JMIR MEDICAL INFORMATICS

Sheikhalishahi et al

ReviewNatural Language Processing of Clinical Notes on Chronic
Diseases: Systematic Review
Seyedmostafa Sheikhalishahi1,2, MSc; Riccardo Miotto3, PhD; Joel T Dudley3, PhD; Alberto Lavelli4, MSc; Fabio
Rinaldi5, PhD; Venet Osmani1, PhD
1eHealth Research Group, Fondazione Bruno Kessler Research Institute, Trento, Italy2

Department of Information Engineering and Computer Science, University of Trento, Trento, Italy

3

Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY,
United States
4

NLP Research Group, Fondazione Bruno Kessler Research Institute, Trento, Italy5

Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland

Corresponding Author:
Venet Osmani, PhD
eHealth Research Group
Fondazione Bruno Kessler Research InstituteVia Sommarive 18, Povo
Trento, 38123
Italy
Phone: 39 0461 31 2479
Email: vosmani@fbk.eu

Abstract
Background: Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic
diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary useof electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods basedon machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic diseaserisk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinicalhistories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is
contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into
structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset.
Objective: The goal of the research was to provide a comprehensive overview of the development and uptake of NLP methods
applied to free-text clinical notes related to chronic diseases, including the investigation of challenges faced by NLP methodologies
in understanding clinical narratives.
Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed andsearches were conducted in 5 databases using “clinical notes,” “natural language processing,” and “chronic disease” and theirvariations as keywords to maximize coverage of the articles.
Results: Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identificationof 43 chronic diseases, which were then further classified into 10 disease categories using the International Classification of
Diseases, 10th Revision. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine andmetabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which
typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus
more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant
increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain
emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers
addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable
use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretabilityof predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data

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may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings fromclinical notes.
Conclusions: Efforts are still required to improve (1) progression of clinical NLP methods from extraction toward understanding;(2) recognition of relations among entities rather than entities in isolation; (3) temporal extraction to understand past, current,
and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale,de-identified clinical corpora.
(JMIR Med Inform 2019;7(2):e12239) doi: 10.2196/12239
KEYWORDS
electronic health records; clinical notes; chronic diseases; natural language processing; machine learning; deep learning; heartdisease; stroke; cancer; diabetes; lung disease

Introduction
Overview
The burden of chronic diseases, such as cancers, diabetes, andhypertension, is widely accepted as one of the principal
challenges of health care. While immense progress has been
made in the discovery of new treatments and prevention
strategies, this challenge not only persists, but its incidence isexhibiting an upward trend [1], with significant impact onpatient quality of life and care costs. Consequently, there is aneed for novel approaches to complement and go beyond current
evidence-based medicine that can reduce the impact of chronic
conditions on modern society.
A promising direction is the secondary use of electronic healthrecords (EHRs) to analyze patient data, advance medical
research, and better inform clinical decision making. Methods
based in analysis of EHRs [2] are resulting in improved
understanding of patient clinical trajectories [3] while enabling
better patient stratification and risk prediction [4-6]. In
particular, use of machine learning and especially deep learning
to process EHRs is creating a unique opportunity to derivepreviously unknown clinical insights [7]. This is especiallyrelevant for chronic diseases as their longitudinal nature provides
a very large and continuous stream of data, where clinicallymeaningful patterns can be extracted and used to guide clinicaldecisions, including delaying or preventing disease onset.
However, EHRs are challenging to represent and model due totheir high dimensionality, noise, heterogeneity, sparseness,
incompleteness, random errors, and systematic biases. Moreover,a wealth of information about patient clinical history is generally
locked behind free-text clinical narratives [8] since writing text
remains the most natural and expressive method to document
clinical events. Development of natural language processing(NLP) methods is essential to automatically transform clinicaltext into structured clinical data that can be directly processedusing machine learning algorithms. Use of NLP in the clinicaldomain is seeing an increasing uptake with diverse applications,
including identification of biomedical concepts from radiology
reports [9], nursing documentation [10], and discharge
summaries [11]. Frameworks based on NLP applied to clinicalnarratives, however, have not been widely used in clinicalsettings to help decision support systems or workflows.

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Motivation
Clinically relevant information from clinical notes has been
historically extracted via manual review by clinical experts,
leading to scalability and cost issues. This is of particular
relevance for chronic diseases since clinical notes dominate
over structured data (for example, Wei et al [12] graphicallyquantify the amount of clinical notes over structured data forchronic diseases such as rheumatoid arthritis, Parkinson disease,and Alzheimer disease). Availability of these data creates animmense opportunity for NLP to automatically extract clinicallymeaningful information that may delay or prevent disease onset,
giving rise, however, to several challenges. In this paper weaimed to identify directions that could speed up the adoptionof NLP of clinical notes for chronic diseases and provide anunderstanding of the current challenges and state of the art.
Systematic reviews related to processing of clinical notes havebeen published in the past [13-18]; however, none have focused
specifically on chronic diseases, making it difficult to derive
conclusions and recommendations on this specific and very
diverse domain. In particular, this paper investigates NLP
challenges related to 43 unique chronic diseases identified byour systematic review and discusses the trends of applying
various NLP methods for clinical translational research. Basedon the outcomes of this review, we also devised a number ofrecommendations on future research directions, including (1)evolution of clinical NLP methods from extraction toward
understanding; (2) recognition of relations among entities, rather
than entities in isolation; (3) temporal extraction in order tounderstand past, current, and future clinical events; (4)exploitation of alternative sources of clinical knowledge; and(5) availability of large-scale deidentified and annotated clinicalcorpora.

Methods
Search Strategy and Information Sources
We followed the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) guidelines [19]. We
carried out a search of several databases to identify all
potentially relevant articles published from January 1, 2007, toFebruary 6, 2018, including Scopus, Web of Science (including
MEDLINE) and PubMed, and the Association for Computing
Machinery (ACM) Digital Library. We have limited the search
to journal articles written in English. In all the searches we usedthe combination of the following groups of keywords: (1)JMIR Med Inform 2019 | vol. 7 | iss. 2 | e12239 | p. 2
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JMIR MEDICAL INFORMATICS“clinical notes,” “medical notes,” or “clinical narratives”; (2)“natural language processing,” “medical language processing,”“text mining,” or “information extraction”; and (3) “chronic
disease,” “heart disease,” “stroke,” “cancer,” “diabetes,” or“lung disease” (where the last set of keywords reflects the top
five chronic diseases). The search keywords were selected to
be exhaustive to maximize coverage of the articles. The exact
queries are provided in Multimedia Appendix 1.

Article Selection
In the initial queries we also included the following terms:“electronic health records,” “EHR,” “electronic medical
records,” and “EMR.” This led to a total of 2652 retrieved
articles. However, upon reviewing these articles, we noticed
that the scope was too broad, providing results outside of focusof this review. Consequently, we narrowed the search strategy
to the keywords specified in the previous section, obtaining a

Sheikhalishahi et al
total of 478 articles, with 401 articles from Scopus, 58 fromWeb of Science (including PubMed), 13 from ACM Digital
Library, and 6 added manually, including 4 conference papers.
After removing 46 duplicates, 432 articles were retained, andtwo authors (MS and VO) reviewed their titles and abstracts(216 articles each). After this screening phase, 159 articles wereretained for further analysis.
In the second screening stage, five authors independently
reviewed the 159 full-text articles, resulting in 106 articles
fulfilling our criteria that are discussed in this review. The mostcommon reason for exclusion was that the work was not directly
related to chronic diseases (n=32); another reason was the work
was not topical (eg, the article was not a journal paper or we
could not retrieve the text). A flowchart and description of theselection process are provided in Figure 1 and Multimedia
Appendix 2, respectively.

Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses article selection flowchart. ACM: Association for Computing Machinery;
NLP: natural language processing.

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Results
Categorization of Diseases
The 106 articles reviewed were largely related to 43 uniquechronic diseases (as shown in Multimedia Appendix 2). One of

our aims was to understand the extent of NLP for specificdisease categories and their associated clinical notes. Therefore,
we grouped the 43 unique chronic diseases into 10 diseasecategories using the International Classification of Diseases,
10th Revision (ICD-10) as shown in Table 1.

Table 1. Classifications of chronic conditions studied (n=102) and the corresponding number of papers found.
Classification of chronic condition

Studies, n (%)

Conditions included

Diseases of the circulatory system

38 (35.8)

Congestive heart disease (2), coronary artery disease (6), heart disease (6), heart failure (7),hypertension (5), peripheral arterial disease (3), pulmonary disease (4)Neoplasms

34 (32.1)

Breast cancer (8), colorectal cancer (7), prostate cancer (4), lymphoma (2)Endocrine, nutritional, andmetabolic diseases

14 (13.2)

Type 2 diabetes mellitus (12), obesity (2)Other diseases

16 (15.1)

Diseases of the digestive system (3), diseases of the genitourinary system (3), diseases of themusculoskeletal system and connective tissue (3), diseases of the respiratory system (2),mental and behavioral disorders (2), multidisease (3)Figure 2. Relationship between chronic diseases (black sectors) and articles included in the review (for clarity we have included only diseases that are
addressed by three or more articles).

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The top three disease groups were (1) diseases of the circulatory
system (n=38) (such as coronary artery disease [20] andhypertension [21]); (2) neoplasms (n=34) (such as breast cancer[22] and prostate cancer [23]); and (3) endocrine, nutritional,
and metabolic diseases (n=14) (such as type 2 diabetes [24] andobesity [25]). An overview of the diseases studied and thecorresponding articles is shown in Figure 2.
An unexpected finding is that despite the higher incidence of
metabolic diseases in the general population [26] comparedwith diseases of circulatory system [27], the use of NLP inclinical narratives of these diseases exhibits an opposite trend.
Diseases of the circulatory system are represented in muchgreater numbers with respect to metabolic diseases (n=38 vs
n=14, respectively). We hypothesize that the structure of datacontained in EHRs may explain this finding. Medical records
related to metabolic diseases typically contain much morestructured data (for example, numerical values for various
physiological and physical parameters) than medical records
for diseases of the circulatory system, which focus more on
unstructured data [28]. This creates a more pressing need to use
NLP to extract information from notes related to diseases of thecirculatory system, whereas EHRs of patients with metabolic
diseases in large part may already contain data that can be usedby algorithms with minimal preprocessing. In the sections that
follow we summarize the most representative papers (thecomplete list is provided in Multimedia Appendix 2).

Disease Groups
Diseases of the Circulatory System
Cardiovascular Diseases
Most of the work in this area focused on using NLP to estimate
the risk of heart disease. As an example, Chen et al [29]developed a hybrid pipeline based on both machine learningand rules to identify medically relevant information related toheart disease risk and track the disease progression over sets oflongitudinal patient records, including clinical notes (similarly
to Torri et al [30]). Karystianis et al [31] and Yang et al [32]evaluated the identification of heart disease risk factors fromthe clinical notes of diabetic patients. In a slightly different
approach, Roberts et al [33] focused on estimating heart diseaserisk based on classification of 8 risk triggers (for example,
aspirin). Other studies in this area have focused on evaluating
the use of aspirin as a risk factor [34,35], extracting heart
function measurements from echocardiograms [36], deep veinthrombosis and pulmonary embolism [37], and low-density
lipoprotein level and statins use [38].
Risk of stroke and major bleeding in patients with atrial
fibrillation has been predicted using structured data and clinicalnotes [39], while patients with heart failure have been identified
using clinical notes only [40]. Moreover, medical reports written
in the Italian language have been used to identify arrhythmia
events [41].
Peripheral and Coronary Arterial Disease
Several studies used NLP to extract cases of peripheral arterial
disease (PAD) and critical limb ischemia from clinical notes[42,43], including a genome-wide associated study, focusing
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on PAD to identify drugs, diseases, signs/symptoms, anatomicalsites, and procedures [44]. Leeper et al [45] used NLP to identify
PAD patients to conduct a safety surveillance study on exposure
to Cilostazol, finding complications of malignant arrhythmia
and sudden death not observed in association with the drug.
Furthermore, Clinical Text Analysis Knowledge Extraction
System (cTAKES) has been used to process clinical history of
diabetic patients to predict development of PAD [46].
Hypertension
Work on hypertension has been principally focused on NLP to
extract relevant indicators, comorbidities, and drug therapies[21]. Analysis of clinical narratives in the Bulgarian languageof 100 million outpatient notes was used to extract numerical
blood pressure values with a high sensitivity and recall [47],while term hypertension was extracted from free-text notes,
using a rule-based, open-source tool [48]. Clinical notes andseveral types of medical documents were also used to identify
hypertensive individuals using open-source medication
information extraction (IE) system MedEx [49].
Right-Sided, Left-Sided, and Congestive Heart Failure
Byrd et al [50] and Jonnagaddala et al [20] proposed a hybrid
NLP model to identify Framingham heart failure signs andsymptoms from clinical notes and EHRs (ie, classifying whether
Framingham criteria are asserted). Left ventricular ejection
fraction was extracted from free-text echocardiogram reports[51], while unstructured, longitudinal EHRs of diabetic patientswere used to extract relevant information of heart disease, usingnaïve Bayes and conditional random field (CRF) classifiers[52].
Wang et al [53] proposed a system for the identification ofcongestive heart failure (CHF) from EHRs, which theyprospectively validated. Furthermore, left ventricular ejection
fraction plus the associated qualitative and quantitative values
were used to identify patients at risk of CHF [54], while free-text
notes were used to distinguish left and right heart failure [55].
Heart Failure Identification
Topaz et al [56] developed an algorithm to identify heart failure(HF) patients with ineffective self-management of diet, physicalactivity, adherence to medication, and clinical appointments
using discharge summary notes, while Garvin et al [57] focusedon the quality of care for HF patients. Vijayakrishnan et al [58]explored the application of a previously validated text anddata-mining tool to identify the presence of HF signs andsymptoms criteria in the EHRs of a large primary care
population. They found that HF signs and symptoms were
documented much more frequently among the eventual HF
cases, years before the first diagnosis as well, thus suggesting
a potential future role for early detection of HF. Last, regularexpressions were used to identify predefined psychosocial
factors that served as predictors of the likelihood to bereadmitted to the hospital after a case of HF [59].

Neoplasms
Overview
This section reviews a number of cancer-related studies,including detection of multiple types of cancer [60,61],
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extracting tumor characteristics and tumor-related information[62-64], disease trajectories of patients with cancer [65], cancer
recurrence [23,66], and detection of stage of cancer [67,68].
Kasthurirathne et al [60] evaluated the performance of common
classification algorithms to detect cancer cases from free-text
pathology reports using nondictionary approaches. Yim et al[62] explored a machine learning algorithm to extract tumor
characteristics by applying reference resolution on radiology
reports. Jensen et al [65] developed a methodology that allows
disease trajectories of cancer patients to be estimated from theclinical text. Napolitano et al [67] facilitated the extraction of
information relevant to cancer staging, proposing a model forsemistructured reports that outperformed the model for
unstructured reports alone.
A number of studies have focused on different applications of
NLP in pathology, histopathology, and radiology reports [69],including extracting relevant domain entities from narrativecancer pathology reports [70], negation detection of medical
entities in pathology reports [71], sentence translation from
pathology reports into graph representations [72], extracting
information from pathology reports and pathology classifications[73,74], and named entity recognition from histopathology notes[75].
The three most common types of cancers found are breast cancer(n=8), colorectal cancer (n=7), and prostate cancer (n=4).
Breast Cancer
Carrell et al [66] proposed an NLP system to process clinicaltext to identify breast cancer recurrences, while Castro et al [22]addressed the automated Breast Imaging-Reporting and Data
System (BI-RADS) categories extraction from breast radiology
reports. Miller et al [76] proposed a tool for coreference
resolution in clinical texts evaluated within the domain (coloncancer) and between domains (breast cancer). Mykowiecka etal [77] propose a rule-based IE system evaluated on
mammography reports. Bozkurt et al [78] developed NLP
methods to recognize lesions in free-text mammography reports
and extract their corresponding relationships, producing acomplete information frame for each lesion.
Colorectal and Prostate Cancer
EHRs and NLP were used to identify patients in need ofcolorectal cancer screening [79] and detect colonoscopy-related
concepts as well as temporal-related information [80].
Additionally, EHRs and NLP were used to also identify patientswith prostate biopsies positive for prostatic adenocarcinoma[81].
Liver and Pancreatic Cancer
Ping et al [82] extracted textual information concerning a setof predefined clinical concepts from a variety of clinical reports
for patients with liver cancer, while Al-Haddad et al [83]identified patients with confirmed surgical pathology diagnoses
of intraductal papillary mucinous neoplasms.

Endocrine, Nutritional, and Metabolic Diseases
Applications of NLP in the domain of endocrine, nutritional,
and metabolic diseases include negation detection and mention
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Sheikhalishahi et alof family history in free-text notes [84] and assigning temporaltags to medical concepts [85]; obesity [25,86] and diabetes
identification [77,87-89]; and diabetes complications such asfoot examination findings [90], vision loss [91], and quantifying
the occurrence of hypoglycemia [24].
Two support vector machines (SVMs) were combined toautomatically identify obesity types by extracting obesity anddiabetes-related concepts from clinical text [86] in addition topatient identification [92]. An SVM-based system wasdeveloped and validated to identify EHR progress notes
pertaining to diabetes [87], while foot examination findings
from clinical reports [90] were used to predict quality of life[93]. Additionally, an analysis of a large EHR database wasused to quantify occurrence of hypoglycemia [24].

Other Disease Categories
The remaining 16 papers focused on processing clinical notesof different types of chronic diseases. Three studies concern
diseases of the musculoskeletal system and connective tissue,
in particular classification of snippets of text related to axial
spondyloarthritis in the EMRs of US military veterans using
NLP and SVM [94], phenotyping systemic lupus erythematosus[95], and identification of rheumatoid arthritis patients via
ontology-based NLP and logistic regression [96]. In the domainof diseases of the digestive system, Chen et al [97] used natural
language features from pathology reports to identify celiac
disease patients, Soguero-Ruiz et al [98] used feature selectionand SVMs to detect early complications after colorectal cancer,
and Chang et al [99] integrated rule-based NLP on notes with
ICD-9s and lab values in an algorithm to better define andrisk-stratify patients with cirrhosis.
Two papers evaluated deep learning in a multidisease domain.
In particular, Miotto et al [3] derived a general purpose patient
representation from aggregated EHRs (structured clinical dataand clinical notes) based on neural networks that facilitatesclinical predictive modeling given the patient status. Clinicalnotes were parsed using the National Center for Biomedical
Ontology’s Open Biomedical Annotator to extract medical terms
and further processed using topic modeling (latent Dirichlet
allocation). Shi et al [100] proposed assessing disease risk frompatient clinical notes using word embeddings and convolutional
neural networks with full connection layer.
Neural networks were also used to process clinical notes forphenotyping psychiatric diagnosis [101]. In particular, thismodel included two neural networks, one highly accurate at
rejecting patients but poor at identifying suitable ones and theother one with the opposite capabilities. In the same domain of
mental and behavioral disorders, comorbidity networks were
derived from the patient notes at the largest Danish psychiatrichospital in order to extract disease correlations [102].
IE from clinical notes based on NLP was also used to (1) screen
computed tomography reports for invasive pulmonary mold[103], (2) discover the co-occurrences of chronic obstructive
pulmonary disease with other medical terms [104], (3) quantify
the relationship between aggregated preoperative risk factors
and cataract surgery complications [105], (4) detect patientswith multiple sclerosis from the clinical notes prior to the initial
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Sheikhalishahi et alrecognition by their health care providers [106], and (5) identify
patients on dialysis in the Multiparameter Intelligent Monitoringin Intensive Care II (MIMIC-II) publicly available dataset [107].

and normalize temporal expressions [29], while a series of SVM
models in conjunction with manually built lexicons were used
to classify triggers specific to each risk factor [33].

Last, Pivovarov and Elhadad [108] used clinical notes of patientswith chronic kidney disease to validate a novel model tocompute the similarity of two medical concepts by combining
complementary information derived from usage patterns ofclinical documentation, accepted definitions, and position of
the concepts in an ontology.

We identified 24 papers that focused on comparison between
performance of rule-based and machine learning methods.
Typically, the rule-based methods were used as a baseline totest the performance against machine learning algorithms.

Information Extraction Methods
In order to understand trends in NLP methods for chronic
diseases, in this review we have analyzed papers with respect
to the methods employed (machine vs rule-based learning).
While there is an increasing use of machine learning methods
in comparison to rule-based (as shown in Figure 3), it is not as
pronounced as we had expected considering the superior
performance of machine learning algorithms shown in the NLP
literature [109]. This result may reflect the fact that we are still
currently witnessing a transition from rule-based methods tomachine learning algorithms, with rule-based methods used asa baseline to compare the performance of machine learning
approaches.
Our review identified 16 papers that employed hybrid
approaches combining rule-based and machine learning
methods. Out of these, 2 papers describe work to identify
diseases, risk factors, medications, and time attributes. In
particular, a hybrid pipeline based on CRFs, SVMs, andrule-based approaches was used to identify negation information

As for rule-based approaches, the methods in this review include
dictionary lookup [110-112], terminology identification basedon domain ontologies [3,42,45,58], various types of manually
defined rules [37,113], and regular expressions patterns[114,115].
The most widely used machine learning approach is SVMs,
having been used for predicting heart disease in medical records[32,46], identifying EHR progress notes pertaining to diabetes[94], and categorizing breast radiology reports according toBI-RADS [22].
Naïve Bayes was the second most frequent approach, being
used to predict heart disease in medical records [30,80], classify
smoking status [52], search EMR records to identify multiple
sclerosis [106], and classify EMR records for obesity [86] andcancer [60,65,67]. CRFs are the third most frequent approach,have been used to predict heart disease in medical records[29,32], identify EHR progress notes pertaining to diabetes [85],categorize breast radiology reports [22], and identify tumor
attributes in radiology reports [63]. Lastly, random forests wereused for predicting heart disease [53], classifying cancer types[60], and identifying hypertension [49].

Figure 3. Natural language processing rule-based methods versus machine learning for chronic diseases.

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Table 2. Most frequently used natural language processing methods and the corresponding number of papers.
MethodPapers (n)Support vector machine18Naïve Bayes

11

Conditional random fields

7

Random forest

4

Maximum entropy

3

Decision tree

3

Deep neural networks

3

Logistic regression

3

Rule-based methods

74

It is interesting to note that there are only 3 papers using
approaches based on deep learning [3,100,101], as shown in
Table 2. In particular, Geraci et al [101] apply deep neuralnetworks to EMRs to identify suitable candidates for a study
on youth depression. Miotto et al [3] present a method to derivea patient representation that facilitates clinical predictivemodeling from aggregate EHRs, including clinical narratives.
They represented free-text notes using topic modeling. Thismethod significantly outperformed those achieved by standard
feature learning strategies. Finally, Shi et al [100] propose a
disease assessment model based on clinical notes, using
convolutional neural network for disease risk assessment. The
experiment involved patients with cerebral infarction, pulmonary
infection, and coronary atherosclerotic heart disease.

Natural Language Processing Tasks, Methods, andDatasets
The NLP works described in the reviewed papers and associatedapproaches reveal that the most frequently described tasks are
text classification and entity recognition. The majority of thepapers describe text classification tasks using standard
approaches in NLP such as SVM (n=12) and naïve Bayes (n=4).
Entity recognition approaches are based on manually developed
resources (dictionary, regular expressions, handwritten rules)
as well as methods based on machine learning. As for theformer, there are dictionary-based approaches (n=5) and thoserelying on regular expressions (n=12). As for the latter, theapproaches are mainly based on standard machine language
techniques such as CRF and deep learning. A few papers
describe approaches to coreference resolution (n=2) and negation
detection (n=3). Coreference resolution is addressed using SVM,
while negation detection is based on SVM (n=2) or manualrules (n=1).
Regarding datasets, the majority of the papers describe
experiments run on datasets that are not publicly available(typically clinical data collected at research-based health careinstitutions and exploited by in-house NLP teams). On the other
hand, out of 16 papers involving publicly available corpora, 12
exploit the Informatics for Integrating Biology and the Bedside(i2b2) datasets. The other 4 public datasets used are MIMIC-II[107], PhenoCHF [116], Temporal Histories of Your Medical
Event (THYME), and Cancer Deep Phenotype Extraction(DeepPhe) [76].
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Comparisons to Other Systematic Reviews
Interest in using NLP for the automated processing of medical
records, and in particular of free-text clinical notes, is increasing,exemplified by a number of recent reviews of the field. Yet
none of these works focuses solely on chronic diseases, where
the amount of patient clinical notes tends to be larger than otherdomains or provides specific recommendations on how toadvance the field toward a clinical adoption that helps in treating
people with chronic conditions. Here we briefly provide asummary of previous works partially related to the work
presented in this paper.
Ford et al [13] present a systematic review of 67 papers using
IE techniques applied to medical records for the purpose of casedetection (ie, finding occurrences of specific medical
conditions). Similarly, Kreimeyer et al [117] review 86 papers
focusing on clinical NLP systems and a set of 71 associated
NLP tasks.
The work by Shivade et al [14] reviews 97 papers aiming at
identifying patient cohorts for further medical studies. Different
from our work, theirs is not limited to investigation of studies
using NLP and text mining but includes rule-based approaches,
which do not make use of the textual part of the medical records.
They observe, however, that the use of machine learning andstatistical and NLP methods is on the rise compared torule-based systems.
Abbe et al [118] consider applications of text mining in
psychiatry through a PRISMA-based review. The study
evaluates the application of specific NLP techniques in relation
to the goal of the studies, first qualitatively, and then with acluster analysis of the topics of selected abstracts. It identifies
four main themes in the publications taken into consideration:(1) psychopathology (2), patient perspective, (3) medical
records, and (4) medical literature. The scope of this review
only partially overlaps with our own, given the narrow thematic
analysis and inclusion of studies that deal with IE from othertextual resources, such as patient perspectives.
The review by Spasic et al [119] focuses on cancer research.
The authors classify the studies by cancer type and type ofprocessed document. They do not focus solely on studies basedon medical records or other types of clinical documents but also
include meta-studies that apply text mining techniques to
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PubMed publications. They classify NLP applications in four
categories: named entity recognition, IE, text classification, andinformation retrieval. Their investigation reveals a predominanceof symbolic approaches (dictionary and rule-based).
The work by Pons et al [120] is a systematic review of NLP
applications in the area of radiology. After initial preselection
based on abstracts, a detailed review of the full text of theselected papers ultimately yields 67 publications, all deemed
to consider practical applications of NLP in radiology. The
selected publications are then grouped into five broad categories
depending on the specific application: diagnostic surveillance,cohort building, query-case retrieval, quality assessment ofradiological practice, and clinical support services. The authors
provide a detailed comparative analysis of the performance
reported in each publication, grouping them by application
category.
Closest to our work is a systematic review by Wang et al [18]that has focused on IE applications; however, our review

Sheikhalishahi et al
additionally includes methodologies used in analysis of clinicalnotes, providing a wider set of articles. We believe that ourreview has a broader and more recent coverage of chronic
diseases, followed by detailed analysis for each disease,
compared with previous reviews, which have focused on specificconditions such as cancer [119], psychiatry [118], radiology[120], or IE applications [18].

Publication Venues
The 106 articles considered in this review were published in 50
unique venues. Figure 4 illustrates how we manually sorted
publication venues into three categories: (1) clinical medicine,(2) medical informatics, and (3) computer science. We observedthat most of the studies were published in medical informatics
journals. Figure 5 shows an increasing trend in number ofpublications over the years (except for the year 2018 due topartial-year retrieval) implying an increasing interest in theapplication of NLP in both clinical and informatics research forchronic diseases.

Figure 4. Categorization of the publication venues.

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Sheikhalishahi et al

Figure 5. Distribution of included studies according to publication venues.

Discussion
Principal Findings
Our systematic review has shown that NLP has a wide range
of applications for processing clinical notes of diverse chronic
diseases (43 unique chronic diseases identified in the analysis).
In this respect, there is a significant increase in the use ofmachine learning compared with rule-based methods. Despite
the potential offered by deep learning, the majority of papers
still rely on shallow classifiers. In fact, only a handful of studies(ie, 3 papers) made use of deep classifiers or general deeplearning methods for NLP. This was unexpected, considering
the potential of deep learning for text processing [121]. Our
hypothesis is that since deep learning is still an emerging area,
initial applications in the clinical domain may have been
published in workshops, conference proceedings, and the e-print
repository arXiv rather than journals, the focus of this review.
In this respect, a keyword search in arXiv for “deep learning,”and “clinical notes,” “medical notes,” or “clinical narratives”
for the previous five years (2013-2018) shows a significant
growth of papers: 7 from 2013 to 2015, 13 in 2016, 19 in 2017,and 22 in 2018. In addition, the longer review time for journals
has likely contributed to this outcome for the more recent papers.
We expect this result to shift in the coming years as anincreasing amount of work based on deep learning to processclinical notes is published in peer-reviewed journals.
Another finding from our review is that the majority of papers
reviewed identify risk factors for a particular disease and classify
a clinical note by a certain disease phenotype. However, there
are only a handful of papers that extract comorbidities from thefree-text or integrate clinical notes with structured data for
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prediction and longitudinal modeling of trajectories of patientswith chronic diseases. Such an outcome could be related to theuse of data analysis methods and algorithms (such as shallow
classifiers and rule-based approaches highlighted earlier) that
do not have the capability to capture temporal and longitudinal
relationships between clinical variables and in turn capture
disease evolution. Tools (such as MetaMap) and methods (suchas mapping n-grams to ontologies) used may have been otherinfluencing factors. While these tools allow extracting
meaningful medical information from the text, inherently they
reduce the possibility to derive more complex relationships,
principally due to phrase structure (for example “breast andlung cancer” may be identified only as “breast” and “lungcancer” rather than both “breast cancer” and “lung cancer”).
However, the use of relatively simple methods is advantageous
in terms of interpretability of predictions—a highly important
aspect in clinical domain—whereas it still represents asignificant issue for more complex methods.
Our review has retrieved only a few studies on the topic ofextracting word embeddings from clinical notes. This may be
due to insufficient available data to train the algorithms as wellas the fact that embedding methods have been developed only
recently. The issue of insufficient training data could be
addressed using transfer learning methods, while using
precomputed embeddings for specific diseases or categories of
diseases could be useful to effectively capture longitudinal
relationships.
Our review has shown that SVM and naïve Bayes algorithms
were most often used for machine learning–based tasks or in
combination with rule-based methods. This may be due to the
popularity of these algorithms as well as because naïve Bayes,
being a relatively simple algorithm, requires relatively small
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JMIR MEDICAL INFORMATICS
amount of training data (in comparison with deep classifiers,
for example). Although it is not feasible to directly compare
algorithmic performance of the studies that we considered (dueto both diversity of data and challenges addressed), we havenoted that the most commonly reported performance measures
were sensitivity (recall), positive predictive value (precision),and F score.
Finally, our review has reinforced the fact that availability of
public datasets remains scarce. This outcome was largely
expected given the sensitivity of clinical data in addition to all
the legal and regulatory issues, including the Health Insurance
Portability and Accountability Act and the Data Protection
Directive (Directive 95/46/EC) of the European Law (supersededby the General Data Protection Regulation 2016/679). As aresult, the studies reviewed in this paper typically came fromresearch-based health care institutions with in-house NLP teams
having access to clinical data. Therefore, the need remains forshared tasks such as i2b2 and access to data that would increase
participation in clinical NLP and contribute to improvements
of NLP methods and algorithms targeting clinical applications.

Sheikhalishahi et al
not encountered work that attempts to bridge the gap between
concept extraction and concept understanding.
We have devised the following specific recommendations:1.

2.

Limitations
This review has examined the last 11 years of clinical IE
applications literature and may have the following limitations.
The review is limited to journal articles written in the English
language, and papers written in other languages, especiallypapers that consider clinical narratives, may provide additional
results. In addition, papers using clinical articles from non-EHR
systems have not been considered. Finally, focusing on theclinical domain may have introduced a bias with respect to themethods reviewed (rule-based vs machine learning), as
rule-based methods are more prevalent in the clinical domaincompared with other domains [122].

Recommendations
Our review has shown that there is a clear necessity for clinical
NLP methods to evolve beyond extraction of clinical concepts
and focus more on concept understanding (ie, not only
understanding of relationships between concepts but
incorporation of clinical facts, domain knowledge, and generalknowledge in the reasoning process). In this review, we have

3.

4.

Focus on recognition of relationships among clinicalconcepts and entities. While progress has been made inrecognizing entities in textual narratives (such as diseases,
drugs, procedures), further efforts must be focused on
automatic inference of relationships between these entities(for example, drug A causes adverse event B for chronic
disease C), which in turn would allow deeper understandingof clinical text.
Temporal extraction, automated mark-up and normalizationof temporal information from natural language texts, is an
important aspect. This is especially relevant for clinical text
as disease progression and clinical events are typicallyrecorded chronologically, with specific events being
significant only in a particular temporal context. As such,significant attention should be given to temporal extraction
considering its implication in clinical context, especiallysince none of the works in this review dealt with temporal
extraction (or used crude methods such as timestamps ofclinical notes).
Scarcity of annotated clinical corpora has raised the needto exploit alternative sources of domain knowledge. In
addition to mainstream sources such as biomedical
literature, encyclopedias, and textbooks, automatic
diagnostic and decision support systems could be
exploitable (such as DXplain [123]). Transfer learning, amethod of transferring knowledge from existing corpora inother domains to the clinical domain, also holds great
potential and should be investigated in more detail.
Significant advances in effective clinical NLP will dependon large-scale corpora becoming available to researchers.
While shared tasks such as i2b2 and its successor n2c2 aresteps in the right direction, further incentives will be
required such as developing mechanisms that would
empower patients to donate their anonymized data or evenproviding algorithms that run on clinical text inside careinstitutions.

Acknowledgments
This work was partially supported by the European Union's Horizon 2020 research and innovation program under grant agreement#769765.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Search strategy.
[PDF File (Adobe PDF File), 178KB-Multimedia Appendix 1]

Multimedia Appendix 2
Complete list of reviewed papers, chronic diseases and their classifications, algorithms used, publication venues, and excluded
papers.
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Sheikhalishahi et al[XLSX File (Microsoft Excel File), 107KB-Multimedia Appendix 2]

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Abbreviations
BI-RADS: Breast Imaging-Reporting and Data System
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Sheikhalishahi et al

CHF: congestive heart failure
CRF: conditional random field
DeepPhe: Cancer Deep Phenotype Extraction
EHR: electronic health record
EMR: electronic medical record
HF: heart failure
i2b2: Informatics for Integrating Biology and the Bedside
ICD: International Classification of Diseases, 10th Revision
IE: information extraction
MIMIC II: Multiparameter Intelligent Monitoring in Intensive Care II
NLP: natural language processing
PAD: peripheral arterial disease
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
SVM: support vector machine
THYME: Temporal Histories of Your Medical Event

Edited by G Eysenbach; submitted 17.09.18; peer-reviewed by C Shivade, M Torii; comments to author 07.01.19; revised version
received 04.03.19; accepted 24.03.19; published 27.04.19
Please cite as:
Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review
JMIR Med Inform 2019;7(2):e12239
URL: http://medinform.jmir.org/2019/2/e12239/
doi: 10.2196/12239
PMID: 31066697©Seyedmostafa Sheikhalishahi, Riccardo Miotto, Joel T Dudley, Alberto Lavelli, Fabio Rinaldi, Venet Osmani. Originallypublished in JMIR Medical Informatics (http://medinform.jmir.org), 27.04.2019. This is an open-access article distributed under
the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, isproperly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as wellas this copyright and license information must be included.

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