Classification of Radiology Reports Using Neural Attention Models

The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value. Distinguished from previous studies, our approach embraces a double-annotated dataset and strays away from obscure "black-box" models to comprehensive deep learning models. In this paper, we present a novel neural attention mechanism that not only classifies clinically important findings. Specifically, convolutional neural networks (CNN) with attention analysis are used to classify radiology head computed tomography reports based on five categories that radiologists would account for in assessing acute and communicable findings in daily practice. The experiments show that our CNN attention models outperform non-neural models, especially when trained on a larger dataset. Our attention analysis demonstrates the intuition behind the classifier's decision by generating a heatmap that highlights attended terms used by the CNN model; this is valuable when potential downstream medical decisions are to be performed by human experts or the classifier information is to be used in cohort construction such as for epidemiological studies

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

How Can Business Analytics Induce Creativity: The Performance Effects of User Interaction with Business Analytics

Most organizations today use business analytics systems mainly for efficiency; reducing cost by contacting the right customer, generating revenue by reducing churn, etc. Nevertheless, business analytics holds promise in generating insights and in making users more creative in their decision making process. Analytics technology is becoming sophisticated with very advanced technical capabilities. However, behavioral aspects (i.e. user interaction) of using business analytics software have not reached the same level of sophistication. Very little research in this field discusses how to implement analytical systems and what outcomes will it produce. We are looking at conditions that can enhance user interaction with business analytics systems leading to certain performance outcomes. We propose that the fit between users’ cognitive style (intuitive vs. rational), business analytics model representations (decision tree vs. clustering), and task type (convergent vs. divergent) can lead to efficiency but can have adverse effects on creativity because that might lead to mindlessness in the decision making process

How Can Business Analytics Induce Creativity: The Performance Effects of User Interaction with Business Analytics

Most organizations today use business analytics systems mainly for efficiency; reducing cost by contacting the right customer, generating revenue by reducing churn, etc. Nevertheless, business analytics holds promise in generating insights and in making users more creative in their decision making process. Analytics technology is becoming sophisticated with very advanced technical capabilities. However, behavioral aspects (i.e. user interaction) of using business analytics software have not reached the same level of sophistication. Very little research in this field discusses how to implement analytical systems and what outcomes will it produce. We are looking at conditions that can enhance user interaction with business analytics systems leading to certain performance outcomes. We propose that the fit between users’ cognitive style (intuitive vs. rational), business analytics model representations (decision tree vs. clustering), and task type (convergent vs. divergent) can lead to efficiency but can have adverse effects on creativity because that might lead to mindlessness in the decision making process

How Can Business Analytics Induce Creativity: The Performance Effects of User Interaction with Business Analytics

Most organizations today use business analytics systems mainly for efficiency; reducing cost by contacting the right customer, generating revenue by reducing churn, etc. Nevertheless, business analytics holds promise in generating insights and in making users more creative in their decision making process. Analytics technology is becoming sophisticated with very advanced technical capabilities. However, behavioral aspects (i.e. user interaction) of using business analytics software have not reached the same level of sophistication. Very little research in this field discusses how to implement analytical systems and what outcomes will it produce. We are looking at conditions that can enhance user interaction with business analytics systems leading to certain performance outcomes. We propose that the fit between users’ cognitive style (intuitive vs. rational), business analytics model representations (decision tree vs. clustering), and task type (convergent vs. divergent) can lead to efficiency but can have adverse effects on creativity because that might lead to mindlessness in the decision making process

LUNA: A Model-Based Universal Analysis Framework for Large Language Models

Over the past decade, Artificial Intelligence (AI) has had great success recently and is being used in a wide range of academic and industrial fields. More recently, LLMs have made rapid advancements that have propelled AI to a new level, enabling even more diverse applications and industrial domains with intelligence, particularly in areas like software engineering and natural language processing. Nevertheless, a number of emerging trustworthiness concerns and issues exhibited in LLMs have already recently received much attention, without properly solving which the widespread adoption of LLMs could be greatly hindered in practice. The distinctive characteristics of LLMs, such as the self-attention mechanism, extremely large model scale, and autoregressive generation schema, differ from classic AI software based on CNNs and RNNs and present new challenges for quality analysis. Up to the present, it still lacks universal and systematic analysis techniques for LLMs despite the urgent industrial demand. Towards bridging this gap, we initiate an early exploratory study and propose a universal analysis framework for LLMs, LUNA, designed to be general and extensible, to enable versatile analysis of LLMs from multiple quality perspectives in a human-interpretable manner. In particular, we first leverage the data from desired trustworthiness perspectives to construct an abstract model as an auxiliary analysis asset, which is empowered by various abstract model construction methods. To assess the quality of the abstract model, we collect and define a number of evaluation metrics, aiming at both abstract model level and the semantics level. Then, the semantics, which is the degree of satisfaction of the LLM w.r.t. the trustworthiness perspective, is bound to and enriches the abstract model with semantics, which enables more detailed analysis applications for diverse purposes.Comment: 44 pages, 9 figure

Copying Machine Learning Classifiers

We study copying of machine learning classifiers, an agnostic technique to replicate the decision behavior of any classifier. We develop the theory behind the problem of copying, highlighting its properties, and propose a framework to copy the decision behavior of any classifier using no prior knowledge of its parameters or training data distribution. We validate this framework through extensive experiments using data from a series of well-known problems. To further validate this concept, we use three different use cases where desiderata such as interpretability, fairness or productivization constrains need to be addressed. Results show that copies can be exploited to enhance existing solutions and improve them adding new features and characteristics