The prospect of precision medicine, driven by machine learning, in tailoring the diagnostics used in individualized treatments to improve patient health and outcomes is very promising. While many life sciences and healthcare organizations are excited about this prospect, they are still faced with data challenges of accessing patient data and generating clinically actionable insights necessary to generate hypotheses, diagnose disease, and classify patient risk. Cambridge Healthtech Institute’s Inaugural Data Science, Precision Medicine and Machine Learning symposium explores these challenges and accelerated solutions. Through interactive sessions and panel discussions, leading researchers and thought leaders will discuss efforts of AI and ML-driven technologies in accelerating R&D and optimizing therapies based on data. The symposium will also feature experts outside of life sciences who are implementing and adopting data models and AI technologies. Hearing their case studies and learning their best practices may be helpful in your own work.   

Who Should Attend: Directors, Managers, Researchers, and Scientists from Pharma, Biotechs, Academia, Government and Healthcare Organizations working in AI, Bioinformatics, Biomarkers and Personalized Medicine, Business Development, Computational & Systems Biology, Data Operations, Data Science, Diagnostics/Diagnostics Research, Machine Learning, Precision Medicine Technology, R&D, and Statistical Science

Final Agenda

Thursday, March 14

7:00 am Registration Open and Morning Coffee

DATA SCIENCE OVERVIEW

8:25 Chairperson’s Opening Remarks

Eleanor Howe, PhD, Founder and CEO, Diamond Age Data Science

8:30 Decoding Data Science

Grishma Jena, Cognitive Software Engineer, IBM Watson Customer Engagement

Data Science is one of the biggest buzzwords out there. Companies are jumping on the data science bandwagon, hoping to predict diseases, create precision medicine, process records efficiently and do many more tasks. But what exactly does Data Science mean? What is the need for it and what questions can it answer? What is the Data Science pipeline and how to use it to answer those questions? Join me to unravel these mysteries using relevant examples from the field of Life Sciences.

9:00 Training Your Data Scientist

Lotem Peled, Data Science and NLP Lecturer, NAYA-College

9:30 The Impact of AI and Big Data Analytics in Healthcare and Life Sciences

Speaker to be Announced

10:00 Sponsored Presentation (Opportunity Available)

10:30 Coffee Break in the Exhibit Hall with Poster Viewing

DATA SCIENCE FOR PERSONALIZED MEDICINE:

APPROACHES, OPEN CHALLENGES, FUTURE DIRECTIONS

11:15 The Role of Data Science in Healthcare, Life Sciences & Precision Medicine: Key Application Areas for Growth

Sudeep Basu, PhD, Practice Leader, TechVision-Innovation Services, Frost & Sullivan

11:45 Streamlining New Industry Models and Precision Medicine Collaborations

Kenneth S. Ramos, MD, PhD, PharmB, Associate Vice President for Precision Health Sciences; Executive Director, Center for Applied Genetics and Genomic Medicine; Professor of Medicine, University of Arizona Health Sciences

12:15 pm Sponsored Presentation (Opportunity Available)

12:30 Session Break

12:40 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:15 Session Break

MINING AND VISUALIZING ASSOCIATIONS OF DATA AND KNOWLEDGE

1:55 Chairperson’s Remarks

Yuval Itan, PhD, Assistant Processor, Department of Genetics and Genomic Sciences; Member, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY

2:00 Text-Based Knowledge Scanning for Drug Development

Corrado Priami, PhD, President and CEO, The Microsoft Research - University of Trento Centre for Computational and Systems Biology (CoSBi)

We have recently developed a natural language process technology to mine scientific literature, patents and clinical trials and to graphically visualize the outcome to help scientists and practitioners being up-to-date with the latest findings in their fields. An application of the technology will be discussed.

2:20 Scaling Ontology and Knowledge Graph Development for Childhood Health, Cancer, and Other Domains

James McCusker, PhD, Director, Data Operation, Rensselaer Polytechnic Institute

2:40 Using Blockchain to Improve Clinical Decision-Making and Address Issues in Biomedical Research

Noah Zimmermann, PhD, Director, Health Data and Design Innovation Center, Icahn School of Medicine at Mount Sinai

3:00 Sponsored Presentation (Opportunity Available)

3:30 Refreshment Break and Poster Competition Winner Announced in the Exhibit Hall

EFFICIENT COMPUTATIONAL AND COMPUTER-BASED MODELING METHODS

4:15 Computational Identification & Screening for Deleterious Mutants

Andrzej Kloczkowski, PhD, Computational Biologist; Principal Investigator, Battelle Center for Mathematical Medicine in the Research Institute of the Nationwide Children’s Hospital; Tenured Professor, Pediatrics, Department of Pediatrics, The Ohio State University College of Medicine

The practice of precision medicine relies on individual patient genome sequences, among other data. The challenge for the interpretation of genome sequences is to distinguish between the normal and the disease-related mutations. The human proteome contains over 20,000 structures, and the consideration of possible mutations increases the number of potential protein structures enormously. This presentation discusses the analysis of big sequential and structural data and application of machine learning lead to identification of deleterious mutants and to the advancement of precision medicine.

4:45 New Methods to Detect Pathogenic Mutations and Genes Using Machine Learning

Yuval Itan, PhD, Assistant Processor, Department of Genetics and Genomic Sciences; Member, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY

Whole exome and whole genome sequencing provide hundreds of thousands of genetic variants per patient, of them only very few are pathogenic. Current computational methods are inefficient in differentiating pathogenic mutations from neutral genetic variants that are predicted to be damaging and cannot predict the functional outcome of mutations. We will present deep learning approaches and machine learning methods in the role of detecting pathogenic mutations. Visualization tools for better utilizing NGS data will be presented to understand human disease genomics.

5:15 Turning the Tide of Failed Neurology and Psychiatry Trials: Using Quantitative Systems Pharmacology-Based Modeling of Individual Virtual Patients to Support Clinical Trial Design

Hugo Geerts, PhD, MBA, BA, CSO, Computational Neuropharmacology, In Silico Biosciences; Adjunct Associate Professor, Perelman School of Medicine, University of Pennsylvania

Mechanistic Disease Modeling or Quantitative Systems Pharmacology is a completely new approach for supporting Drug Discovery and Development. By integrating domain expertise in a formalized way, the computer model can go beyond associations and correlations that is often the outcome of bio-informatics studies with Big Data. With the emphasis on ‘Pharmacology’ the effects of therapeutic interventions on the underlying pathology can be simulated in a more quantitative way and key issues can be detected and corrected long before they become a problem in clinical trials.

5:45 Reception in the Exhibit Hall with Poster Viewing

6:45 Close of Day

Friday, March 15

8:00 am Registration Open and Morning Coffee

DEVELOPING AND USING AI TECHNOLOGIES FOR PRECISION MEDICINE IN HEALTH & CANCER TREATMENTS

8:25 Chairperson’s Remarks

8:30 Building a Computational Model of the Human Anatomy Using Medical Images

Nina Miolane, PhD, Postdoc & Lecturer, Statistics for Neuroimaging, Stanford University; Laureate of the 2016 French L’Oréal-Unesco Fellowship for Women in Science

Neurodegenerative diseases, like Alzheimer’s, are characterized by brain shape changes observable on magnetic resonance imaging. Likewise, many diseases can be detected by looking at organ shapes on various medical imaging modalities. We present the promising results of computational anatomy, a field building a computational model of the human body, with applications in diagnosis.

9:00 Novel Biomarker Discovery and Validation through AI for Oncology and Neurological Diseases

Michael Kiebish, PhD, Chief Precision Medicine Officer, BERG

9:30 Medical Big Data Efforts in China: An Integration of Medical Informatics, Bioinformatics, and AI

Weike Mo, PhD, FAACC, Vice President, Precision Medicine, Digital China Health

China National Cancer Center is building a centralized medical data platform for all Chinese cancer patients with Digital China Health. To date, we have collected data for more than 6 million patients from over 30 oncology specialty hospitals. We have applied medical informatics, bioinformatics, and artificial intelligence technologies to clean up, structure, and utilize all the data. Although we are at an early stage of understanding the big data set, we see potential to revolutionize precision medicine practice in hospitals, insurance companies, and pharma companies.

10:00 Sponsored Presentation (Opportunity Available)

10:30 Coffee Break in the Exhibit Hall with Poster Viewing

IMPLEMENTATION AND ADOPTION OF DATA MODELS AND AI TECHNOLOGIES: CASE STUDIES FROM OUTSIDE LIFE SCIENCES

11:15 From Neurons to a Brain: Integrating AI into Your Startup’s Workflow

Emily Dresner, CTO, Upside Travel

In travel, we have flight data, weather data, flight equipment stats, hotel information, hotel ratings and none of this data is consistent in what data it provides, let alone format or how it correlates across data points. We’ve found three areas to focus on organizationally to launch our program and integrate ML throughout the machine learning engineering, data pipelining and the ETL to make data useable and useful, and product management. This talk explores the three areas, focusing on the last two—our dirty data challenges and how product thinking helps to fuel data hypothesis and research.


11:45 Challenges of Data Handling and Interface Designs

Shannon Gatta, Flight Software Engineer, NASA Langley Research Center

In my role, I analyze software requirements and architectures while assisting in documenting control and data interface designs. I also create designs that develop and test software modules assisting in the integration and testing of software subsystems while providing documentation such as reports, presentations or graphs. I will discuss some of the challenges of data handling and interface designs.

12:15 pm KEYNOTE PRESENTATION: 10 Misconceptions of AI in Medicine & Healthcare

Anthony Chang, MD, MBA, MPH, MS, Medical Director, Heart Failure Program and Chief Intelligence & Innovation Officer, Founding Director of The Sharon Disney Lund Medical Intelligence and Innovation Institute (Mi3), Children’s Hospital of Orange County (CHOC); Author of Medical Intelligence: Principles and Applications of Artificial Intelligence in Healthcare and Medicine (releasing in Spring 2019)

Medical intelligence is derived from pairing machine intelligence with human intelligence. As AI and machine learning approaches and methods continue to evolve, they impact how physicians practice and perform their tasks. As a practicing cardiologist and chief intelligence and innovation officer with a data science background, Dr. Chang will share how he is cautiously embracing this paradigm shift in practicing medicine and the “hype vs. reality” in healthcare’s adoption of AI.

12:45 Close of Symposium