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The Role of Text Analytics in Healthcare: A Review of Recent
Developments and Applications
Mahmoud Elbattah1, Émilien Arnaud2, Maxime Gignon2 and Gilles Dequen1
1Laboratoire MIS, Université de Picardie Jules Verne, Amiens, France
2Emergency Department, Amiens-Picardy University, Amiens France
{mahmoud.elbattah, gilles.dequen}@u-picardie.fr, {arnaud.emilien, maxime.gignon}@chu-amiens.fr
Keywords: Text Analytics, Natural Language Processing, Unstructured Data, Healthcare Analytics.
Abstract: The implementation of Data Analytics has achieved a significant momentum across a very wide range of
domains. Part of that progress is directly linked to the implementation of Text Analytics solutions.
Organisations increasingly seek to harness the power of Text Analytics to automate the process of gleaning
insights from unstructured textual data. In this respect, this study aims to provide a meeting point for
discussing the state-of-the-art applications of Text Analytics in the healthcare domain in particular. It is aimed
to explore how healthcare providers could make use of Text Analytics for different purposes and contexts. To
this end, the study reviews key studies published over the past 6 years in two major digital libraries including
IEEE Xplore, and ScienceDirect. In general, the study provides a selective review that spans a broad spectrum
of applications and use cases in healthcare. Further aspects are also discussed, which could help reinforce the
utilisation of Text Analytics in the healthcare arena.
1 INTRODUCTION
“Most of the knowledge in the world in the future
is going to be extracted by machines and will
reside in machines”, (LeCun, 2014).
The above-mentioned statement describes the ever-
rising a bundance of data -driven knowledge, which
continuously calls for further utilisation of Machine
Learning (ML). By the same token, healthca re is
delivered in da ta-rich environments where a broad
variety of data sources can be created at the individual
and population levels. The forma t of heath data
ranges from Electronic Health Records (EHR) to
images, time series, or unstructured textual notes.
Data Analytics ha s been increasingly considered
as an enabling a rtefact to leverage health data for
competitive advantage. Using a diversity of ML
techniques, ana lytics has been widely utilised to
summa rise, explain, a nd get insights into the
interrelationships underlying complex da tasets in
novel ways. Such insights can play a positive role in
various medical a nd operational aspects including
diagnosis, hea lth monitoring and assessment,
hea lthcare planning, and management of hospita ls
and health services.
However, one of the key challenges for hea lthcare
analytics is to deal with huge da ta volumes in the form
of unstructured text. Examples include nursing notes,
clinical protocols, medical tra nscriptions, medical
publica tions, and many others. In this respect, the use
of Text Analytics ha s increasingly come into
prominence in order to deliver benefits for hea lth
organisations in a wide range of applications.
Text Ana lytics, or Text Mining, is generally
defined as the methodology followed to derive quality
and actionable insights from textual da ta (Sarkar,
2019). Text Analytics represents an overarching field
of techniques a nd technologies including Na tural
Language Processing (NLP), ML, and Information
Retrieva l. The power of Text Ana lytics is to extract
information that could allow for forming and
exploring new facts or hypotheses from unstructured
textua l da ta (Hea rst, 1999).
Compa red to conventional ta sks, the obvious
cha llenge of Text Analytics is to extract pa tterns from
natural-language text, rather than well-structured
data bases. Textual data are largely stored in an
unstructured form, which does not adhere to any pre-
defined schema or data model. Further, standa rd ML
algorithms were genuinely crafted to deal with
num eric data. As such, Text Analytics need to apply
especially designed techniques and transformations
to effectively operate over textua l data.
The potentials of NLP have been constantly
discussed in the healthca re literature (e.g. Demner-
Fushman, Cha pman, and McDonald, 2009; Jensen,
Jensen, and Brunak, 2012; Spasić, Uzuner, and Zhou,
2020). In this respect, the main motivation for this
study was to explore the recent developments and
applications in this context. The study provides a
selective review tha t spans a broad spectrum of the
applications and use ca ses of Text Analytics in the
hea lthcare doma in particularly.
2 REVIEW METHODOLOGY
The review a imed to explore the sta te-of-the-art
approaches and applica tions of Text Analytics in the
hea lthcare context. We were generally motivated by
a set of exploratory questions as below:
• What are the potential data sources for applying
Text Analytics in hea lthcare?
• What a re the recent technological advances in
implementing Text Analytics in this context?
• How could Text Analytics help healthcare
providers make better decisions?
• What are the challenges of integrating NLP
tools into hea lthcare systems?
• What are the key limitations of Text Analytics
in the healthcare domain?
The review incorporated two main stages. The
initial stage included the screening and selection of
studies retrieved from the search results.
Subsequently, we analysed a set of representa tive
studies to be included in the literature review. The
study sought to largely follow the procedures of a
system atic literature review as informed by (Booth,
Sutton, and Papaioannou, 2011).
The search of literature was conducted to find
relevant studies in two major digital libra ries
including: i) IEEE Xplore, and ii) ScienceDirect. It is
acknowledged tha t other relevant studies could have
been published in other conferences or journals, but
we believe that the selected venues generally
provided excellent representative studies. The review
timeframe stretched through the past 6 yea rs (i.e.
2015-2020).
The inclusion of studies was conducted over a
three-step process for screening and classifying
studies. First, potential studies were screened based
on the title. Second, the abstracts were initially
inspected to confirm the suitability for full-text
review. Eventually, the final decision of inclusion
was made ba sed on the full-text inspection. Figure 1
sketches a flowcha rt of the review process. Ta ble 1
summa rises the search strategy.
Figure 1: The process of screening a nd selecting
studies in the review.
Table 1. Summary of search strategy.
Digital Libraries
IEEE Xplore,
ScienceDirect
Search Terms
Text Analytics Healthcare,
Text Mining Healthcare,
NLP Healthcare
Search Items
Title, Abstract, Keywords
Types of
Document
Conference Proceedings,
Journal Articles
Timespan
2015-2020
Language
English
3 REVIEW ANALYSIS
This section aims to provide an analysis of the studies
reviewed. The sea rch results included about 200
publica tions overall. Eventually, a set of 35 studies
were included in the review based on the process of
screening and analysis as described before.
The review is organised into two broad categories
of Text Analytics. On one hand, the first part presents
selective studies that a pplied Text Mining in the
context of healthca re. On the other hand, the second
part describes Text Analytics in a diversity of
predictive a pplications to support the clinical decision
ma king. The review is unavoidably selective rather
tha n exhaustive. However, it is believed that the study
could adequa tely provide representative studies in
each category.
3.1 Text Mining Applications in
Healthcare
Text Mining consists of two phases as follows. The
initial phase typically includes the application of text
refining procedures, which transform free-text
documents into another intermediate form.
Subsequently, the process of knowledge extraction,
which attempts to learn patterns or insights from that
intermediate form (Tan, 1999). This section provides
selective studies that applied Text Mining with
different moda lities and for various purposes in the
hea lthcare context.
(Han, Nandan, and Sun, 2015) presented a rule-
based system for question retrieval. The goa l was to
search for similar questions in a large corpus of
questions posted on online health forums. The system
was mainly based on the RAKE algorithm (Rose,
Engel, Cramer, a nd Cowley, 2010) to perform the
automatic extraction of keywords. Additional NLP
methods were applied using the popular NLTK
library (Bird, Klein, a nd Loper, 2009).
In another applica tion of Text Mining, a study
aimed to develop a utomated methods for extracting
information from the application webpages on the
iTunes App Store (Paglialonga, Riboldi, Tognola,
and Caiani, 2017). The study considered around 86K
applications under the categories of Medicine, and
Hea lth/Fitness. They used the NLP capabilities
provided by the IBM Watson API to identify the
medical specialty (e.g. cardiology, nutrition,
neurology, etc.), and the type of sponsor (e.g. industry
ma nufacturer, or government organisation).
Likewise, (Paglialonga et al., 2017) applied Text
Mining to automate the extraction of meaningful
information about health apps on the web.
(Lieder et al., 2019) developed a system that
could mine millions of public business webpages to
extract a multi-faceted representation of customers. In
addition, the extracted da ta were enriched with
external informa tion collected from Wikipedia. In
this respect, a large-scale knowledge graph was
constructed including millions of inter-connected
entities, which could be continuously enriched and
connected to new entities. The system could be
applied to industry use cases, such as healthca re, to
support insight discovery in real time.
In addition, several studies applied Text Mining to
extract informa tion or insights from online forums or
discussions. For instance, (Suta r, 2017) presented an
interesting a pplication of Text Mining to extract
hea lthcare-related informa tion from the user-
generated content on social media. Using a dataset
from a cancer-related forum, they developed a system
tha t could be used to extract practical information
such as treatments, medication na mes, a nd side
effects. The dataset included a set of unstructured and
semi-structured textual fields. Similarly, (Deng, Zhou,
Zhang, and Abbasi, 2019) proposed a framework to
support the a nalytics of online discussions. The
framework was named as Discussion Logic-based
Text Analytics (DiLTA). The DiLTA framework
attempted to extract features that could reveal the
discussion logic underlying online forums. The
framework was experimented using a case study
related to healthca re forum s.
(Martínez et a l., 2016) discussed exploiting the
hea lth-related online content into actionable
knowledge using Text Mining. To this end, they
developed an approa ch to help monitor online user-
generated strea ms on social Media. An NLP-ba sed
processing pipeline was applied to extract and
transform informa tion stemming from real-time
streams of social media. The system could not only
extract the mention of diseases and drugs, but a lso it
could identify useful relationships among
medications, indications, and adverse drug reactions.
(James, Calderon, and Cook, 2017) ana lysed
unstructured textual feedback of physicians. They
aimed to extract sentiments and topics perta ining to
the quality of healthcare service. Specifically, they
attempted to identify the tones and topics tha t could
shape the service ratings. In this regard, more than
20K patient reviews of more than about 4K
physicians were analysed using the Latent Dirichlet
Alloca tion (LDA) method. Further, a dictionary-
based text ana lysis was applied to determine the tone
elements in the physician reviews.
(Pendyala, a nd Figueira, 2017) explored the
potentials of Text Mining for automating the medical
diagnosis. They study applied the Bag-of-Words
representation to medical documents. To simplify the
text representa tion, the Bag-of-Words model builds a
histogram of the words, while each word count is
considered a s a feature (Goldberg, 2017). As such,
each document can be simply represented as a “bag”
of words, while disregarding the order, sequence, and
gramma r of text. Though using a small data set, their
experiments demonstrated promising results for that
application. More recently, (van Dijk et al., 2020)
applied Text Mining to EHR da ta to validate the
screening eligibility of trial patients. The study was
based on a multi-centre, and multi-EHR systems as
well. The accuracy of the Text-Ming approa ch was
compa red to the standard process produced by
research personnel. The accuracy of the automatically
extracted data was about 88.0%.
(Chang et al., 2016) developed a workflow using
Text Mining to search, extract, a nd synthesise
information about Comparative Effectiveness
Resea rch (CER) in healthca re. The study included the
development of an NLP-ba sed pipeline to extract
information from unstructured CER da ta sources. The
Text-Mining solution could allow for the generation
of timely a lerts, a nd the collection of systematic
reviews as well. Their approach was experimented
using trial data from multiple sources including
Clinica lTrials.gov, WHO International Clinical Trials
Registry Platform (ICTRP), and Citeline Trialtrove.
While other contributions focused on exploiting
Text Mining techniques for extracting concepts and
association rules from the scholarly literature. For
instance, (Kumari, a nd Ma halakshmi, 2019) applied
Text Mining to a subset of the biomedical literature
on PubMed. They aimed to discover informa tion
related to the phytochemical properties of medicinal
plants. In another applica tion, (Ji, Tian, Shen, and
Tran, 2016) developed a scalable approach to extract
associations among biomedical concepts in scientific
articles. Biomedical concepts were derived by
ma tching the text elements with the Unified Medical
Language System (UMLS) thesaurus. A MapReduce-
based algorithm was used to calculate the strength of
associations. The experimenta l dataset included a
large set of about 34K full-text articles. Their results
generally demonstrated that meaningful a ssociation
rules were highly ranked.
Recent studies considered more sophisticated
implementations ba sed on the Bidirectiona l Encoder
Representa tions from Transformers (BERT), a state-
of-the-a rt NLP model (Devlin, Chang, Lee, and
Toutanova, 2019). The BERT approach brings the
adva ntage of allowing pre-trained models to ta ckle a
broa d set of NLP tasks. In this regard, (Peterson,
Jiang, and Liu, 2020) developed a framework for
transforming free-text descriptions into a
standardised form based on the Health Level 7 (HL7)
standards. They utilised a combination of domain-
specific knowledgebases in ta ndem with the BERT
models. It was demonstrated that the BERT-based
language representation contributed significantly to
the model performance. Likewise, the literature
includes recent contributions that ma de use of the
BERT approach for a va riety of Text Mining ta sks
such as (Fan, Fan, and Smith, 2020), (Liao et al.,
2020), and (Vinod et al., 2020).
Furthermore, a major pa rt of the recent
contributions ha s been positioned in the COVID-19
context. For instance, (Jelodar, Wang, Orji, and
Huang, 2020) used Text Mining to extra ct the
COVID-19 discussions from social media. They
applied topic modeling of public opinions to gain
insights into the various issues perta ining to the
COVID-19 pandemic. In addition, they implemented
an LSTM model for the sentiment classification of
comments. While (Bha rti et al., 2020) developed a
Multilingua l conversational bot to provide primary
hea lthcare education, information, and advice to
chronic patients. Using NLP methods, the chatbot
was aimed to a ct as a personal virtual doctor to
interact with pa tients like human beings.
3.2 Text Analytics for Clinical Decision
Support
(Tvardik et al., 2018) developed a Text-Analytics
solution for the automatic detection of medical events
using EHR data. The textua l records included data
collected from three University hospitals ba sed in
France over the period October 2009 to December
2010. The data set spa nned a variety of medical
surgica l specialities including neurosurgery,
orthopa edic surgery, a nd digestive surgery. The
system performa nce was compared with sta ndard
methods. The overall sensitivity and specificity were
about 84%. The study generally confirmed the
fea sibility of using NLP-based methods to automate
the detection and monitoring of healthcare-a ssociated
events in hospital facilities.
In another interesting applica tion, (Brown, and
Marotta, 2017) developed a set of classification
models to predict the protocol and priority of MRI
brain examina tions. They used the narrative clinical
information provided by clinicians. The models were
trained to ma ke predictions on three tasks including:
i) Selection of exa mination protocols, ii) Evalua tion
of the need for contrast administration, and iii)
Estima tion of priority. The data set consisted of about
14K MRI brain examina tions over the period of
Janua ry 2013 to June 2015. The empirical results
la rgely demonstrated tha t the models could be
effectively employed to assist the clinical decision
support in this regard.
In the context of radiology, several studies sought
to explore the a pplication of NLP methods to extract
information from the mammography reports. For
exa mple, (Ca stro et a l., 2017) developed a system to
automate the annota tion a nd classification of the
Breast Imaging Reporting a nd Data System (BI-
RADS) categories. Specifically, the system tackled
two tasks including: i) Annotation of the BI-RADS
categories, and ii) Classification of the laterality for
each BI-RADS ca tegory. The study included about
2K radiology reports collected from 18 hospitals of
the University of Pittsburgh from 2003 to 2015.
While (Miao et al., 2018) applied Deep Lea rning to
extract the BI-RADS ca tegories from breast
ultrasound reports in Chinese. The experiments
included a dataset of 540 manually annotated reports.
The model accuracy could achieve F1-score of 0.904.
(Afzal et al., 2018) applied NLP for the automatic
identification of Critical limb ischemia (CLI). The
data set included na rrative clinical notes retrieved
form the EHR database. The model performance was
validated compa red to the human abstraction of
clinical notes. Specifically, a physician reviewed and
interpreted the information in the EHR data for each
patient in the dataset. Overall, the method could
achieve an excellent F1-score of about 90%.
Using a Text-Analytics approa ch, (Carchiolo et
al., 2019) proposed a system for the automatic
classification of medical prescriptions (i.e. granta ble
or not). Initially, the textual data were sca nned from
medical prescription documents. They could develop
an effective classifier based on the data about
patient/doctor personal da ta, symptoms, pathology,
diagnosis, a nd suggested treatments. Their results
reported tha t only 5% of the prescriptions could not
be automatically classified.
Another recent study developed a framework to
realise scalable Text Ana lytics (Ge, Isah, Zulkernine,
and Kha n, 2019). The framework aimed to support
real-time analytics for decision support in a variety of
dom ains such a s healthca re for example. Deep
Learning was a pplied for NLP tasks including
language understa nding and sentiment analysis. The
framework utilised a set of open -source tools
including Spa rk Streaming for real-time text
processing a long with Zeppelin a nd Ba nana for data
visualisation. In a ddition, an LSTM model was
trained for the sentiment analysis. They practically
demonstrated the functiona lity of the framework
using a scenario with Twitter da ta.
(Kidwai, a nd Nadesh, 2020) discussed the
application of diagnostic cha tbots in hea lthcare. They
developed a cha tbot that makes use of NLP methods
to understand the user queries. After collecting the
initial symptoms, the chatbot would guide the user
through a sequence of questions towards ma king the
appropriate diagnosis. The system uses decision trees
and follows a top-down a pproa ch to conclude the
diagnosis. The cha tbot was experimented using a
medical database of about 150 disea ses.
While plentiful studies sought to develop
predictive models to help stream line hospital
admissions. Increasing contributions a ttempted to
utilise unstructured data such as free-text notes made
by nurses or physicians at the Emergency Department
(ED). For insta nce, (Sterling, Pa tzer, Di, and
Schrager, 2019) utilised the bag-of-words
representation of triage free-text notes. Using a
data set of over 250K ED visits, neural network
models were trained to predict hospital admissions.
They could achieve a promising accuracy with ROC-
AUC≈0.74. Further, (Chen et al., 2020) aimed to
compa re the performance of ML models with the
inclusion of textual elements. They applied Deep
Learning along with Word Embeddings using clinical
narratives. They practically demonstrated tha t the
model accuracy generally improved with the addition
of free-text fields.
Similarly, (Arnaud, Elbatta h, Gignon, and
Dequen, 2020) presented an approa ch based on
integrating structured data with unstructured textual
notes recorded a t the triage stage. The key idea was
to apply a multi-input of mixed da ta for training a
classification model to predict hospitalisation. On one
hand, a standard Multi-Layer Perceptron (MLP)
model was used with the sta ndard set of features (i.e.
num eric a nd categorical). On the other hand, a
Convolutional Neural Network (CNN) was used to
operate over the textua l data . Their empirical results
demonstrated that the classifier could achieve a very
good a ccuracy with ROC-AUC≈0.83.
The use of ontologies ha s a lso drawn attention in
a variety of medical a nd hea lthcare applications. To
name a few, (Chakraba rty, and Roy, 2016) used
ontology alignment for the personalisation of cancer
treatment. A patient ontology was ma pped to the
disease ontology to dyna mically transform general
treatment options into individua l intervention plans,
personalised for the patient. In a nother application,
(Comelli, Agnello, and Vita bile, 2015) proposed an
ontology-ba sed indexing and retrieva l system for the
ma mmography reports. Using a n improved
radiological ontology, medical terms were organised
in a hierarchy, which could measure the semantic
simila rity between unstructured reports. The system
was tested using a dataset of 126 ma mmographic
reports in the Italian language, provided by the
University Hospital of Palermo Policlinico.
Furthermore, part of the recent efforts explored
the applica bility of Text Analytics to predict the
Interna tional Classification of Diseases (ICD) codes.
The manual encoding process is usua lly time-
consuming, and prone to va rious errors as well. In this
regard, (Teng et a l., 2020) applied medical topic
mining and Deep Learning to automatically predict
the ICD codes from free-text medical records. The
study used the MIMIC-III dataset, which provides a
large freely a ccessible repository of ICU records
(Johnson et al. 2016). The reported results indicated
tha t their method could increase the F1-score
approximately by 5% compared to ea rlier work.
Similarly, (Gangavarapu et a l., 2020) developed an
approach to help predict the ICD-9 code groups ba sed
on unstructured nursing notes. They applied vector
space and topic modeling to structure the raw clinical
data , which allowed for ca pturing the sema ntic
information in the free-text notes.
4 DISCUSSION
Over the pa st five years, there ha ve been pronounced
innovations in the NLP research including novel
approaches and technologies, which in turn have
resona ted in the healthcare domain. Most remarkably,
Deep Learning has been increasingly a pplied for
developing large-scale langua ge models. Deep
architectures of CNNs have introduced a potent
mechanism for learning feature representations from
raw data automatica lly (LeCun et al. 1989; LeCun,
Bottou, Bengio, a nd Haffner, 1998). Equally
important, recent a pplications have sta rted to adopt
the BERT-based a pproach, which avails of Transfer
Learning for NLP tasks. Furthermore, scalable
analytics platforms ha ve been utilised for real-time
data processing. Examples include Apache Spark,
and IBM Watson.
In terms of data sources, it appears that Text
Analytics was applied against a broa d va riety of
hea lthcare data. The da tasets ranged from standard
EHR datasets, medical reports, free-text notes,
scientific literature, to user-generated content on
online forums or social media. In this regard, Text
Analytics was implemented for considerable
problems including extracting evidence-based ca re
interventions, and patient outcomes, or identifying
the population at risk for example. To this end, NLP
pipelines have been intensively developed for a
variety of text-processing tasks such a s: i) Named
entity recognition, ii) Topic modeling, iii) Semantic
labelling, iv) Relationship extraction, v) Question
answering, vi) Text summarisation, vii) Sentiment
analysis, and others.
Nevertheless, a set of hurdles stands in opposition
to a widesprea d implementa tion of Text Analytics in
the healthcare domain. A key challenge is the
availability of qua lity data, which is a fundamental
fa ctor for building robust NLP models, and for ML in
general. Beyond that, the underlying data biases pose
multiple ethical concerns for the deploym ent of NLP
models. Such ethical issues have been recently
discussed in the literature (e.g. Davenport, and
Kalakota, 2019; Baclic et al., 2020). While other
technical cha llenges ma y relate to the integration of
Text Analytics tools with existing healthca re systems.
The conventional IT systems may not be well-poised
to be integrated with sophisticated Text Ana lytics,
which requires an advanced infrastructure and a
highly technical skillset as well. Furthermore, the
implementation of Text Ana lytics typically requires
intensive development cycles.
In summary, it is conceived that the future holds
ma ny interesting opportunities for implementing Text
Analytics in a multitude of healthcare applications.
The need for leveraging unstructured textua l data
should bring up new practical areas for taking
adva ntage of the Text Analytics potentials.
5 CONCLUSIONS
There is an obvious need to leverage unstructured
textua l da ta to support the operations of healthca re in
ma ny aspects. A large proportion of the clinica l data
is una voidably stockpiled into unstructured, or semi-
structured, documents or notes. Text Ana lytics should
therefore play a key role in transforming textua l data
into actionable insights.
This study endeavoured to review the state-of-the-
art applica tions of Text Ana lytics in healthca re. In
this regard, the applica tions could be broadly
summa rised as follows:
• Informa tion extraction from free-text data
stored in EHR data bases, clinical reports,
nursing notes, scientific literature, a nd user-
generated content.
• Applying vector-ba sed representations to a
variety of clinical documents, which transforms
the textual data into an amenable form for ML.
• Sequence-based modeling to address ta sks, such
as sentiment ana lysis, using notes in clinical
reports, or comments posted on online forums.
• Predictive analytics applications to support the
clinical decision ma king.
• Implementations of Conversational AI
technologies to use chatbots to intera ct with
patients in a human-like way.
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