Jeff Shrager

AIIM Journal 2023 Journal Article

Virtual Trials: Causally-validated treatment effects efficiently learned from an observational cancer registry

• Asher Wasserman
• Al Musella
• Mark Shapiro
• Jeff Shrager

Randomized controlled trials (RCTs) offer a clear causal interpretation of treatment effects, but are inefficient in terms of information gain per patient. Moreover, because they are intended to test cohort-level effects, RCTs rarely provide information to support precision medicine, which strives to choose the best treatment for an individual patient. If causal information could be efficiently extracted from widely available real-world data, the rapidity of treatment validation could be increased, and its costs reduced. Moreover, inferences could be made across larger, more diverse patient populations. We created a “virtual trial” by fitting a multilevel Bayesian survival model to treatment and outcome records self-reported by 451 brain cancer patients. The model recovers group-level treatment effects comparable to RCTs representing over 3200 patients. The model additionally discovers the feature-treatment interactions needed to make individual-level predictions for precision medicine. By learning from heterogeneous real-world data, virtual trials can generate more causal estimates with fewer patients than RCTs, and they can do so without artificially limiting the patient population. This demonstrates the value of virtual trials as a complement to large randomized controlled trials, especially in highly heterogeneous or rare diseases.

AIIM Journal 2006 Journal Article

Constructing explanatory process models from biological data and knowledge

• Pat Langley
• Oren Shiran
• Jeff Shrager
• Ljupčo Todorovski
• Andrew Pohorille

Objective We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation. Methods We cast both models and background knowledge in terms of processes that interact to account for behavior. We also describe IPM, an algorithm for inducing quantitative process models from such input. Results We demonstrate IPM’s use both on photosynthesis and on a second domain, biochemical kinetics, reporting the models induced and their fit to observations. Conclusion We consider the generality of our approach, discuss related research on biological modeling, and suggest directions for future work.

ICAPS Conference 1994 Conference Paper

Reactive and Automatic Behavior in Plan Execution

• Pat Langley
• Wayne Iba
• Jeff Shrager

Much of the work on execution assumes that the agent constantly senses the environment, which lets it respond immediately to errors or unexpected events. In this paper, we argue that this purely reactive strategy is only optimal if sensing is inexpensive, and we formulate a simple model of execution that incorporates the cost of sensing. We present an average-case analysis of this model, which shows that in domains with high sensing cost or low probability of error, a more automatic strategy, one with long intervals between sensing, can lead to less expensive execution. The analysis also shows that the distance to the goal has no effect on the optimal sensing interval. These results run counter to the prevailing wisdom in the planning community, but they promise a more balanced approach to the interleaving of execution and sensing.

AIJ Journal 1989 Journal Article

Induction: Process of inference, learning and discovery

• Jeff Shrager

AIJ Journal 1987 Journal Article

Universal subgoaling and chunking: The automatic generation and learning of goal hierarchies

• Jeff Shrager

AAAI Conference 1982 Conference Paper

An Expert System that Volunteers Advice

• Jeff Shrager

This paper describes the design and implementation of an expert system that provides novice users with help in using the Vax/VMS operating system. The most interesting feature of our advisor is that it follows the user’s interactions with the system and volunteers its help when it believes that the user would benefit from advice. The user need not ask for help or raise an error condition. The adivsor recognizes correct yet inefficient command sequences and helps the beginner become more proficient by indicating how these tasks may be done more efficiently.