HTL Model: A Model for Extracting and Visualizing Medical Events
from Narrative Text in Electronic Health Records
Eddie Paul Hernández
1
, Alexandra Pomares Quimbaya
1
and Oscar Mauricio Muñoz
1,2
1
Pontificia Universidad Javeriana, Bogotá, Colombia
2
Hospital Universitario San Ignacio, Bogotá, Colombia
Keywords: Text Mining, Clinical Practice Guidelines, Medical Events, Temporal Expressions,
Electronic Health Records.
Abstract: Electronic health records contain important information of a patient and it may serve as source to analyze
and audit the process of diagnosis and treatment of a specific clinical condition. This information is
registered in narrative text, which generates a limitation to identify medical events like doctor appointments,
medications, treatments, surgical procedures, etc. As it is difficult to identify medical events in electronic
health records, it is not easy to find a point of comparison between this electronic information with
recommendations given by clinical practice guidelines. Such guides correspond to recommendations
systematically developed to assist health professionals in taking appropriate decisions with respect to
illness. This article presents “Health Text Line Model HTL”, a model for extraction, structuring and
viewing medical events from narrative text in electronic health records. The HTL model was implemented
in a framework that integrates the aforementioned processes to identify and timing medical events. HTL
was validated in a general hospital giving good results on precision and recall.
1 INTRODUCTION
With the undeniable progress of information science
and the expansion of its use in other areas of
knowledge or even in some situations of our lives,
we can find a large amount of data produced and
stored on a daily basis. Discovering relationships,
patterns and knowledge in these large volumes of
data is being investigated and has become a great
challenge (Oboler et al., 2011). One of the biggest
challenges is text mining, which consists of applying
a set of techniques to extract relevant and unknown
information from large volumes of textual
information, usually in unstructured natural
language (Bentham and Hand, 2012).
Although there are many advances in the health
area, there are still many unexplored fields and
unsolved problems. One of these problems is given
by the information contained in electronic health
records (EHRs) due to limitations of applications,
tools and models to improve the structuring, analysis
and visualization of this information (Bentham and
Hand, 2012). This problem is caused by a possible
inaccuracy when studying the information recorded
in these EHRs, because records are usually written
in natural language (Laguna and Zaldumbide, 2007).
Therefore, the analysis of textual content in EHRs is
expensive in terms of time, and limited when we
want to identify medical events, because usually the
information from EHRs is stored in an unstructured
or narrative text. Due to the limitation of identifying
medical events (appointments, medical prescription,
treatments, surgical procedures, etc.), it is very
difficult to compare the information recorded in the
EHRs with treatment guidelines. The
recommendations given by clinical practice
Guidelines are syntheses of best available evidence
that support decision making by clinicians,
managers, and policy makers (Gagliardi et al.,
2011).To solve these problems, it is presented the
“HTL Health Text Line” model, a model for
extracting, structuring and visualizing of medical
events from narrative text recorded in EHRs. This
model helps health professionals to evaluate
adherence to clinical practice guidelines, giving
clinicians tools to audit and feedback as a good
strategy to improve professional practice, either on
its own or as a key component of multifaceted
quality improvement interventions (Ivers et al.,
2012). HTL is based on a model that uses techniques
of natural language processing techniques, timing of
events and text mining. HTL has three major
Hernández, E., Quimbaya, A. and Muñoz, O.
HTL Model: A Model for Extracting and Visualizing Medical Events from Narrative Text in Electronic Health Records.
In Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2016), pages 107-114
ISBN: 978-989-758-180-9
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
107
processes (Extraction, Structure and Display). The
extraction process is responsible for obtaining facts
from the EHR and timing of medical events
(corresponding to the structuring of medical events),
this process is applied once the information has been
collected from medical records, and later there is the
visualization process that allows medical events to
be represented as a timeline of EHRs.
This article presents in Section 2 an analysis and
classification of related works for structuring and
viewing of narrative text. Section 3 presents an
overview of the “Health Text Line Model HTL” for
extraction and structuring of medical events from
narrative text in EHRs. Section 4 describes the
functionality of the model through an HTL
Framework. Later, in Section 5 the model is
validated through a case study and Section 6
addresses the conclusions and future work.
2 STATE OF THE ART
The processing of unstructured or narrative text to
extract knowledge applies robust analytical
techniques used in Natural Language Processing
(NLP) (Mani et al 2005), Information Extraction
(IE) (Kianmehr et al., 2009), Sentiment Analysis
(SA) (Jeonghee et al., 2003), extraction of temporal
information or events (TIE) ( Cheng et al, 2007).,
such as annotation of entities, regular expressions,
automatic generation of summaries, event extraction,
textual polarity, clusters, time expressions or
temporal relationships. Although significant efforts
have been made in the notation, representation and
knowledge extraction from narrative texts, in recent
years it has increased the interest in the extraction of
temporal information in the medical domain (Clark
et al., 2011). Considering EHRs contain an
overwhelming amount of information stored in
narrative text (Savova et al., 2010), to organize this
information in a temporal way will lead to, for
example, determine whether a drug has been
prescribed, if a patient has had some disease
symptoms or when an lab test, assessment or
treatment is scheduled. Jindal and Roth proposed
three tasks to do: Removal of (a) events, (b)
temporary expressions and (c) temporal
relationships. They use clinical reports (unstructured
data) to automatically annotate medical events.
Denny et al. developed a system to extract, from
EHRs, the clinical concepts, dates to identify the
status of colon and rectal cancer, in order to save
time and effort in analyzing the medical records of
patients with this pathology. Liu et al. built a
framework to extract patients who had been exposed
to drugs. They identified if a patient had any
exposure to the drug warfarin when being admitted
to the hospital. Ferro et al., Mani et al. and Zhou et
al. scored narrative temporal expressions in EHRs.
HeidelTime and SUTime extracted and normalized
time expressions through rules, patterns and regular
expressions (Strötgen and Gertz, 2012). Although
there are studies that focus on these aspects (Savova
et al., 2011), it is important to consider the
identification of medical events and temporal
relationships. This aspect is reflected in TimeML,
which provides an annotation scheme for identifying
and orienting events on a timeline (Pustejovsky et
al., 2003). The Framework TimeML denotes the
aspects mentioned above supported by: TIMEX3,
EVENT and TLINK (Sauri et al, 2005); TIIMEX3 is
mainly used to make explicit temporal expressions,
such as times, dates, duration, etc. EVENT identifies
events and TLINK tag is used to represent temporal
relationships between events or between an event
and a temporary expression.
One of the most influential works in the
identification of medical events and their relationship to
time expressions shows an automatic system to extract
events and the relationship of these using an natural
language processing architecture. Its scores reach 90%
for extracting temporal expressions and 87% for
extracting clinical events (Kovacevic et al., 2013).
There are different approaches and works for the
extraction and structuring of medical events that
have been supported for decision-making and
analysis of information in EHRs. However, there are
some limitations that should be considered to
improve or ensure a complete chronology of events
of a medical patient. The criteria and aspects
evaluated are: Entity types, temporary expressions
identification, medical events, occurrence and
temporal relationships as defined below:
i) Entity Types: indicates whether the work takes
into account the following medical institutions:
drugs, diseases, lab tests, medical dosage units.
ii) Time Expressions: indicates whether the
project studies in depth adverbs of time and
time units.
iii) Medical Events: indicates whether the project
gives importance to medical events. A medical
event can be defined as anything that is
clinically important and which can also be
assigned to a timeline.
iv) Occurrence: indicates whether the project
considers this criterion to describe if an event
actually occurred; that is, the level of
uncertainty of the medical event.
ICT4AWE 2016 - 2nd International Conference on Information and Communication Technologies for Ageing Well and e-Health
108
Table 1: Related Works.
PAPERS Entity types Time expressions Mediical events Occurrence
Temporal
relations
(Kovacevic et al., 2013) X X X ?
(Denny et al., 2010) X X ?
(Jindal and Roth, 2013) X X X
(Savova et al., 2011) X X X ? X
(Wenzina and Kaiser, 2013) X X
(Sun et al., 2013) X X X
(Raghavan et al., 2012) X X X
(Uzuner et al., 2013) X X X X
(Sauri et al., 2012) X X X
(Reeves et al., 2012) X X
v) Temporal Relations: indicates whether the
project takes into account if there is a temporary link
between two events. The possible events can be:
change dosage schedule changes of drugs and / or
changes in the characteristics of the tests.
The "X" symbol indicates that the investigation
contains the criteria of the table. The symbol "?"
Indicates that the investigation does not specify the
criteria, but it may have used it. In conclusion, there
is no project containing all the criteria and some of
them have limitations; for this reason we proposed
HTL Model (Health Text Line).
3 HTL HEALTH TEXT LINE
HTL (Health Text Line) is a model that identifies
medical events associated with medications, diseases
and tests from the narrative texts contained in EHRs.
HTL is based on narrative text analysis to identify
the medical event and the time when it occurred, to
establish the level of certainty of that event.
Additionally, it is a model that uses text mining
techniques and natural language processing as: Stop
words, Tokenization, Splitter, Part of speech (POS),
Named Entity Extraction, Gazetteer, among others.
The model also takes into account the most relevant
criteria for knowledge extraction or removal,
structuring and viewing medical events: entity types,
temporary expressions, medical events, occurrence,
temporal relations and event display.
HTL is composed by three processes (see Figure
1). The first process is the Extraction of medical
event, which consists of a set of threads, methods
and techniques to prepare the narrative texts of
EHRs for major operations of knowledge discovery.
This process allows the identification of entities
(Medication, Test and disease) and medical events.
The second process is the structuring of medical
events, which is in charge of structuring the events
and sub-events with their respective medical
context; by identifying grammatical connectors,
tenses and regular expressions. The third process is
the visualization of medical events, whose input is
obtained as a result of the above two processes and
allowing initiating the visualization of the patient’s
lifeline.
3.1 Extraction Process
The extraction process is divided into four
subprocesses. It has entries (Narrative text HCE and
knowledge base Gazetter) and the result of this
process is stored in a repository called repository of
Extraction of Medical Event). The subprocess that
establishes the extraction process includes:
3.1.1 Stop Words
A method or filter process, which main objective is
to eliminate the words that provide little information
or not represent any important content (Paass G et
al.,2005).
3.1.2 Tokenization
Tokenization divides the text into simple tokens,
such as numbers, punctuation and words of different
types. (Paass G et al., 2005).
HTL Model: A Model for Extracting and Visualizing Medical Events from Narrative Text in Electronic Health Records
109
Figure 1: Model Extraction, Structuring and Visualization of Medical Events.
3.1.3 Splitter
The Splitter is a cascade of finite-state transducers
that segments the text into sentences. The divider
uses a list of abbreviations gazetteer to help
distinguish a new paragraph. Each phrase is annotated
with the type 'Sentence' (Paass G et al., 2005).
3.1.4 Part of Speech (POS)
It is the process of assigning (or tag) to each of the
words in a text their grammatical category. This
process can be executed in accordance with the
definition of the word or the context in which it
appears (Paass G et al., 2005).
3.2 Structuring Process
The structuring process consists of three
subprocesses: recognition of the organization,
identification of subevents and identification of
context). It takes the information from the repository
of Extraction of Medical Event and Knowledge Base
Gazetter to generate a repository of Structuring of
Medical Event. The subprocesses included are:
3.2.1 Entity Recognition
This process is responsible for identifying the entity
where the medical event comes from. It relies on the
various gazetteers lists of entities that conforms the
base of knowledge. The lists of entities are: diseases,
lab test, medical units, drug, adverbs of time,
connector’s grammar, verbs and time units.
3.2.2 Identification of Subevents
This is the process for identifying subevents (dosage
changes, changes of medication schedules and / or
changes in the lab tests characteristics)
corresponding to one entity and a medical event.
This process consists of the following steps:
i) Identification of Grammatical Connectors:
This process is responsible for identifying the
different grammatical connectors that exist on
EHRs, which set the pattern for an event split
in sub-events.
ii) Recognition of Verbs and Tenses: Process
responsible for identifying from a list of verbs,
the verbs commonly used in EHRs. Verbs in
ICT4AWE 2016 - 2nd International Conference on Information and Communication Technologies for Ageing Well and e-Health
110
past, present and future due to the need to
recognize whether the sub-events occur in the
past, or are a current event or are an event that
will happen in the future.
iii) Identification of Regular Expressions: A
process supported by a set of rules or patterns
defined in JAPE (Java Annotation Patterns
Engine).
3.2.3 Context Identification
Process supported by defined characteristics as
Quantum (entity, characteristic of quantity, time
characteristic and adverb of time), which can
identify the context of a medical subevent in EHRs.
The steps for making the identification process of
the context and Quantum characteristics are
described below:
i) Extraction of Adverb of Time: Process
responsible for identifying the different adverbs
of time that exist in the EHRs, to be applied to
the sub-events (complement the process
initiated by the identification of verbs and
verbal tenses).
ii) Characteristic Identification Associated to
Quantity: Process responsible for identifying
the characteristics associated with an amount
(dosage or intensity) of an entity in a sub-event
that means, the change of the quantity unit in
an entity
iii) Identification of Time Characteristics: The
process responsible for identifying the
characteristics associated with time (days, day,
night, fasting, month, week) of an entity in a
sub-event, whether an entity changes the unit
of time.
iv) Occurrence Identification or Medical Event
Uncertainty: The process classifies the level
of uncertainty of the medical event in Medical
probable event and
Medical uncertain Event; it
can identify whether a medical event has a high
probability of occurrence or not (see Figure 2).
Medical Probable Event: It is identified
when there is an important probability of
occurrence of a medical event; although there
is some uncertainty about the date of the
medical attention, there is an approach about
the reference date. Using the previous EHRs.
Medical Uncertain Event: It is identified
when there is very little certainty of the
occurrence of a medical event; unlike the
probable date there is a very high level of
uncertainty (impossibility of identification
relate to the reference date).
3.3 Visualization Process
This process of the model HTL is aimed at
displaying medical events that have been previously
extracted and structured. It allows health
professionals to have a point of comparison between
that recorded in the EHRs and clinical practice
guidelines. The visualization process consists of
three threads: Align, Rank and Filter. The lifeline
was made by the University of Maryland and the
Institute for Advanced Computer Studies (UMIACS)
(Plaisant, et al., 1998). The subprocesses that
integrate the Visualization process are:
3.3.1 Align
Process that allows aligning all EHRs for a specific
event type (for example diabetes).
3.3.2 Rank
Process that classifies EHRs depending on the
number of occurrences of a particular event. For
example, the number of surgeries to a patient or the
number of abnormal results in taking blood pressure.
It can also be ordered considering the judgment of
the medical specialist, the most relevant or important
events of this classification.
3.3.3 Filter
Interactive process that allows health professionals
to filter EHRs to find temporal patterns of medical
events. For example, high glucose, diabetes or
ibuprofen, headache. That is, the process of filtering
enables searching for particular sequences of events,
including both the presence and absence of events.
4 IMPLEMENTATION
To perform the validation of the strategy proposed
by the HTL model, a software tool that follows each
of its phases and processes was implemented. This
software tool is called HTL Framework, a software
that allows the extraction of medical events,
structuring of medical events and visualization of
medical events from the narrative text in EHRs.
As it can be seen in Figure 2, the identification of
medical events takes into account whether the
occurrence of these events are uncertain or probable.
These are placed in the lifeline depending on the
HTL Model: A Model for Extracting and Visualizing Medical Events from Narrative Text in Electronic Health Records
111
Figure 2: Lifeline2.
date calculated in the extraction process. Besides,
medical events are represented by the type of entity
by a specific color:
A Software Engineering process was used to
implement the HTL framework applying the Agile
Model Driven Development (AMDD) (Scott et al.,
2008). The development of the Framework was
made using the programming language Java JDK7.1.
5 VALIDATION
This evaluation and validation is performed through
a case study applied to the narrative of EHRs. These
EHRs stored information of diagnosis, treatment and
monitoring of patients in a general hospital. In the
study case functionality tests were performed
comparing the results generated manually against all
HTL generated results (response). To perform this
task the metrics precision and recall were measured.
5.1 Precision Metric
The precision metric (P) is defined as the proportion
of relevant retrieved events among all retrieved
events.
5.2 Recall Metric
The Recall metric (R) is defined as the proportion of
the relevant events that were recovered from all the
relevant events available.
5.3 Results Analysis
Three initial iterations were performed to the model
formed of a set of 40 EHRs. These EHRs were
randomly selected in a period of one year.
When performing the iterations with the three
initial data sets, there were able to obtain
percentages of 81% and 72% related to the
identification of entities and medical events.
Although these data are satisfactory, it was
necessary to optimize the rules and refine the
knowledge base to increase the percentage of
metrics. The results of the precision and recall
metrics obtained by the model for the set of
evaluation are shown below in Table 2.
The values of accuracy and recall criteria for
identification of Temporal Expressions and Medical
Events were compared with the works that achieved
better results in the analysis accomplished in the
literature, Tang et al., Raghavan et al. and Kovacevic
et al. HTL yields better results when identifying
temporal expressions and the narrative of EHRs.
Table 2: Accuracy and Recall of HTL.
Criteria P (%) R (%)
Type of Entity 96% 93%
Temporal Expressions 94% 92%
Clinical Events 92% 89%
Occurrence 85% 82%
Temporal Relations 84% 80%
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112
It is concluded that the results of accuracy and
completeness to identify medical events are
satisfactory for refining the knowledge base and
optimizing the rules. However, not closer to 100%
accuracy values are reached due to the failure to
identify certain entities within the narrative were
misspelled, and therefore did not belong to the
knowledge base.
6 CONCLUSIONS AND FUTURE
WORK
There is a wide range of benefits in the different
areas of application of text mining, from a greater
understanding of customer needs until the discovery
of fraud in banks. A clear example is the many
advances in the health area and more specifically in
the study of the information recorded in EHRs.
However, there are still limitations in identifying
medical events because usually the information from
EHRs is stored in an unstructured or narrative text,
which leads to the loss of much potentially
important information. From the analysis realized of
the related literature it was established that although
there are different approaches and works for
structuring and visualization of medical events from
EHRs, there are still some limitations that should be
considered to improve or ensure a complete
chronology of events of a medical patient record.
HTL is a model that identifies medical events
associated with medications, diseases and tests from
the narrative contained in electronic health records.
It is based on the analysis of narrative text to
identify the medical event and the time at which it
occurred. HTL includes the extraction, structuring
and visualization process of medical events which
are of vital importance when performing tasks or
procedures of medical reasoning. Likewise, in the
process of viewing it creates a lifeline easier to
understand for health professionals to have a point
of comparison between what is recorded in the
EHRs and clinical practice guidelines. The strategy
proposed by the HTL model, was implemented in a
Framework. HTL gives to the health professional a
tool to evaluate the occurrence of some medical
events.
The model and framework were validated
through a case applied to the narrative text of EHRs
of a general university hospital. In the study case,
functional tests were performed using the precision
and recall metrics, which returned values of 94%,
92%, 92% and 89% respectively for the
identification of temporal expressions and medical
events. These values exceeded the digits obtained by
others research. It is important to stand out that HTL
model results can be strategically used to more
easily understand and analyze the overall structure
for EHRs, due to the benefits in delivering structured
information and its visual display on a lifeline. By
understanding and exploiting this lifeline, the time
can be reduced, improve accuracy in the results of
medical research and thus discover unknown
information.
As future work HTL could be used and validated
with other health professionals (nutritionists,
veterinarians, and psychologists), although they do
not use EHRs yet, they store information in narrative
form. In addition, it would be created a module in
the framework to allow users the option of create
and modify regular expressions or patterns
contemplate by HTL in order to increase the
accuracy of the model and narrative analysis of
EHRs.
ACKNOWLEDGEMENTS
This work is part of the projects funded by Hospital
Universitario San Ignacio and Pontificia Universidad
Javeriana for improving the analysis of electronic
health records.
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ICT4AWE 2016 - 2nd International Conference on Information and Communication Technologies for Ageing Well and e-Health
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