Natural Language Processing (NLP) systems often make use of machine learning techniques that are unfamiliar to end-users who are interested in analyzing clinical records. Although NLP has been widely used in extracting information from clinical text, current systems generally do not support model revision based on feedback from domain experts. We present a prototype tool that allows end users to visualize and review the outputs of an NLP system that extracts binary variables from clinical text. Our tool combines multiple visualizations to help the users understand these results and make any necessary corrections, thus forming a feedback loop and helping improve the accuracy of the NLP models. We have tested our prototype in a formative think-aloud user study with clinicians and researchers involved in colonoscopy research. Results from semi-structured interviews and a System Usability Scale (SUS) analysis show that the users are able to quickly start refining NLP models, despite having very little or no experience with machine learning. Observations from these sessions suggest revisions to the interface to better support review workflow and interpretation of results.
Other efforts have taken this idea even further to build interactive machine learning systems that learn iteratively from their end-users. Sometimes referred to as methods, these techniques involve a learning system whose output is used by the end-user to further inform the system about the learning task. This forms a closed loop that can be used to build continuously improving models of prediction. Some examples include applications in interactive document clustering @cite_21 , document retrieval @cite_13 , image segmentation @cite_17 , bug triaging @cite_10 and even music composition @cite_8 . These successes suggest that it may be promising to use feedback to improve machine learning models in the clinical domain. good!
@cite_13 @cite_8 @cite_21 @cite_10 @cite_17
1992492534 1999043044 2000122588 2157018954 2003238113
Performing exhaustive searches over a large number of text documents can be tedious, since it is very hard to formulate search queries or define filter criteria that capture an analyst's information need adequately. Classification through machine learning has the potential to improve search and filter tasks encompassing either complex or very specific information needs, individually. Unfortunately, analysts who are knowledgeable in their field are typically not machine learning specialists. Most classification methods, however, require a certain expertise regarding their parametrization to achieve good results. Supervised machine learning algorithms, in contrast, rely on labeled data, which can be provided by analysts. However, the effort for labeling can be very high, which shifts the problem from composing complex queries or defining accurate filters to another laborious task, in addition to the need for judging the trained classifier's quality. We therefore compare three approaches for interactive classifier training in a user study. All of the approaches are potential candidates for the integration into a larger retrieval system. They incorporate active learning to various degrees in order to reduce the labeling effort as well as to increase effectiveness. Two of them encompass interactive visualization for letting users explore the status of the classifier in context of the labeled documents, as well as for judging the quality of the classifier in iterative feedback loops. We see our work as a step towards introducing user controlled classification methods in addition to text search and filtering for increasing recall in analytics scenarios involving large corpora. Model evaluation plays a special role in interactive machine learning (IML) systems in which users rely on their assessment of a model's performance in order to determine how to improve it. A better understanding of what model criteria are important to users can therefore inform the design of user interfaces for model evaluation as well as the choice and design of learning algorithms. We present work studying the evaluation practices of end users interactively building supervised learning systems for real-world gesture analysis problems. We examine users' model evaluation criteria, which span conventionally relevant criteria such as accuracy and cost, as well as novel criteria such as unexpectedness. We observed that users employed evaluation techniques---including cross-validation and direct, real-time evaluation---not only to make relevant judgments of algorithms' performance and interactively improve the trained models, but also to learn to provide more effective training data. Furthermore, we observed that evaluation taught users about what types of models were easy or possible to build, and users sometimes used this information to modify the learning problem definition or their plans for using the trained models in practice. We discuss the implications of these findings with regard to the role of generalization accuracy in IML, the design of new algorithms and interfaces, and the scope of potential benefits of incorporating human interaction in the design of supervised learning systems. Extracting useful knowledge from large network datasets has become a fundamental challenge in many domains, from scientific literature to social networks and the web. We introduce Apolo, a system that uses a mixed-initiative approach - combining visualization, rich user interaction and machine learning - to guide the user to incrementally and interactively explore large network data and make sense of it. Apolo engages the user in bottom-up sensemaking to gradually build up an understanding over time by starting small, rather than starting big and drilling down. Apolo also helps users find relevant information by specifying exemplars, and then using a machine learning method called Belief Propagation to infer which other nodes may be of interest. We evaluated Apolo with twelve participants in a between-subjects study, with the task being to find relevant new papers to update an existing survey paper. Using expert judges, participants using Apolo found significantly more relevant papers. Subjective feedback of Apolo was also very positive. Network alarm triage refers to grouping and prioritizing a stream of low-level device health information to help operators find and fix problems. Today, this process tends to be largely manual because existing tools cannot easily evolve with the network. We present CueT, a system that uses interactive machine learning to learn from the triaging decisions of operators. It then uses that learning in novel visualizations to help them quickly and accurately triage alarms. Unlike prior interactive machine learning systems, CueT handles a highly dynamic environment where the groups of interest are not known a-priori and evolve constantly. A user study with real operators and data from a large network shows that CueT significantly improves the speed and accuracy of alarm triage compared to the network's current practice. Perceptual user interfaces (PUIs) are an important part of ubiquitous computing. Creating such interfaces is difficult because of the image and signal processing knowledge required for creating classifiers. We propose an interactive machine-learning (IML) model that allows users to train, classify view and correct the classifications. The concept and implementation details of IML are discussed and contrasted with classical machine learning models. Evaluations of two algorithms are also presented. We also briefly describe Image Processing with Crayons (Crayons), which is a tool for creating new camera-based interfaces using a simple painting metaphor. The Crayons tool embodies our notions of interactive machine learning