=Paper=
{{Paper
|id=None
|storemode=property
|title=Medical Record Retrieval and Extraction for Professional Information Access
|pdfUrl=https://ceur-ws.org/Vol-968/irps_5.pdf
|volume=Vol-968
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==Medical Record Retrieval and Extraction for Professional Information Access==
https://ceur-ws.org/Vol-968/irps_5.pdf
Medical Record Retrieval and Extraction
for Professional Information Access
Chia-Chun Lee, Hen-Hsen Huang, and Hsin-Hsi Chen
Department of Computer Science and Information Engineering
National Taiwan University
Taipei, Taiwan
{cclee, hhhuang}@nlg.csie.ntu.edu.tw, hhchen@ntu.edu.tw
Abstract
This paper analyzes the linguistic phenomena in medical records in
different departments, including average record size, vocabulary,
entropy of medical languages, grammaticality, and so on. Five retrieval
models with six pre-processing strategies on different parts of medical
records are explored on an NTUH medical record dataset. Both
coarse-grained relevance evaluation on department level and
fine-grained relevance evaluation on course and treatment level are
conducted. Query accesses to the medical records in medical
languages of smaller entropy tend to have better performance. The
departments related to generic parts of body such as Departments of
Internal Medicine and Surgery may confuse the retrieval, in particular,
for Departments of Oncology and Neurology. Okapi model with
stemming achieves the best performance on both department and
course and treatment levels.
1 Introduction
Mining wisdom of crowds from heterogeneous domains to support various applications has attracted much attention
in this decade. The contributors of knowledge may be various from common users to domain experts. Internet
forums, weblogs, and wikis are contributed by general users, while scientific documents and medical records are
written by experts. Literature mining aims at extracting knowledge from biomedical literature, constructing a
knowledge base (semi-)automatically, and finding new knowledge [Jen06]. Comparatively, medical text mining
aims at discovering medical knowledge from electronic patient records. There are many potential applications,
e.g., comorbidities and disease correlations [Got12], acute myocardial infarction mining [Hei01], assessment of
healthcare utilization and treatments [Ram11], outpatient department recommendation [Hua12], virtual patient in
health care education, and so on.
Finding relevant information is the first step to mining knowledge from diverse sources. Different information
retrieval systems have been developed to meet these needs. This paper focuses on professional information access
and addresses the supports for experts of medical domain. PubMed, which comprises more than 22 million
citations for biomedical literature from MEDLINE, provides information retrieval engines for finding biomedical
documents. Information retrieval on medical records has been introduced to improve healthcare services
[Her09][Hua12]. Medical records are similar to scientific documents in that both are written by domain experts, but
they are different from several aspects such as authorship, genre, structure, grammaticality, source, and privacy.
Biomedical literatures are research findings of researchers. The layout of a scientific paper published in journals
and conference proceedings are often composed of problem specification, solutions, experimental setup, results,
discussion and conclusion. To gain more impacts, scientific literatures are often made available to the public.
Grammatical correctness and readability are the basic requirements for publication.
In contrast, medical records are patients’ treatments by physicians when patients visit hospitals. The basic layout
consists of a chief complaint, a brief history, and a course and treatment. From the ethical and legal aspects, medical
records are privacy-sensitive. Release of medical records is restricted by government laws. Medical records are
frequently below par in grammaticality. That is not a problem for the understanding by physicians, but is an issue
for retrieval.
Copyright©by the paper’s authors. Copying permitted only for private and academic purposes.
In: M. Lupu, M. Salampasis, N. Fuhr, A. Hanbury, B. Larsen, H. Strindberg (eds.): Proceedings of the Integrating IR technologies
for Professional Search Workshop, Moscow, Russia, 24-March-2013, published at http://ceur-ws.org
17
Medical Record Retrieval and Extraction for Professional Information Access
Case study is indispensable for learning medical knowledge. The course and treatments of similar cases provide
important references, in particular, for medical students or junior physicians. How to retrieve relevant medical
records effectively and efficiently is an essential research topic. TREC 2011 [Voo11] and 2012 [Voo12] Medical
Records track provides test collections for patient retrieval based on a set of clinical criteria. Several approaches
such as concept-based [Koo11], query expansion [Din11], and knowledge-based [Dem11] have been proposed to
improve the retrieval performance. In this paper, we investigate medical record retrieval on an NTUH dataset
provided by National Taiwan University Hospital. Given a chief complaint and/or a brief history, we would like to
find the related medical records, and propose examination, medicine and surgery that may be performed for the
input case.
The structure of this paper is organized as follows. The characteristics of the domain-specific dataset are
addressed and analyzed in Section 2. Several information retrieval models and medical term extraction methods
are explored on the dataset in Section 3. Both coarse-grained relevance evaluation on department level and
fine-grained relevance evaluation on course and treatment level are conducted and discussed in Section 4. Finally,
Section 5 concludes the remarks.
2 Description of the NTUH Medical Record Dataset
In the NTUH dataset, almost all medical records are written in English. A medical record is composed of three
major parts, including a chief complaint, a brief history, and a course and treatment. A chief complaint is a short
statement specifying the purpose of a patient’s visit and the patient’s physical discomfort, e.g., Epigastralgia for 10
days, Tarry stool twice since last night, and so on. It describes the symptoms found by the patient and the duration
of these symptoms. A brief history summarizes the personal information, the physical conditions, and the past
medical treatment of the patient. In an example shown in Figure 1, the first paragraph lists the personal information
and the physical conditions, and the second paragraph shows the past medical treatment. A course and treatment
describes the treatment processes and the treatment outcomes in detail. Figure 2 is an example of a course and
treatment, where medication administration, inspection, and surgery are enclosed in , , and Ballooming was also
performed. …
Figure 2: A Course and Treatment
Table 1: Mean and Standard Deviation of Medical Records in Words
component mean (µ) standard deviation (σ)
chief complaint (CC) 7.88 3.75
brief history (BH) 233.46 163.69
course and treatment (CT) 110.28 145.04
medical record (MR) 351.62 248.51
Table 2: Distribution of the Medical Records w.r.t. Department Type
Dental 1,253 Dermatology 1,258 Ear, Nose & Throat 7,680
Internal Medicine 34,396 Neurology 2,739 Obstetrics & Gynecology 5,679
Oncology 4,226 Ophthalmology 3,400 Orthopedics 8,814
Pediatrics 11,468 Rehabilitation 1,935 Psychiatry 1,656
Surgery 23,303 Urology 5,818
Table 3: Mean and Standard Deviation of Medical Records in Each Department Type
µ σ µ σ µ σ
Dental Dermatology Ear, Nose & Throat
CC 9.14 4.13 11.25 3.93 7.17 2.3
BH 138.97 65.45 232.4 108.58 158.33 84.81
CT 23.31 35.93 123.71 140.3 47.46 27.31
MR 171.41 87.27 367.35 197.93 212.95 95.48
Internal Medicine Neurology Obstetrics & Gynecology
CC 7.8 4.75 10.17 3.64 7.8 2.69
BH 278.72 154.61 251.87 127.61 175.13 129.89
CT 162.28 182.69 141.87 115.52 53.45 55.53
MR 448.8 257.04 403.91 190.02 236.38 156.11
Oncology Ophthalmology Orthopedics
CC 8.29 3.34 8.21 2.44 8.42 3.73
BH 418.46 201.19 117.93 47.73 131.96 70.26
CT 170.44 193.36 49.59 32.04 44.75 38.0
MR 597.19 301.34 175.73 65.87 185.14 88.71
Pediatrics Rehabilitation Psychiatry
CC 7.52 2.84 9.27 2.82 10.01 4.79
BH 293.46 189.83 346.09 186.26 521.73 287.7
CT 137.77 184.69 183.4 101.47 162.44 96.51
MR 438.75 291.07 538.77 227.96 694.19 320.3
Surgery Urology
CC 7.73 3.04 6.26 2.78
BH 191.03 126.37 152.96 121.31
CT 84.22 103.71 44.33 59.26
MR 282.98 179.89 203.54 148.27
From the linguistic point of view, we also investigate the vocabulary size and entropy of the medical language
overall for the dataset and individually for each department. Table 4 summarizes the statistics. Shannon [Sha51]
estimated word entropy for English as 11.82 bits per word, but there has been some debate about this estimate, with
Grignetti [Gri64] estimating entropy to be 9.8 bits per word. In the NTUH medical dataset, the entropy is 11.15
bits per word, a little smaller than Shannon entropy and larger than Grignetti entropy. Departments related to
definite parts of body, e.g., dental, ear, nose & throat, ophthalmology and orthopedics, have lower entropy.
19
Medical Record Retrieval and Extraction for Professional Information Access
Comparatively, departments related to generic parts have larger entropy. In particular, Department of
Ophthalmology has the lowest entropy, while Department of Internal Medicine has the largest entropy. Medical
records are frequently below par in grammaticality. Spelling errors are very common in this dataset. Some common
erroneous words and their correct forms enclosed in parentheses are listed below for reference: histropy (history),
ag (ago/age), withour (without), denid (denied), and recieved (received). Some words are ambiguous in the
erroneous form, e.g., “ag” can be interpreted as “ago” or “age” depending on its context. Besides grammatical
problems, shorthand notation or abbreviation occurs very often. For example, “opd” is an abbreviation of
“outpatient department” and “yrs” is a shorthand notation of “years-old”. Furthermore, physicians tend to mix
English and Chinese in the NTUH dataset. In Department of Psychiatry, the chief complaint of psychiatric disorder
patients is more descriptive, and it is hard to write down the descriptions completely in English. Physicians
express the patients’ descriptions bilingually, e.g., “Chronic insomnia for 10+ years 10 FM2 ,
. Furthermore, physicians tend to name hospitals in Chinese.
Table 4: Vocabulary Size and Entropy of the Medical Language w.r.t. Department Type
Vocabulary Size Entropy Vocabulary Size Entropy Vocabulary Size Entropy
Dental Dermatology Ear, Nose & Throat
15,036 9.74 26,914 10.32 48,452 9.88
Internal Medicine Neurology Obstetrics & Gynecology
415,279 11.06 55,301 10.62 65,760 10.46
Oncology Ophthalmology Orthopedics
101,361 10.81 27,765 9.70 47,082 9.79
Pediatrics Rehabilitation Psychiatry
175,555 10.86 51,328 10.50 67,390 10.64
Surgery Urology Overall
203,677 10.76 53,853 10.25 786,666 11.15
3 Retrieval and Extraction Models
The retrieval scenario is specified as follows. Given a chief complaint and/or a brief history, physicians plan to
retrieve the similar cases from the historical medical records and reference to the possible course and treatments.
Chief complaints and/or brief histories in the historical medical records can be regarded as queries. Words may be
stemmed and stop words may be removed before indexing. Spelling checker is introduced to deal with
grammaticality issue. Besides words, medical terms are also recognized as indices. Different IR models can be
explored on different parts of medical records. In the empirical study, Lemur Toolkit (http://www.lemurproject.org/)
is adopted and five retrieval models including tf-idf, okapi, KL-divergence, cosine, and indri are experimented.
The medical terms such as examination, medicine, and surgery are extracted from the course and treatment of the
retrieved medical records. Medical term recognition [Aba11] is required. Ontology-based and pattern-based
approaches are adopted. The ontology-based approach adopts the resources from the Unified Medical Language
System (UMLS) maintained by National Library of Medicine. The UMLS covers a wide range of terms in
medical domain, and relations between these medical terms. Among these resources, the Metathesaurus organizes
medical terms into groups of concepts. Moreover, each concept is assigned at least one Semantic Type. Semantic
Types provide categorization of concepts at a more general level, and therefore are well-suited to be incorporated.
The pattern-based approach adopts patterns such as “SURGERY was performed on DATE” to extract medical
terms. The idea comes from the special written styles of medical records. A number of patterns frequently
repeat in medical records. The following lists some examples for the pattern “SURGERY was performed on
DATE”: paracentesis was performed on 2010-01-08, repositioning was performed on 2008/04/03, incision and
drainage was performed on 2010-01-15, and tracheostomy was performed on 2010/1/11.
We follow our previous work [Che12] to extract frequent patterns from medical record dataset and apply them to
recognize medical terms. The overall schedule is summarized as follows.
(a) Medical Entity Classification: Recognize medical named entities including surgeries, diseases, drugs, etc. by
the ontology-based approach, transform them into the corresponding medical classes, and derive a new corpus.
(b) Frequent Pattern Extraction: Employ n-gram models in the new corpus to extract a set of frequent patterns.
(c) Linguistic Pattern Extraction: For each pattern, randomly sample sentences having this pattern, parse these
sentences, and keep the pattern if there is at least one parsing sub-tree for it.
(d) Pattern Coverage Finding: Check coverage relations among higher order patterns and lower order patterns,
and remove those lower patterns being covered.
To evaluate the performance of the retrieval and extraction models, 10-fold cross validation is adopted. We
conduct two-phase evaluation. In the first phase, the input query is a chief complaint and the output is the retrieved
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Medical Record Retrieval and Extraction for Professional Information Access
top-n medical records. We aim to evaluate the quality of the returned n medical records. There is no ground
truth or relevance judgments available, surrogate relevance judgments are therefore used. Recall that each medical
record belongs to a department. Let the input chief complaint belong to department d, and the departments of the
top-n retrieved medical records be d1, d2, …, dn. Here, we postulate that medical record i is relevant to the input
chief complaint, if di of medical record i is equal to d. In this way, we can compute precision@k, mean average
precision (MAP), and nDCG as traditional IR. In addition, we can regard the returned n medical records as a
cluster and compute the department distribution of the cluster. The retrieval is regarded as correct if the dominant
department of the cluster is the same as the department of the input query (i.e., the input chief complaint). In this
way, we can compute the confusion matrix among actual and proposed departments and observe the effects on
retrieval performance.
In the second phase, we conduct much finer evaluation. The input is a chief complaint and a brief history, and the
output is top-1 course and treatment selected from the historical NTUH medical records. Recall that examination,
medicine and surgery are three key types of medical entities specified in a course and treatment. We would like to
know if the retrieved medical record adopts the similar course and treatment as the input query. Thus the
evaluation unit is the three types of entities. We extract examinations, medicines and surgeries from the courses
and treatments of an input query and the retrieved medical record, respectively, by medical term recognition.
They are named as GE, GM, and GS for ground truth (i.e., the course and treatment of the input query), and PE, PM,
and PS for the proposed treatment (i.e., the course and treatment of the returned medical record), respectively.
The Jaccard's coefficient between the ground truth and the proposed treatment is a metric indicating if the returned
medical records are relevant and interesting to physicians. It is defined as: total number of common entities in the
ground truth and the proposed answer divided by sum of the entities in the ground truth and the proposed answer for
each query. The evaluation is done for each medical entity type. That is, Jaccard's coefficient for
examination=|GE∩PE|/|GE∪PE|, Jaccard's coefficient for medicine=|GM∩PM|/|GM∪PM|, and Jaccard's
coefficient for surgery=|GS∩PS|/|GS∪PS|. Note that the denominator will be zero, if both the ground truth and
the proposed answer do not contain any medical entities of the designated type. In this case, we set Jaccard's
coefficient to be 1. The average of the Jaccard's coefficients of all the input queries is considered as a metric to
evaluate the performance of the retrieval model on the treatment level.
Table 5: MAP and nDCG of Retrieval Models on the Department Level with Different Strategies
model metric S1 S2 S3 S4 S5 S6
Top 5
MAP 0.6858 0.6776 0.6860 0.6780 0.6700 0.6685
tf-idf
nDCG 0.7529 0.7456 0.7535 0.7461 0.7385 0.7370
MAP 0.6954 0.6871 0.6965 0.6875 0.6800 0.6774
okapi
nDCG 0.7622 0.7545 0.7626 0.7551 0.7489 0.7469
MAP 0.6715 0.6634 0.6692 0.6612 0.6691 0.6654
kl
nDCG 0.7396 0.7316 0.7385 0.7305 0.7380 0.7350
MAP 0.6857 0.6818 0.6868 0.6827 0.6521 0.6503
cos
nDCG 0.7520 0.7485 0.7534 0.7488 0.7217 0.7203
MAP 0.6638 0.6582 0.6604 0.6558 0.6557 0.6527
indri
nDCG 0.7328 0.7274 0.7305 0.7264 0.7251 0.7220
S1 S2 S3 S4 S5 S6
Top 10
MAP 0.6651 0.6584 0.6660 0.6590 0.6502 0.6487
tf-idf
nDCG 0.7481 0.7420 0.7486 0.7422 0.7348 0.7330
MAP 0.6734 0.6672 0.6749 0.6678 0.6588 0.6566
okapi
nDCG 0.7559 0.7498 0.7564 0.7498 0.7427 0.7404
MAP 0.6517 0.6444 0.6499 0.6430 0.6489 0.6465
kl
nDCG 0.7362 0.7297 0.7352 0.7285 0.7329 0.7307
MAP 0.6648 0.6611 0.6660 0.6622 0.6340 0.6331
cos
nDCG 0.7473 0.7437 0.7481 0.7447 0.7186 0.7181
MAP 0.6446 0.6395 0.6422 0.6380 0.6365 0.6339
indri
nDCG 0.7305 0.7256 0.7285 0.7246 0.7221 0.7192
4 Results and Discussion
Table 5 shows the coarse-grained relevance evaluation on department level. Five retrieval models shown in the 1st
column with six strategies (S1)-(S6) are explored. These six strategies are defined as follows. Top 5 and top 10
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Medical Record Retrieval and Extraction for Professional Information Access
medical records are retrieved and compared. For strategies S5 and S6, we extract gender (male/female), age (0-15,
16-45, 46-60, 61+), and other information from brief history besides chief complaints.
S1: using chief complaints
S2: S1 with stop word removal
S3: S1 with porter stemming
S4: S1 with both stop word removal and porter stemming
S5: using chief complaints and the first two sentences in brief histories
S6: S5 with porter stemming
Overall, the performance tendency is okapi>tf-idf>cos>kl>indri no matter which strategies are used. Removing stop
words tend to decrease the performance. Using porter stemming is useful when chief complaints are employed only.
Introducing brief histories decreases the performance. Okapi retrieval model with strategy S3 performs the best
when top 5 medical records are retrieved. In fact, Okapi+S3 is not significantly better than Okapi+S1, but both are
significantly better than Okapi with other strategies (p value <0.0001) on MAP and nDCG. When S3 is adopted,
Okapi is significantly better than the others.
We further evaluate the retrieval models with precision@k shown in Table 6. The five retrieval models at the
setting k=1 are significantly better than those at k=3 and k=5. Most of the precision@k are larger than 0.7 at k=1.
It means the first medical record retrieved is often relevant. Okapi with strategy S3 is still the best under
precision@k. Moreover, we examine the effects of the parameter n in the medical record retrieval. Only the best
two retrieval models in the above experiments, i.e., tf-idf and okapi with strategy S3, are shown in Figure 3. We can
find MAP decreases when n becomes larger in both models. It means noise is introduced when more medical
records are reported. The Okapi+S3 model is better than the tf-idf+S3 model in all the settings.
Table 6: precision@k of Retrieval Models on the Department Level with Different Strategies
model precision@k S1 S2 S3 S4 S5 S6
tf-idf 0.7185 0.7103 0.7188 0.7105 0.7031 0.7013
okapi 0.7280 0.7197 0.7293 0.7203 0.7136 0.7109
kl k=1 0.7041 0.6958 0.7020 0.6933 0.7021 0.6984
cos 0.7184 0.7138 0.7193 0.7149 0.6857 0.6827
indri 0.6960 0.6907 0.6926 0.6879 0.6880 0.6857
tf-idf 0.6259 0.6196 0.6269 0.6204 0.6132 0.6117
okapi 0.6371 0.6316 0.6384 0.6326 0.6238 0.6231
kl k=3 0.6073 0.5997 0.6055 0.5988 0.6120 0.6105
cos 0.6273 0.6236 0.6279 0.6245 0.5983 0.5970
indri 0.5986 0.5947 0.5967 0.5935 0.5986 0.5973
tf-idf 0.5963 0.5911 0.5980 0.5928 0.5863 0.586
okapi 0.6072 0.6034 0.6099 0.605 0.5973 0.5965
kl k=5 0.5775 0.5719 0.5770 0.5725 0.5842 0.5838
cos 0.5972 0.5933 0.5984 0.5951 0.5741 0.5741
indri 0.5698 0.5670 0.5691 0.5676 0.5713 0.5702
0.72
0.7
0.68
0.66 tf-idf
0.64
okapi
0.62
0.6
0.58
5 10 15 20 25 30 35 40 45 50
Figure 3: MAPs of tf-idf and okapi under Different n’s
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Medical Record Retrieval and Extraction for Professional Information Access
Table 7 further shows the retrieval performance in terms of MAP, nDCG and precision@k with respect to
department type. Note four departments have entropy less than 10 shown in Table 4, i.e., Departments of Dental,
Ear, Nose & Throat, Ophthalmology, and Orthopedics. The performances of query accesses to medical records in
these departments are more than 0.8200 in all the metrics. In particular, the retrieval performances for Department
of Ophthalmology are even more than 0.9155. Comparatively, Department of Internal Medicine, which has the
largest entropy, achieves the average performance. Department of Oncology gets the worst retrieval performance
because tumor may occur in different organs. The precision@1 to access medical records in this department is
only 0.3685, which is the worst of all.
Table 8 lists the confusion matrix among department types. The diagonals show how many percentages the
dominant department in the retrieved medical record cluster is the same as the actual department. Larger diagonal
values mean good retrieval performance. The results are quite similar to those in Table 7. The values of
Dental-Dental, Ear&Nose&Throat-Ear&Nose&Throat, Ophthalmology-Ophthalmology and Orthopedics-
Orthopedics are larger than those of other department pairs in the corresponding rows. In contrast, the value of
Oncology-Oncology is 0.1545, which is even smaller than the values of Oncology-Internal Medicine (i.e., 0.4792)
and Oncology-Surgery (i.e., 0.1805). That may be because tumor is often found in Department of Internal
Medicine, and treated in Department of Surgery. Similarly, the access related to Department of Neurology is also
worse in Table 7. Table 8 shows the value of Neurology-Internal Medicine (i.e., 0.2950) is very close to that of
Neurology-Neurology (i.e., 0.3552).
Table 7: Retrieval Performance w.r.t. Department Type Using Okapi Retrieval Model and Strategy S3
Department MAP@5 nDCG@5 MAP@10 nDCG@10 precision@1
Dental 0.8545 0.8825 0.8295 0.8744 0.8755
Dermatology 0.6531 0.7083 0.6263 0.7003 0.6901
Ear, Nose & Throat 0.8443 0.8770 0.8282 0.8715 0.8640
Internal Medicine 0.7001 0.7867 0.6695 0.7688 0.7381
Neurology 0.4843 0.5762 0.4612 0.5731 0.5232
Obstetrics & Gynecology 0.7779 0.8121 0.7635 0.8100 0.8000
Oncology 0.3233 0.3847 0.3236 0.4185 0.3685
Ophthalmology 0.9265 0.9419 0.9155 0.9371 0.9377
Orthopedics 0.8518 0.8888 0.8326 0.8802 0.8736
Pediatrics 0.6667 0.7278 0.6509 0.7290 0.6977
Rehabilitation 0.6088 0.6772 0.5921 0.6771 0.6390
Psychiatry 0.8323 0.8631 0.8183 0.8608 0.8487
Surgery 0.6120 0.6971 0.5889 0.6943 0.6535
Urology 0.7651 0.8035 0.7494 0.8037 0.7873
Table 8: Confusion Matrix among Departments
Actual Dominant Department (%)
Dept. Dent Derm ENT Med Neur O&G Onc Ophth Ortho Pedi Reha Psyc Surg Urol Un
Dent 76.94 0.32 6.62 4.87 0.08 0.40 0.72 0.00 1.44 0.56 0.00 0.00 7.74 0.32 0.00
Derm 0.32 54.45 2.70 22.97 0.79 0.32 0.40 0.32 2.62 2.62 0.08 0.16 11.76 0.48 0.00
ENT 0.56 0.09 83.62 6.80 0.27 0.18 0.87 0.35 0.38 1.00 0.01 0.03 5.64 0.18 0.01
Med 0.09 0.40 1.14 74.39 0.82 0.73 1.08 0.37 1.50 5.14 0.96 0.24 11.66 1.48 0.00
Neur 0.04 0.22 1.10 29.50 35.52 0.29 0.73 1.64 2.08 2.63 11.46 1.57 12.38 0.84 0.00
O&G 0.18 0.05 0.67 11.92 0.07 70.73 0.35 0.11 0.83 1.34 0.19 0.02 10.02 3.52 0.00
Onc 0.35 0.21 6.25 47.92 0.62 1.37 15.45 0.33 4.54 2.70 0.50 0.09 18.05 1.61 0.00
Ophth 0.03 0.06 0.85 3.18 0.06 0.12 0.09 90.91 0.71 0.47 0.03 0.00 3.12 0.38 0.00
Ortho 0.19 0.16 0.68 3.03 0.31 0.15 0.35 0.12 85.28 0.27 0.16 0.03 8.91 0.34 0.01
Pedi 0.17 0.33 1.13 29.30 0.43 0.58 0.35 0.22 0.81 58.07 0.18 0.19 7.39 0.82 0.01
Reha 0.05 0.05 0.47 15.30 8.37 0.21 0.31 0.21 5.37 0.41 57.52 0.05 9.66 2.02 0.00
Psyc 0.00 0.06 0.60 11.78 0.79 0.06 0.60 0.18 0.36 1.57 0.12 80.01 3.74 0.12 0.00
Surg 0.27 0.25 2.73 30.70 1.10 0.97 0.79 0.64 5.18 3.51 0.74 0.11 51.25 1.76 0.00
Urol 0.09 0.03 0.81 12.98 0.07 1.58 0.52 0.14 1.01 1.39 0.22 0.03 10.06 71.04 0.03
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Medical Record Retrieval and Extraction for Professional Information Access
Table 9: Jaccard's Coefficients of Retrieval Models on the Course and Treatment Level with Different Strategies
Top-1 S1 S2 S3 S4 S5 S6
examination 0.3332 0.3109 0.3515 0.3289 0.3728 0.3727
tf-idf medicine 0.2501 0.2445 0.2589 0.2539 0.3166 0.3147
surgery 0.1115 0.1154 0.1131 0.1168 0.1851 0.1835
examination 0.3448 0.3376 0.3499 0.3447 0.3816 0.3810
okapi medicine 0.2995 0.2980 0.3000 0.2988 0.3289 0.3278
surgery 0.1406 0.1397 0.1394 0.1406 0.1954 0.1936
examination 0.4351 0.4017 0.4399 0.4076 0.3690 0.3679
kl medicine 0.2222 0.2370 0.2245 0.2389 0.3112 0.3101
surgery 0.0847 0.0961 0.0844 0.0950 0.1821 0.1803
examination 0.3362 0.3305 0.3437 0.3362 0.3814 0.3826
cos medicine 0.2846 0.2865 0.2897 0.2905 0.3292 0.3291
surgery 0.1358 0.1393 0.1339 0.1376 0.1882 0.1875
examination 0.4501 0.4202 0.4535 0.4259 0.3639 0.3636
indri medicine 0.2035 0.2257 0.2055 0.2267 0.3042 0.3035
surgery 0.0776 0.0898 0.0764 0.0879 0.1758 0.1743
Table 9 lists the fine-grained relevance evaluation on the course and treatment level with Jaccard's coefficient.
Total 663 examinations, 2,165 medicines, and 1,483 surgeries are used in the treatments. Total 54,679, 64,607,
and 88,647 medical records mention examinations, medicines, and surgeries in their treatments. We count the
number of the same examinations (medicines or surgeries) appearing in both ground truth and the treatment of the
top-1 returned medical record. The number is normalized by total number of examinations (medicines or surgeries)
in both treatments for each query. If both do not recommend any examinations (medicines or surgeries), the
Jaccard's coefficient is regarded as 1. The five retrieval models and the six strategies used in the above
experiments are explored again in the fine-grained evaluation. Overall, the performance of examination prediction
is larger than that of medicine prediction, which is larger than that of surgery prediction. Considering brief history
(i.e., strategies S5 and S6) benefits medicine and surgery prediction. The experimental results show that Okapi
model with strategy S5 achieves the best performance on medicine and surgery prediction (i.e., 0.3289 and 0.1954),
and Indri with strategy S3 achieves the best performance on examination prediction (i.e., 0.4535).
5 Conclusion
This paper studies the medical record retrieval and extraction with different retrieval models under different
strategies on department and course and treatment levels. Both coarse-grained and fine-grained relevance
evaluations with various metrics are conducted. The medical records in medical languages of smaller entropy tend
to have better retrieval performance. The departments related to generic parts of body such as Departments of
Internal Medicine and Surgery may confuse the retrieval, in particular, for Departments of Oncology and Neurology.
Okapi model achieves the best on department and treatment levels (in particular, medicine prediction and surgery
prediction). To construct an evaluation dataset for medical record retrieval and extraction is challenging because
the assessors which are domain experts cost much. In this paper, we postulate that the medical records belong to
the same departments as the input queries are relevant. Such an evaluation may be underestimated because cross
department is not necessarily wrong in real cases. For example, the treatment of tumors may be related to more
than one department. Real user study is necessary for advanced evaluation. Besides, medical records may be in
more than one language. Cross language medical retrieval will be explored in the future.
Acknowledgments
Research of this paper was partially supported by National Science Council (Taiwan) under the contract NSC
101-2221-E-002-195-MY3. We are very thankful to National Taiwan University Hospital for providing NTUH
the medical record dataset. The authors also thank anonymous reviewers for their helpful comments.
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