
Big Data Analytics study has found an association between the use of proton-pump inhibitors质子泵抑制剂and the likelihood of incurring a heart attack
Clinical
Data
Notes
Preprocessing
NLP
Knowledge
Knowledge Graph
Medical Images
Deep Learning
Predictive Models
Data Mining
Genomic Big Data Analytics
Hospital-derived data
bigdataanalyticsinmedicine大数据分析公开课课件
Big Data Analytics In Medicine
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Big Data Analytics and Drug Side Effects
Big Data
Electronic Health Records (EHRs) of nearly 3 million people and trillions of pieces of medical data
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Challenges of Big Data Management and Analytics
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Challenges of Big Data Management and Analytics
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Big Data Pipeline
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Precision Medicine
Medical model that proposes the customization of healthcare, with medical decisions, practices, or products being tailored to the individual patient
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• NLP and network analysis technologies
Big Data Management & Analytics
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Big Data Management & Analytics
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Big Data Management & Analytics
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Big Data Management & Analytics
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Big Data: Genomicห้องสมุดไป่ตู้ Data
Genomics from Big Data Point of View
Human Genome consists of 30,000 to 35,000 genes Scalable Methods for pathway analysis and for the discover associations between observed gene expression changes and predicted functional effects Scalable Methods for Reconstruction of Metabolic and Regulatory Networks
[Shah, SH. Clopidogrel Dosing and CYP2C19. Medscape. July 1, 2011. Medscape web site.] 2
Big Data Analytics and Disease Predisposition体质
Big Data
Researchers analyzed more than 7,700 brain images from 1,171 people in various stages of Alzheimer's progression using a variety of techniques including magnetic resonance imaging (MRI) and positron emission tomography (PET).
• Unraveling molecular mechanisms that predict response to different tumor types (signatures)
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Big Data in the Lung Cancer Pilot
• EHRs in Spanish
• PET/CT Images
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Big Data: Medical Image
Medical Image Processing from Big Data Point of View
Large volumes of data produced by Imaging Techniques Volume of medical images is growing exponentially Annotated data or structured methods to annotate medical images is challenging Big Data Analytical Methods allow for the interpretability of depicted contents Scalable Methods for collecting, compressing, sharing, and anonymizing medical data
• Lung cancer is a heterogeneous disease. Characteristics differ among • patients • tumor regions
iASiS will enable:
• Discovery of correlations among tumor spread, prognosis, response to treatment
• Biomedical ontologies and taxonomies • terminology standardization • semantically describing the EHRs
Clinical Big Data Analytics
Medical Vocabularies
Identification of RNAs regulated
by RBP
Comparison with available information
Integration with transcriptomic
data
Identification of key genes and interactions