Which Is The Best of Them All – Hard Decisions, Smart Choices

How do we make the best decisions in face of voluminous, complex, changing, and uncertain information? We describe a multi-disciplinary effort in developing the next generation decision analytic and engineering technologies. We explain the goals, the approaches, the achievements, and the evolving plans of the project. We illustrate the ideas and issues explored in various biomedical domains, with related information ranging from genes to humans.Singapore-MIT Alliance (SMA

Automated Information Extraction to Support Biomedical Decision Model Construction: A Preliminary Design

We propose an information extraction framework to support automated construction of decision models in biomedicine. Our proposed technique classifies text-based documents from a large biomedical literature repository, e.g., MEDLINE, into predefined categories, and identifies important keywords for each category based on their discriminative power. Relevant documents for each category are retrieved based on the keywords, and a classification algorithm is developed based on machine learning techniques to build the final classifier. We apply the HITS algorithm to select the authoritative and typical documents within a category, and construct templates in the form of Bayesian networks. Data mining and information extraction techniques are then applied to extract the necessary semantic knowledge to fill in the templates to construct the final decision models.Singapore-MIT Alliance (SMA

Federated Learning of Causal Effects from Incomplete Observational Data

Decentralized and incomplete data sources are prevalent in real-world applications, posing a formidable challenge for causal inference. These sources cannot be consolidated into a single entity owing to privacy constraints, and the presence of missing values within them can potentially introduce bias to the causal estimands. We introduce a new approach for federated causal inference from incomplete data, enabling the estimation of causal effects from multiple decentralized and incomplete data sources. Our approach disentangles the loss function into multiple components, each corresponding to a specific data source with missing values. Our approach accounts for the missing data under the missing at random assumption, while also estimating higher-order statistics of the causal estimands. Our method recovers the conditional distribution of missing confounders given the observed confounders from the decentralized data sources to identify causal effects. Our framework estimates heterogeneous causal effects without the sharing of raw training data among sources, which helps to mitigate privacy risks. The efficacy of our approach is demonstrated through a collection of simulated and real-world instances, illustrating its potential and practicality.Comment: Preprin