Workshop 2021: AI in Healthcare

slide of heart rate, respiratory rate, and blood pressure correlation next to portraits of the three researchers mentioned in the caption
LLNL machine learning group researcher Jose Cadena (top right) was one of several presenters applying machine learning to EHRs at the first workshop session on March 11. Other presenters included LLNL postdoctoral researcher Boya Zhang (middle right) and data scientist Braden Soper (bottom right).

Held virtually over three consecutive Thursdays in early spring, this workshop focused on AI applications and possibilities in healthcare. Panelists and speakers included LLNL staff as well as academic and clinical professionals.

Select each day below to access a synopsis of workshop goals, the day’s agenda, a list of speakers, and any available recordings and slide decks.

View or download: welcome slides presented by DSI director Michael Goldman.

Electronic health records (EHRs) provide a wealth of information on the patient’s clinical trajectory, including their demographics, vital signs, medical test results, medications, and doctor’s notes. With EHRs becoming more widely adopted by healthcare providers in recent years, large amounts of rich, temporal, multimodal patient data for a variety of medical conditions are being generated. There is considerable potential for health informatics research and medical knowledge discovery by employing statistical and machine learning methods for analyzing EHRs. EHRs have successfully been used for disease prediction, patient risk stratification, and latent health state discovery, among others.

The goal of this session was to discuss recent developments, challenges, and opportunities in data science for EHRs. The session will feature a series of talk on recent work in statistics and machine learning specific to this type of data. We also hosted a panel to discuss the main challenges in this area. Data privacy considerations and standardization across healthcare providers is one key issue. On the methodological front, one current challenge is to address the lack of clinical controls and developing counterfactual methods for EHR analysis.

View or download:

Actionable AI for healthcare refers to models and processes that lead to knowledge discovery or information that can be acted upon by domain experts. The main goal when developing actionable models is not just maximizing accuracy or predictive power; rather, the focus is on generating results that are human-interpretable, consider realistic constraints, and can be taken into consideration to make an informed decision.

For example, when modeling the risk of death of a patient given his/her medical history, in actionable AI, it is not sufficient to develop a model that accurately gives a probability of death. An analyst should be able to understand how the model produced the answer and which aspects of the patient’s history are most relevant to predict risk. Health experts should be able to take these results into consideration to decide which course of action to take in the patient’s treatment.

The goal of this session was twofold. First, we surveyed recent applications of actionable and interpretable AI techniques to healthcare problems through technical talks presented by academic, national lab, and clinical researchers. Second, we provided a forum to discuss current challenges and opportunities to further integrate machine learning models into medical practice and decision-making.

View or download:

COVID-19 is an infectious respiratory disease caused by the SARS-CoV-2 virus that emerged in December 2019 and quickly spread throughout the globe. The COVID-19 pandemic is a severe global health threat that has taken millions of lives, exerted significant pressure on often limited healthcare resources, and devastated countries and economies. The long-term COVID-19 impacts are still largely unknown, but is expected to be experienced for decades.

The goal of this session was to discuss and identify potential use of AI technologies to facilitate rapid response to COVID-19, such as improving patient diagnosis, developing new treatments, and improving overall medical and hospital care. We also hosted a panel of experts in the medical and AI fields to discuss ongoing research efforts and point out challenges for further progress in medical AI systems with focus on infectious diseases and quick response to healthcare-related crises.

View or download: