8 Applications of AI in Healthcare and Biopharma in 2022
Of all the ways that artificial intelligence is being used in the real world, healthcare and biopharma continue to lead the way. Whether it’s for helping doctors make the right decisions for treating patients or developing new medications and treatments, AI has been a welcome hand to these spaces. At ODSC East 2022, there will be a number of talks — some available for free with a Bronze Pass — that showcase the benefits of using artificial intelligence in healthcare or biopharma.
1. AI for Drug Assessment
Anti-drug antibodies developed against therapeutic proteins have been shown to reduce medication efficacy, and in the worst case, this immunogenicity can have safety implications for the patient. Understanding and predicting the immunogenic potential of therapeutic proteins has been a major challenge in the process of biotherapeutic drug discovery. In recent years, many AI methods have been applied to protein fragments to evaluate their immunogenicity potential and have shown encouraging accuracies. This presentation will provide an overview of popular AI techniques in this space, including natural language processing, position-specific scoring matrix, deep motif deconvolution, etc. Current use cases of such tools, research gaps, and future opportunities will also be discussed.
Session: Using AI for Immunogenicity Potential Assessment in Drug Discovery: Jiayi Cox, PhD | Data Scientist | Novartis
2. Identifying Patients Who Need Palliative Care
Palliative care can improve patient experience and satisfaction but identifying patients appropriate for referral remains a challenge. The difficulty compounds when there is a need to develop consistent referral processes across multiple patient populations diverse in age, health, and coverage status. This is a common paradigm in healthcare data science — there is often a need to develop predictive recommendation systems that are consistent and equitable across vastly different inputs. In this talk, Evie will discuss how predictive analytics and machine learning can be utilized to identify patients appropriate for a palliative care path.
Session: Data Science and Contextual Approaches to Palliative Care Need Prediction: Evie Fowler | Manager, Data Science Product Owner | Highmark Health
3. A Better Standard for Research Data
A mature data operations strategy for ensuring high-quality health data is critical to the development of patient-level prediction and machine learning models. In this tutorial session, attendees will learn how a set of open source tools can be leveraged to perform standardization, characterization, and data quality assessment for various health data sources. Open source tools including Synthea, ETL-Synthea, Achilles, Data Quality Dashboard, and Ares will be reviewed and demonstrated in a data operations pipeline. We will demonstrate how the global health information community leverages this strategy to ensure research-ready health data.
Session: Data Operations for Research Quality Health Data: Frank DeFalco | Director, Observational Health Data Analytics | Janssen Research & Development
4. Analyzing Sensitive Data
The privacy risks of analyzing/sharing sensitive data about individuals have never been more apparent, due to increasingly sophisticated attacks that demonstrate that private information can be leaked even from aggregate statistics or trained models.
Differential privacy addresses these risks with a rigorous, mathematically proven model of privacy protection, which has broad applications, from analytics to machine learning to synthetic data. By adding carefully calibrated noise to statistical calculations, it is possible to ensure strong privacy and still enjoy statistically accurate results.
Session: Analyzing Sensitive Data Using Differential Privacy: Ashwin Machanavajjhala, PhD | Associate Professor, Co-Founder | Duke University, Tumult Labs / Michael Hay, PhD | Associate Professor, Founder & CTO | Colgate University, Tumult Labs
5. Improving Digital Health Measures
Clinical drug development has historically been a costly and complex task. Advances in AI/ML and digital health promise increased efficiency, better outcomes for pharmaceutical companies, and drive patient centricity by making research more diverse and available. This talk will focus on the takeaways from our multi-year experience in applying AI/ML methods to develop, validate, and deploy solutions enabling digital health measures at the Pfizer Innovation Research (PfIRe) Laboratory and the Digital Medicine & Translational Imaging (DMTI) group.
Session: Applying AI/ML Methods to Generate Digital Endpoints in Clinical Trials: Tomasz Adamusiak, MD, PhD | Director of Data Science | Pfizer Innovation Research (PfIRe) Lab
6. Improved Clinical Care
Effective clinical care planning requires clinician users to understand patient health status and needs to deliver appropriate support. The proliferation of healthcare data including massive volumes of clinical free-text documents creates a significant challenge for users, but a major opportunity for advanced clinical analytics. Novel Artificial Intelligence (AI)-driven solutions can help optimize care planning, reducing inefficiency and increasing focus on the most salient information, leading to better clinical decision making and improved patient outcomes. This talk will focus on various AI-based use cases developed as part of our advanced care planning initiatives.
Session: AI for Clinical Care Planning and Decision Support: Sadid Hasan, PhD | Executive Director of Artificial Intelligence | CVS Health
7. Treating Difficult Problems
Psilocybin therapy has experienced a renaissance in recent years with regard to the treatment of various mental health disorders. COMPASS Pathways is leading this effort, developing its COMP360 psilocybin therapy to help patients suffering from treatment-resistant depression (TRD) as well as other indications. As part of the investigational COMP360 therapy protocol, a specially trained therapist provides psychological support to the patient before, during, and after the psilocybin session. In this talk, we will describe how we have built out an NLP platform to monitor these interactions and provide tools to therapists in quasi-real-time, allowing them to provide better support to their patients.
Session: Utilizing NLP in the Context of COMP360 Psilocybin Therapy for Treatment-Resistant Depression: Gregory Ryslik, PhD | Senior VP | Compass
8. Developing mRNA Therapies
In this talk, Andrew will highlight the way that digital and platform mindsets have shaped the way Moderna carries out its mission to deliver on the promise of mRNA therapies to help patients. Andrew will introduce his group, discuss their vision for data science and AI at Moderna, and highlight a recent example of applying machine learning to the challenge of protein sequence design.
Session: Data Science and AI at Moderna: Andrew Giessel, PhD | Director of Data Science and Artificial Intelligence | Moderna
More Sessions:
- Creating a Benchmark for a Large-Scale Image Captioning Pipeline: Jennifer Dawn Davis, PhD | Staff Field Data Scientist | Domino Data Lab
- Accelerating Your Advanced Analytics & ESG (Environmental, Social, and Governance) Journey: James Olejniczak | Product Manager of Visualizations, Data Management Solutions | S&P Global Market Intelligence
- Spark NLP for Healthcare: Modular Approach to Solve Problems at Scale in Healthcare NLP: Veysel Kocaman, PhD | Lead Data Scientist | John Snow Labs
- AI for Healthcare: A Practical Application of AI/ML in Pediatric Behavioral Healthcare: Halim Abbas | Chief AI Officer | Cognoa
Create the future of AI in healthcare and biopharma at ODSC East 2022
As you can see, there are tried-and-true and novel ways of using AI in healthcare and biopharma settings. At ODSC East 2022, there will be an entire track devoted to machine learning in healthcare and biopharma, all highlighting real-world applications of AI in the industry. Register now for 20% off all ticket types — the event is next week!
