論文 / 著書情報
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Title

Sequential Pattern Mining on Electronic Medical Records with HandlingTime Intervals and the Efficacy of Medicines

AuthorKeishiro Uragaki, Tomoyuki Hosaka, Yoshitaka Arahori, Muneo
Kushima, Tomoyoshi Yamazaki, Kenji Araki, Haruo Yokota

Journal/Book nameProc. of the 21st IEEE International Symposium on Computers andCommunications, , ,Issue date

2016, 6

DOI

http://dx.doi.org/10.1109/ISCC.2016.7543708

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http://www.ieee.org/index.html

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Sequential Pattern Mining on Electronic Medical Records
with Handling Time Intervals and the Efficacy of Medicines
Keishiro Uragaki∗ , Tomoyuki Hosaka∗ , Yoshitaka Arahori∗ , Muneo Kushima∗∗ ,Tomoyoshi Yamazaki∗∗ , Kenji Araki∗∗ and Haruo Yokota∗∗ Department of Computer Science, Tokyo Institute of Technology,
2-12-1 Oookayama, Meguro-ku, Tokyo 152, Japan
Email:yokota@cs.titech.ac.jp∗∗ Faculty of Medicine, University of Miyazaki Hospital,
5200 Machikihara, Kiyotake-Cho, Miyazaki-shi, Miyazaki 889, Japan
Email: jyoho support@med.miyazaki-u.ac.jp

Abstract—It is useful to employ electronic medical records to improvemedical studies. Based on their experience, medical workers conventionally prepare clinical pathways as guidelines for the typical flow for the
medical treatment of each disease. In this study, we propose an approachfor verifying existing clinical pathways and recommend variants or new
pathways by analyzing historical records. We propose a method basedon the application of sequential pattern mining to record logs withhandling time intervals between treatments. We also focus on the efficacy
of medicines instead of their names because various medicines have thesame efficacy and they change dynamically. We evaluated the proposed
method using actual logs and the results demonstrated that the proposed
method is effective.

I. I NTRODUCTION
Most large-scale hospitals have recently adopted electronic medical
record (EMR) systems to support medical workers with the simple
maintenance and checking of patient information, which can becompared rapidly with written records. Moreover, the secondary usesof electronic medical records have attracted attention with respect
to the standardization of medical treatments. Medical workers tend
to use clinical pathways as guidelines for their medical actions. A
clinical pathway defines the typical flow for the medical treatment ofeach disease and it is conventionally generated by medical workers
based on their experience. However, this human-based approachis very time-consuming and much effort is required to produce
correct pathways, while there is also the possibility of including
inappropriate components in the results obtained. Clinical pathways
can be extracted from long historical EMRs, which are kept in
hospitals for secondary usage. Machine-generated clinical pathways
can be used to verify existing clinical pathways and to recommend
variants or new pathways.
In this study, we propose a method based on the application ofa sequential pattern mining algorithm to EMR logs for extracting
clinical pathways. The time intervals between medical treatments
are important, such as between checkups and doses; therefore,
the proposed method considers the time intervals between medical
treatments. Moreover, various medicines have the same efficacy andnew medicines may appear dynamically, so we consider the efficacy
of medicines instead of their names in the proposed method. We used
actual EMR logs from a university hospital to evaluate the proposed
method and the experimental results demonstrated that the proposed
method is effective.
The remainder of this paper is organized as follows. Related
research is reviewed in Section II. The proposed method is describedand evaluated in Sections III and IV, respectively. We provide ourconclusions in Section V.

II. R ELATED W ORK
Many studies have addressed the secondary uses of EMRs related
to clinical pathways. Okada et al. proposed an electronic clinicalpathway system based on a semistructured data model that uses a personal digital assistant for on-site access [1]. Wakamiya and Yamauchi
considered standard functions for electronic clinical pathways [2].
Hirano and Tsumoto proposed a method for extracting typical medical
processes by analyzing the logs stored in a hospital information
system [3], where they clustered the medical treatment data from each
implementation date to create candidate clinical pathways, although
they did not employ a sequential pattern mining approach.
In this study, we propose the application of a sequential patternmining algorithm to EMRs to extract clinical pathways. The sequential pattern mining algorithm is an Apriori-based frequent patternmining algorithm, which is well known [4]. However, this algorithm
is very time-consuming with large data sets and it generates a large
number of irrelevant patterns as results. Moreover, it cannot handle
time intervals between items.
The time-interval sequential pattern mining (TI-SPM) algorithm
was proposed to handle the time intervals between items [6]. Because
the TI-SPM algorithm is based on PrefixSpan [7], it is much fasterthan the Apriori-based algorithm. However, TI-SPM still generates
irrelevant patterns, so the concept of closed sequential pattern mining[5] can be applied to remove irrelevant patterns from the results.
TI-SPM represents the time interval between items using a timeinterval set, which is a group that comprises a finite number ofnumeric values. Multi-TI-SPM (MTI-SPM) was proposed [8] for
determining the time interval between multiple items in patterns. BothTI-SPM and MTI-SPM are effective when the time interval between
items does not change greatly, but they are not suitable if the timeinterval between items changes in a dynamic manner, as found withmedical treatments.
In this study, we propose a method called T-PrefixSpan for
calculating the time interval between medical treatments based on
statistical information to extract clinical pathways. Although Huang
et al. employed a similar approach [9], ours differs with respect
to the calculation methods and medicinal treatments, especially theefficacy of medicines. Lin et al. proposed a method for mining
time dependency patterns in clinical pathways [18], which is anotherapproach to the secondary usage of EMRs that considers time.
III. M ETHODS
A. Handling Time Interval between Items
In medical care, it is essential to manage the time interval during
sequential medical treatments. The existing TI-SPM [6] handles a

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set of time intervals by dividing them into time ranges. However,more accurate information about time is required to specify clinical pathways. Thus, we propose a method called T-PrefixSpan forderiving statistical information about time intervals by considering
outliers. Huang et al. proposed a similar approach for calculating timeintervals based on sequential pattern mining [9], but their approachto the statistical calculations differs from ours because they did nothandle outliers or consider the efficacy of medicines.
First, we define the concepts required to introduce our method
before we explain T-PrefixSpan.
Definition 1: T-item (i, t)
Let I be the set of items and t is the time when an item i has
occurred. We define a T-item (i, t) as a pair of i and t.
Definition 2: T-sequence s and O-sequence Os
T-sequence s is a sequence of T-items, which is denoted by s =<(i1 , t1 ), (i2 , t2 ), ..., (in , tn ) >. T-items that occur at the same timeshould be arranged in alphabetical order. Furthermore, let n be thelength of T-sequence s and let an O-sequence of s be the sequence
Os =< i1 , i2 , ..., in >.
Definition 3: time interval T Ik
Given a T-sequence s =< (i1 , t1 ), (i2 , t2 ), ..., (in , tn ) >, timeinterval T Ik is defined as follows:
T Ik ≡ tk+1 − tk (k = 1, 2, ..., n − 2, n − 1).
Definition 4: T-sequential database D and O-sequential
database OD
Given a set of T-sequences, T-sequential database D is defined asfollows, where the identifier sid of the element of D has a uniquevalue for each sequence.
D ≡ {(sid, s) | sid, s ∈ S}
Furthermore, let an O-sequential database OD be a sequential
database that comprises O-sequences configured from all of the Tsequences in D. Let Size(D) be Size(O( D)), which is the numberof sequences in OD
Definition 5: T-frequent sequential pattern P
Let M inSup (0 ≤ M inSup ≤ 1) be a minimum
support and D is a T-sequential database. Given P =<i1 , X1 , i2 , X2 , ..., in−1 , Xn−1 , in > (∀j ij is an item, ∀k Xk
is a set of five values, i.e., (mink , modk , avek , medk ,maxk ), andwe can configure a sequence OP =< i1 , i2 , ..., in−1 , in >.
We define P as a T-frequent sequential pattern if Op is a frequent
sequential pattern in an O-sequential database configured from D(i.e., Sup(P ) =| {Seq|Op ⊆ Seq, (sid, Seq) ∈ OD , where sid is
an identifier of Seq} |≥ Size(OD ) × M inSup).
Let OP be the O-Pattern of P . The set of five values is defined asfollows.
Given all of the T-sequences with O-sequences containing
OP in D, let S be one of them, where S=<
i′1 , t1 , i′2 , t2 , ..., i′m−1 , tm−1 , i′m >. By using j1 , j2 , ..., jn−1 , jn ,which satisfies (1) 1 ≤ j1 < j2 < ... < jn−1 < jn ≤ m and (2)ik = i′jk , ik+1 = i′jk+1 , we can configure sets of time intervals:
SetT I1 , SetT I2 , ..., SetT In−1 , where T Ik = t′jk+1 − t′jk . Moreover,in Xk = (mink , modk , avek , medk , maxk ), we define the fivevalues as follows.
1) mink = min SetT Ik
2) modk = the most frequent value in SetT Ik
3) avek = the average of the values in SetT Ik

4) medk = the intermediate value of the values in SetT Ik
5) maxk = max SetT Ik
Given a time interval Xj = (minj , modj , avej , medj , maxj ) (1 ≤j < n), if the equation minj = M axj holds, then the time interval
between item ij and item ij+1 is consistent; in particular, if theequation minj = M axj = 0 holds, then these two items occurredat the same time.
Definition 6: T-closed frequent sequential∑ pattern A
Given a T-sequential database D, letbe a set of T-frequent
sequential patterns
extracted
from
D
and
A
is
a T-frequent sequential∑
pattern of. A is a T-closed frequent∑sequential pattern if B
satisfying the following does not exist in\A.
1) If we let A′ and B ′ be O-Patterns of A and B, respectively,
then A′ ⊆ B ′ .
2) Sup(A) ≤ Sup(B), where we define a support of the Tfrequent sequential pattern as Sup(A) ≡| {s | s ⊆S, (sid, S) ∈ D, wheresid is the identifier of S in D} |.
3) If we let A and B be < a1 , T1 , a2 , T2 , ..., an−1 , Tn−1 , an >′and < b1 , T1′ , b2 , T2′ , ..., bm−1 , Tm−1
, bm >, respectively,
the j1 , j2 , ..., jn exist that satisfy (1) 1 ≤ j1 < j2 <... < jn ≤ m and (2) ak = bjk , ak+1 = bjk+1 .
Thus, for all Tk = (mink , modk , avek , medk , maxk ) andTj′k = (min′jk , mod′jk , ave′jk , med′jk , max′jk ), equations (1)mink ≥ min′jk and (2) maxk ≤ max′jk hold.
For example, if we are extracting T-frequent sequential patterns
from a T-sequential database such as Table I under the minimum
support M inSup = 0.4, then because the O-sequential database of
D is shown in Table II, the frequent sequential patterns based on theminimum support M inSup = 0.4 are < A >, < B >, < E >,< A, B >, < B, E >, and < A, B, E >. The frequent sequential
patterns with one item in OD are T-frequent sequential patterns inD, so < A >, < B >, and < E > are T-frequent sequential patternsin D.
Considering the time between item A and item B in the sequence < A, B >, the set of time intervals calculated from D is{2, 3, 3, 4, 5}. By considering the minimum, most frequent value,
average, median, and maximum, then < A, (2, 3, 3, 3, 5), B > isa T-frequent sequential pattern in D (2+3+3+4+5=17, [17/5]=3).
Similarly, if we calculate the T-frequent sequential pattern from
< B, E > and < A, B, E >, we have < B, (3, 5, 5, 5, 7), E > and< A, (2, 2, 2, 2, 3), B, (3, 5, 5, 5, 7), E >. If more than two different
values are the most frequent values, then their average is the mostfrequent value. Therefore, the T-frequent sequential patterns in D
under the minimum support M inSup = 0.4 are < A >, < B >,< E >, < A, (2, 3, 3, 3, 5), B >, < B, (3, 5, 5, 5, 7), E >, and< A, (2, 2, 2, 2, 3), B, (3, 5, 5, 5, 7), E >, and the T-closed frequent
sequential patterns are < A >,< B >, < A, (2, 3, 3, 3, 5), B > and< A, (2, 2, 2, 2, 3), B, (3, 5, 5, 5, 7), E >.
We developed T-PrefixSpan from PrefixSpan [7]. The T-PrefixSpan
algorithm is described in Algorithm 1, where an O-sequential
database of a T-sequential database D is O(D), an O-sequence ofa T-sequence S is O(S), and the connection of sequence A withsequence B is AB. Furthermore, the N-th element of a set X is
XN , the N-th item of a sequence S is SN , and the time when theN-th T-item of T-sequence A occurred is TAN .
We use the Smirnov–Grubbs test as an arbitrary function to exclude
outliers from the set of time intervals, where we exclude outliers
below the significance level of α = 0.05.

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TABLE I
T- SEQUENTIAL DATABASE D
Identifier for a T-sequence
10
20
30
40
50

T-sequence< (A, 1), (B, 3), (C, 7), (E, 10) >< (A, 1), (B, 4), (E, 7) >< (A, 2), (B, 6), (B, 9) >< (A, 2), (B, 5) >< (A, 2), (B, 7) >TABLE IIOD (O- SEQUENTIAL DATABASE OF D)
Identifier for a T-sequence
10
20
30
40
50

T-sequence< A, B, C, E >< A, B, E >< A, B, B >< A, B >< A, B >B. Handling Medicines
We represent a medical treatment item as a set of four subitems:(Class; Description; Code; Name). Class denotes the classificationof a medical treatment, Description is its detailed diagnostic record,
Code is a medicinal code that represents the unique efficacy of the
medicine considered, and Name is the name of the medicine. The total
number of Class types is finite. If a medical treatment administered
to a patient is not a medicine, Code and Name are set to ”null” inorder to clarify that they are blank.
For example, when a medical treatment designated as “prescribe”
an “internal medicine” where the medicinal Code is “613” and thename is “Fine Cefzon 10% for Children” appears in a log, the item
is represented in the form: (prescription; internal medicine; 613; Fine
Cefzon 10% for Children). In this example, Code 613 is an “antibiotic
preparation that acts mainly against Gram-positive, Gram-negative
bacteria.” If we need to represent a medical treatment that comprises
a “nursing task” of “replacing the sheets,” then the item is represented
in the form: (nursing task; replacing the sheets; null; null).
Next, we introduce some techniques for organizing a medical
treatment as an item. First, the medicines used during surgery varyfor each patient, so the Code and Name of the medicine employedin the medical treatment where the Class does not include a word
related to surgery (e.g., “surgery” and “anesthesia”) are set as “null”
because the information about the medicine is not medically useful.
Second, if the medical treatment is determined only by the contentsof the Class such as “sampling” (which depends on the EMR), thenthe Description, Code, and Name are set as “null” because they
are not medically useful. Next, depending on the language used in
an EMR, Description is the first (or last) clause of a sentence inthe description given by a medical worker because the important
information is written in the first (or last) clause. In Japanese, thefirst clause is important. Finally, we delete the sequences that do notcontain items where the Class lacks words related to surgery because
they are not medically useful. We specify the group of four medical
data values (Class, Description, Code, Name) as an item and we
configure a T-item by adding the time when a medical treatment isapplied. We configure a T-sequence comprising T-items, which are
medical treatments received by one patient until their discharge from
hospital. If the T-items occurred at the same time in a T-sequence andthe items in the T-items are the same, we regard these T-items as one
item. We sort the T-items in a T-sequence alphabetically according
to the order of Class, Description, Code, and Name. Finally we
configure a T-sequential database based on these T-sequences. We

Algorithm 1 T-PrefixSpan
Input: D: a T-sequential database, M inSup: a minimum support
Output: P : the set of T-frequent sequential patterns
Call: T-PrefixSpan(<>,D)
Procedure: T-PrefixSpan(α,D |α )1: D ′ |α = O(D |α )2: if α ! = null then3:
P ← GetProperTime(α, D |α , D′ |α )4: end if
5: B ← {β | (s ⊆ D ′ |α , β ∈ s) ∧ (Sup(β) ≥ Size(D) ×
M insup)}
6: for β ∈ B do
7:
D |αβ ← {< sid, s >∈ D |α | αβ ⊆ O(s)}8:Call T-PrefixSpan(αβ, D |αβ )9: end for
Subroutine: GetProperTime(α,D |α ,D′ |α )1: if length(α) == 1 then2:
return α
3: end if
4: K ← {k |< sid, s >∈ D |α ,O(s)∈ D ′ |α , k ⊆ s,O(k) == α}
5: T = {{}, {}, ..., {}}(| T |= length(α) − 1)
6: for k ∈ K do
7:
for i = 0, ..., length(k − 1) do
8:Ti ← T (ki+1 ) − T (ki )9:end for
10: end for
11: W =< α0 , α1 , ..., αlength(α)−1 >12: for i = 0, ..., length(α) − 2 do
13:
Ti = an arbitrary function for excluding outliers from Ti
14:mini = min Ti
15:modi =the most frequent value of Ti
16:avei =the average of the values of Ti
17:medi =the intermediate value of the values of Ti
18:maxi = max Ti
19:
Xi = (mini , modi , avei , medi , maxi )20:
W =< α0 , ..., αi , Xi , αi+1 ..., αlength(α)−1 >21: end for
22: return W

consider T-sequences as different T-sequences if the patients are thesame but their admission–discharge periods differ.
C. Efficacy of Medicines
In the previous section, we described the representation of an item
as a set of four text types because it is difficult to extract patterns with
only the Class, Description, and Name fields as various medicines
have the same efficacy and they may change dynamically. Wright etal. [10] conducted mining by focusing on attributes other than thename of the medicine, where they extracted frequent patterns from asequential database based on sequences that contained items represented by medicines administered to diabetic patients. Their method
focused on the classification of medicines and they regarded items as
the same if their classifications matched with each other. The results
obtained showed that their method focusing on the classification of
medicines could predict the next item for administration with a higher
probability compared with a naive method that lacked this focus.
Based on the method to focus on attributes other than names of
medicines, our proposed method focuses on the efficacy of medicines
and we regard items as the same if their Class, Description, and Codematch even if their Name differs, so we can extract medically useful

4

patterns by reducing the number of item types. A related method that
focused on the efficacy of medicines was described by [11].
IV. E XPERIMENT

C. Results and Discussion
The numbers of outputs are shown in Fig. 1 and Fig. 2, the average
lengths of the outputs are shown in Fig. 3 and Fig. 4, and Fig. 5 andFig. 6 show the proportion of patterns including medical treatmentsthat involved medicines relative to the total.

Based on our proposed method for handling information about
medicines and the time intervals between items, we evaluated thechanges in the output when we focused only on the efficacy of
medicines compared with the actual data.

A. Experimental Data
We used target medical treatment data based on the clinicalpathways recorded from November 19, 1991 to October 4, 2015 in the
EMRs at the Faculty of Medicine, University of Miyazaki Hospital.
These medical data were acquired using an EMR system WATATUMI[12] employed by the Faculty of Medicine, University of Miyazaki
Hospital. We did not include information that could identify a patient
uniquely to ensure the protection of personal information.
When we extracted the medical treatment data, we used anonymous
patient IDs, which were impossible to determine. The data we extracted from the EMRs to support medical treatments at the Faculty of
Medicine, University of Miyazaki Hospital were described previouslyin [13] and they can be accessed at the website of the University ofMiyazaki. Our study was approved by the Ethics Review Board of the
University of Miyazaki and the Research Ethics Review Committeeof Tokyo Institute of Technology.
Our target data comprised medical treatments based on two clinical pathways: (1) cryptorchidism fusion surgery and (2) TUR-Bt(Transurethral Resection of the Bladder tumor), which were includedin the EMRs. We selected these two clinical pathways because (1)cryptorchidism fusion surgery is a clinical pathway where the flowof the medical treatments was fixed, whereas (2) TUR-Bt is a clinicalpathway for which the flow was not clearly defined.

Fig. 1.

Cryptorchidism fusion surgery: number of outputs

Fig. 2. TUR-Bt: number of outputs

B. Experimental Procedure
In the experiment, we compared two methods for using theminimum support, as follows: (1) a “Normal” method that extracted
patterns using the four values, i.e., Class, Description, Code, andName; and (2) a “Focusing on Efficacy” method that extracted patterns
using only three values, i.e., Class, Description, and Code. The output
patterns were extracted by T-PrefixSpan. The indicators compared
were the number of outputs, the average length of the outputs, andthe proportion of patterns including medical treatments that involved
medicines relative to the total. After the comparative experiments,
we checked whether the typical flow obtained using our extraction
method agreed with the clinical pathway prepared by a medical doctor
based on their experience.
The number of sequences, average length of the sequences, minimum length of the sequences, and maximum length of the sequences
for the two data sets, i.e., (1) cryptorchidism fusion surgery and (2)TUR-Bt, are shown in Table III.

Fig. 3.

Cryptorchidism fusion surgery: average length of outputs

TABLE IIITARGET DATASET
datasetNumber of sequences
Average length
Minimum length
Maximum length

Cryptorchidism fusion surgery
Normal
Focusing on Efficacy
265
19.64
19.16
10
9
460
465

Normal
53.21
11
655

TUR-Btt
Focusing on Efficacy
488
49.89
11
485

Fig. 4. TUR-Bt: average length of outputs

5Fig. 5. Cryptorchidism fusion surgery: proportion of patterns involving
medicines

Fig. 7. Cryptorchidism fusion surgery: the typical flow obtained by our
extraction method

Fig. 6. TUR-Bt: proportion of patterns involving medicines

Based on the results of the experimental comparisons, we determined the following. Figures 1 and 2 indicate that the number of sequences was higher using the Focusing on Efficacy method comparedwith that using the Normal method with either cryptorchidism fusion
surgery or TUR-Bt. There was an increase in the variety of items
for which the frequency exceeded the minimum support because weregarded items with different Name fields as the same item if they
had the same efficacy.
The execution time was higher using the Focusing on Efficacy
method compared with that using the Normal method because therecursion number increased with T-PrefixSpan when the frequency ofthe number of item types exceeded the minimum support. According
to Fig. 3 and Fig. 4, the average length obtained using the Focusing
on Efficacy method tended to be larger than that using the Normal
method for both data sets, because medical workers frequently
administer medicines with the same name that differ in their efficacy
among patients. As shown in Fig. 5 and Fig. 6, the proportionof patterns involving medicines was higher using the Focusing on
Efficacy method compared with that using the Normal method, whichshows that the former method could extract more medical treatmentsthat involved medicines than the Normal method.
For all of the outputs obtained under minimum support values
ranging from 0.02 to 1.0, with surgery as day 0 and the items
that occurred on each implementation date, we found that the flow
included most of the outputs, as shown in Fig. 7. It is not possible toextract latent medical treatments by normal data mining according to[14]. Thus, we regarded items with the same probability of more than
50% of occurring the day before the surgery as one item. Using thismethod, we extracted the items that appeared only with low support.
In Fig. 7, a blue item represents surgery, red items are themedical treatments that involved medicines, and green items are
medical treatments that did not involve medicines. Figure 7 shows acommon set of patterns, including surgery, where the time interval
between two items was constant when their minimum and maximum
values matched. We excluded medical treatments with indefinite

Fig. 8. Cryptorchidism fusion surgery: the clinical pathway produced by a
medical doctor based on their experienceFig. 9. Cryptorchidism fusion surgery: medicinal classification table

6

implementation dates. This approach loosely represents items that
occur on the same day as a daily set, so it is possible that a branch
exists.
A medicinal classification table representing the Code field andefficacy is shown in Fig. 9, which was created based on [15], as
provided by the Ministry of Health, Labor, and Welfare in Japan.
Figure 8 shows the clinical pathway created by medical doctors according to their experience, where medical treatments with indefinite
implementation dates were excluded. In our comparison of Fig. 7 andFig. 8, items with consistent implementation dates are denoted by red”A” and the items with implementation dates that differ from thosein the clinical pathway are denoted by blue ”B”. Thus, by comparing
Fig. 7 and Fig. 8, we can see that for the medical treatments involving
medicines, a high proportion of the item contents and implementation
dates were consistent, whereas for the medical treatments that did not
involve medicines, many of the implementation dates in the outputs
did not match with those in the clinical pathway.
Thus, the actual flows of the medical treatments differed from theflows assumed by the medical workers. We could not extract someitems involving medicines from the datasets because we could not
obtain the items by mining under the minimum support value of
M inSup = 0.02. It will be necessary to determine whether theprocesses that did not match with the actual pathway are medicallyimportant based on discussions with medical workers.

V. C ONCLUSIONSIn this study, we proposed a method for extracting clinicalpathways from historical EMRs. We developed T-PrefixSpan as asequential pattern mining algorithm, which can handle time intervals
to derive their statistical information, as well as considering theefficacy of medicines instead of their names. In the proposed method,
medical workers can refer to the time intervals between medical
treatments based on five values, i.e., the minimum, mode, average,
median, and maximum.
We evaluated the proposed method using actual logs from a
university hospital. The experimental results showed that the numberof patterns obtained by focusing on the efficacy of medicines was
greater than that based on the names of medicines. We obtained atypical flow for cryptorchidism fusion surgery and the result was
similar to the clinical pathway generated by medical doctors basedon their experience regarding items not involving medicines. The
generated patterns can be used to verify existing clinical pathways andto recommend variants or new pathways. In future research, we plan
to evaluate the proposed method with other diseases. In addition, toextend the proposed method, it will be necessary to introduce a faster
algorithm for mining under a low minimum support, which cannot
be calculated in real time with T-PrefixSpan. We aim to consider
the suitability of faster sequential pattern mining algorithms suchas CloSpan [5], Clasp [16], and CSpan [17] for time-interval-based
extraction.
The method used to measure the similarity between the medical
treatments applied to patients and the clinical pathway was describedin [19]. During evaluations, we would normally expect to the use theminimum and maximum values of the time intervals between items,
but it is possible to incorporate the most frequent value, average, andmedian.
Medical workers should be asked to evaluate whether the outputs
are medically important. It would be useful to develop a sophisticatedtechnique for presenting the outputs to medical workers by considering the branching flow of medical treatments.

ACKNOWLEDGEMENTS
This research was supported in part by a JSPS Grant-in-Aid for
Scientific Research (A) (#25240014).
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