OBJECTIVE: To explore new graphical methods for reducing and analyzing large data sets in which the data are coded with a hierarchical terminology. METHODS: We use a hierarchical terminology to organize a data set and display it in a graph. We reduce the size and complexity of the data set by considering the terminological structure and the data set itself (using a variety of thresholds) as well as contributions of child level nodes to parent level nodes. RESULTS: We found that our methods can reduce large data sets to manageable size and highlight the differences among graphs. The thresholds used as filters to reduce the data set can be used alone or in combination. We applied our methods to two data sets containing information about how nurses and physicians query online knowledge resources. The reduced graphs make the differences between the two groups readily apparent. CONCLUSIONS: This is a new approach to reduce size and complexity of large data sets and to simplify visualization. This approach can be applied to any data sets that are coded with hierarchical terminologies.
OBJECTIVE: To explore new graphical methods for reducing and analyzing large data sets in which the data are coded with a hierarchical terminology. METHODS: We use a hierarchical terminology to organize a data set and display it in a graph. We reduce the size and complexity of the data set by considering the terminological structure and the data set itself (using a variety of thresholds) as well as contributions of child level nodes to parent level nodes. RESULTS: We found that our methods can reduce large data sets to manageable size and highlight the differences among graphs. The thresholds used as filters to reduce the data set can be used alone or in combination. We applied our methods to two data sets containing information about how nurses and physicians query online knowledge resources. The reduced graphs make the differences between the two groups readily apparent. CONCLUSIONS: This is a new approach to reduce size and complexity of large data sets and to simplify visualization. This approach can be applied to any data sets that are coded with hierarchical terminologies.
Authors: Alison Callahan; Vladimir Polony; José D Posada; Juan M Banda; Saurabh Gombar; Nigam H Shah Journal: J Am Med Inform Assoc Date: 2021-07-14 Impact factor: 4.497
Authors: Xia Jing; Matthew Emerson; David Masters; Matthew Brooks; Jacob Buskirk; Nasseef Abukamail; Chang Liu; James J Cimino; Jay Shubrook; Sonsoles De Lacalle; Yuchun Zhou; Vimla L Patel Journal: BMC Med Inform Decis Mak Date: 2019-02-14 Impact factor: 2.796
Authors: Xia Jing; Vimla L Patel; James J Cimino; Jay H Shubrook; Yuchun Zhou; Chang Liu; Sonsoles De Lacalle Journal: JMIR Res Protoc Date: 2022-07-18