CD8+ T-cell differentiation and dysfunction inform treatment response in acute myeloid leukemia.

The interplay between T-cell states of differentiation, dysfunction, and treatment response in acute myeloid leukemia (AML) remains unclear. Here, we leveraged a multimodal approach encompassing high-dimensional flow cytometry and single-cell transcriptomics and found that early memory CD8+ T cells are associated with therapy response and exhibit a bifurcation into 2 distinct terminal end states. One state is enriched for markers of activation, whereas the other expresses natural killer (NK)-like and senescence markers.

Learning Predictive and Interpretable Timeseries Summaries from ICU Data.

Machine learning models that utilize patient data across time (rather than just the most recent measurements) have increased performance for many risk stratification tasks in the intensive care unit. However, many of these models and their learned representations are complex and therefore difficult for clinicians to interpret, creating challenges for validation. Our work proposes a new procedure to learn summaries of clinical timeseries that are both predictive and easily understood by humans.

Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise As Much As Possible.

We propose Preferential MoE, a novel human-ML mixture-of-experts model that augments human expertise in decision making with a data-based classifier only when necessary for predictive performance. Our model exhibits an interpretable gating function that provides information on when human rules should be followed or avoided. The gating function is maximized for using human-based rules, and classification errors are minimized.