Visualizing the Overview of Temporal Patterns of Patients’ Activities
Kazuo Misue, Yuta Kanai, and Hideaki Takagi
University of Tsukuba, Tsukuba, JapanAbstract
It is necessary to determine patients’ activities for effective management in hospitals and affiliated regional medicalinstitutions. The purpose of this study is to support the identification of features from temporal patterns of patients’
activities using information visualization. This paper introduces a technique called “ChronoView” for visualizing
temporal patterns. ChronoView provides an overview of many temporal patterns and enables us to easily determine
the similarity and anomaly of temporal patterns. In order to illustrate a use case, temporal patterns of patients’
activities were extracted from an actual data set of patients of the University of Tsukuba Hospital. This paper
reports some features of the temporal patterns identified using ChronoView.

Figure 1. Daily temporal patterns of admission
visualized using ChronoView. Each blue circle
represents a clinical group. Admission time of mostclinical groups is concentrated between 10:00 and 11:00.
Admission time for Group HK (Emergency group) and a
few other groups is independent of the time of day.

Figure 2. Weekly temporal patterns of transition intowards. Each red circle represents a ward. Transition into
most wards is independent of the day of the week.
Transition into Ward 930 changed and was concentrated
on the weekend. The gray polyline represents the moving
history of Ward 930.

1 Introduction
It is necessary to determine activity patterns of patients for effective management in hospitals and affiliated regional
medical institutions. Activity patterns need to be determined from various viewpoints. It is important to determine
the temporal patterns of patients’ activities. What type of features does each clinical group have in temporal patternsof admission? What kinds of differences exist among wards in temporal patterns of moving in? Are temporal
patterns of admission changing? Determining patients’ activities and their changes with respect to the previously
mentioned viewpoints is necessary to manage clinical groups that are accepted and to allocate beds in wards.
Date and time can be expressed as simple numerical data. However, understanding the significance of date and time
converts the analysis of temporal patterns into a complex process. Time is a linear as well as periodic entity. Thus,
we cannot disregard the periodicities of date and time in the temporal patterns of human activities. Therefore, evenfor a single pattern, we have to observe the pattern from several viewpoints such as a day-period, week-period, andmonth-period. Moreover, it is difficult to observe many temporal patterns simultaneously. Consequently, theanalysis of temporal patterns can be very complicated and problematic. To solve these problems, we need certain
techniques to visualize the overview of many temporal patterns.
The purpose of this study is to support the analysis of patients’ activities. In particular, we aim to improve theefficiency of identifying features from many temporal patterns by focusing on the temporal aspects of activities.

We have already proposed a technique called “ChronoView” for visualizing many temporal patterns1. This
technique represents a temporal pattern, i.e., a set of time-stamps, using a position on a two-dimensional plane.
Therefore, it provides high space efficiency and contributes toward determining the similarity between temporal
patterns. We have developed an analysis tool that provides ChronoView. Using this analysis tool, we have been able
to visualize a data set of patients.
2 Related Work
Powsner and Tufte proposed a graphical summary of patient status2. They used small repeated graphs with identicalformats to show the time-oriented status of a patient. Plaisant et al. proposed LifeLines, which provides a generalvisualization environment for personal patient histories3. These seminal works focus on offering information about
an individual patient. There are certain studies that focus on patients’ activities. For example, Brodbeck et al.
presented a case study using Masterplan, which provides several different views of patients’ flows4. In this study, we
aim to determine the features of temporal patterns of patients’ activities such as admission, transition, discharge, andother clinical events.
For visualizing temporal patterns, various visualization techniques have been developed. One of the most commontechniques is plotting each event on a time axis5. LifeLines adopted this technique. A time axis can be drawn as acircle or a spiral to reveal the periodicity of temporal patterns6. Another technique adopted a representation similar
to a river to express the time-oriented quantitative data7. There are various techniques similar to those mentionedabove; however, it is difficult to represent many temporal patterns together using these techniques. The scalability ofvisual representations for a large number of temporal patterns is an important issue.
3 Temporal Patterns and Their Overview
This section presents a formal definition of a temporal pattern, and describes an issue of visualizing the patterns.
3.1 Formalization of Temporal Pattern
Events or activities related to inpatients are accompanied by date and time, i.e., time-stamps. Every event is recorded
using a time-stamp. Assume that a patient 𝑝 of Group PB (obstetric group) is admitted to Ward 300 at time 𝑡. This isconsidered as an event and is accompanied by a time-stamp 𝑡 and other attributes such as patient 𝑝, admission, PB,and Ward 300. Events that satisfy certain conditions are considered as an event group. For example, events of alladmissions into Ward 300 comprise an event group. A temporal pattern is a set of time-stamps that accompany anevent group. We define a temporal pattern as follows: Let 𝐸 be a set of events. Each event 𝑒 ∈ 𝐸 has a time-stamp𝑡(𝑒). Each event may also have certain other attributes: a patient, one or two ward(s), action (admission, transition
between wards, discharge, and so on), and a clinical group. Let, 𝑎(𝑒) and 𝑔(𝑒) denote an action and a clinical grouprelated to an event 𝑒, respectively. For example 𝐸!",!"# = {𝑒 ∈ 𝐸|𝑔 𝑒 = 𝑃𝐵  and  𝑎 𝑒 = 𝐴𝑑𝑚. } denotes a set ofevents that represent admissions to Group PB. Moreover, 𝑇!",!"# = {𝑡(𝑒)|𝑒 ∈ 𝐸!",!" } denotes a temporal patternthat accompanies events in 𝐸!",!" . By analyzing the set, we determine a pattern of admission related to the obstetricgroup.
3.2 Overview of Many Temporal Patterns
If we focus on a single temporal pattern, plotting every time-stamp in the pattern on a time axis allows us to easily
determine the pattern. A histogram is also useful to understand the frequency distribution of events. For example,
when we want to identify the monthly distribution of admission to the obstetric group, such methods work well.
However, we want to identify not only the distribution of an event group but also the remarkable features in many
event groups. There are many sets of events to be observed and the temporal patterns of these sets should beobserved with respect to various periodicities. One naive method for our requirement is to draw all possible
histograms and to compare them. This impractical method fills the computer display with many histograms for large
groups. We require a more practical method to observe an overview of all event groups efficiently.
4 ChronoView
ChronoView is a technique for visualizing many event groups with time-stamps. This technique represents each
event group as a position on a two-dimensional plane. Event groups are placed on a plane similar to the clock face ofan analog clock. First, we give the twelve o'clock position on the clock face to the time-stamp 𝑡! . Then, moving

clockwise, we then place all time-stamps on the circumference according tothe time elapsed from 𝑡! . If we suppose that 𝑈 is the set of all time-stamps,
the position of the time-stamps is expressed by the function 𝑓! : 𝑈 → ℝ!
given by

022
20

𝑓! 𝑡 = (𝑟 ∙ cos 𝜃 , 𝑟 ∙ sin 𝜃)
where 𝑟 denotes the radius of the clock face circle, and𝜃=!
!

− 2𝜋!!!!
!

!
!

4B

186A.

Suppose that 𝑇 is a finite set of time-stamps, i.e., 𝑇 = {𝑡! , 𝑡! , … , 𝑡! }. We
assign the barycenter of the positions of 𝑡! (𝑖 = 1, … , 𝑛) to the set 𝑇. Thepositions of a set of time-stamps can be represented by the function
𝑓: 2! ∖ {∅} → ℝ! .
𝑓 𝑇 =2!∈! 𝑓! 𝑡 .

16

814

1210Figure 3. Basic idea of ChronoView.

For example, let us consider the case where  𝑐 = 24h. Figure 3 illustrates the basic idea of ChronoView and shows
two event groups: A and B. If events in Group A occurred at 00:00, 08:00, and 14:00, the group is placed near thecenter of the clock face. If events in Group B occurred at 18:00 and 22:00, then the group is placed close to 20:00 on
the chord between 18:00 and 22:00. It is easy to observe whether events in the group have occurred in a wide rangeof time-periods, or only during certain times, by representing an event group using such positions. In Figure 3, aperiod 𝑐 is set to 24h and 𝑡! is 00:00 (midnight). When 𝑐 is seven days, it represents a diagram of a one-week period.
In a similar way, ChronoView can represent a one-month or one-year period.
5 Visual Tool for Temporal Patterns
We have implemented ChronoView in a visual tool. The tool
provides some functions as follows.
• A time-period can be selected from one-hour, one-day, oneweek, and one-month.
• Individual time-stamps are plotted. ChronoView has someambiguity in representation, because it represents a set oftime-stamps by a position on a two-dimensional plane. To
eliminate this ambiguity, bullets that represent individual
time-stamps are drawn on the circumference of a circle
expressing an event group (see Figure 4).
• Moving averages of event groups are shown. ChronoViewshows the barycenter, i.e., the average of time-stamps. The
length of the range to compute the average can be selectedusing a slider. The range can also be changed. Movement of
the barycenter according to the movement of the range can bepresented as an animation.

Figure 4. (Above) Normal view, without
individual time-stamps. (Below) Individual
time-stamps are plotted on the circumference ofeach blue circle.

• The moving history of an event group can be drawn. Moving
averages are presented as an animation. However, the animation is not helpful in determining an exact route of amoving average. The tool draws the trajectory of the moving average of a selected group explicitly (see Figure 5).
6 Use CaseUTH is affiliated to the University of Tsukuba, and is located about 40 miles northeast of Tokyo, Japan. It has
approximately 30 clinical groups and 23 wards consisting of 800 beds8. A complete data set of all inpatients’
activities (with encrypted id's), ranging over a two-year period from April 2010 to March 2012, was provided byUTH. The Ethics Committee of UTH has approved the statistical treatment of this data and the resulting publicationof research findings in our project. We extracted various event groups from the data set and visualized temporal
patterns of the groups using the analysis tool.

6.1 Remarkable Features of Event Groups
We consider admission to each clinical group as anevent group. Figure 1 shows temporal patterns of all
event groups represented as a ChronoView. Thetime range is three months from April 1, 2010 toJune 30, 2010. Each blue circle represents a clinicalgroup. As is shown, most circles are concentrated
from 10:00 to 11:00 on the circumference of theclock face. We can infer that the admission time ofmost clinical groups is concentrated between 10:00
and 11:00. This shows that admissions of planned
hospitalization are performed in the morning.
In contrast, Group AN (Sleep Disordered Breathing
Figure 5. Moving history of Ward 930. (Left) Ward 930
group) was placed close to 20:00. This suggests thatmoved to Sunday in late December 2010 and (Right) Ward 930
all the patients of Group AN were hospitalized for
returned to the center of the clock face.
tests. Group HK (Emergency group) was placed
close to the center of the clock face. This suggeststhat the admission of Group HK occurred independent of the time of day. This feature is in the nature of the role ofGroup HK. Group PB (Obstetric group) and Group EB (Child Health group) were also placed near the center of theclock face. We can say that the admission of these groups also occurred independently of the time of day.
6.2 Change of Features of Event Groups
Here, we consider transitions to each ward as an event group. Figure 2 presents the temporal patterns of all event
groups represented in a one-week period. Each red circle denotes the moving average of a ward for three months. Inaddition, the moving history of Ward 930 is drawn with gray lines. We see that Ward 930 moved to Sunday and thenreturned to the center of the clock face (see Figure 5). This suggests that in late December 2010, the transition toWard 930 was concentrated on the weekend.
7 Conclusion
We have shown that we can understand the overview of features of patients’ activities by visualizing temporal
pattern data extracted from the data set of movement of patients. The results of the study shown so far are based
only on the temporal patterns extracted from the data set. We did not use the expertise of the hospital for ourconjecture. We have two objectives for future work. The first is the improvement of visual representation toefficiently express the information required by medical professionals. The second is to design a smooth connection
between ChronoView and other visual representation methods for detailed information because ChronoView canonly show the overview of temporal patterns.
We thank the following staff of the University of Tsukuba Hospital: Dr. Tetsuya Igarashi, Dr. Hiroyuki Hoshimoto,and Mr. Masaki Suzuki. This work was partially supported by JSPS KAKENHI Grant Number 23241047.
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