An interactive and user-centered computer system to predict physician’s disease judgments in discharge summaries

AbstractPurpose: This article describes a formative natural language processing (NLP) system that is grounded in user-centered design, simplification, and transparency of function. Methods: The NLP system was tasked to classify diseases within patient discharge summaries and is evaluated against clinician judgment during the 2008 i2b2 Shared Task competition. Text classification is performed by interactive, fully supervised learning using rule-based processes and support vector machines (SVMs). Results: The macro-averaged F-score for textual (t) and intuitive (i) classification were 0.614(t) and 0.629(i), while micro-averaged F-scores were recorded at 0.966(t) and 0.954(i) for the competition. These results were comparable to the top 10 performing systems. Discussion: The results of this study indicate that an interactive training method, de novo knowledge base with no external data sources, and simplified text mining processes can achieve a comparably high performance in classifying health-related texts. Further research is needed to determine if the user-centered advantages of a NLP system translate into real world benefits

KNODWAT: A scientific framework application for testing knowledge discovery methods for the biomedical domain

BACKGROUND: Professionals in the biomedical domain are confronted with an increasing mass of data. Developing methods to assist professional end users in the field of Knowledge Discovery to identify, extract, visualize and understand useful information from these huge amounts of data is a huge challenge. However, there are so many diverse methods and methodologies available, that for biomedical researchers who are inexperienced in the use of even relatively popular knowledge discovery methods, it can be very difficult to select the most appropriate method for their particular research problem. RESULTS: A web application, called KNODWAT (KNOwledge Discovery With Advanced Techniques) has been developed, using Java on Spring framework 3.1. and following a user-centered approach. The software runs on Java 1.6 and above and requires a web server such as Apache Tomcat and a database server such as the MySQL Server. For frontend functionality and styling, Twitter Bootstrap was used as well as jQuery for interactive user interface operations. CONCLUSIONS: The framework presented is user-centric, highly extensible and flexible. Since it enables methods for testing using existing data to assess suitability and performance, it is especially suitable for inexperienced biomedical researchers, new to the field of knowledge discovery and data mining. For testing purposes two algorithms, CART and C4.5 were implemented using the WEKA data mining framework

MINING FACEBOOK PAGE FOR BI-PARTISAN ANALYSIS

Social media, particularly Facebook, has become ubiquitous in everyday life. Almost all news sources have adopted Facebook as a platform for dissemination of news. There are many opinions and studies on the partisanship of journalism. What makes social media interesting is that people do not only consume but also interact with others centered around a news article or post. Depending on the partisan bias of both the provider and the consumer, the interactions, and thus the conversation may vary. This research is a preliminary step towards mining these interactions and conversations pivoted against the topic of “fake news” from CNN and Fox News. We used several techniques of data mining, data analytics, and text analytics to generate summaries and descriptive statistics to explore user behavior. Our findings suggest that CNN follower base is more interactive and gregarious. Additionally, CNN followers’ use of Facebook reactions is more diverse, favoring the “haha” (funny / sarcastic) reaction, while those on Fox News’ inclined more towards “like” and “love” (agreement)

Identifying Agile Requirements Engineering Patterns in Industry

Agile Software Development (ASD) is gaining in popularity in today´s business world. Industry is adopting agile methodologies both to accelerate value delivery and to enhance the ability to deal with changing requirements. However, ASD has a great impact on how Requirements Engineering (RE) is carried out in agile environments. The integration of Human-Centered Design (HCD) plays an important role due to the focus on user and stakeholder involvement. To this end, we aim to introduce agile RE patterns as main objective of this paper. On the one hand, we will describe our pattern mining process based on empirical research in literature and industry. On the other hand, we will discuss our results and provide two examples of agile RE patterns. In sum, the pattern mining process identifies 41 agile RE patterns. The accumulated knowledge will be shared by means of a web application.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Economía y Competitividad TIN2015-71938-RED

Towards a human eye behavior model by applying Data Mining Techniques on Gaze Information from IEC

In this paper, we firstly present what is Interactive Evolutionary Computation (IEC) and rapidly how we have combined this artificial intelligence technique with an eye-tracker for visual optimization. Next, in order to correctly parameterize our application, we present results from applying data mining techniques on gaze information coming from experiments conducted on about 80 human individuals

Design Driven Development of a Web-Enabled System for Data Mining in Arthroplasty Registry

This research was inspired by the work at the Norwegian Arthroplasty Registry, which serves as a national resource for understanding the longevity of implanted prostheses, analyzing risks, and patient outcomes in general. At this moment, they have no online system that would help and enable several user groups to take advantage of the data for clinical, research, and informative purposes. This thesis has contributed with a high-fidelity prototype of a desktop application named LeddPOR. The system is dedicated to three user groups: patients, physicians, and researchers. The project was completed in collaboration with three other master students, comprising a back-end and front-end development team. Knut T. Hufthamer and Sølve Ånneland, who provided valuable data mining tasks to be incorporated in the prototype, and Arle Farsund Solheim created visualizations that allow interactive data exploration. The project followed the User-Centered Design approach, as a method to produce a prototype that would be appreciated by real users. The Design Science Research methodology allowed five iterations, within which prototypes from low- to high fidelity have taken form. The final, fully interactive prototype is intended for physicians, researchers, and patients. There are two dedicated parts; one for hip, and the other for knee. Under those, a number of data mining tasks could be performed at the convenience of the expert user. The sessions can be saved and reviewed according to users' preferences and needs. The patient part of the system is offering mainly information, but also some resources such as formerly developed applications supporting post-operative care. During this development, we have defined two patient personas, acknowledging their different needs. On the expert side, two personas were created, one for physicians and one for researchers. Usability testing was conducted with both expert and novice users, which suggested a high success rate. The final System Usability Score (SUS) of 95 points, as well as feedback from evaluation, indicate a potential to develop a product that could be valuable for several user groups.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

Supporting Data mining of large databases by visual feedback queries

In this paper, we describe a query system that provides visual relevance feedback in querying large databases. Our goal is to support the process of data mining by representing as many data items as possible on the display. By arranging and coloring the data items as pixels according to their relevance for the query, the user gets a visual impression of the resulting data set. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. Furthermore, by using multiple windows for different parts of a complex query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. Our system allows to represent the largest amount of data that can be visualized on current display technology, provides valuable feedback in querying the database, and allows the user to find results which, otherwise, would remain hidden in the database

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care