The DOI matches its Crossref record; 152 of the 161 references it cites were found in a registry or an index.
Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review
Abstract
Background Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. Methodology After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: (1) medical note classification, (2) clinical entity recognition, (3) text summarisation, (4) deep learning (DL) and transfer learning architecture, (5) information extraction, (6) Medical language translation and (7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Result and discussion EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. Conclusion We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“To address this, natural language processing (NLP) techniques such as named entity recognition (NER), text classification, and relation extraction are used [38].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [38]
Citations
Cites
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741
The Stanford CoreNLP Natural Language Processing Toolkit
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S. J., & McClosky, D. (2014) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.3115/v1/p14-5010 · Cited by 7,252
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312
Big Data and Machine Learning in Health Care
Beam, A. L., & Kohane, I. S. (2018) · JAMA · doi:10.1001/jama.2017.18391 · Cited by 2,506
Domain-specific language model pretraining for biomedical natural language processing
Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3458754 · Cited by 2,170
Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001560 · Cited by 1,967
Gray literature: An important resource in systematic reviews
Paez, A. (2017) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.12266 · Cited by 1,459
2013 IEEE International Conference on Big Data
(2013) · doi:10.1109/bigdata21311.2013 · Cited by 1,406
Hyperband: A Novel Bandit-Based Approach to Hyperparameter OptimizationPreprint
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06560 · Cited by 1,368
(2015) · neural information processing systems · Cited by 1,305
Reliable Accuracy Estimates from k-Fold Cross Validation
Wong, T.-T., & Yeh, P.-Y. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2912815 · Cited by 1,074
Clinical information extraction applications: A literature review
Wang, Y., Wang, L., Rastegar-Mojarad, M., Moon, S., Shen, F., Afzal, N., Liu, S., Zeng, Y., Mehrabi, S., Sohn, S., et al. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.11.011 · Cited by 910
Ambati, B. R., Deoskar, T., Johnson, M., & Steedman, M. J. (2015) · The Association for Computational Linguistics · Cited by 860
Yacouby, R., & Axman, D. (2020) · Workshop on Evaluation and Comparison of NLP Systems (Eval4NLP) · doi:10.18653/v1/2020.eval4nlp-1.9 · Cited by 858
Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
Waring, J., Lindvall, C., & Umeton, R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101822 · Cited by 834
Electronic health records to facilitate clinical research
Cowie, M. R., Blomster, J. I., Curtis, L. H., Duclaux, S., Ford, I., Fritz, F., Goldman, S., Janmohamed, S., Kreuzer, J., Leenay, M., et al. (2017) · Clinical Research in Cardiology · doi:10.1007/s00392-016-1025-6 · Cited by 651
TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning
Olson, R. S., & Moore, J. H. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-05318-5_8 · Cited by 621
BEHRT: Transformer for Electronic Health Records
Li, Y., Rao, S., Solares, J. R. A., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., & Salimi-Khorshidi, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-62922-y · Cited by 595
Prediction Models for Suicide Attempts and Deaths: A Systematic Review and Simulation
Belsher, B. E., Smolenski, D. J., Pruitt, L. D., Bush, N. E., Beech, E. H., Workman, D. E., Morgan, R. L., Evatt, D. P., Tucker, J., & Skopp, N. A. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.0174 · Cited by 540
Deep learning in clinical natural language processing: a methodical review
Wu, S., Roberts, K. E., Datta, S., Du, J., Ji, Z., Si, Y., Soni, S., Wang, Q., Wei, Q., Xiang, Y., et al. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz200 · Cited by 524
NAS-Unet: Neural Architecture Search for Medical Image Segmentation
Weng, Y., Zhou, T., Li, Y., & Qiu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2908991 · Cited by 487
Santos, M. S., Soares, J. P., Abreu, P. H., Araújo, H. J., & Santos, J. A. M. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2866730 · Cited by 424
Koleck, T. A., Dreisbach, C., Bourne, P. E., & Henry, S. B. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy173 · Cited by 409
Simon, G. E., Johnson, E., Lawrence, J. M., Rossom, R. C., Ahmedani, B., Lynch, F. L., Beck, A., Waitzfelder, B., Ziebell, R., Penfold, R. B., et al. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.17101167 · Cited by 400 · cited 1×
“In addition, risk assessment mainly depends on patient-reported data [37] and, and patients may be prone to hide suicidal notions [38].”
BackgroundA machine learning-based framework to identify type 2 diabetes through electronic health records
Zheng, T., Xie, W., Xu, L., He, X., Zhang, Y., You, M., Yang, G., & Chen, Y. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.09.014 · Cited by 380
Automatic generation of textual summaries from neonatal intensive care data
Portet, F., Reiter, E., Albert Gatt, A., Jim Hunter, A., Sripada, S. G., Freer, Y., & Cindy Sykes, B. (2008) · Artificial Intelligence · doi:10.1016/j.artint.2008.12.002 · Cited by 318
Faes, L., Wagner, S. K., Fu, D. J., Liu, X., Korot, E., Ledsam, J. R., Back, T., Chopra, R., Pontikos, N., Kern, C., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30108-6 · Cited by 317
Juhn, Y. J., & Liu, H. (2019) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2019.12.897 · Cited by 300
MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs
Johnson, A. E. W., Pollard, T. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-Y., Peng, Y., Lu, Z., Mark, R. G., Berkowitz, S. J., & Horng, S. (2019) · arXiv · doi:10.48550/arxiv.1901.07042 · Cited by 269
Privacy-Preserving Federated Learning Framework Based on Chained Secure Multiparty Computing
Li, Y., Zhou, Y., Jolfaei, A., Yu, D., Xu, G., & Zheng, X. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3022911 · Cited by 268
Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning
Walsh, C. G., Ribeiro, J. D., & Franklin, J. C. (2018) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12916 · Cited by 264
Ye, C., Fu, T., Hao, S., Zhang, Y., Wang, O., Jin, B., Xia, M., Liu, M., Zhou, X., Wu, Q., et al. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9268 · Cited by 250
Steele, A. J., Cakiroglu, S. A., Shah, A. D., Denaxas, S., Hemingway, H., & Luscombe, N. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0202344 · Cited by 246
2008 IEEE International Conference on Bioinformatics and Biomedicine
(2008) · doi:10.1109/bibm14813.2008 · Cited by 220
Natural Language Processing for EHR-Based Computational Phenotyping
Zeng, Z., Deng, Y., Li, X., Naumann, T., & Luo, Y. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2849968 · Cited by 215
Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review
Luo, Y., Thompson, W. K., Herr, T. M., Zeng, Z., Berendsen, M. A., Jonnalagadda, S. R., Carson, M. B., & Starren, J. (2017) · Drug Safety · doi:10.1007/s40264-017-0558-6 · Cited by 200
Survey of Deep Representation Learning for Speech Emotion Recognition
Latif, S., Rana, R. K., Khalifa, S., Jurdak, R., Qadir, J., & Schuller, B. W. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3114365 · Cited by 193
Self-citationWeng, W., Wagholikar, K. B., McCray, A. T., Szolovits, P., & Chueh, H. C. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0556-8 · Cited by 175
Radiomics and Machine Learning in Oral Healthcare
Leite, A. F., Vasconcelos, K. D. F., Willems, H., & Jacobs, R. (2020) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.201900040 · Cited by 166
Automated ICD-9 Coding via A Deep Learning Approach
Li, M., Fei, Z., Zeng, M., Wu, F., Li, Y., Pan, Y. F., & Wang, J. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2817488 · Cited by 145
Lu, H., Ehwerhemuepha, L., & Rakovski, C. S. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01665-y · Cited by 136
DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx
Mehrabi, S., Krishnan, A., Sohn, S., Roch, A. M., Schmidt, H., Kesterson, J., Beesley, C., Dexter, P. R., Schmidt, C. M., Liu, H., et al. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.02.010 · Cited by 132
Language models are an effective representation learning technique for electronic health record data
Steinberg, E., Jung, K., Fries, J. A., Corbin, C. K., Pfohl, S. R., & Shah, N. H. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103637 · Cited by 132
Using Clinical Notes and Natural Language Processing for Automated HIV Risk Assessment
Feller, D. J., Zucker, J., Yin, M. T., Gordon, P., & Elhadad, N. (2018) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0000000000001580 · Cited by 130
The Value of Unstructured Electronic Health Record Data in Geriatric Syndrome Case Identification
Kharrazi, H., Anzaldi, L. J., Hernandez, L., Davison, A., Boyd, C. M., Leff, B., Kimura, J., & Weiner, J. P. (2018) · Journal of the American Geriatrics Society · doi:10.1111/jgs.15411 · Cited by 128
Learning to Summarize Radiology Findings
Zhang, Y., Ding, D. Y., Qian, T., Manning, C. D., & Langlotz, C. P. (2018) · Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis · doi:10.18653/v1/w18-5623 · Cited by 126
Wu, C., Kuo, C., Su, C.-H., Wang, S., & Dai, H.-J. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.09.044 · Cited by 122
Assessing stroke severity using electronic health record data: a machine learning approach
Kogan, E., Twyman, K., Heap, J., Milentijevic, D., Lin, J., & Alberts, M. J. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-1010-x · Cited by 117
Carson, N. J., Mullin, B. O., Román, M. J. S., Lu, F. Q., Yang, K. G., Menezes, M., & Cook, B. L. (2019) · PLoS ONE · doi:10.1371/journal.pone.0211116 · Cited by 113
Automated misspelling detection and correction in clinical free-text records
Lai, K. H., Topaz, M., Goss, F. R., & Zhou, L. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.04.008 · Cited by 113
Employability skills: Profiling data scientists in the digital labour market
Smaldone, F., Ippolito, A., Lagger, J., & Pellicano, M. (2022) · European Management Journal · doi:10.1016/j.emj.2022.05.005 · Cited by 112
Bhosale, Y. H., & Patnaik, K. S. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104445 · Cited by 109
Liang, J., Li, Y., Zhang, Z., Shen, D., Jie, X., Zheng, X., Wang, T., Tang, B., Lei, J., & Zhang, J. (2021) · Journal of Medical Internet Research · doi:10.2196/24813 · Cited by 106
Wang, L., Sha, L., Lakin, J. R., Bynum, J., Bates, D. W., Hong, P., & Zhou, L. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.6972 · Cited by 105
Tsui, F. R., Shi, L., Ruiz, V., Ryan, N. D., Biernesser, C. L., Iyengar, S., Walsh, C. G., & Brent, D. A. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab011 · Cited by 105
Deep Learning Techniques for Biomedical and Health Informatics
(2019) · Studies in big data · doi:10.1007/978-3-030-33966-1 · Cited by 104
Smit, A., Jain, S., Rajpurkar, P., Pareek, A., Ng, A. Y., & Lungren, M. P. (2020) · arXiv · doi:10.48550/arxiv.2004.09167 · Cited by 103
Su, X., Chen, N., Sun, H., Liu, Y., Yang, X., Wang, W., Zhang, S., Tan, Q., Su, J., Gong, Q., et al. (2019) · Neuro-Oncology · doi:10.1093/neuonc/noz184 · Cited by 102
Yuan, Q., Cai, T. A., Hong, C., Du, M., Johnson, B. E., Lanuti, M. D., Cai, T., & Christiani, D. C. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.14723 · Cited by 102
Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review
Bhosale, Y. H., & Patnaik, K. S. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-11023-0 · Cited by 101
Monitoring Suicidal Patients in Primary Care Using Electronic Health Records
Anderson, H. D., Pace, W. D., Brandt, E., Nielsen, R., Allen, R. R., Libby, A. M., West, D. R., & Valuck, R. J. (2015) · The Journal of the American Board of Family Medicine · doi:10.3122/jabfm.2015.01.140181 · Cited by 101
Lindvall, C. J., Lilley, E. J., Zupanc, S. N., Chien, I., Udelsman, B. V., Walling, A. M., Cooper, Z. R., & Tulsky, J. A. (2018) · Journal of Palliative Medicine · doi:10.1089/jpm.2018.0326 · Cited by 100
Natural language processing of clinical notes for identification of critical limb ischemia
Afzal, N., Mallipeddi, V. P., Sohn, S., Liu, H., Chaudhry, R., Scott, C. G., Kullo, I. J., & Arruda-Olson, A. M. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.12.024 · Cited by 95
Irving, J., Patel, R., Oliver, D., Colling, C., Pritchard, M., Broadbent, M., Baldwin, H., Stahl, D., Stewart, R., & Fusar-Poli, P. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa126 · Cited by 92
Sholle, E. T., Pinheiro, L. C., Adekkanattu, P., Davila, M. A., Johnson, S. B., Pathak, J., Sinha, S., Li, C., Lubansky, S. A., Safford, M. M., et al. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz040 · Cited by 89
Predicting dementia with routine care EMR data
Miled, Z. B., Haas, K., Black, C. M., Khandker, R. K., Chandrasekaran, V., Lipton, R. B., & Boustani, M. A. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101771 · Cited by 88
Levis, M., Leonard Westgate, C., Gui, J., Watts, B. V., & Shiner, B. (2021) · Psychological Medicine · doi:10.1017/s0033291720000173 · Cited by 87
Shao, Y., Zeng, Q. T., Chen, K. K., Shutes‐David, A., Thielke, S. M., & Tsuang, D. W. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0846-4 · Cited by 83
Anzaldi, L. J., Davison, A., Boyd, C. M., Leff, B. A., & Kharrazi, H. H. K. (2017) · BMC Geriatrics · doi:10.1186/s12877-017-0645-7 · Cited by 83
Building the evidence-base to reduce electronic health record–related clinician burden
Dymek, C., Kim, B. I., Melton, G. B., Payne, T. H., Singh, H., & Hsiao, C. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa238 · Cited by 83
Lee, R. Y., Brumback, L. C., Lober, W. B., Sibley, J., Nielsen, E. L., Treece, P. D., Kross, E. K., Loggers, E. T., Fausto, J. A., Lindvall, C. J., et al. (2020) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2020.08.024 · Cited by 81
Li, X., Cui, M., Li, J., Bai, R., Lu, Z., & Aickelin, U. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.02.069 · Cited by 75
Le, D. V., Montgomery, J., Kirkby, K. C., & Scanlan, J. D. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.08.007 · Cited by 73
Hong, N., Wen, A., Stone, D. J., Tsuji, S., Kingsbury, P., Rasmussen, L. V., Pacheco, J. A., Adekkanattu, P. M., Wang, F., Luo, Y., et al. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103310 · Cited by 69
Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems
Evaluation Team, Eger, S., Gao, Y., et al. (2020) · doi:10.18653/v1/2020.eval4nlp-1 · Cited by 69
Automated disease cohort selection using word embeddings from Electronic Health Records
Glicksberg, B. S., Miotto, R., Johnson, K. W., Shameer, K., Li, L., Chen, R., & Dudley, J. T. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0014 · Cited by 67
De-identification of electronic health record using neural network
Ahmed, T., Aziz, M. M. A., & Mohammed, N. (2020) · Scientific Reports · doi:10.1038/s41598-020-75544-1 · Cited by 66
Jonnalagadda, S., Adupa, A. K., Garg, R., Corona-Cox, J., & Shah, S. J. (2017) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-017-9752-2 · Cited by 64
Natural language processing for structuring clinical text data on depression using UK-CRIS
Vaci, N., Liu, Q., Kormilitzin, A., Crescenzo, F. D., Kurtulmuş, A., Harvey, J., O’Dell, B., Innocent, S. H. S., Tomlinson, A., Cipriani, A., et al. (2020) · Evidence-Based Mental Health · doi:10.1136/ebmental-2019-300134 · Cited by 64
A Comparative Analysis of Active Learning for Biomedical Text Mining
Naseem, U., Khushi, M., Khan, S. K., Shaukat, K., & Moni, M. A. (2021) · Applied System Innovation · doi:10.3390/asi4010023 · Cited by 64
MT-clinical BERT: scaling clinical information extraction with multitask learning
Mulyar, A., Uzuner, Ö., & McInnes, B. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab126 · Cited by 63
Afshar, M., Joyce, C., Dligach, D., Sharma, B., Kania, R., Xie, M., Swope, K., Salisbury‐Afshar, E., & Karnik, N. S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0219717 · Cited by 61
Clinical named entity recognition: Challenges and opportunities
Kundeti, S. R., Vijayananda, J., Mujjiga, S., & Kalyan, M. S. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840814 · Cited by 59
Alaa, A. M., & van der Schaar, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.07207 · Cited by 57
Accurate Blood-Based Diagnostic Biosignatures for Alzheimer’s Disease via Automated Machine Learning
Karaglani, M., Gourlia, K., Tsamardinos, I., & Chatzaki, Ε. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9093016 · Cited by 57
Fu, S., Wyles, C. C., Osmon, D. R., Carvour, M. L., Sagheb, E., Ramazanian, T., Kremers, W. K., Lewallen, D. G., Berry, D. J., Sohn, S., et al. (2020) · The Journal of Arthroplasty · doi:10.1016/j.arth.2020.07.076 · Cited by 56
A Review of Automatic Phenotyping Approaches using Electronic Health Records
Alzoubi, H., Alzubi, R., Ramzan, N., West, D., Al-Hadhrami, T., & Alazab, M. (2019) · Electronics · doi:10.3390/electronics8111235 · Cited by 56
Kıcı, D., Malik, G., Çevik, M., Parikh, D., & Başar, A. (2021) · doi:10.21428/594757db.a4880a62 · Cited by 56
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction
Liu, L., Shen, J., Zhang, M., Wang, Z., & Tang, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11307 · Cited by 55
Downs, J. M., Velupillai, S. U., Gkotsis, G., Holden, R., Kikoler, M., Dean, H., Fernandes, A. C., & Dutta, R. (2017) · PubMed Central · Cited by 54
Ascertainment of Delirium Status Using Natural Language Processing From Electronic Health Records
Fu, S., Lopes, G. S., Pagali, S. R., Thorsteinsdottir, B., LeBrasseur, N. K., Wen, A., Liu, H., Rocca, W. A., Olson, J. E., Sauver, J. L. S., et al. (2020) · The Journals of Gerontology Series A · doi:10.1093/gerona/glaa275 · Cited by 52
Solomon, M. D., Tabada, G. H., Allen, A., Sung, S. H., & Go, A. S. (2021) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2021.03.003 · Cited by 52
Generation of Synthetic Electronic Medical Record Text
Guan, J., Li, R., Yu, S., & Zhang, X. (2018) · 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621223 · Cited by 49
An automated machine learning approach to predict brain age from cortical anatomical measures
Dafflon, J., Pinaya, W. H. L., Turkheimer, F., Cole, J. H., Leech, R., Harris, M. A., Cox, S. R., Whalley, H. C., McIntosh, A. M., & Hellyer, P. J. (2020) · Human Brain Mapping · doi:10.1002/hbm.25028 · Cited by 48
Kim, I. K., Lee, K., Park, J. H., Baek, J., & Lee, W. K. (2020) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2020-316108 · Cited by 47
Luo, G., Stone, B. L., Johnson, M. D., Tarczy-Hornoch, P. Z., Wilcox, A. B., Mooney, S. D., Sheng, X., Haug, P. J., & Nkoy, F. L. (2017) · JMIR Research Protocols · doi:10.2196/resprot.7757 · Cited by 45
Dorr, D. A., Bejan Cosmin, A., Pizzimenti, C. L., Sumeet, S., Storer, M., & Quiñones, A. R. (2019) · Studies in health technology and informatics · doi:10.3233/shti190482 · Cited by 45
Zhao, S. S., Hong, C., Cai, T. A., Xu, C. Q., Huang, J., Ermann, J., Goodson, N. J., Solomon, D. H., Cai, T., & Liao, K. P. (2019) · Lara D. Veeken · doi:10.1093/rheumatology/kez375 · Cited by 45
Dynamic Feature Induction: The Last Gist to the State-of-the-Art
Choi, J. D. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1031 · Cited by 42
Viani, N., Botelle, R., Kerwin, J., Yin, L., Patel, R., Stewart, R. J., & Velupillai, S. U. (2021) · Scientific Reports · doi:10.1038/s41598-020-80457-0 · Cited by 42
Weissler, E., Zhang, J.-K., Lippmann, S. J., Rusincovitch, S. A., Henao, R., & Jones, W. (2020) · Circulation. Cardiovascular Interventions · doi:10.1161/circinterventions.120.009447 · Cited by 41
Use of abbreviations by healthcare professionals: what is the way forward?
Sinha, S., McDermott, F. D., Srinivas, G., & Houghton, P. W. J. (2011) · Postgraduate Medical Journal · doi:10.1136/pgmj.2010.097394 · Cited by 39
Chowdhury, M., Cervantes, E. G., Chan, W.-Y., & Seitz, D. P. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.738466 · Cited by 39
Augmenting Generative Adversarial Networks for Speech Emotion Recognition
Latif, S., Asim, M., Rana, R. K., Khalifa, S., Jurdak, R., & Schuller, B. W. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-3194 · Cited by 38
Self-citationChen, T., Dredze, M. H., Weiner, J. P., & Kharrazi, H. H. K. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz093 · Cited by 37
The Prevalence and Correlates of Self-Harm in the Perinatal Period
Ayre, K., Gordon, H. G., Dutta, R., Hodsoll, J. P., & Howard, L. M. (2019) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.19r12773 · Cited by 37
Tsang, G., Zhou, S., & Xie, X. (2020) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2020.3040236 · Cited by 36
CAB: Classifying Arrhythmias based on Imbalanced Sensor Data
Wang, Y., Sun, L., & Subramani, S. (2021) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2021.07.001 · Cited by 35
Ni, Y., Bachtel, A., Nause, K., & Beal, S. J. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab116 · Cited by 33
Leiter, R. E., Santus, E., Jin, Z., Lee, K. C., Yusufov, M., Chien, I., Ramaswamy, A., Moseley, E. T., Qian, Y., Schrag, D., et al. (2020) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2020.06.010 · Cited by 31
Ford, E. M., Sheppard, J., Oliver, S., Rooney, P., Banerjee, S., & Cassell, J. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-039248 · Cited by 31
Puri, M. (2019) · Assay and Drug Development Technologies · doi:10.1089/adt.2019.919 · Cited by 30
Just Add Data: Automated Predictive Modeling and BioSignature DiscoveryPreprint
Tsamardinos, I., Charonyktakis, P., Lakiotaki, K., Borboudakis, G., Zenklusen, J. C., Juhl, H. H., Chatzaki, Ε., & Lagani, V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.04.075747 · Cited by 30
Jabali, A. K., Waris, A., Khan, D. I., Ahmed, S., & Hourani, R. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100872 · Cited by 30
Zhu, R., Tu, X., & Huang, J. X. (2019) · Studies in big data · doi:10.1007/978-3-030-33966-1_13 · Cited by 29
Jaber, A., & Martı́nez, P. (2022) · Methods of Information in Medicine · doi:10.1055/s-0042-1742388 · Cited by 28
Classifying Pseudogout Using Machine Learning Approaches With Electronic Health Record Data
Tedeschi, S. K., Cai, T. A., He, Z., Ahuja, Y., Hong, C., Yates, K. A., Dahal, K. P., Xu, C. Q., Lyu, H., Yoshida, K., et al. (2020) · Arthritis Care & Research · doi:10.1002/acr.24132 · Cited by 28
Unsupervised Clinical Language Translation
Weng, W., Chung, Y.-A., & Peter Szolovits, B. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330710 · Cited by 27
Shi, J., Liu, S., Pruitt, L. C. C., Luppens, C. L., Ferraro, J. P., Gundlapalli, A. V., Chapman, W. W., & Bucher, B. T. (2019) · PubMed Central · Cited by 27
Hodgkins, A. J., Mullan, J., Mayne, D. J., Boyages, C. S., & Bonney, A. D. (2020) · Australian Journal of General Practice · doi:10.31128/ajgp-07-19-5024 · Cited by 25
Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis
(2022) · Studies in computational intelligence · doi:10.1007/978-981-19-1076-0 · Cited by 25
Text Classification to Inform Suicide Risk Assessment in Electronic Health Records
Bittar, A., Velupillai, S. U., Roberts, A., & Dutta, R. (2019) · Studies in health technology and informatics · doi:10.3233/shti190179 · Cited by 24
Ayre, K., Bittar, A., Kam, J., Verma, S., Howard, L. M., & Dutta, R. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253809 · Cited by 24
A Blockchain and NLP Based Electronic Health Record System: Indian Subcontinent Context
Bharimalla, P. K., Choudhury, H. S., Parida, S., Mallick, D. K., & Dash, S. R. (2021) · Informatica · doi:10.31449/inf.v45i4.3503 · Cited by 22
Envisioning the potential of Natural Language Processing (NLP) in Health Care Management
Sivarethinamohan, R., Sujatha, S., & Biswas, P. (2021) · International Engineering Conference on Sustainable Technology and Development (IEC) · doi:10.1109/iec52205.2021.9476131 · Cited by 22
Bhat, G. S., Shankar, N., & Panahi, I. M. S. (2020) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc44109.2020.9175693 · Cited by 20
Self-Service Data Science in Healthcare with Automated Machine Learning
Ooms, R., & Spruit, M. R. (2020) · Applied Sciences · doi:10.3390/app10092992 · Cited by 15
Extracting Smoking Status from Electronic Health Records Using NLP and Deep Learning.
Rajendran, S., & Topaloĝlu, Ü. (2020) · PubMed · Cited by 15
Kovacs, M. D., Mesterhazy, J., Avrin, D. E., Urbania, T. H., & Mongan, J. (2017) · Radiographics · doi:10.1148/rg.2017160195 · Cited by 14
NLP/Deep Learning Techniques in Healthcare for Decision Making
Jain, K. K., & Prajapati, V. (2021) · doi:10.35248/2167-1079.21.11.373 · Cited by 14
Cairns, K. A., Rawlins, M. D. M., Unwin, S., Doukas, F. F., Burke, R. A., Tong, E., Henderson, A., & Cheng, A. C. (2021) · Infectious Diseases and Therapy · doi:10.1007/s40121-020-00392-5 · Cited by 14
Multiple Sclerosis Severity Classification From Clinical Text
D’Costa, A., Denkovski, S., Malyska, M., Moon, S. Y., Rufino, B., Yang, Z., Killian, T. W., & Ghassemi, M. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.2 · Cited by 14
Sarker, A., Al-Garadi, M. A., Yang, Y., Choi, J. D., Quyyumi, A. A., & Martin, G. S. (2021) · JMIR Medical Informatics · doi:10.2196/18471 · Cited by 13
Borkowski, A., Wilson, C. P., Borkowski, S. A., Thomas, L. B., Deland, L. A., Grewe, S., & Mastorides, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.08057 · Cited by 12
Moore, C., Jain, S., Haas, S., Yadav, H., Whitsel, E. A., Rosamand, W., HEISS, G. M., & Kucharska‐Newton, A. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-047356 · Cited by 11
Query-Focused EHR Summarization to Aid Imaging DiagnosisPreprint
McInerney, D. J., Dabiri, B. E., Touret, A.-S., Young, G. S., van de Meent, J.-W., & Wallace, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.04645 · Cited by 10
Query Focused Multi-document Summarisation of Biomedical TextsPreprint
Mollá, D., Jones, C., & Nguyen, V. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.11986 · Cited by 10
Timely and Efficient AI Insights on EHR: System Design.
Suryanarayanan, P., Epstein, E. A., Malvankar, A., Lewis, B. L., Degenaro, L., Liang, J. J., Tsou, C.-H., & Pathak, D. (2020) · PubMed Central · Cited by 10
Survey: Deep Learning Concepts and Techniques for Electronic Health Record
Alaiad, A., Duwairi, R., & Fraihat, M. (2018) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2018.8612827 · Cited by 9
Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health RecordsPreprint
Liu, X., Xu, K., Xie, P., & Xing, E. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08040 · Cited by 9
Galatzan, B. J., Carrington, J. M., & Gephart, S. M. (2021) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000000732 · Cited by 9
The Competing Demands of Patient Privacy and Clinical Research
Ulrich, C. M., Grady, C., Demiris, G., & Richmond, T. S. (2021) · Ethics & Human Research · doi:10.1002/eahr.500076 · Cited by 8
Small-world networks for summarization of biomedical articlesPreprint
Moradi, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.02861 · Cited by 5
A Benchmark Dataset for Understandable Medical Language Translation.Preprint
Luo, J., Zheng, Z., Ye, H., Ye, M., Wang, Y., You, Q., Xiao, C., & Ma, F. (2020) · arXiv (Cornell University) · Cited by 5
Feature Extraction Method from Electronic Health Records in Russia
Gusev, A. S., Korsakov, I., Novitskiy, R. E., Serova, L., & Gavrilov, D. V. (2020) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.5281/zenodo.4007407 · Cited by 5
Miller, T. A., Avillach, P., & Mandl, K. D. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa016 · Cited by 4
A SEMI-AUTOMATED APPROACH TO IDENTIFYING CHRONIC COUGH IN ELECTRONIC HEALTH RECORDS
Weiner, M., Weaver, J. P., Dexter, P. R., Roberts, A., Liu, Z., Hui, S. L., Church, A., Doshi, I., & Heithoff, K. A. (2018) · Annals of Allergy Asthma & Immunology · doi:10.1016/j.anai.2018.09.187 · Cited by 3
Natural language processing to identify lupus nephritis phenotype in electronic health recordsPreprint
Deng, Y., Pacheco, J. A., Chung, A. H., Mao, C., Smith, J., Zhao, J., Wei, W., Barnado, A., Weng, C., Liu, C., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.10821 · Cited by 3
NLP Applications for Big Data Analytics Within Healthcare
Choudhary, A., Choudhary, A., & Suman, S. (2022) · Studies in computational intelligence · doi:10.1007/978-981-19-1076-0_13 · Cited by 2
(2021) · CIN Computers Informatics Nursing · doi:10.1097/01.ncn.0000755944.18537.e0 · Cited by 1
Research works printed but not linked: 5
- Juhn, 2019, Natural language processing to advance EHR-based clinical research in Allergy, Asthma, and Immunology, J. Allergy Clin. Immunol., 145
- Kormilitzin, 2021, Med7: a transferable clinical natural language processing model for electronic health records, Artif. Intell. Med., 118, 10.1016/j.artmed.2021.102086 · DOI
- Gligic, 2020, Named entity recognition in electronic health records using transfer learning bootstrapped neural networks, Neural Netw., 121, 132, 10.1016/j.neunet.2019.08.032 · DOI
- Shen, 2021, Analysis of electronic health records based on deep learning with natural language processing, Arab. J. Sci. Eng., 1
- kormilitzin, 2021, Med7: a transferable clinical natural language processing model for electronic health records, Artif. Intell. Med., 118, 10.1016/j.artmed.2021.102086 · DOI
Cited by
Sorted by citations
Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions
Abd-alrazaq, A., AlSaad, R., Alhuwail, D., Ahmed, A., Healy, P. M., Latifi, S., Aziz, S., Damseh, R., Alabed Alrazak, S., & Sheikh, J. (2023) · JMIR Medical Education · doi:10.2196/48291 · Cited by 722
Future of Artificial Intelligence—Machine Learning Trends in Pathology and Medicine
Hanna, M. G., Pantanowitz, L., Dash, R., Harrison, J. H., Deebajah, M., Pantanowitz, J., & Rashidi, H. H. (2025) · Modern Pathology · doi:10.1016/j.modpat.2025.100705 · Cited by 231
Hirani, R., Noruzi, K. A., Khuram, H., Hussaini, A. S., Aifuwa, E., Ely, K., Lewis, J. M., Gabr, A. E., Smiley, A., Tiwari, R. K., et al. (2024) · Life · doi:10.3390/life14050557 · Cited by 276
“…employs various , algorithms, and Multiple analysis techniques fall under the umbrella of AI, which employs various methods, algorithms, and data science approaches to analyze large datasets to extract meaningful information depending on the data type [38–40]. depending on the data type [38–40].”
MethodGala, D., Behl, H., Shah, M., & Makaryus, A. N. (2024) · Healthcare · doi:10.3390/healthcare12040481 · Cited by 216
“Moreover, NLP algorithms contribute to data analysis within medical records, unveiling invaluable patterns and insights that aid in identifying trends and optimizing patient care pathways [12,13].”
The integration of AI in nursing: addressing current applications, challenges, and future directions
Wei, Q., Pan, S., Liu, X., Hong, M., Nong, C., & Zhang, W. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1545420 · Cited by 143
Metadata
- Authors
- Hossain, E., Rana, R., Higgins, N., Soar, J., Barua, P. D., Pisani, A. R., & Turner, K.
- Affiliations
- North South University (BD)
- University of Southern Queensland (AU)
- Queensland University of Technology (AU)
- University of Rochester Medicine (US)
- Corresponding author
- Hossain, Elias
- ORCID
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 155
- Pages
- 106649
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W4319985906
- Cited by
- 407
- References
- 161
- Citations by year
- 2026: 103 · 2025: 177 · 2024: 105 · 2023: 22
- FWCI
- 8.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- natural language processing, electronic health records, healthcare decision-making, named entity recognition, clinical information extraction, ICD-9, transfer learning, class imbalance, clinical text
- MeSH terms
- Machine Learning, Delivery of Health Care, Humans, Natural Language Processing, Information Storage and Retrieval, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
