No retraction, correction or concern found when last checked on Oct 17, 2026.

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Seyedmostafa Sheikhalishahi, Riccardo Miotto, Joel T Dudley and 3 more

JMIR Medical Informatics · 2019 · Review

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 use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories 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 and searches were conducted in 5 databases using “clinical notes,” “natural language processing,” and “chronic disease” and their variations 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 identification of 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 and metabolic 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 interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical 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.

Full text

Its licence does not allow us to show the full text. Read it at PubMed Central:

References 123

1.
World Health Organization.
Not in our library
2.
Kruse CS, Kothman K, Anerobi K, Abanaka L. Adoption factors of the electronic health record: a systematic review. JMIR Med Inform. 2016;4(2):e19.
Not in our library10.2196/medinform.5525
3.
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records · 2016
No notice
4.
Jensen P, Jensen L, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012;13(6):395-405.
Not in our library10.1038/nrg3208
5.
Goldstein BA, Navar AM, Pencina MJ, Ioannidis JPA. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc. 2017;24(1):198-208.
Not in our library10.1093/jamia/ocw042
6.
Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning · 2018
No notice
7.
Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2017.
Not in our library10.1093/bib/bbx044
8.
Jensen K, Soguero-Ruiz C, Oyvind MK, Lindsetmo R, Kouskoumvekaki I, Girolami M, Olav SS, Augestad KM. Analysis of free text in electronic health records for identification of cancer patient trajectories. Sci Rep. 2017;7:46226.
Not in our library10.1038/srep46226
9.
Flynn R, Macdonald TM, Schembri N, Murray GD, Doney ASF. Automated data capture from free-text radiology reports to enhance accuracy of hospital inpatient stroke codes. Pharmacoepidemiol Drug Saf. 2010;19(8):843-847.
Not in our library10.1002/pds.1981
10.
Popejoy LL, Khalilia MA, Popescu M, Galambos C, Lyons V, Rantz M, Hicks L, Stetzer F. Quantifying care coordination using natural language processing and domain-specific ontology. J Am Med Inform Assoc. 2015;22(e1):e93-e103.
Not in our library10.1136/amiajnl-2014-002702
11.
Yang H, Spasic I, Keane JA, Nenadic G. A text mining approach to the prediction of disease status from clinical discharge summaries. J Am Med Inform Assoc. 2009;16(4):596-600.
Not in our library10.1197/jamia.m3096
12.
Wei W, Teixeira PL, Mo H, Cronin RM, Warner JL, Denny JC. Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance. J Am Med Inform Assoc. 2016;23(e1):e20-e27.
Not in our library10.1093/jamia/ocv130
13.
Extracting information from the text of electronic medical records to improve case detection: a systematic review · 2016
No notice
14.
Shivade C, Raghavan P, Fosler-Lussier E, Embi PJ, Elhadad N, Johnson SB, Lai AM. A review of approaches to identifying patient phenotype cohorts using electronic health records. J Am Med Inform Assoc. 2014;21(2):221-230.
Not in our library10.1136/amiajnl-2013-001935
15.
Abbe A, Grouin C, Zweigenbaum P, Falissard B. Text mining applications in psychiatry: a systematic literature review. Int J Methods Psychiatr Res. 2016;25(2):86-100.
Not in our library10.1002/mpr.1481
16.
Spasić I, Livsey J, Keane JA, Nenadić G. Text mining of cancer-related information: review of current status and future directions. Int J Med Inform. 2014;83(9):605-623.
Not in our library10.1016/j.ijmedinf.2014.06.009
17.
Pons E, Braun LMM, Hunink MGM, Kors JA. Natural language processing in radiology: a systematic review. Radiology. 2016;279(2):329-343.
Not in our library10.1148/radiol.16142770
18.
Wang Y, Wang ZF, Rastegar-Mojarad M, Moon S, Shen F, Afzal N, Liu S, Zeng Y, Mehrabi S, Sohn S, Liu H. Clinical information extraction applications: a literature review. J Biomed Inform. 2018;77:34-49.
Not in our library10.1016/j.jbi.2017.11.011
19.
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement · 2009
No notice
20.
Jonnagaddala J, Liaw S, Ray P, Kumar M, Chang N, Dai H. Coronary artery disease risk assessment from unstructured electronic health records using text mining. J Biomed Inform. 2015;58 Suppl:S203-S210.
Not in our library10.1016/j.jbi.2015.08.003
21.
Backenroth D, Chase HS, Wei Y, Friedman C. Monitoring prescribing patterns using regression and electronic health records. BMC Med Inform Decis Mak. 2017;17(1):175.
Not in our library10.1186/s12911-017-0575-5
22.
Castro SM, Tseytlin E, Medvedeva O, Mitchell K, Visweswaran S, Bekhuis T, Jacobson RS. Automated annotation and classification of BI-RADS assessment from radiology reports. J Biomed Inform. 2017;69:177-187.
Not in our library10.1016/j.jbi.2017.04.011
23.
Heintzelman NH, Taylor RJ, Simonsen L, Lustig R, Anderko D, Haythornthwaite JA, Childs LC, Bova GS. Longitudinal analysis of pain in patients with metastatic prostate cancer using natural language processing of medical record text. J Am Med Inform Assoc. 2013;20(5):898-905.
Not in our library10.1136/amiajnl-2012-001076
24.
Nunes AP, Yang J, Radican L, Engel SS, Kurtyka K, Tunceli K, Yu S, Iglay K, Doherty MC, Dore DD. Assessing occurrence of hypoglycemia and its severity from electronic health records of patients with type 2 diabetes mellitus. Diabetes Res Clin Pract. 2016;121:192-203.
Not in our library10.1016/j.diabres.2016.09.012
25.
Ware H, Mullett CJ, Jagannathan V. Natural language processing framework to assess clinical conditions. J Am Med Inform Assoc. 2009;16(4):585-589.
Not in our library10.1197/jamia.m3091
26.
Golden SH, Robinson KA, Saldanha I, Anton B, Ladenson PW. Clinical review: prevalence and incidence of endocrine and metabolic disorders in the United States: a comprehensive review. J Clin Endocrinol Metab. 2009;94(6):1853-1878.
Not in our library10.1210/jc.2008-2291
27.
Sanchis-Gomar F, Perez-Quilis C, Leischik R, Lucia A. Epidemiology of coronary heart disease and acute coronary syndrome. Ann Transl Med. 2016;4(13):256.
Not in our library10.21037/atm.2016.06.33
28.
Liao KP, Ananthakrishnan AN, Kumar V, Xia Z, Cagan A, Gainer VS, Goryachev S, Chen P, Savova GK, Agniel D, Churchill S, Lee J, Murphy SN, Plenge RM, Szolovits P, Kohane I, Shaw SY, Karlson EW, Cai T. Methods to develop an electronic medical record phenotype algorithm to compare the risk of coronary artery disease across 3 chronic disease cohorts. PLoS One. 2015;10(8):e0136651.
Not in our library10.1371/journal.pone.0136651
29.
Chen Q, Li H, Tang B, Wang X, Liu X, Liu Z, Liu S, Wang W, Deng Q, Zhu S, Chen Y, Wang J. An automatic system to identify heart disease risk factors in clinical texts over time. J Biomed Inform. 2015;58 Suppl:S158-S163.
Not in our library10.1016/j.jbi.2015.09.002
30.
Torii M, Fan J, Yang W, Lee T, Wiley MT, Zisook DS, Huang Y. Risk factor detection for heart disease by applying text analytics in electronic medical records. J Biomed Inform. 2015;58 Suppl:S164-S170.
Not in our library10.1016/j.jbi.2015.08.011
31.
Karystianis G, Dehghan A, Kovacevic A, Keane JA, Nenadic G. Using local lexicalized rules to identify heart disease risk factors in clinical notes. J Biomed Inform. 2015;58 Suppl:S183-S188.
Not in our library10.1016/j.jbi.2015.06.013
32.
Yang H, Garibaldi JM. A hybrid model for automatic identification of risk factors for heart disease. J Biomed Inform. 2015;58 Suppl:S171-S182.
Not in our library10.1016/j.jbi.2015.09.006
33.
Roberts K, Shooshan SE, Rodriguez L, Abhyankar S, Kilicoglu H, Demner-Fushman D. The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs. J Biomed Inform. 2015;58 Suppl:S111-S119.
Not in our library10.1016/j.jbi.2015.06.010
34.
Pakhomov S, Shah N, Hanson P, Balasubramaniam S, Smith S. Automated processing of electronic medical records is a reliable method of determining aspirin use in populations at risk for cardiovascular events. Inform Prim Care. 2010;18(2):125-133.
Not in our library10.14236/jhi.v18i2.762
35.
Zheng C, Rashid N, Koblick R, An J. Medication extraction from electronic clinical notes in an integrated health system: a study on aspirin use in patients with nonvalvular atrial fibrillation. Clin Ther. 2015;37(9):2048-2052.
Not in our library10.1016/j.clinthera.2015.07.002
36.
Patterson OV, Freiberg MS, Skanderson M, Brandt CA, DuVall SL. Unlocking echocardiogram measurements for heart disease research through natural language processing. BMC Cardiovasc Disord. 2017;17(1):151.
Not in our library10.1186/s12872-017-0580-8
37.
Tian Z, Sun S, Eguale T, Rochefort CM. Automated extraction of VTE events from narrative radiology reports in electronic health records: a validation study. Med Care. 2017;55(10):e73-e80.
Not in our library10.1097/mlr.0000000000000346
38.
Ross EG, Shah N, Leeper N. Statin intensity or achieved LDL? Practice-based evidence for the evaluation of new cholesterol treatment guidelines. PLoS One. 2016;11(5):e0154952.
Not in our library10.1371/journal.pone.0154952
39.
Wang SV, Rogers JR, Jin Y, Bates DW, Fischer MA. Use of electronic healthcare records to identify complex patients with atrial fibrillation for targeted intervention. J Am Med Inform Assoc. 2017;24(2):339-344.
Not in our library10.1093/jamia/ocw082
40.
Pakhomov S, Weston S, Jacobsen S, Chute C, Meverden R, Roger V. Electronic medical records for clinical research: application to the identification of heart failure. Am J Manag Care. 2007;13(6 Part 1):281-288.
Not in our library

All 123 references

Cited by papers we hold 2

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare · 2021
No notice
Artificial intelligence for precision medicine in neurodevelopmental disorders · 2019
No notice

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.