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Tue Dec 14 05:30 AM -- 02:30 PM (PST)
Bridging the Gap: from Machine Learning Research to Clinical Practice
Julia Vogt · Ece Ozkan · Sonali Parbhoo · Melanie F. Pradier · Patrick Schwab · Shengpu Tang · Mario Wieser · Jiayu Yao

Workshop Home Page

Machine learning (ML) methods often achieve superhuman performance levels, however, most existing machine learning research in the medical domain is stalled at the research paper level and is not implemented into daily clinical practice. To achieve the overarching goal of realizing the promise of cutting-edge ML techniques and bring this exciting research to fruition, we must bridge the gap between research and clinics. In this workshop, we aim to bring together ML researchers and clinicians to discuss the challenges and potential solutions on how to enable the use of state-of-the-art ML techniques in the daily clinical practice and ultimately improve healthcare by trying to answer questions like: what are the procedures that bring humans-in-the-loop for auditing ML systems for healthcare? Are the proposed ML methods robust to changes in population, distribution shifts, or other types of biases? What should the ML methods/systems fulfill to successfully deploy them in the clinics? What are failure modes of ML models for healthcare? How can we develop methods for improved interpretability of ML predictions in the context of healthcare? And many others. We will further discuss translational and implementational aspects and talk about challenges and lessons learned from integrating an ML system into clinical workflow.

Opening remarks by the organizers (Short intro)
Invited talk (Clinical) - Sven Wellmann (Invited talk)
Invited talk (ML) - Michael Brudno (Invited talk)
Moderated Q&A (Topic: pediatrics) (Moderated Q&A)
Informal exchange to encourage discussions and collaborations (Round table discussion)
Spotlight Presentations (Spotlight)
Poster Session 1 (Poster Session)
Lunch Break (Break)
Invited talk (ML) - Rich Caruana (Invited talk)
Invited talk (Clinical) - Bram Stieljes (Invited talk)
Moderated Q&A (Topic: tbd) (Moderated Q&A)
Invited talk (ML) - Barbara Engelhardt (Invited talk (ML))
Invited talk (Clinical) - Roy Perlis (Invited talk)
Moderated Q&A (Topic: tbd) (Moderated Q&A)
Poster Session 2 (Poster Session)
Closing remarks by the organizers (Short intro)
Workshop ends (Break)
Survival-oriented embeddings for improving accessibility to complex data structures (Poster)
Rethinking Generalization Performance of Surgical Phase Recognition with Expert-Generated Annotations (Poster)
Predicting Sufficiency for Hemorrhage Resuscitation Using Non-invasive Physiological Data without Reference to Personal Baselines (Poster)
A Conservative Q-Learning approach for handling distributional shift in sepsis treatment strategies (Poster)
What Do You See in this Patient? Behavioral Testing of Clinical NLP Models (Poster)
Harmonizing Attention: Attention Map Consistency For Unsupervised Fine-Tuning (Poster)
Robust Interpretable Rule Learning to Identify Expertise Transfer Opportunities in Healthcare (Poster)
GAM Changer: Editing Generalized Additive Models with Interactive Visualization (Poster)
Interpretable Electrocardiogram Mapping to Detect Decreased Cardiac Contraction (Poster)
Automated Supervised Feature Selection for Differentiated Patterns of Care (Poster)
Transferring Multi-Omics Survival Models to Clinical Settings Through Linear Surrogate Models (Poster)
Neuroweaver: Towards a Platform for Designing Translatable Intelligent Closed-loop Neuromodulation Systems (Poster)
Contextualized Representation Learning in Biomedical Word Sense Disambiguation (Poster)
Longitudinal Fairness with Censorship (Poster)
Interpretable Data Analysis for Bench-to-Bedside Research (Poster)
Post-discovery Analysis of Anomalous Subsets (Poster)
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation (Poster)
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources (Poster)
Type Safety and Disambiguation of Depression (Poster)
Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data (Poster)