The analyses upon which this publication is based were performed under Contract Number HHSM C sponsored by the Center for Medicare and Medicaid Services, Department of Health and Human Services. Xiaoqian Jiang, PhD MED 264 introduction

MED 264 Introduction Introduction and class overview Topics and expectations A brief introduction of biomedical informatics 2

MED students 10 weeks, 2 course per week Class website: 3

DateLecturerTitle 10/2/2014Xiaoqian JiangIntroduction to MED264 10/7/2014 Mary Linn Bergstrom Systematic Reviews: principles and processes 10/9/2014Mike Hogarth Public health information systems and interoperability and data standards in public health informatics 10/14/2014Zhuowen TuIntroduction to information retrieval and data fusion 10/16/2014 Lucila Ohno- Machado Research methods (study design, sample size, evaluation of models) 10/21/2014Claudiu FarcasProject management and software engineering related to informatics projects 10/23/2014Shuang WangIntroduction to R with Shiny for sharing interactive biomedical research results 10/28/2014N/ANo lecture scheduled (due to conference) 10/30/2014Yunan ChenEvaluation of information systems to provide feedback for system improvement 11/4/2014Jihoon KimStatistics for biomedical research 11/6/2014Cui TaoApplying Ontology and Semantic Web Technologies to Clinical and Biomedical Studies 11/13/2014Edna ShenviImpact of clinical information systems on users and patients 11/18/2014Chun-Nan HsuNLP applications in biomedicine 11/20/2014Son DoanIntroduction to biomedical natural language processing 11/25/2014 Robert El- Kareh Clinical Decision Support 12/2/2014Xiaoqian JiangPrivacy policy and technologies for healthcare research 12/4/2014Cleo MaeharaImaging informatics 12/9,11/2014Presentations 4

Grading Policies: Course grades will be based on 1) Attendance (10%), 2) Assignment and mid-term project review (30%), 3) Project oral presentation (15%), 4) Project participation (15%), 5) Final project report (30%) 5

Healthcare Systems Medical Informatics Bioinformatics Algorithms Controlled vocabularies Ontologies Data management Information retrieval Pharmacogenomics Personalized Medicine Biomedical Informatics Electronic Health Records Decision Support Systems Hospital Information Systems Genomics Transcriptomics Proteomics Epigenetics

Big Data Today: Some data on a lot of individuals –Example: observational data from EHRs A lot of data on some individuals –Example: sensor data Tomorrow: A lot of data on a lot of individuals –International collaborations

Personalized Care and Population Health Genomics –SNP-based therapy (cancer) ‘Phenomics’ –Electronic Health Records –Personal monitoring Blood pressure, glucose –Behavior Adherence to medication, exercise Public Health and Environment –Air quality, food –Surveillance Source: DOE

UC ReX - Research eXchange Clinical Data Warehouses from 5 Medical Centers and affiliated institutions (>10 million patients) Aggregate and individual-level patient data to be exchanged according to data use agreements, internal review boards Funded by the University of California Office of the President 9

iDASH 10

Integrating Different Types of Data Genotype RNA Metabolites transcription translation genome transcriptome laboratory Physiologytests Proteinproteome Phenotypephysical exam, imaging, monitoring systems

What can we do? Build access to large data repositories to improve research –Enhance policy and technological solutions to the problem of individual and institutional privacy –Donate data Aggregate data from different countries and use for new analyses –Provide tools to integrate and analyze data

Privacy Protection – Use of clinical, experimental, and genetic data for research not primarily for clinical practice (i.e., not for health care) not primarily for quality improvement (i.e., not for IRB exempt activities – regulatory ethics committee) – iDASH will host and disseminatte data according to Consents from individuals Data owner (institutional) requirements Federal and state rules and regulations 13funded by NIH U54HL108460

Shared Model Building and Evaluation 14 Wu Y, Jiang X, Kim J, Ohno-Machado L. Grid Binary LOgistic REgression (GLORE): Building Shared Models Without Sharing Data. JAMIA 2012

Prevention –Risk Assessment Genomics Diagnosis and Therapy –Decision support Pharmacogenomics Big Data - Secure Cloud Environment Electronic Medical Records Genetic Data Personalizing Medicine

22%

16%

“this program shows the estimated health risks of people with your same age, gender, and risk factor levels” Your Risk p=1 x

“this means that 5 of 100 people with this level of risk will have a heart attack or die”

Input space “people with your same age, gender, and risk factor levels” People “like you” Output space “people with this level of risk” p p=1 x

Who should get a liver transplant? risk p

Individualized Confidence Interval 25 Probability estimate Large Individual Confidence Interval Narrow C.I.

Patients “like you” get predictions like you, but different confidence intervals height gender risk me 1 14 me 1 p 14 Probability Estimate = 0.3 C.I. = [0.2, 0.4] Probability Estimate = 0.3 C.I. = [0.05, 0.55]

Confidence Interval (CI) Near the Boundary 27

Far from the Boundary 28

C.I. depends on Density 2011 summer internship program funded by NIH U54HL

Sparse region, larger C.I. 30

Adaptive Calibration 31 Probability estimate Large Individual Confidence Interval Narrow C.I.

Adaptive Calibration 32 Probabilit y estimate Recalibrated prediction 2/4 = 0.5 Recalibrated prediction 1/3 =0.33 Jiang X, Osl M, Kim J, Ohno-Machado L. Calibration of Predictive Model Estimated to Support Personalized Medicine. J Amer Med Inform Assoc 2012

Adaptive Calibration of Predictions 33

Original Estimates 34

Recalibrated Estimates 35

Who should get a liver transplant? risk 0 2 me p 1 ELIGIBLE FOR TRANSPLANTATION NOT ELIGIBLE FOR TRANSPLANTATION

Biomedical Informatics Data compression Dimensionality reduction Information retrieval Data annotation Visualization Genotype-phenotype associations Temporal associations

Research Service Education Change