Learning parametric policies and transition probability models of markov decision processes from data.

We consider the problem of estimating the policy and transition probability model of a Markov Decision Process from data (state, action, next state tuples). The transition probability and policy are assumed to be parametric functions of a sparse set of features associated with the tuples. We propose two regularized maximum likelihood estimation algorithms for learning the transition probability model and policy, respectively.

A neural circuit model for a contextual association task inspired by recommender systems.

Behavioral data shows that humans and animals have the capacity to learn rules of associations applied to specific examples, and generalize these rules to a broad variety of contexts. This article focuses on neural circuit mechanisms to perform a context-dependent association task that requires linking sensory stimuli to behavioral responses and generalizing to multiple other symmetrical contexts. The model uses neural gating units that regulate the pattern of physiological connectivity within the circuit.

Learning from animals: How to Navigate Complex Terrains.

We develop a method to learn a bio-inspired motion control policy using data collected from hawkmoths navigating in a virtual forest. A Markov Decision Process (MDP) framework is introduced to model the dynamics of moths and sparse logistic regression is used to learn control policy parameters from the data. The results show that moths do not favor detailed obstacle location information in navigation, but rely heavily on optical flow.

Classification of Pulmonary Nodular Findings based on Characterization of Change using Radiology Reports.

Radiology reports contain descriptions of radiological observations followed by diagnosis and follow up recommendations, transcribed by radiologists while reading medical images. One of the most challenging tasks in a radiology workflow is to extract, characterize and structure such content to be able to pair each observation with an appropriate action. This requires classification of the findings based on the provided characterization.

Context-Driven Concept Annotation in Radiology Reports: Anatomical Phrase Labeling.

During a radiology reading session, it is common that the radiologist refers back to the prior history of the patient for comparison. As a result, structuring of radiology report content for seamless, fast, and accurate access is in high demand in Radiology Information Systems (RIS). A common approach for defining a structure is based on the anatomical sites of radiological observations.

Negation Scope Detection in Clinical Notes and Scientific Abstracts: A Feature-enriched LSTM-based Approach.

Electronic Health Records contain a wealth of clinical information that can potentially be used for a variety of clinical tasks. Clinical narratives contain information about the existence or absence of medical conditions as well as clinical findings. It is essential to be able to distinguish between the two since the negated events and the non-negated events often have very different prognostic value.

Neural circuits for learning context-dependent associations of stimuli.

The use of reinforcement learning combined with neural networks provides a powerful framework for solving certain tasks in engineering and cognitive science. Previous research shows that neural networks have the power to automatically extract features and learn hierarchical decision rules. In this work, we investigate reinforcement learning methods for performing a context-dependent association task using two kinds of neural network models (using continuous firing rate neurons), as well as a neural circuit gating model.

Automatic Normalization of Anatomical Phrases in Radiology Reports Using Unsupervised Learning.

In today's radiology workflow, free-text reporting is established as the most common medium to capture, store, and communicate clinical information. Radiologists routinely refer to prior radiology reports of a patient to recall critical information for new diagnosis, which is quite tedious, time consuming, and prone to human error. Automatic structuring of report content is desired to facilitate such inquiry of information.