The use of deep neural models for diagnosis prediction from clinical text has shown promising results. However, in clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results. We introduce ProtoPatient, a novel method based on prototypical networks and label-wise attention with both of these abilities. ProtoPatient makes predictions based on parts of the text that are similar to prototypical patients—providing justifications that doctors understand. We evaluate the model on two publicly available clinical datasets and show that it outperforms existing baselines. Quantitative and qualitative evaluations with medical doctors further demonstrate that the model provides valuable explanations for clinical decision support.

Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in a static setting and unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently entity relations are very dynamic over time. In this work, we propose a neural network-based approach that leverages public attention as supervision. Our model is capable of learning rich and different entity representations in a joint framework. Through extensive experiments on large-scale datasets, we demonstrate that our method achieves better results than competitive baselines.

Textual entailment has been recognized as a generic task that captures major semantic inference needs across many natural language processing applications. However, to date, textual entailment has not been considered in a cross-corpus setting, nor for user generated content. Given the emergence of Medicine 2.0, medical blogs are becoming an increasingly accepted source of information. However, given the characteristics of blogs( which tend to be noisy and informal; or contain a interspersing of subjective and factual sentences) a potentially large amount of irrelevant information may be present. Given the potential noise, the overarching problem with respect to information extraction from social media is achieving the correct level of sentence filtering - as opposed to document or blog post level. Specifically for the task of medical intelligence gathering. In this paper, we propose an approach to textual entailment with uses the text from one source of user generated content (T text) for sentence-level filtering within a new and less amenable one (H text), when the underlying domain, tasks or semantic information is the same, or overlaps.