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AlShehri Y, Sidhu A, Lakshmanan LVS, Lefaivre KA. Applications of Natural Language Processing for Automated Clinical Data Analysis in Orthopaedics. J Am Acad Orthop Surg 2024; 32:439-446. [PMID: 38626429 DOI: 10.5435/jaaos-d-23-00839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/20/2024] [Indexed: 04/18/2024] Open
Abstract
Natural language processing is an exciting and emerging field in health care that can transform the field of orthopaedics. It can aid in the process of automated clinical data analysis, changing the way we extract data for various purposes including research and registry formation, diagnosis, and medical billing. This scoping review will look at the various applications of NLP in orthopaedics. Specific examples of NLP applications include identification of essential data elements from surgical and imaging reports, patient feedback analysis, and use of AI conversational agents for patient engagement. We will demonstrate how NLP has proven itself to be a powerful and valuable tool. Despite these potential advantages, there are drawbacks we must consider. Concerns with data quality, bias, privacy, and accessibility may stand as barriers in the way of widespread implementation of NLP technology. As natural language processing technology continues to develop, it has the potential to revolutionize orthopaedic research and clinical practices and enhance patient outcomes.
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Affiliation(s)
- Yasir AlShehri
- From the Department of Orthopedics, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia (AlShehri), the Department of Orthopaedics, Faculty of Medicine, The University of British Columbia, Vancouver, BC, Canada (Sidhu and Lefaivre), and the Department of Computer Science, The University of British Columbia, Vancouver, BC, Canada (Lakshmanan)
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Bhatnagar A, Kekatpure AL, Velagala VR, Kekatpure A. A Review on the Use of Artificial Intelligence in Fracture Detection. Cureus 2024; 16:e58364. [PMID: 38756254 PMCID: PMC11097122 DOI: 10.7759/cureus.58364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Artificial intelligence (AI) simulates intelligent behavior using computers with minimum human intervention. Recent advances in AI, especially deep learning, have made significant progress in perceptual operations, enabling computers to convey and comprehend complicated input more accurately. Worldwide, fractures affect people of all ages and in all regions of the planet. One of the most prevalent causes of inaccurate diagnosis and medical lawsuits is overlooked fractures on radiographs taken in the emergency room, which can range from 2% to 9%. The workforce will soon be under a great deal of strain due to the growing demand for fracture detection on multiple imaging modalities. A dearth of radiologists worsens this rise in demand as a result of a delay in hiring and a significant percentage of radiologists close to retirement. Additionally, the process of interpreting diagnostic images can sometimes be challenging and tedious. Integrating orthopedic radio-diagnosis with AI presents a promising solution to these problems. There has recently been a noticeable rise in the application of deep learning techniques, namely convolutional neural networks (CNNs), in medical imaging. In the field of orthopedic trauma, CNNs are being documented to operate at the proficiency of expert orthopedic surgeons and radiologists in the identification and categorization of fractures. CNNs can analyze vast amounts of data at a rate that surpasses that of human observations. In this review, we discuss the use of deep learning methods in fracture detection and classification, the integration of AI with various imaging modalities, and the benefits and disadvantages of integrating AI with radio-diagnostics.
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Affiliation(s)
- Aayushi Bhatnagar
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aditya L Kekatpure
- Orthopedic Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Vivek R Velagala
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aashay Kekatpure
- Orthopedic Surgery, Narendra Kumar Prasadrao Salve Institute of Medical Sciences and Research, Nagpur, IND
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Bazoge A, Morin E, Daille B, Gourraud PA. Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review. JMIR Med Inform 2023; 11:e42477. [PMID: 38100200 PMCID: PMC10757232 DOI: 10.2196/42477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 01/16/2023] [Accepted: 09/07/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. OBJECTIVE The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. METHODS This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. RESULTS We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). CONCLUSIONS CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.
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Affiliation(s)
- Adrien Bazoge
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
- Nantes Université, CHU de Nantes, Pôle Hospitalo-Universitaire 11: Santé Publique, Clinique des données, INSERM, CIC 1413, F-44000 Nantes, France
| | - Emmanuel Morin
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
| | - Béatrice Daille
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
| | - Pierre-Antoine Gourraud
- Nantes Université, CHU de Nantes, Pôle Hospitalo-Universitaire 11: Santé Publique, Clinique des données, INSERM, CIC 1413, F-44000 Nantes, France
- Nantes Université, INSERM, CHU de Nantes, École Centrale Nantes, Centre de Recherche Translationnelle en Transplantation et Immunologie, CR2TI, F-44000 Nantes, France
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Wang M, Seibel MJ. Approach to the Patient With Bone Fracture: Making the First Fracture the Last. J Clin Endocrinol Metab 2023; 108:3345-3352. [PMID: 37290052 PMCID: PMC10655538 DOI: 10.1210/clinem/dgad345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/02/2023] [Accepted: 06/05/2023] [Indexed: 06/10/2023]
Abstract
The global burden of osteoporosis and osteoporotic fractures will increase significantly as we enter a rapidly aging population. Osteoporotic fractures lead to increased morbidity, mortality, and risk of subsequent fractures if left untreated. However, studies have shown that the majority of patients who suffer an osteoporotic fracture are not investigated or treated for osteoporosis, leading to an inexcusable "osteoporosis care gap." Systematic and coordinated models of care in secondary fracture prevention known as fracture liaison services (FLS) have been established to streamline and improve the care of patients with osteoporotic fractures, and employ core principles of identification, investigation, and initiation of treatment. Our approach to the multifaceted care of secondary fracture prevention at a hospital-based FLS is illustrated through several case vignettes.
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Affiliation(s)
- Mawson Wang
- The University of Sydney, Bone Research Program, ANZAC Research Institute, Concord, NSW 2139, Australia
| | - Markus J Seibel
- The University of Sydney, Bone Research Program, ANZAC Research Institute, Concord, NSW 2139, Australia
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Russe MF, Fink A, Ngo H, Tran H, Bamberg F, Reisert M, Rau A. Performance of ChatGPT, human radiologists, and context-aware ChatGPT in identifying AO codes from radiology reports. Sci Rep 2023; 13:14215. [PMID: 37648742 PMCID: PMC10468502 DOI: 10.1038/s41598-023-41512-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/28/2023] [Indexed: 09/01/2023] Open
Abstract
While radiologists can describe a fracture's morphology and complexity with ease, the translation into classification systems such as the Arbeitsgemeinschaft Osteosynthesefragen (AO) Fracture and Dislocation Classification Compendium is more challenging. We tested the performance of generic chatbots and chatbots aware of specific knowledge of the AO classification provided by a vector-index and compared it to human readers. In the 100 radiological reports we created based on random AO codes, chatbots provided AO codes significantly faster than humans (mean 3.2 s per case vs. 50 s per case, p < .001) though not reaching human performance (max. chatbot performance of 86% correct full AO codes vs. 95% in human readers). In general, chatbots based on GPT 4 outperformed the ones based on GPT 3.5-Turbo. Further, we found that providing specific knowledge substantially enhances the chatbot's performance and consistency as the context-aware chatbot based on GPT 4 provided 71% consistent correct full AO codes for the compared to the 2% consistent correct full AO codes for the generic ChatGPT 4. This provides evidence, that refining and providing specific context to ChatGPT will be the next essential step in harnessing its power.
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Affiliation(s)
- Maximilian F Russe
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany.
| | - Anna Fink
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany
| | - Helen Ngo
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany
| | - Hien Tran
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany
| | - Marco Reisert
- Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Alexander Rau
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany
- Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Tan WM, Ng WL, Ganggayah MD, Hoe VCW, Rahmat K, Zaini HS, Mohd Taib NA, Dhillon SK. Natural language processing in narrative breast radiology reporting in University Malaya Medical Centre. Health Informatics J 2023; 29:14604582231203763. [PMID: 37740904 DOI: 10.1177/14604582231203763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/25/2023]
Abstract
Radiology reporting is narrative, and its content depends on the clinician's ability to interpret the images accurately. A tertiary hospital, such as anonymous institute, focuses on writing reports narratively as part of training for medical personnel. Nevertheless, free-text reports make it inconvenient to extract information for clinical audits and data mining. Therefore, we aim to convert unstructured breast radiology reports into structured formats using natural language processing (NLP) algorithm. This study used 327 de-identified breast radiology reports from the anonymous institute. The radiologist identified the significant data elements to be extracted. Our NLP algorithm achieved 97% and 94.9% accuracy in training and testing data, respectively. Henceforth, the structured information was used to build the predictive model for predicting the value of the BIRADS category. The model based on random forest generated the highest accuracy of 92%. Our study not only fulfilled the demands of clinicians by enhancing communication between medical personnel, but it also demonstrated the usefulness of mineable structured data in yielding significant insights.
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Affiliation(s)
- Wee Ming Tan
- Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Wei Lin Ng
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Mogana Darshini Ganggayah
- Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Victor Chee Wai Hoe
- Department of Social and Preventive Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Kartini Rahmat
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Hana Salwani Zaini
- Department of Information Technology, University of Malaya Medical Centre, Kuala Lumpur, Malaysia
| | - Nur Aishah Mohd Taib
- Department of Surgery, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Sarinder Kaur Dhillon
- Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
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Galbusera F, Cina A, Bassani T, Panico M, Sconfienza LM. Automatic Diagnosis of Spinal Disorders on Radiographic Images: Leveraging Existing Unstructured Datasets With Natural Language Processing. Global Spine J 2023; 13:1257-1266. [PMID: 34219477 PMCID: PMC10416592 DOI: 10.1177/21925682211026910] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
STUDY DESIGN Retrospective study. OBJECTIVES Huge amounts of images and medical reports are being generated in radiology departments. While these datasets can potentially be employed to train artificial intelligence tools to detect findings on radiological images, the unstructured nature of the reports limits the accessibility of information. In this study, we tested if natural language processing (NLP) can be useful to generate training data for deep learning models analyzing planar radiographs of the lumbar spine. METHODS NLP classifiers based on the Bidirectional Encoder Representations from Transformers (BERT) model able to extract structured information from radiological reports were developed and used to generate annotations for a large set of radiographic images of the lumbar spine (N = 10 287). Deep learning (ResNet-18) models aimed at detecting radiological findings directly from the images were then trained and tested on a set of 204 human-annotated images. RESULTS The NLP models had accuracies between 0.88 and 0.98 and specificities between 0.84 and 0.99; 7 out of 12 radiological findings had sensitivity >0.90. The ResNet-18 models showed performances dependent on the specific radiological findings with sensitivities and specificities between 0.53 and 0.93. CONCLUSIONS NLP generates valuable data to train deep learning models able to detect radiological findings in spine images. Despite the noisy nature of reports and NLP predictions, this approach effectively mitigates the difficulties associated with the manual annotation of large quantities of data and opens the way to the era of big data for artificial intelligence in musculoskeletal radiology.
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Affiliation(s)
| | - Andrea Cina
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Tito Bassani
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Matteo Panico
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Chemistry, Materials and Chemical Engineering “Giulio Natta,” Politecnico di Milano, Milan, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
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Zhang Y, Grant BMM, Hope AJ, Hung RJ, Warkentin MT, Lam ACL, Aggawal R, Xu M, Shepherd FA, Tsao MS, Xu W, Pakkal M, Liu G, McInnis MC. Using Recurrent Neural Networks to Extract High-Quality Information From Lung Cancer Screening Computerized Tomography Reports for Inter-Radiologist Audit and Feedback Quality Improvement. JCO Clin Cancer Inform 2023; 7:e2200153. [PMID: 36930839 DOI: 10.1200/cci.22.00153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023] Open
Abstract
PURPOSE Lung cancer screening programs generate a high volume of low-dose computed tomography (LDCT) reports that contain valuable information, typically in a free-text format. High-performance named-entity recognition (NER) models can extract relevant information from these reports automatically for inter-radiologist quality control. METHODS Using LDCT report data from a longitudinal lung cancer screening program (8,305 reports; 3,124 participants; 2006-2019), we trained a rule-based model and two bidirectional long short-term memory (Bi-LSTM) NER neural network models to detect clinically relevant information from LDCT reports. Model performance was tested using F1 scores and compared with a published open-source radiology NER model (Stanza) in an independent evaluation set of 150 reports. The top performing model was applied to a data set of 6,948 reports for an inter-radiologist quality control assessment. RESULTS The best performing model, a Bi-LSTM NER recurrent neural network model, had an overall F1 score of 0.950, which outperformed Stanza (F1 score = 0.872) and a rule-based NER model (F1 score = 0.809). Recall (sensitivity) for the best Bi-LSTM model ranged from 0.916 to 0.991 for different entity types; precision (positive predictive value) ranged from 0.892 to 0.997. Test performance remained stable across time periods. There was an average of a 2.86-fold difference in the number of identified entities between the most and the least detailed radiologists. CONCLUSION We built an open-source Bi-LSTM NER model that outperformed other open-source or rule-based radiology NER models. This model can efficiently extract clinically relevant information from lung cancer screening computerized tomography reports with high accuracy, enabling efficient audit and feedback to improve quality of patient care.
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Affiliation(s)
- Yucheng Zhang
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Benjamin M M Grant
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Andrew J Hope
- Radiation Medicine Program, Princess Margaret Cancer Centre, and Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Rayjean J Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health Systems, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Matthew T Warkentin
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health Systems, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Andrew C L Lam
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Reenika Aggawal
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Maria Xu
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Frances A Shepherd
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Ming-Sound Tsao
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Laboratory Medicine and Pathology, University Health Network, Toronto, ON, Canada
| | - Wei Xu
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Computational Biology and Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Mini Pakkal
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Division of Cardiothoracic Imaging, Joint Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada
| | - Geoffrey Liu
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Micheal C McInnis
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Division of Cardiothoracic Imaging, Joint Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada
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Gaviria-Valencia S, Murphy SP, Kaggal VC, McBane Ii RD, Rooke TW, Chaudhry R, Alzate-Aguirre M, Arruda-Olson AM. Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study. JMIR Med Inform 2023; 11:e40964. [PMID: 36826984 PMCID: PMC10007015 DOI: 10.2196/40964] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 12/29/2022] [Accepted: 01/19/2023] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Management of abdominal aortic aneurysms (AAAs) requires serial imaging surveillance to evaluate the aneurysm dimension. Natural language processing (NLP) has been previously developed to retrospectively identify patients with AAA from electronic health records (EHRs). However, there are no reported studies that use NLP to identify patients with AAA in near real-time from radiology reports. OBJECTIVE This study aims to develop and validate a rule-based NLP algorithm for near real-time automatic extraction of AAA diagnosis from radiology reports for case identification. METHODS The AAA-NLP algorithm was developed and deployed to an EHR big data infrastructure for near real-time processing of radiology reports from May 1, 2019, to September 2020. NLP extracted named entities for AAA case identification and classified subjects as cases and controls. The reference standard to assess algorithm performance was a manual review of processed radiology reports by trained physicians following standardized criteria. Reviewers were blinded to the diagnosis of each subject. The AAA-NLP algorithm was refined in 3 successive iterations. For each iteration, the AAA-NLP algorithm was modified based on performance compared to the reference standard. RESULTS A total of 360 reports were reviewed, of which 120 radiology reports were randomly selected for each iteration. At each iteration, the AAA-NLP algorithm performance improved. The algorithm identified AAA cases in near real-time with high positive predictive value (0.98), sensitivity (0.95), specificity (0.98), F1 score (0.97), and accuracy (0.97). CONCLUSIONS Implementation of NLP for accurate identification of AAA cases from radiology reports with high performance in near real time is feasible. This NLP technique will support automated input for patient care and clinical decision support tools for the management of patients with AAA. .
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Affiliation(s)
- Simon Gaviria-Valencia
- Divisions of Preventive Cardiology and Cardiovascular Ultrasound, Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Sean P Murphy
- Advanced Analytics Services Unit (Natural Language Processing), Department of Information Technology, Mayo Clinic, Rochester, MN, United States
| | - Vinod C Kaggal
- Enterprise Technology Services (Natural Language Processing), Department of Information Technology, Mayo Clinic, Rochester, MN, United States
| | - Robert D McBane Ii
- Gonda Vascular Center, Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Thom W Rooke
- Gonda Vascular Center, Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Rajeev Chaudhry
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States
| | - Mateo Alzate-Aguirre
- Divisions of Preventive Cardiology and Cardiovascular Ultrasound, Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Adelaide M Arruda-Olson
- Divisions of Preventive Cardiology and Cardiovascular Ultrasound, Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
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Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model. J Digit Imaging 2023; 36:91-104. [PMID: 36253581 PMCID: PMC9576130 DOI: 10.1007/s10278-022-00717-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 08/31/2022] [Accepted: 09/30/2022] [Indexed: 11/16/2022] Open
Abstract
Radiology reports contain a diverse and rich set of clinical abnormalities documented by radiologists during their interpretation of the images. Comprehensive semantic representations of radiological findings would enable a wide range of secondary use applications to support diagnosis, triage, outcomes prediction, and clinical research. In this paper, we present a new corpus of radiology reports annotated with clinical findings. Our annotation schema captures detailed representations of pathologic findings that are observable on imaging ("lesions") and other types of clinical problems ("medical problems"). The schema used an event-based representation to capture fine-grained details, including assertion, anatomy, characteristics, size, and count. Our gold standard corpus contained a total of 500 annotated computed tomography (CT) reports. We extracted triggers and argument entities using two state-of-the-art deep learning architectures, including BERT. We then predicted the linkages between trigger and argument entities (referred to as argument roles) using a BERT-based relation extraction model. We achieved the best extraction performance using a BERT model pre-trained on 3 million radiology reports from our institution: 90.9-93.4% F1 for finding triggers and 72.0-85.6% F1 for argument roles. To assess model generalizability, we used an external validation set randomly sampled from the MIMIC Chest X-ray (MIMIC-CXR) database. The extraction performance on this validation set was 95.6% for finding triggers and 79.1-89.7% for argument roles, demonstrating that the model generalized well to the cross-institutional data with a different imaging modality. We extracted the finding events from all the radiology reports in the MIMIC-CXR database and provided the extractions to the research community.
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IKAR: An Interdisciplinary Knowledge-Based Automatic Retrieval Method from Chinese Electronic Medical Record. INFORMATION 2023. [DOI: 10.3390/info14010049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
To date, information retrieval methods in the medical field have mainly focused on English medical reports, but little work has studied Chinese electronic medical reports, especially in the field of obstetrics and gynecology. In this paper, a dataset of 180,000 complete Chinese ultrasound reports in obstetrics and gynecology was established and made publicly available. Based on the ultrasound reports in the dataset, a new information retrieval method (IKAR) is proposed to extract key information from the ultrasound reports and automatically generate the corresponding ultrasound diagnostic results. The model can both extract what is already in the report and analyze what is not in the report by inference. After applying the IKAR method to the dataset, it is proved that the method could achieve 89.38% accuracy, 91.09% recall, and 90.23% F-score. Moreover, the method achieves an F-score of over 90% on 50% of the 10 components of the report. This study provides a quality dataset for the field of electronic medical records and offers a reference for information retrieval methods in the field of obstetrics and gynecology or in other fields.
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Natural Language Processing in Radiology: Update on Clinical Applications. J Am Coll Radiol 2022; 19:1271-1285. [PMID: 36029890 DOI: 10.1016/j.jacr.2022.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/25/2022] [Accepted: 06/03/2022] [Indexed: 11/24/2022]
Abstract
Radiological reports are a valuable source of information used to guide clinical care and support research. Organizing and managing this content, however, frequently requires several manual curations due to the more common unstructured nature of the reports. However, manual review of these reports for clinical knowledge extraction is costly and time-consuming. Natural language processing (NLP) is a set of methods developed to extract structured meaning from a body of text and can be used to optimize the workflow of health care professionals. Specifically, NLP methods can help radiologists as decision support systems and improve the management of patients' medical data. In this study, we highlight the opportunities offered by NLP in the field of radiology. A comprehensive review of the most commonly used NLP methods to extract information from radiological reports and the development of tools to improve radiological workflow using this information is presented. Finally, we review the important limitations of these tools and discuss the relevant observations and trends in the application of NLP to radiology that could benefit the field in the future.
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13
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Song G, Chung SJ, Seo JY, Yang SY, Jin EH, Chung GE, Shim SR, Sa S, Hong MS, Kim KH, Jang E, Lee CW, Bae JH, Han HW. Natural Language Processing for Information Extraction of Gastric Diseases and Its Application in Large-Scale Clinical Research. J Clin Med 2022; 11:jcm11112967. [PMID: 35683353 PMCID: PMC9181010 DOI: 10.3390/jcm11112967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/16/2022] [Accepted: 05/23/2022] [Indexed: 11/21/2022] Open
Abstract
Background and Aims: The utility of clinical information from esophagogastroduodenoscopy (EGD) reports has been limited because of its unstructured narrative format. We developed a natural language processing (NLP) pipeline that automatically extracts information about gastric diseases from unstructured EGD reports and demonstrated its applicability in clinical research. Methods: An NLP pipeline was developed using 2000 EGD and associated pathology reports that were retrieved from a single healthcare center. The pipeline extracted clinical information, including the presence, location, and size, for 10 gastric diseases from the EGD reports. It was validated with 1000 EGD reports by evaluating sensitivity, positive predictive value (PPV), accuracy, and F1 score. The pipeline was applied to 248,966 EGD reports from 2010–2019 to identify patient demographics and clinical information for 10 gastric diseases. Results: For gastritis information extraction, we achieved an overall sensitivity, PPV, accuracy, and F1 score of 0.966, 0.972, 0.996, and 0.967, respectively. Other gastric diseases, such as ulcers, and neoplastic diseases achieved an overall sensitivity, PPV, accuracy, and F1 score of 0.975, 0.982, 0.999, and 0.978, respectively. The study of EGD data of over 10 years revealed the demographics of patients with gastric diseases by sex and age. In addition, the study identified the extent and locations of gastritis and other gastric diseases, respectively. Conclusions: We demonstrated the feasibility of the NLP pipeline providing an automated extraction of gastric disease information from EGD reports. Incorporating the pipeline can facilitate large-scale clinical research to better understand gastric diseases.
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Affiliation(s)
- Gyuseon Song
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea; (G.S.); (S.R.S.); (S.S.); (M.S.H.); (K.H.K.); (E.J.); (C.W.L.)
- Institute for Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea
| | - Su Jin Chung
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, 39FL Gangnam Finance Center 152, Teheran-ro, Gangnam-gu, Seoul 06236, Korea; (S.J.C.); (J.Y.S.); (S.Y.Y.); (E.H.J.); (G.E.C.)
| | - Ji Yeon Seo
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, 39FL Gangnam Finance Center 152, Teheran-ro, Gangnam-gu, Seoul 06236, Korea; (S.J.C.); (J.Y.S.); (S.Y.Y.); (E.H.J.); (G.E.C.)
| | - Sun Young Yang
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, 39FL Gangnam Finance Center 152, Teheran-ro, Gangnam-gu, Seoul 06236, Korea; (S.J.C.); (J.Y.S.); (S.Y.Y.); (E.H.J.); (G.E.C.)
| | - Eun Hyo Jin
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, 39FL Gangnam Finance Center 152, Teheran-ro, Gangnam-gu, Seoul 06236, Korea; (S.J.C.); (J.Y.S.); (S.Y.Y.); (E.H.J.); (G.E.C.)
| | - Goh Eun Chung
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, 39FL Gangnam Finance Center 152, Teheran-ro, Gangnam-gu, Seoul 06236, Korea; (S.J.C.); (J.Y.S.); (S.Y.Y.); (E.H.J.); (G.E.C.)
| | - Sung Ryul Shim
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea; (G.S.); (S.R.S.); (S.S.); (M.S.H.); (K.H.K.); (E.J.); (C.W.L.)
- Institute for Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea
- Department of Health and Medical Informatics, Kyungnam University College of Health Sciences, Changwon 51767, Korea
| | - Soonok Sa
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea; (G.S.); (S.R.S.); (S.S.); (M.S.H.); (K.H.K.); (E.J.); (C.W.L.)
- Institute for Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea
| | - Moongi Simon Hong
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea; (G.S.); (S.R.S.); (S.S.); (M.S.H.); (K.H.K.); (E.J.); (C.W.L.)
- Institute for Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea
| | - Kang Hyun Kim
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea; (G.S.); (S.R.S.); (S.S.); (M.S.H.); (K.H.K.); (E.J.); (C.W.L.)
- Institute for Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea
| | - Eunchan Jang
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea; (G.S.); (S.R.S.); (S.S.); (M.S.H.); (K.H.K.); (E.J.); (C.W.L.)
- Institute for Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea
| | - Chae Won Lee
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea; (G.S.); (S.R.S.); (S.S.); (M.S.H.); (K.H.K.); (E.J.); (C.W.L.)
- Institute for Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea
| | - Jung Ho Bae
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea; (G.S.); (S.R.S.); (S.S.); (M.S.H.); (K.H.K.); (E.J.); (C.W.L.)
- Institute for Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, 39FL Gangnam Finance Center 152, Teheran-ro, Gangnam-gu, Seoul 06236, Korea; (S.J.C.); (J.Y.S.); (S.Y.Y.); (E.H.J.); (G.E.C.)
- Correspondence: (J.H.B.); (H.W.H.); Tel.: +82-2-2112-5574 (J.H.B.); +82-31-881-7109 (H.W.H.); Fax: +82-2-2112-5635 (J.H.B.); +82-31-881-7069 (H.W.H.)
| | - Hyun Wook Han
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea; (G.S.); (S.R.S.); (S.S.); (M.S.H.); (K.H.K.); (E.J.); (C.W.L.)
- Institute for Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea
- Correspondence: (J.H.B.); (H.W.H.); Tel.: +82-2-2112-5574 (J.H.B.); +82-31-881-7109 (H.W.H.); Fax: +82-2-2112-5635 (J.H.B.); +82-31-881-7069 (H.W.H.)
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14
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Blaker K, Wijewardene A, White E, Stokes G, Chong S, Ganda K, Ridley L, Brown S, White C, Clifton-Bligh R, Seibel MJ. Electronic search programs are effective in identifying patients with minimal trauma fractures. Osteoporos Int 2022; 33:435-441. [PMID: 34510231 DOI: 10.1007/s00198-021-06105-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 08/16/2021] [Indexed: 11/24/2022]
Abstract
UNLABELLED We assessed two electronic search tools that screen medical records for documented fractures. Both programs reliably identified patients with any fracture but missed individuals with minimal trauma fracture to different degrees. A hybrid tool combining the methodology of both tools is likely to improve the identification of those with osteoporosis. PURPOSE Most patients who suffer a minimal trauma fracture remain undiagnosed, placing them at high risk of refracture. Case finding can be improved by electronic search tools that screen medical records for documented fractures. Here, we assessed the efficacy of two new programs, AES and XRAIT, in identifying patients with minimal trauma fracture. METHODS Each tool was applied to search the electronic medical record and/or radiology reports at two tertiary hospitals in Sydney, Australia, from 1 July to 31 December 2018. Samples of the extracted reports were then manually reviewed to determine the sensitivity of each program in detecting minimal trauma fractures. RESULTS At the two centers, AES detected 872 and 1364 cases, whereas XRAIT identified 1414 and 2180 patients with fractures, respectively. The true positive rate for "any fracture" was similar for both instruments (77-88%). However, the ability to detect "minimal trauma fractures" differed between programs and centers (53-75% accuracy), with each tool identifying separate subsets of patients. Concordance between both tools was less than half of the combined total number of minimal trauma fractures (43-45%). Considering the total number of minimal trauma fractures detected by both tools combined, AES correctly identified 52-55% of cases while XRAIT identified 88-93% of cases. CONCLUSION Both programs reliably identified patients with any fracture but missed individuals with minimal trauma fracture to different degrees. Hybrid tools combining the methodology of XRAIT and AES are likely to improve the identification of patients who require investigation and treatment for osteoporosis.
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Affiliation(s)
- K Blaker
- Department of Endocrinology & Metabolism, Concord Repatriation General Hospital, Concord, NSW, 2139, Australia
| | - A Wijewardene
- Department of Endocrinology & Metabolism, Concord Repatriation General Hospital, Concord, NSW, 2139, Australia.
- Department of Endocrinology, Royal North Shore Hospital, St Leonards, NSW, 2065, Australia.
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia.
| | - E White
- Department of Endocrinology, Royal North Shore Hospital, St Leonards, NSW, 2065, Australia
| | - G Stokes
- Department of Endocrinology & Metabolism, Prince of Wales Hospital, Randwick, NSW, 2031, Australia
| | - S Chong
- Department of Endocrinology & Metabolism, Concord Repatriation General Hospital, Concord, NSW, 2139, Australia
| | - K Ganda
- Department of Endocrinology & Metabolism, Concord Repatriation General Hospital, Concord, NSW, 2139, Australia
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia
- Bone Research Program, ANZAC Research Institute, Concord, NSW, 2139, Australia
| | - L Ridley
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia
- Department of Radiology, Concord Repatriation General Hospital, Concord, NSW, 2139, Australia
| | - S Brown
- Abbot Diagnostics, Macquarie Park, NSW, 2113, Australia
| | - C White
- Department of Endocrinology & Metabolism, Prince of Wales Hospital, Randwick, NSW, 2031, Australia
| | - R Clifton-Bligh
- Department of Endocrinology, Royal North Shore Hospital, St Leonards, NSW, 2065, Australia
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia
| | - M J Seibel
- Department of Endocrinology & Metabolism, Concord Repatriation General Hospital, Concord, NSW, 2139, Australia
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia
- Bone Research Program, ANZAC Research Institute, Concord, NSW, 2139, Australia
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15
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Jungmann F, Kämpgen B, Hahn F, Wagner D, Mildenberger P, Düber C, Kloeckner R. Natural language processing of radiology reports to investigate the effects of the COVID-19 pandemic on the incidence and age distribution of fractures. Skeletal Radiol 2022; 51:375-380. [PMID: 33851252 PMCID: PMC8043440 DOI: 10.1007/s00256-021-03760-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 03/15/2021] [Accepted: 03/16/2021] [Indexed: 02/02/2023]
Abstract
OBJECTIVE During the COVID-19 pandemic, the number of patients presenting in hospitals because of emergency conditions decreased. Radiology is thus confronted with the effects of the pandemic. The aim of this study was to use natural language processing (NLP) to automatically analyze the number and distribution of fractures during the pandemic and in the 5 years before the pandemic. MATERIALS AND METHODS We used a pre-trained commercially available NLP engine to automatically categorize 5397 radiological reports of radiographs (hand/wrist, elbow, shoulder, ankle, knee, pelvis/hip) within a 6-week period from March to April in 2015-2020 into "fracture affirmed" or "fracture not affirmed." The NLP engine achieved an F1 score of 0.81 compared to human annotators. RESULTS In 2020, we found a significant decrease of fractures in general (p < 0.001); the average number of fractures in 2015-2019 was 295, whereas it was 233 in 2020. In children and adolescents (p < 0.001), and in adults up to 65 years (p = 0.006), significantly fewer fractures were reported in 2020. The number of fractures in the elderly did not change (p = 0.15). The number of hand/wrist fractures (p < 0.001) and fractures of the elbow (p < 0.001) was significantly lower in 2020 compared with the average in the years 2015-2019. CONCLUSION NLP can be used to identify relevant changes in the number of pathologies as shown here for the use case fracture detection. This may trigger root cause analysis and enable automated real-time monitoring in radiology.
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Affiliation(s)
- Florian Jungmann
- grid.410607.4Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckst. 1, 55131 Mainz, Germany
| | - B. Kämpgen
- grid.424427.3Empolis Information Management, Kaiserslautern, Germany
| | - F. Hahn
- grid.410607.4Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckst. 1, 55131 Mainz, Germany
| | - D. Wagner
- grid.410607.4Department of Orthopedics and Traumatology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - P. Mildenberger
- grid.410607.4Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckst. 1, 55131 Mainz, Germany
| | - C. Düber
- grid.410607.4Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckst. 1, 55131 Mainz, Germany
| | - R. Kloeckner
- grid.410607.4Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckst. 1, 55131 Mainz, Germany
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16
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Mozayan A, Fabbri AR, Maneevese M, Tocino I, Chheang S. Practical Guide to Natural Language Processing for Radiology. Radiographics 2021; 41:1446-1453. [PMID: 34469212 DOI: 10.1148/rg.2021200113] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Natural language processing (NLP) is the subset of artificial intelligence focused on the computer interpretation of human language. It is an invaluable tool in the analysis, aggregation, and simplification of free text. It has already demonstrated significant potential in the analysis of radiology reports. There are abundant open-source libraries and tools available that facilitate its application to the benefit of radiology. Radiologists who understand its limitations and potential will be better positioned to evaluate NLP models, understand how they can improve clinical workflow, and facilitate research endeavors involving large amounts of human language. The advent of increasingly affordable and powerful computer processing, the large quantities of medical and radiologic data, and advances in machine learning algorithms have contributed to the large potential of NLP. In turn, radiology has significant potential to benefit from the ability of NLP to convert relatively standardized radiology reports to machine-readable data. NLP benefits from standardized reporting, but because of its ability to interpret free text by using context clues, NLP does not necessarily depend on it. An overview and practical approach to NLP is featured, with specific emphasis on its applications to radiology. A brief history of NLP, the strengths and challenges inherent to its use, and freely available resources and tools are covered to guide further exploration and study within the field. Particular attention is devoted to the recent development of the Word2Vec and BERT (Bidirectional Encoder Representations from Transformers) language models, which have exponentially increased the power and utility of NLP for a variety of applications. Online supplemental material is available for this article. ©RSNA, 2021.
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Affiliation(s)
- Ali Mozayan
- From the Department of Radiology and Biomedical Imaging, Yale School of Medicine, PO Box 208042, Tompkins East 2, New Haven, CT 06520 (A.M., M.M., I.T., S.C.); and Department of Computer Science, Yale University, New Haven, Conn (A.R.F.)
| | - Alexander R Fabbri
- From the Department of Radiology and Biomedical Imaging, Yale School of Medicine, PO Box 208042, Tompkins East 2, New Haven, CT 06520 (A.M., M.M., I.T., S.C.); and Department of Computer Science, Yale University, New Haven, Conn (A.R.F.)
| | - Michelle Maneevese
- From the Department of Radiology and Biomedical Imaging, Yale School of Medicine, PO Box 208042, Tompkins East 2, New Haven, CT 06520 (A.M., M.M., I.T., S.C.); and Department of Computer Science, Yale University, New Haven, Conn (A.R.F.)
| | - Irena Tocino
- From the Department of Radiology and Biomedical Imaging, Yale School of Medicine, PO Box 208042, Tompkins East 2, New Haven, CT 06520 (A.M., M.M., I.T., S.C.); and Department of Computer Science, Yale University, New Haven, Conn (A.R.F.)
| | - Sophie Chheang
- From the Department of Radiology and Biomedical Imaging, Yale School of Medicine, PO Box 208042, Tompkins East 2, New Haven, CT 06520 (A.M., M.M., I.T., S.C.); and Department of Computer Science, Yale University, New Haven, Conn (A.R.F.)
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17
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Olthof AW, Shouche P, Fennema EM, IJpma FFA, Koolstra RHC, Stirler VMA, van Ooijen PMA, Cornelissen LJ. Machine learning based natural language processing of radiology reports in orthopaedic trauma. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106304. [PMID: 34333208 DOI: 10.1016/j.cmpb.2021.106304] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 07/18/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVES To compare different Machine Learning (ML) Natural Language Processing (NLP) methods to classify radiology reports in orthopaedic trauma for the presence of injuries. Assessing NLP performance is a prerequisite for downstream tasks and therefore of importance from a clinical perspective (avoiding missed injuries, quality check, insight in diagnostic yield) as well as from a research perspective (identification of patient cohorts, annotation of radiographs). METHODS Datasets of Dutch radiology reports of injured extremities (n = 2469, 33% fractures) and chest radiographs (n = 799, 20% pneumothorax) were collected in two different hospitals and labeled by radiologists and trauma surgeons for the presence or absence of injuries. NLP classification was applied and optimized by testing different preprocessing steps and different classifiers (Rule-based, ML, and Bidirectional Encoder Representations from Transformers (BERT)). Performance was assessed by F1-score, AUC, sensitivity, specificity and accuracy. RESULTS The deep learning based BERT model outperforms all other classification methods which were assessed. The model achieved an F1-score of (95 ± 2)% and accuracy of (96 ± 1)% on a dataset of simple reports (n= 2469), and an F1 of (83 ± 7)% with accuracy (93 ± 2)% on a dataset of complex reports (n= 799). CONCLUSION BERT NLP outperforms traditional ML and rule-base classifiers when applied to Dutch radiology reports in orthopaedic trauma.
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Affiliation(s)
- A W Olthof
- Department of Radiology, Treant Health Care Group, Dr. G.H. Amshoffweg 1, Hoogeveen, the Netherlands; Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, the Netherlands.
| | - P Shouche
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, the Netherlands
| | - E M Fennema
- Department of Trauma Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, the Netherlands
| | - F F A IJpma
- Department of Trauma Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, the Netherlands
| | - R H C Koolstra
- Department of Radiology, Treant Health Care Group, Dr. G.H. Amshoffweg 1, Hoogeveen, the Netherlands
| | - V M A Stirler
- Department of Trauma Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, the Netherlands
| | - P M A van Ooijen
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, the Netherlands; Machine Learning Lab, Data Science Center in Health (DASH),University Medical Center Groningen, University of Groningen, L.J. Zielstraweg 2, Groningen, the Netherlands
| | - L J Cornelissen
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, the Netherlands; COSMONiO Imaging BV, L.J. Zielstraweg 2, Groningen, the Netherlands
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18
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Abstract
Natural language processing (NLP) is an interdisciplinary field, combining linguistics, computer science, and artificial intelligence to enable machines to read and understand human language for meaningful purposes. Recent advancements in deep learning have begun to offer significant improvements in NLP task performance. These techniques have the potential to create new automated tools that could improve clinical workflows and unlock unstructured textual information contained in radiology and clinical reports for the development of radiology and clinical artificial intelligence applications. These applications will combine the appropriate application of classic linguistic and NLP preprocessing techniques, modern NLP techniques, and modern deep learning techniques.
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Affiliation(s)
- Jack W Luo
- Department of Radiology, McGill University, 1001 Decarie Boulevard, Room B02.9375, Montreal, QC H4A 3J1, Canada
| | - Jaron J R Chong
- Department of Medical Imaging, Western University, 800 Commissioners Road East, Room C1-609, London, ON N6A 5W9, Canada.
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19
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Kolanu N, Brown AS, Beech A, Center JR, White CP. Natural language processing of radiology reports for the identification of patients with fracture. Arch Osteoporos 2021; 16:6. [PMID: 33403479 DOI: 10.1007/s11657-020-00859-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 11/13/2020] [Indexed: 02/03/2023]
Abstract
UNLABELLED Text-search software can be used to identify people at risk of re-fracture. The software studied identified a threefold higher number of people with fractures compared with conventional case finding. Automated software could assist fracture liaison services to identify more people at risk than traditional case finding. PURPOSE Fracture liaison services address the post-fracture treatment gap in osteoporosis (OP). Natural language processing (NLP) is able to identify previously unrecognized patients by screening large volumes of radiology reports. The aim of this study was to compare an NLP software tool, XRAIT (X-Ray Artificial Intelligence Tool), with a traditional fracture liaison service at its development site (Prince of Wales Hospital [POWH], Sydney) and externally validate it in an adjudicated cohort from the Dubbo Osteoporosis Epidemiology Study (DOES). METHODS XRAIT searches radiology reports for fracture-related terms. At the development site (POWH), XRAIT and a blinded fracture liaison clinician (FLC) reviewed 5,089 reports and 224 presentations, respectively, of people 50 years or over during a simultaneous 3-month period. In the external cohort of DOES, XRAIT was used without modification to analyse digitally readable radiology reports (n = 327) to calculate its sensitivity and specificity. RESULTS XRAIT flagged 433 fractures after searching 5,089 reports (421 true fractures, positive predictive value of 97%). It identified more than a threefold higher number of fractures (421 fractures/339 individuals) compared with manual case finding (98 individuals). Unadjusted for the local reporting style in an external cohort (DOES), XRAIT had a sensitivity of 70% and specificity of 92%. CONCLUSION XRAIT identifies significantly more clinically significant fractures than manual case finding. High specificity in an untrained cohort suggests that it could be used at other sites. Automated methods of fracture identification may assist fracture liaison services so that limited resources can be spent on treatment rather than case finding.
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Affiliation(s)
- Nithin Kolanu
- Clinical Epidemiology/Healthy Ageing Division, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia. .,Prince of Wales Hospital, Randwick, Sydney, NSW, Australia.
| | - A Shane Brown
- Royal Hospital for Women, Randwick, Sydney, NSW, Australia
| | - Amanda Beech
- Prince of Wales Hospital, Randwick, Sydney, NSW, Australia.,Royal Hospital for Women, Randwick, Sydney, NSW, Australia.,University of New South Wales, Sydney, NSW, Australia
| | - Jacqueline R Center
- Clinical Epidemiology/Healthy Ageing Division, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia.,University of New South Wales, Sydney, NSW, Australia.,St Vincent's Hospital Clinical School, Darlinghurst, NSW, Australia
| | - Christopher P White
- Clinical Epidemiology/Healthy Ageing Division, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia.,Prince of Wales Hospital, Randwick, Sydney, NSW, Australia.,Royal Hospital for Women, Randwick, Sydney, NSW, Australia.,University of New South Wales, Sydney, NSW, Australia
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20
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Vydiswaran VGV, Zhang Y, Wang Y, Xu H. Special issue of BMC medical informatics and decision making on health natural language processing. BMC Med Inform Decis Mak 2019; 19:76. [PMID: 30943961 PMCID: PMC6448180 DOI: 10.1186/s12911-019-0777-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
| | - Yaoyun Zhang
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Yanshan Wang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
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