Visualization of unstructured text sequences of nursing narratives

Abstract

This paper presents a keyword-based information visualization technique for nursing record sequences. Visualizing the trend information rooted in unstructured and fragmented abstract text data is a largely unaddressed problem. In our technique, multiple hierarchical keyword based visualizations are used to explore unstructured text data from nursing records. First, each text data set is broken up into a list of keywords to enable the visualization of keyword occurrences over time and the relative distribution of keywords. A graphical user interface is provided to enable selection and classification of keywords. Users may select one or more data sets to compare, in addition to one or more groups of keywords to add to the visualization. Colors are used to distinguish quickly and easily between groups of keywords present in the visualization. At the second level of hierarchy, keywords for visualization are discovered through a predetermined automatic detection and scoring based mechanism. The aggregate frequency trend of keywords from all data sets is also provided in both hierarchies as a way to visualize overall trends and analyze various events in time.

EMRVisualization for Patient Progress Tracking: Interface Design and System Implementation

“…In the existing works, the visualization systems have been proposed with a diversity of purposes. For example, [2] provided a general practitioner with a mobile electronic medical record (EMR) application for house visits; [3] supported a doctor to find frequency trends of keywords in unstructured texts of nursing narratives; [5] generated communication facilities about prostate cancer health risk for patients and doctors; [6] discovered patient groups for better patient care; [8] supported clinical decision making in psychotherapy; [11] aimed at visual analytics; [12] supported the therapy of diabetic patients; [14] analyzed clinical notes for clinical preparation task; [21] supported patient progress tracking using group-based tracking graph analysis.…”

Term distribution visualizations with Focus+Context

“…We have already done some preliminary work on applying this visualization model to the problem of digital forensic string search [15], and we foresee many more possible applications in the field of information security. Other applications to be pursued include the use of these visualizations for conventional text search tasks as was done in [7], and for trend analysis as has been explored in [9] and [5].…”

Electronic Medical Record Visualization for Patient Progress Tracking

“…Medical data visualization has received much attention all over the world due to its usefulness and significance as discussed in [12]. The existing visualization systems are often divided into two groups: the systems in [1,2,5,7,[9][10][11] for single patient-related visualization, and the ones in [3,4,[6][7][8] for patient group-related visualization.…”

“…Those systems have been proposed with a diversity of purposes. For example, [2] provided a general practitioner with a mobile electronic medical record (EMR) application for house visits; [3] supported a doctor to find frequency trends of keywords in unstructured texts of nursing narratives; [5] generated communication facilities about prostate cancer health risk for patients and doctors; [6] discovered patient groups for better patient care; [7] supported clinical decision making in psychotherapy; [9] aimed at visual analytics; [10] supported the therapy of diabetic patients; [11] analyzed clinical notes for clinical preparation task; [16] supported patient progress tracking using group-based tracking graph analysis.…”

“…Such differences lead to our distinct visualization design for presentation and interaction on tablet computers. Besides, our work considers and visualizes unstructured symptom data, which are common in healthcare, in a quantitative approach, instead of a keyword-based approach in [3]. Moreover, in our system, medical data are supported in various types.…”