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Thematische Bibliographien / Clinical Natural Language Processing / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Clinical Natural Language Processing“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 31. Juli 2025

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1

K Gautam, Leena. "Natural Language Processing - Based Structured Data Extraction from Unstructured Clinical Notes." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 1541–44. http://dx.doi.org/10.21275/sr24422134801.

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Yandell, Mark D., and William H. Majoros. "Genomics and natural language processing." Nature Reviews Genetics 3, no. 8 (2002): 601–10. http://dx.doi.org/10.1038/nrg861.

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Ware, H., C. J. Mullett, and V. Jagannathan. "Natural Language Processing Framework to Assess Clinical Conditions." Journal of the American Medical Informatics Association 16, no. 4 (2009): 585–89. http://dx.doi.org/10.1197/jamia.m3091.

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Sager, N., M. Lyman, C. Bucknall, N. Nhan, and L. J. Tick. "Natural Language Processing and the Representation of Clinical Data." Journal of the American Medical Informatics Association 1, no. 2 (1994): 142–60. http://dx.doi.org/10.1136/jamia.1994.95236145.

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Friedman, C., G. Hripcsak, W. DuMouchel, S. B. Johnson, and P. D. Clayton. "Natural language processing in an operational clinical information system." Natural Language Engineering 1, no. 1 (1995): 83–108. http://dx.doi.org/10.1017/s1351324900000061.

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AbstractThis paper describes a natural language text extraction system, called MEDLEE, that has been applied to the medical domain. The system extracts, structures, and encodes clinical information from textual patient reports. It was integrated with the Clinical Information System (CIS), which was developed at Columbia-Presbyterian Medical Center (CPMC) to help improve patient care. MEDLEE is currently used on a daily basis to routinely process radiological reports of patients at CPMC.In order to describe how the natural language system was made compatible with the existing CIS, this paper wi

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Denny, Joshua C., Lisa Bastarache, Elizabeth Ann Sastre, and Anderson Spickard. "Tracking medical students’ clinical experiences using natural language processing." Journal of Biomedical Informatics 42, no. 5 (2009): 781–89. http://dx.doi.org/10.1016/j.jbi.2009.02.004.

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Wu, Stephen T., Vinod C. Kaggal, Dmitriy Dligach, et al. "A common type system for clinical natural language processing." Journal of Biomedical Semantics 4, no. 1 (2013): 1. http://dx.doi.org/10.1186/2041-1480-4-1.

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Mateussi, Nadayca, Michael P. Rogers, Emily A. Grimsley, et al. "Clinical Applications of Machine Learning." Annals of Surgery Open 5, no. 2 (2024): e423. http://dx.doi.org/10.1097/as9.0000000000000423.

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Objective: This review introduces interpretable predictive machine learning approaches, natural language processing, image recognition, and reinforcement learning methodologies to familiarize end users. Background: As machine learning, artificial intelligence, and generative artificial intelligence become increasingly utilized in clinical medicine, it is imperative that end users understand the underlying methodologies. Methods: This review describes publicly available datasets that can be used with interpretable predictive approaches, natural language processing, image recognition, and reinfo

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Legnar, Maximilian, Philipp Daumke, Jürgen Hesser, et al. "Natural Language Processing in Diagnostic Texts from Nephropathology." Diagnostics 12, no. 7 (2022): 1726. http://dx.doi.org/10.3390/diagnostics12071726.

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Introduction: This study investigates whether it is possible to predict a final diagnosis based on a written nephropathological description—as a surrogate for image analysis—using various NLP methods. Methods: For this work, 1107 unlabelled nephropathological reports were included. (i) First, after separating each report into its microscopic description and diagnosis section, the diagnosis sections were clustered unsupervised to less than 20 diagnostic groups using different clustering techniques. (ii) Second, different text classification methods were used to predict the diagnostic group base

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da Rocha, Naila Camila, Abner Macola Pacheco Barbosa, Yaron Oliveira Schnr, et al. "Natural Language Processing to Extract Information from Portuguese-Language Medical Records." Data 8, no. 1 (2022): 11. http://dx.doi.org/10.3390/data8010011.

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Studies that use medical records are often impeded due to the information presented in narrative fields. However, recent studies have used artificial intelligence to extract and process secondary health data from electronic medical records. The aim of this study was to develop a neural network that uses data from unstructured medical records to capture information regarding symptoms, diagnoses, medications, conditions, exams, and treatment. Data from 30,000 medical records of patients hospitalized in the Clinical Hospital of the Botucatu Medical School (HCFMB), São Paulo, Brazil, were obtained

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Schiekiera, Louis, Jonathan Diederichs, and Helen Niemeyer. "Classifying Positive Results in Clinical Psychology Using Natural Language Processing." Zeitschrift für Psychologie 232, no. 3 (2024): 147–59. http://dx.doi.org/10.1027/2151-2604/a000563.

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Abstract: This study addresses the gap in machine learning tools for positive results classification by evaluating the performance of SciBERT, a transformer model pretrained on scientific text, and random forest in clinical psychology abstracts. Over 1,900 abstracts were annotated into two categories: positive results only and mixed or negative results. Model performance was evaluated on three benchmarks. The best-performing model was utilized to analyze trends in over 20,000 psychotherapy study abstracts. SciBERT outperformed all benchmarks and random forest in in-domain and out-of-domain dat

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Friedman, Carol, Lyudmila Shagina, Yves Lussier, and George Hripcsak. "Automated Encoding of Clinical Documents Based on Natural Language Processing." Journal of the American Medical Informatics Association 11, no. 5 (2004): 392–402. http://dx.doi.org/10.1197/jamia.m1552.

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Demner-Fushman, Dina, Wendy W. Chapman, and Clement J. McDonald. "What can natural language processing do for clinical decision support?" Journal of Biomedical Informatics 42, no. 5 (2009): 760–72. http://dx.doi.org/10.1016/j.jbi.2009.08.007.

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Leaman, Robert, Ritu Khare, and Zhiyong Lu. "Challenges in clinical natural language processing for automated disorder normalization." Journal of Biomedical Informatics 57 (October 2015): 28–37. http://dx.doi.org/10.1016/j.jbi.2015.07.010.

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Kalyan, Katikapalli Subramanyam, and S. Sangeetha. "SECNLP: A survey of embeddings in clinical natural language processing." Journal of Biomedical Informatics 101 (January 2020): 103323. http://dx.doi.org/10.1016/j.jbi.2019.103323.

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Cai, Tianrun, Andreas A. Giannopoulos, Sheng Yu, et al. "Natural Language Processing Technologies in Radiology Research and Clinical Applications." RadioGraphics 36, no. 1 (2016): 176–91. http://dx.doi.org/10.1148/rg.2016150080.

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Wu, Stephen, Kirk Roberts, Surabhi Datta, et al. "Deep learning in clinical natural language processing: a methodical review." Journal of the American Medical Informatics Association 27, no. 3 (2019): 457–70. http://dx.doi.org/10.1093/jamia/ocz200.

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Abstract Objective This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. Materials and Methods We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variable

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Bobba, Pratheek S., Anne Sailer, James A. Pruneski, et al. "Natural language processing in radiology: Clinical applications and future directions." Clinical Imaging 97 (May 2023): 55–61. http://dx.doi.org/10.1016/j.clinimag.2023.02.014.

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Luo, Jack W., and Jaron J. R. Chong. "Review of Natural Language Processing in Radiology." Neuroimaging Clinics of North America 30, no. 4 (2020): 447–58. http://dx.doi.org/10.1016/j.nic.2020.08.001.

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Diab, Kareem Mahmoud, Jamie Deng, Yusen Wu, Yelena Yesha, Fernando Collado-Mesa, and Phuong Nguyen. "Natural Language Processing for Breast Imaging: A Systematic Review." Diagnostics 13, no. 8 (2023): 1420. http://dx.doi.org/10.3390/diagnostics13081420.

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Natural Language Processing (NLP) has gained prominence in diagnostic radiology, offering a promising tool for improving breast imaging triage, diagnosis, lesion characterization, and treatment management in breast cancer and other breast diseases. This review provides a comprehensive overview of recent advances in NLP for breast imaging, covering the main techniques and applications in this field. Specifically, we discuss various NLP methods used to extract relevant information from clinical notes, radiology reports, and pathology reports and their potential impact on the accuracy and efficie

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Ozonoff, Al, Carly E. Milliren, Kerri Fournier, et al. "Electronic surveillance of patient safety events using natural language processing." Health Informatics Journal 28, no. 4 (2022): 146045822211324. http://dx.doi.org/10.1177/14604582221132429.

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Objective We describe our approach to surveillance of reportable safety events captured in hospital data including free-text clinical notes. We hypothesize that a) some patient safety events are documented only in the clinical notes and not in any other accessible source; and b) large-scale abstraction of event data from clinical notes is feasible. Materials and Methods We use regular expressions to generate a training data set for a machine learning model and apply this model to the full set of clinical notes and conduct further review to identify safety events of interest. We demonstrate thi

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Gunjan, Dhole, and Nilesh Uke Dr. "Medical Information Extraction Using Natural Language Interpretation." Advances in Vision Computing: An International Journal (AVC) 1, no. 1 (2014): 19–25. https://doi.org/10.5281/zenodo.3352046.

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NLP Based Retrieval of Medical Information is the extraction of medical data from narrative clinical documents. In this paper, we review Natural Language Processing (NLP) applications designed to extract medical problems from narrative text clinical documents. This paper also covers the methods used in this field and it also describes the architecture of the proposed system. However extraction of medical information is the difficult task due to complex symptom names and complex disease names. Proposed system is an expert system which will try to understand the input that can be the question ab

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Gunjan, Dhole, and Nilesh Uke Dr. "Medical Information Extraction Using Natural Language Interpretation." Advances in Vision Computing: An International Journal (AVC) 1, no. 1 (2014): 19–25. https://doi.org/10.5281/zenodo.3548115.

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NLP Based Retrieval of Medical Information is the extraction of medical data from narrative clinical documents. In this paper, we review Natural Language Processing (NLP) applications designed to extract medical problems from narrative text clinical documents. This paper also covers the methods used in this field and it also describes the architecture of the proposed system. However extraction of medical information is the difficult task due to complex symptom names and complex disease names. Proposed system is an expert system which will try to understand the input that can be the question ab

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Bina K., Nalongo. "The Role of Natural Language Processing in Medical Documentation." Research Output Journal of Biological and Applied Science 5, no. 1 (2025): 11–14. https://doi.org/10.59298/rojbas/2025/511114.

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Natural Language Processing (NLP) has emerged as a transformative technology for medical documentation, addressing challenges such as data complexity, interoperability, and errors in record-keeping. This paper explores the fundamental principles of NLP, its applications in healthcare, and its role in automating and improving the accuracy of clinical documentation. Key focus areas include the extraction of unstructured data, real-time transcription, and sentiment analysis, as well as the ethical and privacy considerations critical to maintaining patient confidentiality. The future of NLP in hea

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Karhade, Aditya V., Michiel E. R. Bongers, Olivier Q. Groot, et al. "Natural language processing for automated detection of incidental durotomy." Spine Journal 20, no. 5 (2020): 695–700. http://dx.doi.org/10.1016/j.spinee.2019.12.006.

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Névéol, A., and P. Zweigenbaum. "Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare." Yearbook of Medical Informatics 24, no. 01 (2015): 194–98. http://dx.doi.org/10.15265/iy-2015-035.

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Summary Objective: To summarize recent research and present a selection of the best papers published in 2014 in the field of clinical Natural Language Processing (NLP).Method: A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. Results: The clinical NLP best paper selection s

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Kanaparthi, Vijaya. "Examining Natural Language Processing Techniques in the Education and Healthcare Fields." International Journal of Engineering and Advanced Technology 12, no. 2 (2022): 8–18. http://dx.doi.org/10.35940/ijeat.b3861.1212222.

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Natural language processing is a branch of artificial intelligence currently being used to classify unstructured data. While natural language processing is found throughout several fields, these algorithms are currently being excelled in the education and healthcare fields. The healthcare industry has found various uses of natural language processing models. These algorithms are capable of analyzing large amounts of unstructured data from clinical notes, making it easier for healthcare professionals to identify at-risk patients and analyze consumer healthcare perception. In the education field

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Vijaya, Kanaparthi. "Examining Natural Language Processing Techniques in the Education and Healthcare Fields." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 2 (2022): 8–18. https://doi.org/10.35940/ijeat.B3861.1212222.

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<strong>Abstract:</strong> Natural language processing is a branch of artificial intelligence currently being used to classify unstructured data. While natural language processing is found throughout several fields, these algorithms are currently being excelled in the education and healthcare fields. The healthcare industry has found various uses of natural language processing models. These algorithms are capable of analyzing large amounts of unstructured data from clinical notes, making it easier for healthcare professionals to identify at-risk patients and analyze consumer healthcare percept

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ALLA, Naga Lalitha Valli, Aipeng CHEN, Sean BATONGBACAL, Chandini NEKKANTTI, Hong-Jie Dai, and Jitendra JONNAGADDALA. "Cohort selection for construction of a clinical natural language processing corpus." Computer Methods and Programs in Biomedicine Update 1 (2021): 100024. http://dx.doi.org/10.1016/j.cmpbup.2021.100024.

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Sheikhalishahi, Seyedmostafa, Riccardo Miotto, Joel T. Dudley, Alberto Lavelli, Fabio Rinaldi, and Venet Osmani. "Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review." JMIR Medical Informatics 7, no. 2 (2019): e12239. http://dx.doi.org/10.2196/12239.

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Wu, Stephen, Timothy Miller, James Masanz, et al. "Negation’s Not Solved: Generalizability Versus Optimizability in Clinical Natural Language Processing." PLoS ONE 9, no. 11 (2014): e112774. http://dx.doi.org/10.1371/journal.pone.0112774.

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Benavent, Diego, María Benavent-Núñez, Judith Marin-Corral, et al. "Natural language processing to identify and characterize spondyloarthritis in clinical practice." RMD Open 10, no. 2 (2024): e004302. http://dx.doi.org/10.1136/rmdopen-2024-004302.

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ObjectiveThis study aims to use a novel technology based on natural language processing (NLP) to extract clinical information from electronic health records (EHRs) to characterise the clinical profile of patients diagnosed with spondyloarthritis (SpA) at a large-scale hospital.MethodsAn observational, retrospective analysis was conducted on EHR data from all patients with SpA (including psoriatic arthritis (PsA)) at Hospital Universitario La Paz, between 2020 and 2022. Data were collected using Savana Manager, an NLP-based system, enabling the extraction of information from unstructured, free-

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More, Arjit Amol. "Natural Language Processing - Based Structured Data Extraction from Unstructured Clinical Notes." Journal of Contemporary Medical Practice 6, no. 8 (2024): 327–30. http://dx.doi.org/10.53469/jcmp.2024.06(08).67.

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Electronic Health Records (EHRs) are pivotal in modern healthcare, housing a treasure trove of patient information. They are real-time, patient-centered records that make information available instantly and securely to authorized users. However, a substantial portion of this data resides in unstructured clinical notes, presenting significant challenges for data extraction and utilization. This research paper investigates the issues posed by unstructured clinical notes application of Natural Language Processing (NLP) techniques in the healthcare sector to extract structured patient data from un

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CALVO, RAFAEL A., DAVID N. MILNE, M. SAZZAD HUSSAIN, and HELEN CHRISTENSEN. "Natural language processing in mental health applications using non-clinical texts." Natural Language Engineering 23, no. 5 (2017): 649–85. http://dx.doi.org/10.1017/s1351324916000383.

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AbstractNatural language processing (NLP) techniques can be used to make inferences about peoples’ mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have be

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Trivedi, Gaurav, Phuong Pham, Wendy W. Chapman, Rebecca Hwa, Janyce Wiebe, and Harry Hochheiser. "NLPReViz: an interactive tool for natural language processing on clinical text." Journal of the American Medical Informatics Association 25, no. 1 (2017): 81–87. http://dx.doi.org/10.1093/jamia/ocx070.

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Abstract The gap between domain experts and natural language processing expertise is a barrier to extracting understanding from clinical text. We describe a prototype tool for interactive review and revision of natural language processing models of binary concepts extracted from clinical notes. We evaluated our prototype in a user study involving 9 physicians, who used our tool to build and revise models for 2 colonoscopy quality variables. We report changes in performance relative to the quantity of feedback. Using initial training sets as small as 10 documents, expert review led to final F1s

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Pethani, Farhana, and Adam G. Dunn. "Natural language processing for clinical notes in dentistry: A systematic review." Journal of Biomedical Informatics 138 (February 2023): 104282. http://dx.doi.org/10.1016/j.jbi.2023.104282.

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Li, Chengtai, Yiming Zhang, Ying Weng, Boding Wang, and Zhenzhu Li. "Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology." Diagnostics 13, no. 2 (2023): 286. http://dx.doi.org/10.3390/diagnostics13020286.

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In the era of big data, text-based medical data, such as electronic health records (EHR) and electronic medical records (EMR), are growing rapidly. EHR and EMR are collected from patients to record their basic information, lab tests, vital signs, clinical notes, and reports. EHR and EMR contain the helpful information to assist oncologists in computer-aided diagnosis and decision making. However, it is time consuming for doctors to extract the valuable information they need and analyze the information from the EHR and EMR data. Recently, more and more research works have applied natural langua

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Shaitarova, Anastassia, Jamil Zaghir, Alberto Lavelli, Michael Krauthammer, and Fabio Rinaldi. "Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey." Yearbook of Medical Informatics 32, no. 01 (2023): 230–43. http://dx.doi.org/10.1055/s-0043-1768726.

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Objectives: This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks. Methods: We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE. We query online databases and manually select relevant publications. We also use recent NLP review papers to identify the possible information lacunae. Results: Ou

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Miskov-Zivanov, Natasa, and Stefan Andjelkovic. "Natural language processing to aggregate knowledge of biological networks." Brain Stimulation 14, no. 6 (2021): 1713. http://dx.doi.org/10.1016/j.brs.2021.10.411.

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Hripcsak, G., and C. Friedman. "Evaluating Natural Language Processors in the Clinical Domain." Methods of Information in Medicine 37, no. 04/05 (1998): 334–44. http://dx.doi.org/10.1055/s-0038-1634566.

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AbstractEvaluating natural language processing (NLP) systems in the clinical domain is a difficult task which is important for advancement of the field. A number of NLP systems have been reported that extract information from free-text clinical reports, but not many of the systems have been evaluated. Those that were evaluated noted good performance measures but the results were often weakened by ineffective evaluation methods. In this paper we describe a set of criteria aimed at improving the quality of NLP evaluation studies. We present an overview of NLP evaluations in the clinical domain a

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Joffe, Erel, Emily J. Pettigrew, Jorge R. Herskovic, Charles F. Bearden, and Elmer V. Bernstam. "Expert guided natural language processing using one-class classification." Journal of the American Medical Informatics Association 22, no. 5 (2015): 962–66. http://dx.doi.org/10.1093/jamia/ocv010.

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Abstract Introduction Automatically identifying specific phenotypes in free-text clinical notes is critically important for the reuse of clinical data. In this study, the authors combine expert-guided feature (text) selection with one-class classification for text processing. Objectives To compare the performance of one-class classification to traditional binary classification; to evaluate the utility of feature selection based on expert-selected salient text (snippets); and to determine the robustness of these models with respects to irrelevant surrounding text. Methods The authors trained on

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Koonce, Taneya Y., Dario A. Giuse, Annette M. Williams, et al. "Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition." JMIR Medical Informatics 12 (January 30, 2024): e53516. http://dx.doi.org/10.2196/53516.

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Implementing artificial intelligence to extract insights from large, real-world clinical data sets can supplement and enhance knowledge management efforts for health sciences research and clinical care. At Vanderbilt University Medical Center (VUMC), the in-house developed Word Cloud natural language processing system extracts coded concepts from patient records in VUMC’s electronic health record repository using the Unified Medical Language System terminology. Through this process, the Word Cloud extracts the most prominent concepts found in the clinical documentation of a specific patient or

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Amer, Eslam. "Enhancing Disability Determination Decision Process Through Natural Language Processing." International Journal of Applied Research on Public Health Management 4, no. 2 (2019): 15–28. http://dx.doi.org/10.4018/ijarphm.2019070102.

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In this article, a new approach is introduced that makes use of the valuable information that can be extracted from a patient's electronic healthcare records (EHRs). The approach employs natural language processing and biomedical text mining to handle patient's data. The developed approach extracts relevant medical entities and builds relations between symptoms and other clinical signature modifiers. The extracted features are viewed as evaluation features. The approach utilizes such evaluation features to decide whether an applicant could gain disability benefits or not. Evaluations showed th

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Zweigenbaum, P., and A. Névéol. "Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest." Yearbook of Medical Informatics 25, no. 01 (2016): 234–39. http://dx.doi.org/10.15265/iy-2016-049.

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Summary Objective: To summarize recent research and present a selection of the best papers published in 2015 in the field of clinical Natural Language Processing (NLP). Method: A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Section editors first selected a shortlist of candidate best papers that were then peer-reviewed by independent external reviewers. Results: The clinical NLP best paper selection shows tha

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Dorda, W., B. Haidl, and P. Sachs. "Processing Medical Natural Language Data by the System WAREL." Methods of Information in Medicine 27, no. 02 (1988): 67–72. http://dx.doi.org/10.1055/s-0038-1635521.

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SummaryMany clinical data are in natural language form (diagnoses, therapies, etc.). There is great interest in making these data retrievable to form samples of patients for scientific investigations (statistical analyses, courses of diseases, etc.). To perform this task, “medical natural language data” have to be prepared and stored in a retrieval-oriented database. In this paper, the advantages of processing textual data are shown in contrast to coding. Accordingly, in our system WAREL medical thesauri (like ICD 9 or SNOMED) are not used for codification; they are taken as a knowledge base d

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Soysal, Ergin, Jingqi Wang, Min Jiang, et al. "CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines." Journal of the American Medical Informatics Association 25, no. 3 (2017): 331–36. http://dx.doi.org/10.1093/jamia/ocx132.

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Abstract Existing general clinical natural language processing (NLP) systems such as MetaMap and Clinical Text Analysis and Knowledge Extraction System have been successfully applied to information extraction from clinical text. However, end users often have to customize existing systems for their individual tasks, which can require substantial NLP skills. Here we present CLAMP (Clinical Language Annotation, Modeling, and Processing), a newly developed clinical NLP toolkit that provides not only state-of-the-art NLP components, but also a user-friendly graphic user interface that can help user

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Bitterman, Danielle S., Timothy A. Miller, Raymond H. Mak, and Guergana K. Savova. "Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer." International Journal of Radiation Oncology*Biology*Physics 110, no. 3 (2021): 641–55. http://dx.doi.org/10.1016/j.ijrobp.2021.01.044.

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Kormilitzin, Andrey, Nemanja Vaci, Qiang Liu, and Alejo Nevado-Holgado. "Med7: A transferable clinical natural language processing model for electronic health records." Artificial Intelligence in Medicine 118 (August 2021): 102086. http://dx.doi.org/10.1016/j.artmed.2021.102086.

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Eguia, Hans, Carlos Luis Sánchez-Bocanegra, Franco Vinciarelli, Fernando Alvarez-Lopez, and Francesc Saigí-Rubió. "Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review." Journal of Medical Internet Research 26 (September 30, 2024): e55315. http://dx.doi.org/10.2196/55315.

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Background Ensuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a more streamlined approach. Objective This systematic review aims to assess the current state of artificial intelligence (AI) and natural language processing (NLP) techniques in health care to identify their potential use in electronic health records and automated information searches. Methods A search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online d

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Feller, Daniel J., Jason Zucker, Michael T. Yin, Peter Gordon, and Noémie Elhadad. "Using Clinical Notes and Natural Language Processing for Automated HIV Risk Assessment." JAIDS Journal of Acquired Immune Deficiency Syndromes 77, no. 2 (2018): 160–66. http://dx.doi.org/10.1097/qai.0000000000001580.

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