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Binsfeld Gonçalves L, Nesic I, Obradovic M, Stieltjes B, Weikert T, Bremerich J. Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame. JMIR Med Inform 2022; 10:e40534. [PMID: 36542426 PMCID: PMC9813822 DOI: 10.2196/40534] [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: 06/29/2022] [Revised: 09/13/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND A concise visualization framework of related reports would increase readability and improve patient management. To this end, temporal referrals to prior comparative exams are an essential connection to previous exams in written reports. Due to unstructured narrative texts' variable structure and content, their extraction is hampered by poor computer readability. Natural language processing (NLP) permits the extraction of structured information from unstructured texts automatically and can serve as an essential input for such a novel visualization framework. OBJECTIVE This study proposes and evaluates an NLP-based algorithm capable of extracting the temporal referrals in written radiology reports, applies it to all the radiology reports generated for 10 years, introduces a graphical representation of imaging reports, and investigates its benefits for clinical and research purposes. METHODS In this single-center, university hospital, retrospective study, we developed a convolutional neural network capable of extracting the date of referrals from imaging reports. The model's performance was assessed by calculating precision, recall, and F1-score using an independent test set of 149 reports. Next, the algorithm was applied to our department's radiology reports generated from 2011 to 2021. Finally, the reports and their metadata were represented in a modulable graph. RESULTS For extracting the date of referrals, the named-entity recognition (NER) model had a high precision of 0.93, a recall of 0.95, and an F1-score of 0.94. A total of 1,684,635 reports were included in the analysis. Temporal reference was mentioned in 53.3% (656,852/1,684,635), explicitly stated as not available in 21.0% (258,386/1,684,635), and omitted in 25.7% (317,059/1,684,635) of the reports. Imaging records can be visualized in a directed and modulable graph, in which the referring links represent the connecting arrows. CONCLUSIONS Automatically extracting the date of referrals from unstructured radiology reports using deep learning NLP algorithms is feasible. Graphs refined the selection of distinct pathology pathways, facilitated the revelation of missing comparisons, and enabled the query of specific referring exam sequences. Further work is needed to evaluate its benefits in clinics, research, and resource planning.
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Affiliation(s)
- Laurent Binsfeld Gonçalves
- Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Ivan Nesic
- Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Marko Obradovic
- Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Bram Stieltjes
- Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Thomas Weikert
- Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Jens Bremerich
- Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
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Fei X, Chen P, Wei L, Huang Y, Xin Y, Li J. Quality Management of Pulmonary Nodule Radiology Reports Based on Natural Language Processing. Bioengineering (Basel) 2022; 9:244. [PMID: 35735487 PMCID: PMC9220149 DOI: 10.3390/bioengineering9060244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 11/27/2022] Open
Abstract
To investigate the feasibility of automated follow-up recommendations based on findings in radiology reports, this paper proposed a Natural Language Processing model specific for Pulmonary Nodule Radiology Reports. Unstructured findings used to describe pulmonary nodules in 48,091 radiology reports were processed in this study. We established an NLP model to extract information entities from findings of radiology reports, using deep learning and conditional random-field algorithms. Subsequently, we constructed a knowledge graph comprising 168 entities and four relationships, based on the export recommendations of the internationally renowned Fleischner Society for pulmonary nodules. These were employed in combination with rule templates to automatically generate follow-up recommendations. The automatically generated recommendations were then compared to the impression part of the reports to evaluate the matching rate of proper follow ups in the current situation. The NLP model identified eight types of entities with a recognition accuracy of up to 94.22%. A total of 43,898 out of 48,091 clinical reports were judged to contain appropriate follow-up recommendations, corresponding to the matching rate of 91.28%. The results show that NLP can be used on Chinese radiology reports to extract structured information at the content level, thereby realizing the prompt and intelligent follow-up suggestion generation or post-quality management of follow-up recommendations.
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Affiliation(s)
- Xiaolu Fei
- Information Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; (X.F.); (P.C.); (L.W.); (Y.H.)
| | - Pengyu Chen
- Information Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; (X.F.); (P.C.); (L.W.); (Y.H.)
| | - Lan Wei
- Information Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; (X.F.); (P.C.); (L.W.); (Y.H.)
| | - Yue Huang
- Information Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; (X.F.); (P.C.); (L.W.); (Y.H.)
| | - Yi Xin
- School of Life Science, Beijing Institute of Technology, Beijing 100081,China;
| | - Jia Li
- Information Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; (X.F.); (P.C.); (L.W.); (Y.H.)
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Ascertainment of Aspirin Exposure Using Structured and Unstructured Large-scale Electronic Health Record Data. Med Care 2020; 57:e60-e64. [PMID: 30807451 PMCID: PMC6703965 DOI: 10.1097/mlr.0000000000001065] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Supplemental Digital Content is available in the text. Aspirin impacts risk for important outcomes such as cancer, cardiovascular disease, and gastrointestinal bleeding. However, ascertaining exposure to medications available both by prescription and over-the-counter such as aspirin for research and quality improvement purposes is a challenge.
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Haygood TM, Mullins B, Sun J, Amini B, Bhosale P, Kang HC, Sagebiel T, Mujtaba B. Consultation and citation rates for prior imaging studies and documents in radiology. J Med Imaging (Bellingham) 2018; 5:031409. [PMID: 29750178 PMCID: PMC5938465 DOI: 10.1117/1.jmi.5.3.031409] [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: 10/05/2017] [Accepted: 03/26/2018] [Indexed: 11/20/2022] Open
Abstract
Frequently, the consensus conclusion after quality assurance conferences in radiology is that whatever mistake was made could have been avoided if more prior images or documents had been consulted. It is generally assumed that anything that was not specifically cited in the report had not been consulted. Is it actually safe to assume that an image or document that is not cited was also not consulted? It is this question that this investigation addresses. In this Institutional Review Board-approved study, one observer watched the board-certified radiologists while they interpreted imaging studies and issued reports. He recorded what type of study was being interpreted [either computed tomography, magnetic resonance imaging, or conventional radiography (x-ray)]. He also recorded the number and type of prior imaging studies and documents that were consulted during the interpretation. These observations were then compared with the signed report to determine how many of the consulted imaging studies and documents were cited. Of the 198 previous imaging studies that the radiologists consulted, 116 (58.6%) were cited in a report. Of the 285 documents consulted, 3 (1.1%) were cited in a report. This difference in citation rate was statistically significant (p<0.0001). It cannot be safely assumed that an older radiologic image or medical document was not consulted during radiologic interpretation merely because it is not cited in the report. Radiologists often consult more old studies than they cite, and they do not cite the majority of prior documents that they consult.
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Affiliation(s)
- Tamara Miner Haygood
- University of Texas MD Anderson Cancer Center, Department of Diagnostic Radiology, Houston, Texas, United States
| | - Barry Mullins
- University of Texas MD Anderson Cancer Center, Department of Diagnostic Radiology, Houston, Texas, United States
| | - Jia Sun
- University of Texas MD Anderson Cancer Center, Department of Biostatistics, Houston, Texas, United States
| | - Behrang Amini
- University of Texas MD Anderson Cancer Center, Department of Diagnostic Radiology, Houston, Texas, United States
| | - Priya Bhosale
- University of Texas MD Anderson Cancer Center, Department of Diagnostic Radiology, Houston, Texas, United States
| | - Hyunseon C Kang
- University of Texas MD Anderson Cancer Center, Department of Diagnostic Radiology, Houston, Texas, United States
| | - Tara Sagebiel
- University of Texas MD Anderson Cancer Center, Department of Diagnostic Radiology, Houston, Texas, United States
| | - Bilal Mujtaba
- University of Texas MD Anderson Cancer Center, Department of Diagnostic Radiology, Houston, Texas, United States
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Forsberg D, Gupta A, Mills C, MacAdam B, Rosipko B, Bangert BA, Coffey MD, Kosmas C, Sunshine JL. Synchronized navigation of current and prior studies using image registration improves radiologist's efficiency. Int J Comput Assist Radiol Surg 2016; 12:431-438. [PMID: 27889861 DOI: 10.1007/s11548-016-1506-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Accepted: 11/14/2016] [Indexed: 10/20/2022]
Abstract
PURPOSE The purpose of this study was to investigate how the use of multi-modal rigid image registration integrated within a standard picture archiving and communication system affects the efficiency of a radiologist while performing routine interpretations of cases including prior examinations. METHODS Six radiologists were recruited to read a set of cases (either 16 neuroradiology or 14 musculoskeletal cases) during two crossover reading sessions. Each radiologist read each case twice, one time with synchronized navigation, which enables spatial synchronization across examinations from different study dates, and one time without. Efficiency was evaluated based upon time to read a case and amount of scrolling while browsing a case using Wilcoxon signed rank test. RESULTS Significant improvements in efficiency were found considering either all radiologists simultaneously, the two sections separately and the majority of individual radiologists for time to read and for amount of scrolling. The relative improvement for each individual radiologist ranged from 4 to 32% for time to read and from 14 to 38% for amount of scrolling. CONCLUSION Image registration providing synchronized navigation across examinations from different study dates provides a tool that enables radiologists to work more efficiently while reading cases with one or more prior examinations.
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Affiliation(s)
- Daniel Forsberg
- Sectra AB, Teknikringen 20, 583 30, Linköping, Sweden. .,Department of Radiology, Case Western Reserve University and University Hospitals Case Medical Center, Cleveland, OH, USA.
| | - Amit Gupta
- Department of Radiology, Case Western Reserve University and University Hospitals Case Medical Center, Cleveland, OH, USA
| | - Christopher Mills
- Department of Radiology, Case Western Reserve University and University Hospitals Case Medical Center, Cleveland, OH, USA
| | - Brett MacAdam
- Department of Radiology, Case Western Reserve University and University Hospitals Case Medical Center, Cleveland, OH, USA
| | - Beverly Rosipko
- Department of Radiology, Case Western Reserve University and University Hospitals Case Medical Center, Cleveland, OH, USA
| | - Barbara A Bangert
- Department of Radiology, Case Western Reserve University and University Hospitals Case Medical Center, Cleveland, OH, USA
| | - Michael D Coffey
- Department of Radiology, Case Western Reserve University and University Hospitals Case Medical Center, Cleveland, OH, USA
| | - Christos Kosmas
- Department of Radiology, Case Western Reserve University and University Hospitals Case Medical Center, Cleveland, OH, USA
| | - Jeffrey L Sunshine
- Department of Radiology, Case Western Reserve University and University Hospitals Case Medical Center, Cleveland, OH, USA
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Pons E, Braun LMM, Hunink MGM, Kors JA. Natural Language Processing in Radiology: A Systematic Review. Radiology 2016; 279:329-43. [PMID: 27089187 DOI: 10.1148/radiol.16142770] [Citation(s) in RCA: 300] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Radiological reporting has generated large quantities of digital content within the electronic health record, which is potentially a valuable source of information for improving clinical care and supporting research. Although radiology reports are stored for communication and documentation of diagnostic imaging, harnessing their potential requires efficient and automated information extraction: they exist mainly as free-text clinical narrative, from which it is a major challenge to obtain structured data. Natural language processing (NLP) provides techniques that aid the conversion of text into a structured representation, and thus enables computers to derive meaning from human (ie, natural language) input. Used on radiology reports, NLP techniques enable automatic identification and extraction of information. By exploring the various purposes for their use, this review examines how radiology benefits from NLP. A systematic literature search identified 67 relevant publications describing NLP methods that support practical applications in radiology. This review takes a close look at the individual studies in terms of tasks (ie, the extracted information), the NLP methodology and tools used, and their application purpose and performance results. Additionally, limitations, future challenges, and requirements for advancing NLP in radiology will be discussed.
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Affiliation(s)
- Ewoud Pons
- From the Departments of Radiology (E.P., L.M.M.B., M.G.M.H.) and Medical Informatics (J.A.K.), Erasmus Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands
| | - Loes M M Braun
- From the Departments of Radiology (E.P., L.M.M.B., M.G.M.H.) and Medical Informatics (J.A.K.), Erasmus Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands
| | - M G Myriam Hunink
- From the Departments of Radiology (E.P., L.M.M.B., M.G.M.H.) and Medical Informatics (J.A.K.), Erasmus Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands
| | - Jan A Kors
- From the Departments of Radiology (E.P., L.M.M.B., M.G.M.H.) and Medical Informatics (J.A.K.), Erasmus Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands
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Lakhani P, Langlotz CP. Automated detection of radiology reports that document non-routine communication of critical or significant results. J Digit Imaging 2011; 23:647-57. [PMID: 19826871 PMCID: PMC2978900 DOI: 10.1007/s10278-009-9237-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
The purpose of this investigation is to develop an automated method to accurately detect radiology reports that indicate non-routine communication of critical or significant results. Such a classification system would be valuable for performance monitoring and accreditation. Using a database of 2.3 million free-text radiology reports, a rule-based query algorithm was developed after analyzing hundreds of radiology reports that indicated communication of critical or significant results to a healthcare provider. This algorithm consisted of words and phrases used by radiologists to indicate such communications combined with specific handcrafted rules. This algorithm was iteratively refined and retested on hundreds of reports until the precision and recall did not significantly change between iterations. The algorithm was then validated on the entire database of 2.3 million reports, excluding those reports used during the testing and refinement process. Human review was used as the reference standard. The accuracy of this algorithm was determined using precision, recall, and F measure. Confidence intervals were calculated using the adjusted Wald method. The developed algorithm for detecting critical result communication has a precision of 97.0% (95% CI, 93.5–98.8%), recall 98.2% (95% CI, 93.4–100%), and F measure of 97.6% (ß = 1). Our query algorithm is accurate for identifying radiology reports that contain non-routine communication of critical or significant results. This algorithm can be applied to a radiology reports database for quality control purposes and help satisfy accreditation requirements.
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Affiliation(s)
- Paras Lakhani
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
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Paulett JM, Langlotz CP. Improving language models for radiology speech recognition. J Biomed Inform 2008; 42:53-8. [PMID: 18761109 DOI: 10.1016/j.jbi.2008.08.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2007] [Revised: 08/03/2008] [Accepted: 08/05/2008] [Indexed: 11/26/2022]
Abstract
Speech recognition systems have become increasingly popular as a means to produce radiology reports, for reasons both of efficiency and of cost. However, the suboptimal recognition accuracy of these systems can affect the productivity of the radiologists creating the text reports. We analyzed a database of over two million de-identified radiology reports to determine the strongest determinants of word frequency. Our results showed that body site and imaging modality had a similar influence on the frequency of words and of three-word phrases as did the identity of the speaker. These findings suggest that the accuracy of speech recognition systems could be significantly enhanced by further tailoring their language models to body site and imaging modality, which are readily available at the time of report creation.
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Affiliation(s)
- John M Paulett
- School of Engineering and Applied Science, University of Pennsylvania, USA
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