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Romao P, Neuenschwander S, Zbinden C, Seidel K, Sariyar M. An ontology-based tool for modeling and documenting events in neurosurgery. BMC Med Inform Decis Mak 2024; 24:216. [PMID: 39085883 PMCID: PMC11293115 DOI: 10.1186/s12911-024-02615-y] [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] [Received: 11/14/2023] [Accepted: 07/17/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Intraoperative neurophysiological monitoring (IOM) plays a pivotal role in enhancing patient safety during neurosurgical procedures. This vital technique involves the continuous measurement of evoked potentials to provide early warnings and ensure the preservation of critical neural structures. One of the primary challenges has been the effective documentation of IOM events with semantically enriched characterizations. This study aimed to address this challenge by developing an ontology-based tool. METHODS We structured the development of the IOM Documentation Ontology (IOMDO) and the associated tool into three distinct phases. The initial phase focused on the ontology's creation, drawing from the OBO (Open Biological and Biomedical Ontology) principles. The subsequent phase involved agile software development, a flexible approach to encapsulate the diverse requirements and swiftly produce a prototype. The last phase entailed practical evaluation within real-world documentation settings. This crucial stage enabled us to gather firsthand insights, assessing the tool's functionality and efficacy. The observations made during this phase formed the basis for essential adjustments to ensure the tool's productive utilization. RESULTS The core entities of the ontology revolve around central aspects of IOM, including measurements characterized by timestamp, type, values, and location. Concepts and terms of several ontologies were integrated into IOMDO, e.g., the Foundation Model of Anatomy (FMA), the Human Phenotype Ontology (HPO) and the ontology for surgical process models (OntoSPM) related to general surgical terms. The software tool developed for extending the ontology and the associated knowledge base was built with JavaFX for the user-friendly frontend and Apache Jena for the robust backend. The tool's evaluation involved test users who unanimously found the interface accessible and usable, even for those without extensive technical expertise. CONCLUSIONS Through the establishment of a structured and standardized framework for characterizing IOM events, our ontology-based tool holds the potential to enhance the quality of documentation, benefiting patient care by improving the foundation for informed decision-making. Furthermore, researchers can leverage the semantically enriched data to identify trends, patterns, and areas for surgical practice enhancement. To optimize documentation through ontology-based approaches, it's crucial to address potential modeling issues that are associated with the Ontology of Adverse Events.
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Affiliation(s)
| | | | - Chantal Zbinden
- Department of Neurosurgery, Inselspital, University Hospital, Bern, Switzerland
| | - Kathleen Seidel
- Department of Neurosurgery, Inselspital, University Hospital, Bern, Switzerland
| | - Murat Sariyar
- Bern University of Applied Sciences, Bern, Switzerland.
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Wang H, Alanis N, Haygood L, Swoboda TK, Hoot N, Phillips D, Knowles H, Stinson SA, Mehta P, Sambamoorthi U. Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis. Acad Emerg Med 2024; 31:696-706. [PMID: 38757352 PMCID: PMC11246236 DOI: 10.1111/acem.14937] [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: 02/28/2024] [Revised: 04/15/2024] [Accepted: 04/17/2024] [Indexed: 05/18/2024]
Abstract
OBJECTIVES Natural language processing (NLP) represents one of the adjunct technologies within artificial intelligence and machine learning, creating structure out of unstructured data. This study aims to assess the performance of employing NLP to identify and categorize unstructured data within the emergency medicine (EM) setting. METHODS We systematically searched publications related to EM research and NLP across databases including MEDLINE, Embase, Scopus, CENTRAL, and ProQuest Dissertations & Theses Global. Independent reviewers screened, reviewed, and evaluated article quality and bias. NLP usage was categorized into syndromic surveillance, radiologic interpretation, and identification of specific diseases/events/syndromes, with respective sensitivity analysis reported. Performance metrics for NLP usage were calculated and the overall area under the summary of receiver operating characteristic curve (SROC) was determined. RESULTS A total of 27 studies underwent meta-analysis. Findings indicated an overall mean sensitivity (recall) of 82%-87%, specificity of 95%, with the area under the SROC at 0.96 (95% CI 0.94-0.98). Optimal performance using NLP was observed in radiologic interpretation, demonstrating an overall mean sensitivity of 93% and specificity of 96%. CONCLUSIONS Our analysis revealed a generally favorable performance accuracy in using NLP within EM research, particularly in the realm of radiologic interpretation. Consequently, we advocate for the adoption of NLP-based research to augment EM health care management.
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Affiliation(s)
- Hao Wang
- Department of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104
| | - Naomi Alanis
- Department of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104
| | - Laura Haygood
- Health Sciences Librarian for Public Health, Brown University, 69 Brown St., Providence, RI 02912
| | - Thomas K. Swoboda
- Department of Emergency Medicine, The Valley Health System, Touro University Nevada School of Osteopathic Medicine, 657 N. Town Center Drive, Las Vegas, NV 89144
| | - Nathan Hoot
- Department of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104
| | - Daniel Phillips
- Department of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104
| | - Heidi Knowles
- Department of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104
| | - Sara Ann Stinson
- Mary Couts Burnett Library, Burnett School of Medicine at Texas Christian University, 2800 S. University Dr., Fort Worth, TX 76109
| | - Prachi Mehta
- Department of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104
| | - Usha Sambamoorthi
- College of Pharmacy, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX 76107
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Cusumano G, D'Arrigo S, Terminella A, Lococo F. Artificial Intelligence Applications for Thoracic Surgeons: "The Phenomenal Cosmic Powers of the Magic Lamp". J Clin Med 2024; 13:3750. [PMID: 38999317 PMCID: PMC11242691 DOI: 10.3390/jcm13133750] [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: 05/12/2024] [Revised: 06/17/2024] [Accepted: 06/21/2024] [Indexed: 07/14/2024] Open
Abstract
In the digital age, artificial intelligence (AI) is emerging as a transformative force in various sectors, including medicine. This article explores the potential of AI, which is akin to the magical genie of Aladdin's lamp, particularly within thoracic surgery and lung cancer management. It examines AI applications like machine learning and deep learning in achieving more precise diagnoses, preoperative risk assessment, and improved surgical outcomes. The challenges and advancements in AI integration, especially in computer vision and multi-modal models, are discussed alongside their impact on robotic surgery and operating room management. Despite its transformative potential, implementing AI in medicine faces challenges regarding data scarcity, interpretability issues, and ethical concerns. Collaboration between AI and medical communities is essential to address these challenges and unlock the full potential of AI in revolutionizing clinical practice. This article underscores the importance of further research and interdisciplinary collaboration to ensure the safe and effective deployment of AI in real-world clinical settings.
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Affiliation(s)
- Giacomo Cusumano
- General Thoracic Surgery Unit, Azienda Ospedaliero Universitaria Policlinico "G. Rodolico-San Marco", Via Santa Sofia 78, 95100 Catania, Italy
- Department of Surgery and Medical-Surgical Specialties, University of Catania, Via Santa Sofia 78, 95100 Catania, Italy
| | - Stefano D'Arrigo
- Department of Computer, Control and Management Engineering, Università La Sapienza, 00185 Rome, Italy
| | - Alberto Terminella
- General Thoracic Surgery Unit, Azienda Ospedaliero Universitaria Policlinico "G. Rodolico-San Marco", Via Santa Sofia 78, 95100 Catania, Italy
| | - Filippo Lococo
- General Thoracic Surgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Department of Thoracic Surgery, "Sacro Cuore"-Catholic University, 00168 Rome, Italy
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4
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Berlet M, Jell A, Wagner L, Bernhard L, Fuchtmann J, Wegener L, Feussner H, Friess H, Wilhelm D. Model-based individual life-spanning documentation in visceral surgery: a proof of concept. Int J Comput Assist Radiol Surg 2024:10.1007/s11548-024-03214-y. [PMID: 38884892 DOI: 10.1007/s11548-024-03214-y] [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: 01/08/2024] [Accepted: 06/04/2024] [Indexed: 06/18/2024]
Abstract
INTRODUCTION Surgical documentation has many implications. However, its primary function is to transfer information about surgical procedures to other medical professionals. Thereby, written reports describing procedures in detail are the current standard, impeding comprehensive understanding of patient-individual life-spanning surgical course, especially if surgeries are performed at a timely distance and in diverse facilities. Therefore, we developed a novel model-based approach for documentation of visceral surgeries, denoted as 'Surgical Documentation Markup-Modeling' (SDM-M). MATERIAL AND METHODS For scientific evaluation, we developed a web-based prototype software allowing for creating hierarchical anatomical models that can be modified by individual surgery-related markup information. Thus, a patient's cumulated 'surgical load' can be displayed on a timeline deploying interactive anatomical 3D models. To evaluate the possible impact on daily clinical routine, we performed an evaluation study with 24 surgeons and advanced medical students, elaborating on simulated complex surgical cases, once with classic written reports and once with our prototypical SDM-M software. RESULTS Leveraging SDM-M in an experimental environment reduced the time needed for elaborating simulated complex surgical cases from 354 ± 85 s with the classic approach to 277 ± 128 s. (p = 0.00109) The perceived task load measured by the Raw NASA-TLX was reduced significantly (p = 0.00003) with decreased mental (p = 0.00004) and physical (p = 0.01403) demand. Also, time demand (p = 0.00041), performance (p = 0.00161), effort (p = 0.00024), and frustration (p = 0.00031) were improved significantly. DISCUSSION Model-based approaches for life-spanning surgical documentation could improve the daily clinical elaboration and understanding of complex cases in visceral surgery. Besides reduced workload and time sparing, even a more structured assessment of individual surgical cases could foster improved planning of further surgeries, information transfer, and even scientific evaluation, considering the cumulative 'surgical load.' CONCLUSION Life-spanning model-based documentation of visceral surgical cases could significantly improve surgery and workload.
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Affiliation(s)
- Maximilian Berlet
- TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
- TUM School of Medicine and Health, Klinikum rechts der Isar, Department of Surgery, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
| | - Alissa Jell
- TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
- TUM School of Medicine and Health, Klinikum rechts der Isar, Department of Surgery, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Lars Wagner
- TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Lukas Bernhard
- TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Jonas Fuchtmann
- TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Luca Wegener
- TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Hubertus Feussner
- TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
- TUM School of Medicine and Health, Klinikum rechts der Isar, Department of Surgery, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Helmut Friess
- TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
- TUM School of Medicine and Health, Klinikum rechts der Isar, Department of Surgery, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Dirk Wilhelm
- TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
- TUM School of Medicine and Health, Klinikum rechts der Isar, Department of Surgery, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
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Dencker EE, Bonde A, Troelsen A, Sillesen M. Assessing the utility of natural language processing for detecting postoperative complications from free medical text. BJS Open 2024; 8:zrae020. [PMID: 38593027 PMCID: PMC11003538 DOI: 10.1093/bjsopen/zrae020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 01/29/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Postoperative complication rates are often assessed through administrative data, although this method has proven to be imprecise. Recently, new developments in natural language processing have shown promise in detecting specific phenotypes from free medical text. Using the clinical challenge of extracting four specific and frequently undercoded postoperative complications (pneumonia, urinary tract infection, sepsis, and septic shock), it was hypothesized that natural language processing would capture postoperative complications on a par with human-level curation from electronic health record free medical text. METHODS Electronic health record data were extracted for surgical cases (across 11 surgical sub-specialties) from 18 hospitals in the Capital and Zealand regions of Denmark that were performed between May 2016 and November 2021. The data set was split into training/validation/test sets (30.0%/48.0%/22.0%). Model performance was compared with administrative data and manual extraction of the test data set. RESULTS Data were obtained for 17 486 surgical cases. Natural language processing achieved a receiver operating characteristic area under the curve of 0.989 for urinary tract infection, 0.993 for pneumonia, 0.992 for sepsis, and 0.998 for septic shock, whereas administrative data achieved a receiver operating characteristic area under the curve of 0.595 for urinary tract infection, 0.624 for pneumonia, 0.571 for sepsis, and 0.625 for septic shock. CONCLUSION The natural language processing approach was able to capture complications with acceptable performance, which was superior to administrative data. In addition, the model performance approached that of manual curation and thereby offers a potential pathway for complete real-time coverage of postoperative complications across surgical procedures based on natural language processing assessment of electronic health record free medical text.
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Affiliation(s)
- Emilie Even Dencker
- Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Center for Surgical Translational and Artificial Intelligence Research (CSTAR), Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Alexander Bonde
- Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Center for Surgical Translational and Artificial Intelligence Research (CSTAR), Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Aiomic Aps, Copenhagen, Denmark
| | - Anders Troelsen
- Department of Orthopaedics, Copenhagen University Hospital, Hvidovre, Hvidovre, Denmark
- Institute of Clinical Medicine, University of Copenhagen Medical School, Copenhagen, Denmark
| | - Martin Sillesen
- Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Center for Surgical Translational and Artificial Intelligence Research (CSTAR), Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Institute of Clinical Medicine, University of Copenhagen Medical School, Copenhagen, Denmark
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Boonstra MJ, Weissenbacher D, Moore JH, Gonzalez-Hernandez G, Asselbergs FW. Artificial intelligence: revolutionizing cardiology with large language models. Eur Heart J 2024; 45:332-345. [PMID: 38170821 PMCID: PMC10834163 DOI: 10.1093/eurheartj/ehad838] [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: 06/20/2023] [Revised: 12/01/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
Natural language processing techniques are having an increasing impact on clinical care from patient, clinician, administrator, and research perspective. Among others are automated generation of clinical notes and discharge letters, medical term coding for billing, medical chatbots both for patients and clinicians, data enrichment in the identification of disease symptoms or diagnosis, cohort selection for clinical trial, and auditing purposes. In the review, an overview of the history in natural language processing techniques developed with brief technical background is presented. Subsequently, the review will discuss implementation strategies of natural language processing tools, thereby specifically focusing on large language models, and conclude with future opportunities in the application of such techniques in the field of cardiology.
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Affiliation(s)
- Machteld J Boonstra
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
| | - Davy Weissenbacher
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
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Abid R, Hussein AA, Guru KA. Artificial Intelligence in Urology: Current Status and Future Perspectives. Urol Clin North Am 2024; 51:117-130. [PMID: 37945097 DOI: 10.1016/j.ucl.2023.06.005] [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: 11/12/2023]
Abstract
Surgical fields, especially urology, have shifted increasingly toward the use of artificial intelligence (AI). Advancements in AI have created massive improvements in diagnostics, outcome predictions, and robotic surgery. For robotic surgery to progress from assisting surgeons to eventually reaching autonomous procedures, there must be advancements in machine learning, natural language processing, and computer vision. Moreover, barriers such as data availability, interpretability of autonomous decision-making, Internet connection and security, and ethical concerns must be overcome.
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Affiliation(s)
- Rayyan Abid
- Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA
| | - Ahmed A Hussein
- Department of Urology, Roswell Park Comprehensive Cancer Center
| | - Khurshid A Guru
- Department of Urology, Roswell Park Comprehensive Cancer Center.
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8
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Tsai AY, Carter SR, Greene AC. Artificial intelligence in pediatric surgery. Semin Pediatr Surg 2024; 33:151390. [PMID: 38242061 DOI: 10.1016/j.sempedsurg.2024.151390] [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: 01/21/2024]
Abstract
Artificial intelligence (AI) is rapidly changing the landscape of medicine and is already being utilized in conjunction with medical diagnostics and imaging analysis. We hereby explore AI applications in surgery and examine its relevance to pediatric surgery, covering its evolution, current state, and promising future. The various fields of AI are explored including machine learning and applications to predictive analytics and decision support in surgery, computer vision and image analysis in preoperative planning, image segmentation, surgical navigation, and finally, natural language processing assist in expediting clinical documentation, identification of clinical indications, quality improvement, outcome research, and other types of automated data extraction. The purpose of this review is to familiarize the pediatric surgical community with the rise of AI and highlight the ongoing advancements and challenges in its adoption, including data privacy, regulatory considerations, and the imperative for interdisciplinary collaboration. We hope this review serves as a comprehensive guide to AI's transformative influence on surgery, demonstrating its potential to enhance pediatric surgical patient outcomes, improve precision, and usher in a new era of surgical excellence.
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Affiliation(s)
- Anthony Y Tsai
- Division of Pediatric Surgery, Penn State Health Children's Hospital, 500 University Drive, Hershey, PA 17033, United States.
| | - Stewart R Carter
- Division of Pediatric Surgery, University of Louisville School of Medicine, Louisville, KY, United States
| | - Alicia C Greene
- Division of Pediatric Surgery, Penn State Health Children's Hospital, 500 University Drive, Hershey, PA 17033, United States
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Lou J, Kooragayala K, Williams J, Kalola A, Crudeli C, Sandilos G, Butchy MV, Shersher DD, Burg JM. Diagnostic Workup and Therapeutic Intervention of Hiatal Hernias Discovered as Incidental Findings on Computed Tomography. Am Surg 2024:31348241230096. [PMID: 38279933 DOI: 10.1177/00031348241230096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2024]
Abstract
BACKGROUND Computed tomography imaging routinely detects incidental findings; most research focuses on malignant findings. However, benign diseases such as hiatal hernia also require identification and follow-up. Natural language algorithms can help identify these non-malignant findings. METHODS Imaging of adult trauma patients from 2010 to 2020 who underwent CT chest/abdomen/pelvis was evaluated using an open-source natural language processor to query for hiatal hernias. Patients who underwent subsequent imaging, endoscopy, fluoroscopy, or operation were retrospectively reviewed. RESULTS 1087(10.6%) of 10 299 patients had incidental hiatal hernias: 812 small (74.7%) and 275 moderate/large (25.3%). 224 (20.7%) had subsequent imaging or endoscopic evaluation. Compared to those with small hernias, patients with moderate/large hernias were older (66.3 ± 19.4 vs 79.6 ± 12.6 years, P < .001) and predominantly female (403[49.6%] vs 199[72.4%], P < .001). Moderate/large hernias were not more likely to grow (small vs moderate/large: 13[7.6%] vs 8[15.1%], P = .102). Patients with moderate/large hernias were more likely to have an intervention or referral (small vs moderate/large: 6[3.5%] vs 7[13.2%], P = .008). No patients underwent elective or emergent hernia repair. Three patients had surgical referral; however, only one was seen by a surgeon. One patient death was associated with a large hiatal hernia. CONCLUSIONS We demonstrate a novel utilization of natural language processing to identify patients with incidental hiatal hernia in a large population, and found a 10.6% incidence with only 1.2%. (13/1087) of these receiving a referral for follow-up. While most incidental hiatal hernias are small, moderate/large and symptomatic hernias have high risk of loss-to-follow-up and need referral pipelines to improve patient outcomes.
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Affiliation(s)
- Johanna Lou
- Department of Surgery, Cooper University Hospital, Camden, NJ, USA
| | | | | | - Ami Kalola
- Cooper University Medical School of Rowan University, Camden, NJ, USA
| | - Connor Crudeli
- Cooper University Medical School of Rowan University, Camden, NJ, USA
| | | | | | - David D Shersher
- Department of Surgery, Cooper University Hospital, Camden, NJ, USA
| | - Jennifer M Burg
- Department of Surgery, Maine Medical Center, Portland, ME, USA
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10
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Bonde A, Lorenzen S, Brixen G, Troelsen A, Sillesen M. Assessing the utility of deep neural networks in detecting superficial surgical site infections from free text electronic health record data. Front Digit Health 2024; 5:1249835. [PMID: 38259257 PMCID: PMC10801170 DOI: 10.3389/fdgth.2023.1249835] [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: 06/29/2023] [Accepted: 12/15/2023] [Indexed: 01/24/2024] Open
Abstract
Background High-quality outcomes data is crucial for continued surgical quality improvement. Outcomes are generally captured through structured administrative data or through manual curation of unstructured electronic health record (EHR) data. The aim of this study was to apply natural language processing (NLP) to chart notes in the EHR to accurately capture postoperative superficial surgical site infections (SSSIs). Methods Deep Learning (DL) NLP models were trained on data from 389,865 surgical cases across all 11 hospitals in the Capital Region of Denmark. Surgical cases in the training dataset were performed between January 01st, 2017, and October 30th, 2021. We trained a forward reading and a backward reading universal language model on unlabeled postoperative chart notes recorded within 30 days of a surgical procedure. The two language models were subsequently finetuned on labeled data for the classification of SSSIs. Validation and testing were performed on surgical cases performed during the month of November 2021. We propose two different use cases: a stand-alone machine learning (SAM) pipeline and a human-in-the-loop (HITL) pipeline. Performances of both pipelines were compared to administrative data and to manual curation. Results The models were trained on 3,983,864 unlabeled chart notes and finetuned on 1,231,656 labeled notes. Models had a test area under the receiver operating characteristic curves (ROC AUC) of 0.989 on individual chart notes and 0.980 on an aggregated case level. The SAM pipeline had a sensitivity of 0.604, a specificity of 0.996, a positive predictive value (PPV) of 0.763, and a negative predictive value (NPV) of 0.991. Prior to human review, the HITL pipeline had a sensitivity of 0.854, a specificity of 0.987, a PPV of 0.603, and a NPV of 0.997. Conclusion The performance of the SAM pipeline was superior to administrative data, and significantly outperformed previously published results. The performance of the HITL pipeline approached that of manual curation.
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Affiliation(s)
- Alexander Bonde
- Aiomic, Copenhagen, Denmark
- Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Denmark
| | | | - Gustav Brixen
- Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Anders Troelsen
- Department of Orthopedics, Copenhagen University Hospital, Hvidovre, Denmark
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Martin Sillesen
- Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Denmark
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Center for Surgical Translational and Artificial Intelligence Research (CSTAR), Copenhagen University Hospital, Rigshospitalet, Denmark
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11
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Ali SR, Dobbs TD, Tarafdar A, Strafford H, Fonferko-Shadrach B, Lacey AS, Pickrell WO, Hutchings HA, Whitaker IS. Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings. Br J Surg 2024; 111:znad347. [PMID: 38198154 PMCID: PMC10782209 DOI: 10.1093/bjs/znad347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/23/2023] [Accepted: 10/07/2023] [Indexed: 01/11/2024]
Abstract
BACKGROUND Cancer multidisciplinary team (MDT) meetings are under intense pressure to reform given the rapidly rising incidence of cancer and national mandates for protocolized streaming of cases. The aim of this study was to validate a natural language processing (NLP)-based web platform to automate evidence-based MDT decisions for skin cancer with basal cell carcinoma as a use case. METHODS A novel and validated NLP information extraction model was used to extract perioperative tumour and surgical factors from histopathology reports. A web application with a bespoke application programming interface used data from this model to provide an automated clinical decision support system, mapped to national guidelines and generating a patient letter to communicate ongoing management. Performance was assessed against retrospectively derived recommendations by two independent and blinded expert clinicians. RESULTS There were 893 patients (1045 lesions) used to internally validate the model. High accuracy was observed when compared against human predictions, with an overall value of 0.92. Across all classifiers the virtual skin MDT was highly specific (0.96), while sensitivity was lower (0.72). CONCLUSION This study demonstrates the feasibility of a fully automated, virtual, web-based service model to host the skin MDT with good system performance. This platform could be used to support clinical decision-making during MDTs as 'human in the loop' approach to aid protocolized streaming. Future prospective studies are needed to validate the model in tumour types where guidelines are more complex.
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Affiliation(s)
- Stephen R Ali
- Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, UK
- Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK
| | - Thomas D Dobbs
- Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, UK
- Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK
| | - Adib Tarafdar
- Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, UK
- Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK
| | - Huw Strafford
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK
- Health Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, UK
| | - Beata Fonferko-Shadrach
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK
- Health Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, UK
| | - Arron S Lacey
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK
- Health Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, UK
| | - William Owen Pickrell
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK
- Department of Neurology, Morriston Hospital, Swansea, UK
| | - Hayley A Hutchings
- Faculty of Medicine, Health and Life Science, Swansea University Medical School, Swansea, UK
| | - Iain S Whitaker
- Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, UK
- Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK
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12
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TerKonda SP, TerKonda AA, Sacks JM, Kinney BM, Gurtner GC, Nachbar JM, Reddy SK, Jeffers LL. Artificial Intelligence: Singularity Approaches. Plast Reconstr Surg 2024; 153:204e-217e. [PMID: 37075274 DOI: 10.1097/prs.0000000000010572] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
SUMMARY Artificial intelligence (AI) has been a disruptive technology within health care, from the development of simple care algorithms to complex deep-learning models. AI has the potential to reduce the burden of administrative tasks, advance clinical decision-making, and improve patient outcomes. Unlocking the full potential of AI requires the analysis of vast quantities of clinical information. Although AI holds tremendous promise, widespread adoption within plastic surgery remains limited. Understanding the basics is essential for plastic surgeons to evaluate the potential uses of AI. This review provides an introduction of AI, including the history of AI, key concepts, applications of AI in plastic surgery, and future implications.
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Affiliation(s)
- Sarvam P TerKonda
- From the Division of Plastic and Reconstructive Surgery, Mayo Clinic Florida
| | - Anurag A TerKonda
- Division of Plastic and Reconstructive Surgery, Washington University School of Medicine in St. Louis
| | - Justin M Sacks
- Division of Plastic and Reconstructive Surgery, Washington University School of Medicine in St. Louis
| | - Brian M Kinney
- Division of Plastic Surgery, University of Southern California
| | - Geoff C Gurtner
- Division of Plastic and Reconstructive Surgery, Stanford University
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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] [Received: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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14
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Kovoor JG, Bacchi S, Gupta AK, Stretton B, Nann SD, Aujayeb N, Lu A, Nathin K, Lam L, Jiang M, Lee S, To MS, Ovenden CD, Hewitt JN, Goh R, Gluck S, Reid JL, Khurana S, Dobbins C, Hewett PJ, Padbury RT, Malycha J, Trochsler MI, Hugh TJ, Maddern GJ. Surgery's Rosetta Stone: Natural language processing to predict discharge and readmission after general surgery. Surgery 2023; 174:1309-1314. [PMID: 37778968 DOI: 10.1016/j.surg.2023.08.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/04/2023] [Accepted: 08/16/2023] [Indexed: 10/03/2023]
Abstract
BACKGROUND This study aimed to examine the accuracy with which multiple natural language processing artificial intelligence models could predict discharge and readmissions after general surgery. METHODS Natural language processing models were derived and validated to predict discharge within the next 48 hours and 7 days and readmission within 30 days (based on daily ward round notes and discharge summaries, respectively) for general surgery inpatients at 2 South Australian hospitals. Natural language processing models included logistic regression, artificial neural networks, and Bidirectional Encoder Representations from Transformers. RESULTS For discharge prediction analyses, 14,690 admissions were included. For readmission prediction analyses, 12,457 patients were included. For prediction of discharge within 48 hours, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.86 and 0.86 for Bidirectional Encoder Representations from Transformers, 0.82 and 0.81 for logistic regression, and 0.82 and 0.81 for artificial neural networks. For prediction of discharge within 7 days, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.82 and 0.81 for Bidirectional Encoder Representations from Transformers, 0.75 and 0.72 for logistic regression, and 0.68 and 0.67 for artificial neural networks. For readmission prediction within 30 days, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.55 and 0.59 for Bidirectional Encoder Representations from Transformers and 0.77 and 0.62 for logistic regression. CONCLUSION Modern natural language processing models, particularly Bidirectional Encoder Representations from Transformers, can effectively and accurately identify general surgery patients who will be discharged in the next 48 hours. However, these approaches are less capable of identifying general surgery patients who will be discharged within the next 7 days or who will experience readmission within 30 days of discharge.
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Affiliation(s)
- Joshua G Kovoor
- Department of Surgery, The Queen Elizabeth Hospital, The University of Adelaide, Adelaide, South Australia, Australia; Royal Australasian College of Surgeons, Adelaide, South Australia, Australia; Health and Information, Adelaide, South Australia, Australia. https://twitter.com/josh.kovoor
| | - Stephen Bacchi
- Health and Information, Adelaide, South Australia, Australia; Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia
| | - Aashray K Gupta
- Department of Surgery, The Queen Elizabeth Hospital, The University of Adelaide, Adelaide, South Australia, Australia; Health and Information, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia; Gold Coast University Hospital, Gold Coast, Queensland, Australia
| | - Brandon Stretton
- Department of Surgery, The Queen Elizabeth Hospital, The University of Adelaide, Adelaide, South Australia, Australia; Health and Information, Adelaide, South Australia, Australia; Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia
| | - Silas D Nann
- Health and Information, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia; Gold Coast University Hospital, Gold Coast, Queensland, Australia
| | - Nidhi Aujayeb
- Health and Information, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia
| | - Amy Lu
- Health and Information, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia
| | - Kayla Nathin
- Health and Information, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia
| | - Lydia Lam
- Health and Information, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia
| | - Melinda Jiang
- Health and Information, Adelaide, South Australia, Australia; Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia
| | - Shane Lee
- Health and Information, Adelaide, South Australia, Australia; Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia
| | - Minh-Son To
- Health and Information, Adelaide, South Australia, Australia; Royal Adelaide Hospital, Adelaide, South Australia, Australia; Flinders Medical Centre, Flinders University, Adelaide, South Australia, Australia
| | - Christopher D Ovenden
- Health and Information, Adelaide, South Australia, Australia; Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia
| | - Joseph N Hewitt
- Department of Surgery, The Queen Elizabeth Hospital, The University of Adelaide, Adelaide, South Australia, Australia; Health and Information, Adelaide, South Australia, Australia; Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Rudy Goh
- Health and Information, Adelaide, South Australia, Australia; Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia
| | - Samuel Gluck
- University of Adelaide, Adelaide, South Australia, Australia
| | - Jessica L Reid
- Department of Surgery, The Queen Elizabeth Hospital, The University of Adelaide, Adelaide, South Australia, Australia
| | - Sanjeev Khurana
- Women's and Children's Hospital, Adelaide, South Australia, Australia
| | - Christopher Dobbins
- Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia
| | - Peter J Hewett
- Department of Surgery, The Queen Elizabeth Hospital, The University of Adelaide, Adelaide, South Australia, Australia
| | - Robert T Padbury
- Flinders Medical Centre, Flinders University, Adelaide, South Australia, Australia
| | - James Malycha
- Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia
| | - Markus I Trochsler
- Department of Surgery, The Queen Elizabeth Hospital, The University of Adelaide, Adelaide, South Australia, Australia
| | - Thomas J Hugh
- University of Sydney, Sydney, New South Wales, Australia; Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Guy J Maddern
- Department of Surgery, The Queen Elizabeth Hospital, The University of Adelaide, Adelaide, South Australia, Australia; Royal Australasian College of Surgeons, Adelaide, South Australia, Australia.
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15
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Zhang J, Xu W, Lei C, Pu Y, Zhang Y, Zhang J, Yu H, Su X, Huang Y, Gong R, Zhang L, Shi Q. Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study. JMIR Form Res 2023; 7:e43995. [PMID: 37656501 PMCID: PMC10504630 DOI: 10.2196/43995] [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: 11/02/2022] [Revised: 12/26/2022] [Accepted: 07/24/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND Unlike research project-based health data collection (questionnaires and interviews), social media platforms allow patients to freely discuss their health status and obtain peer support. Previous literature has pointed out that both public and private social platforms can serve as data sources for analysis. OBJECTIVE This study aimed to use natural language processing (NLP) techniques to identify concerns regarding the postoperative quality of life and symptom burdens in patients with uterine fibroids after focused ultrasound ablation surgery. METHODS Screenshots taken from clinician-patient WeChat groups were converted into free texts using image text recognition technology and used as the research object of this study. From 408 patients diagnosed with uterine fibroids in Chongqing Haifu Hospital between 2010 and 2020, we searched for symptom burdens in over 900,000 words of WeChat group chats. We first built a corpus of symptoms by manually coding 30% of the WeChat texts and then used regular expressions in Python to crawl symptom information from the remaining texts based on this corpus. We compared the results with a manual review (gold standard) of the same records. Finally, we analyzed the relationship between the population baseline data and conceptual symptoms; quantitative and qualitative results were examined. RESULTS A total of 408 patients with uterine fibroids were included in the study; 190,000 words of free text were obtained after data cleaning. The mean age of the patients was 39.94 (SD 6.81) years, and their mean BMI was 22.18 (SD 2.78) kg/m2. The median reporting times of the 7 major symptoms were 21, 26, 57, 2, 18, 30, and 49 days. Logistic regression models identified preoperative menstrual duration (odds ratio [OR] 1.14, 95% CI 5.86-6.37; P=.009), age of menophania (OR -1.02 , 95% CI 11.96-13.47; P=.03), and the number (OR 2.34, 95% CI 1.45-1.83; P=.04) and size of fibroids (OR 0.12, 95% CI 2.43-3.51; P=.04) as significant risk factors for postoperative symptoms. CONCLUSIONS Unstructured free texts from social media platforms extracted by NLP technology can be used for analysis. By extracting the conceptual information about patients' health-related quality of life, we can adopt personalized treatment for patients at different stages of recovery to improve their quality of life. Python-based text mining of free-text data can accurately extract symptom burden and save considerable time compared to manual review, maximizing the utility of the extant information in population-based electronic health records for comparative effectiveness research.
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Affiliation(s)
- Jiayuan Zhang
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Wei Xu
- School of Public Health, Chongqing Medical University, Chongqing, China
| | - Cheng Lei
- School of Public Health, Chongqing Medical University, Chongqing, China
| | - Yang Pu
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Yubo Zhang
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Jingyu Zhang
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Hongfan Yu
- School of Public Health, Chongqing Medical University, Chongqing, China
| | - Xueyao Su
- School of Public Health, Chongqing Medical University, Chongqing, China
| | - Yanyan Huang
- School of Public Health, Chongqing Medical University, Chongqing, China
| | - Ruoyan Gong
- School of Public Health, Chongqing Medical University, Chongqing, China
| | - Lijun Zhang
- School of Public Health, Chongqing Medical University, Chongqing, China
| | - Qiuling Shi
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China
- School of Public Health, Chongqing Medical University, Chongqing, China
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16
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Ali SR, Dobbs TD, Jovic M, Strafford H, Fonferko-Shadrach B, Lacey AS, Williams N, Pickrell WO, Hutchings HA, Whitaker IS. Validating a novel natural language processing pathway for automated quality assurance in surgical oncology: incomplete excision rates of 34 955 basal cell carcinomas. Br J Surg 2023; 110:1072-1075. [PMID: 36935397 PMCID: PMC10416688 DOI: 10.1093/bjs/znad055] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 02/06/2023] [Indexed: 03/21/2023]
Affiliation(s)
- Stephen R Ali
- Reconstructive Surgery and Regenerative Medicine Research Centre. Institute of Life Sciences, Swansea University Medical School, Swansea, UK
- Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK
| | - Thomas D Dobbs
- Reconstructive Surgery and Regenerative Medicine Research Centre. Institute of Life Sciences, Swansea University Medical School, Swansea, UK
- Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK
| | - Matthew Jovic
- Reconstructive Surgery and Regenerative Medicine Research Centre. Institute of Life Sciences, Swansea University Medical School, Swansea, UK
| | - Huw Strafford
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK
- Health Data Research UK, Swansea University Medical School, Swansea University, Swansea, UK
| | - Beata Fonferko-Shadrach
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK
- Health Data Research UK, Swansea University Medical School, Swansea University, Swansea, UK
| | - Arron S Lacey
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK
- Health Data Research UK, Swansea University Medical School, Swansea University, Swansea, UK
| | - Namor Williams
- Department of Cellular Pathology, Morriston Hospital, Swansea, UK
| | - William Owen Pickrell
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK
- Department of Neurology, Morriston Hospital, Swansea, UK
| | - Hayley A Hutchings
- Patient and Population Health and Informatics Research, Swansea University Medical School, Swansea, UK
| | - Iain S Whitaker
- Reconstructive Surgery and Regenerative Medicine Research Centre. Institute of Life Sciences, Swansea University Medical School, Swansea, UK
- Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK
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17
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Malhotra K, Wong BNX, Lee S, Franco H, Singh C, Cabrera Silva LA, Iraqi H, Sinha A, Burger S, Breedt DS, Goyal K, Dagli MM, Bawa A. Role of Artificial Intelligence in Global Surgery: A Review of Opportunities and Challenges. Cureus 2023; 15:e43192. [PMID: 37692604 PMCID: PMC10486145 DOI: 10.7759/cureus.43192] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/09/2023] [Indexed: 09/12/2023] Open
Abstract
Global surgery broadly refers to a rapidly expanding multidisciplinary field concerned with providing better and equitable surgical care across international health systems. Global surgery initiatives primarily focus on capacity building, advocacy, education, research, and policy development in low- and middle-income countries (LMICs). The inadequate surgical, anesthetic, and obstetric care currently contributes to 18 million preventable deaths each year. Hence, there is a growing interest in the rapid growth of artificial intelligence (AI) that provides a distinctive opportunity to enhance surgical services in LMICs. AI modalities have been used for personalizing surgical education, automating administrative tasks, and developing realistic and cost-effective simulation-training programs with provisions for people with special needs. Furthermore, AI may assist with providing insights for governance, infrastructure development, and monitoring/predicting stock take or logistics failure that can help in strengthening global surgery pillars. Numerous AI-assisted telemedicine-based platforms have allowed healthcare professionals to virtually assist in complex surgeries that may help to improve surgical accessibility across LMICs. Challenges in implementing AI technology include the misrepresentation of minority populations in the datasets leading to discriminatory bias. Human hesitancy, employment uncertainty, automation bias, and role of confounding factors need to be further studied for equitable utilization of AI. With a focused and evidence-based approach, AI could help several LMICs overcome bureaucratic inefficiency and develop more efficient surgical systems.
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Affiliation(s)
- Kashish Malhotra
- Department of Surgery, Dayanand Medical College and Hospital, Ludhiana, IND
| | | | - Susie Lee
- Department of Orthopaedics, Toowoomba Hospital, Queensland, AUS
| | - Helena Franco
- Department of Surgery, Bond University, Queensland, AUS
| | - Carol Singh
- Department of Surgery, Dayanand Medical College and Hospital, Ludhiana, IND
| | | | - Habab Iraqi
- Department of Surgery, Al-Yarmouk College of Medical Sciences, Khartoum, SDN
| | - Akatya Sinha
- Department of Surgery, MGM (Mahatma Gandhi Mission's) Medical College and Hospital, Mumbai, IND
| | - Sule Burger
- Department of Surgery, Ngwelezana Hospital, KwaZulu-Natal, ZAF
| | | | - Kashish Goyal
- Department of Internal Medicine, Dayanand Medical College and Hospital, Ludhiana, IND
| | - Mert Marcel Dagli
- Department of Neurosurgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Ashvind Bawa
- Department of Surgery, Dayanand Medical College and Hospital, Ludhiana, IND
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18
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Zimmerman A. A Ghostwriter for the Masses: ChatGPT and the Future of Writing. Ann Surg Oncol 2023; 30:3170-3173. [PMID: 37029868 DOI: 10.1245/s10434-023-13436-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 02/12/2023] [Indexed: 04/09/2023]
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Abstract
The emergence of Big Data has been facilitated by technological advancements in the processing, storage, and analysis of large quantities of data. Its strength is derived from its size, ease of access, and speed of analysis, and it has enabled surgeons to investigate areas of interest that traditional research models have historically been unable to address. In the future, Big Data will likely assist in the incorporation of more advanced technologies into surgical practice, including artificial intelligence and machine learning to realize the full potential of Big Data in Surgery.
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Affiliation(s)
- Christopher Prien
- Department of Colon & Rectal Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Eddy P Lincango
- Department of Colon & Rectal Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Stefan D Holubar
- Department of Colon & Rectal Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA.
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20
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Kobritz M, Patel V, Rindskopf D, Demyan L, Jarrett M, Coppa G, Antonacci AC. Practice-Based Learning and Improvement: Improving Morbidity and Mortality Review Using Natural Language Processing. J Surg Res 2023; 283:351-356. [PMID: 36427445 DOI: 10.1016/j.jss.2022.10.075] [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: 03/01/2022] [Revised: 09/21/2022] [Accepted: 10/18/2022] [Indexed: 11/23/2022]
Abstract
INTRODUCTION Practice-Based Learning and Improvement, a core competency identified by the Accreditation Council for Graduate Medical Education, carries importance throughout a physician's career. Practice-Based Learning and Improvement is cultivated by a critical review of complications, yet methods to accurately identify complications are inadequate. Machine-learning algorithms show promise in improving identification of complications. We compare a manual-supplemented natural language processing (ms-NLP) methodology against a validated electronic morbidity and mortality (MM) database, the Morbidity and Mortality Adverse Event Reporting System (MARS) to understand the utility of NLP in MM review. METHODS The number and severity of complications were compared between MARS and ms-NLP of surgical hospitalization discharge summaries among three academic medical centers. Clavien-Dindo (CD) scores were assigned to cases with identified complications and classified into minor (CD I-II) or major (CD III-IV) harm. RESULTS Of 7774 admissions, 987 cases were identified to have 1659 complications by MARS and 1296 by ms-NLP. MARS identified 611 (62%) cases, whereas ms-NLP identified 670 (68%) cases. Less than one-third of cases (299, 30.3%) were detected by both methods. MARS identified a greater number of complications with major harm (457, 46.30%) than did ms-NLP (P < 0.0001). CONCLUSIONS Both a prospectively maintained MM database and ms-NLP review of discharge summaries fail to identify a significant proportion of postoperative complications and overlap 1/3 of the time. ms-NLP more frequently identifies cases with minor complications, whereas prospective voluntary reporting more frequently identifies major complications. The educational benefit of reporting and analysis of complication data may be supplemented by ms-NLP but not replaced by it at this time.
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Affiliation(s)
- Molly Kobritz
- Northwell Health North Shore/Long Island Jewish General Surgery, Manhasset, New York; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York.
| | - Vihas Patel
- Northwell Health North Shore/Long Island Jewish General Surgery, Manhasset, New York; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - David Rindskopf
- City University of New York, Graduate School And University Center, New York, New York
| | - Lyudmyla Demyan
- Northwell Health North Shore/Long Island Jewish General Surgery, Manhasset, New York; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Mark Jarrett
- Northwell Health North Shore/Long Island Jewish General Surgery, Manhasset, New York; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Gene Coppa
- Northwell Health North Shore/Long Island Jewish General Surgery, Manhasset, New York; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Anthony C Antonacci
- Northwell Health North Shore/Long Island Jewish General Surgery, Manhasset, New York; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
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21
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Cesare M, D’agostino F, Maurici M, Zega M, Zeffiro V, Cocchieri A. Standardized Nursing Diagnoses in a Surgical Hospital Setting: A Retrospective Study Based on Electronic Health Data. SAGE Open Nurs 2023; 9:23779608231158157. [PMID: 36824318 PMCID: PMC9941607 DOI: 10.1177/23779608231158157] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/06/2022] [Accepted: 01/30/2023] [Indexed: 02/22/2023] Open
Abstract
Introduction In electronic health records (EHRs), standardized nursing terminologies (SNTs), such as nursing diagnoses (NDs), are needed to demonstrate the impact of nursing care on patient outcomes. Unfortunately, the use of NDs is not common in clinical practice, especially in surgical settings, and is rarely included in EHRs. Objectives The aim of the study was to describe the prevalence and trend of NDs in a hospital surgical setting by also analyzing the relationship between NDs and hospital outcomes. Methods A retrospective study was conducted. All adult inpatients consecutively admitted to one of the 15 surgical inpatient units of an Italian university hospital across 1 year were included. Data, including the Professional Assessment Instrument and the Hospital Discharge Register, were collected retrospectively from the hospital's EHRs. Results The sample included 5,027 surgical inpatients. There was a mean of 6.3 ± 4.3 NDs per patient. The average distribution of NDs showed a stable trend throughout the year. The most representative NANDA-I ND domain was safety/protection. The total number of NDs on admission was significantly higher for patient whose length of stay was longer. A statistically significant correlation was observed between the number of NDs on admission and the number of intra-hospital patient transfers. Additionally, the mean number of NDs on admission was higher for patients who were later transferred to an intensive care unit compared to those who were not transferred. Conclusion NDs represent the key to understanding the contribution of nurses in the surgical setting. NDs collected upon admission can represent a prognostic factor related to the hospital's key outcomes.
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Affiliation(s)
- Manuele Cesare
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Fabio D’agostino
- Unicamillus, Saint Camillus International University of Health Sciences, Rome, Italy
| | - Massimo Maurici
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Maurizio Zega
- Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy
| | - Valentina Zeffiro
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Antonello Cocchieri
- Section of Hygiene, Woman and Child Health and Public Health, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy,Antonello Cocchieri, Section of Hygiene, Woman and Child Health and Public Health, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Francesco Vito 1, 00168 Rome, Italy.
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Pethani F, Dunn AG. Natural language processing for clinical notes in dentistry: A systematic review. J Biomed Inform 2023; 138:104282. [PMID: 36623780 DOI: 10.1016/j.jbi.2023.104282] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 12/01/2022] [Accepted: 01/04/2023] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To identify and synthesise research on applications of natural language processing (NLP) for information extraction and retrieval from clinical notes in dentistry. MATERIALS AND METHODS A predefined search strategy was applied in EMBASE, CINAHL and Medline. Studies eligible for inclusion were those that that described, evaluated, or applied NLP to clinical notes containing either human or simulated patient information. Quality of the study design and reporting was independently assessed based on a set of questions derived from relevant tools including CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). A narrative synthesis was conducted to present the results. RESULTS Of the 17 included studies, 10 developed and evaluated NLP methods and 7 described applications of NLP-based information retrieval methods in dental records. Studies were published between 2015 and 2021, most were missing key details needed for reproducibility, and there was no consistency in design or reporting. The 10 studies developing or evaluating NLP methods used document classification or entity extraction, and 4 compared NLP methods to non-NLP methods. The quality of reporting on NLP studies in dentistry has modestly improved over time. CONCLUSIONS Study design heterogeneity and incomplete reporting of studies currently limits our ability to synthesise NLP applications in dental records. Standardisation of reporting and improved connections between NLP methods and applied NLP in dentistry may improve how we can make use of clinical notes from dentistry in population health or decision support systems. PROTOCOL REGISTRATION PROSPERO CRD42021227823.
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Affiliation(s)
- Farhana Pethani
- Biomedical Informatics and Digital Health, Faculty of Medicine and Health, the University of Sydney, Sydney, Australia
| | - Adam G Dunn
- Biomedical Informatics and Digital Health, Faculty of Medicine and Health, the University of Sydney, Sydney, Australia.
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23
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Morris MX, Song EY, Rajesh A, Asaad M, Phillips BT. Ethical, Legal, and Financial Considerations of Artificial Intelligence in Surgery. Am Surg 2023; 89:55-60. [PMID: 35978473 DOI: 10.1177/00031348221117042] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Machine learning systems have become integrated into some of the most vital decision-making aspects of humanity, including hiring decisions, loan applications, and automobile safety, to name just a few. As applications increase in both gravity and complexity, the data quality and algorithmic interpretability of the systems must rise to meet those challenges. This is especially vital for navigating the nuances of health care, particularly among the high stakes of surgical operations. In addition to inherent ethical challenges of enabling a "black box" system to influence decision-making in patient care, the creation of biased datasets leads to biased algorithms with the power to perpetuate discrimination and reinforce disparities. Transparency and responsibility are paramount to the implementation of artificial intelligence in surgical decision-making and autonomous robotic surgery. Machine learning has been permeating health care across diverse clinical and surgical contexts but continues to face sizable obstacles, including apprehension from patients and providers alike. To integrate the technology fully while upholding standard of care and patient-provider trust, one must acknowledge and address the ethical, financial, and legal implications of using artificial intelligence for patient care.
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Affiliation(s)
- Miranda X Morris
- 12277Duke University School of Medicine, Durham, NC, USA.,22957Duke Pratt School of Engineering, Durham, NC, USA
| | - Ethan Y Song
- Division of Plastic, Maxillofacial, and Oral Surgery, Department of Surgery, 14742Duke University Hospital, Durham, NC, USA
| | - Aashish Rajesh
- Department of Surgery, 571198University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Malke Asaad
- Department of Plastic Surgery, 22957University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Brett T Phillips
- Division of Plastic, Maxillofacial, and Oral Surgery, Department of Surgery, 14742Duke University Hospital, Durham, NC, USA
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Morris MX, Song EY, Rajesh A, Kass N, Asaad M, Phillips BT. New Frontiers of Natural Language Processing in Surgery. Am Surg 2023; 89:43-48. [PMID: 35969539 DOI: 10.1177/00031348221117039] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The vast and ever-growing volume of electronic health records (EHR) have generated a wealth of information-rich data. Traditional, non-machine learning data extraction techniques are error-prone and laborious, hindering the analytical potential of these massive data sources. Equipped with natural language processing (NLP) tools, surgeons are better able to automate, and customize their review to investigate and implement surgical solutions. We identify current perioperative applications of NLP algorithms as well as research limitations and future avenues to outline the impact and potential of this technology for progressing surgical innovation.
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Affiliation(s)
- Miranda X Morris
- 12277Duke University School of Medicine, Durham, NC, USA.,Duke Pratt School of Engineering, Durham, NC, USA
| | - Ethan Y Song
- Division of Plastic, Maxillofacial, and Oral Surgery, Department of Surgery, 22957Duke University, Durham, NC, USA
| | - Aashish Rajesh
- Department of Surgery, 14742University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Nicolas Kass
- 12317University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Malke Asaad
- Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Brett T Phillips
- Division of Plastic, Maxillofacial, and Oral Surgery, Department of Surgery, 22957Duke University, Durham, NC, USA
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Morris MX, Rajesh A, Asaad M, Hassan A, Saadoun R, Butler CE. Deep Learning Applications in Surgery: Current Uses and Future Directions. Am Surg 2023; 89:36-42. [PMID: 35567312 DOI: 10.1177/00031348221101490] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical fields. Its tremendous capacity for powerful data-driven problem-solving has generated computational breakthroughs in many realms, with the fields of medicine and surgery becoming increasingly prominent avenues. Through its multi-layer architecture of interconnected neural networks, DL enables feature extraction and pattern recognition of highly complex and large-volume data. Across various surgical specialties, DL is being applied to optimize both preoperative planning and intraoperative performance in new and innovative ways. Surgeons are now able to integrate deep learning tools into their practice to improve patient safety and outcomes. Through this review, we explore the applications of deep learning in surgery and related subspecialties with an aim to shed light on the practical utilization of this technology in the present and near future.
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Affiliation(s)
- Miranda X Morris
- 12277Duke University School of Medicine, Durham, NC, USA.,101571Duke Pratt School of Engineering, Durham, NC, USA
| | - Aashish Rajesh
- Department of Surgery, 14742University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Malke Asaad
- Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Abbas Hassan
- Department of Plastic Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rakan Saadoun
- Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Charles E Butler
- Department of Plastic Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Zhang J, Xu W, Lei C, Pu Y, Zhang Y, Zhang J, Yu H, Su X, Huang Y, Gong R, Zhang L, Shi Q. Using WeChat clinician-patient group communication data to identify symptom burdens in patients with uterine fibroids under focused ultrasound ablation surgery treatment :Qualitative Study (Preprint).. [DOI: 10.2196/preprints.43995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
BACKGROUND
Unlike research project-based health data collections(questionnaires, interviews), social media platforms, which allow patients to freely discuss their health status and obtain peer support.Previous literature has pointed out that both public and private social platforms can serve as data sources for analysing.
OBJECTIVE
This study aimed to use natural language processing (NLP) techniques to identify concerns regarding the postoperative quality of life and symptom burdens in uterine fibroids after focused ultrasound ablation surgery.
METHODS
Screenshots taken from the clinician-patient WeChat groups were converted into free texts using image text recognition technology and used as the research object of this study, which used regular expressions in Python to search for symptom burdens in over 900,000 words of WeChat group-chats associated with 408 patients in Chongqing Haifu Hospital diagnosed with uterine fibroids between 2010 and 2020. We first built a corpus of symptoms by manually coding 30% of the WeChat texts, and then used regular expressions to crawl symptom information from the remaining texts based on this corpus. We compared the results with a manual review (gold standard) of the same records. Then we analyzed the relationship between the population baseline data and conceptual symptoms, Quantitative and qualitative results were examined.
RESULTS
A total of 190,000 words of uterine fibroids patients' free text were finally obtained after data cleaning. A total of 408 patients were included in the study. The age of the patients was 39.94±6.81 years, and their BMI was 22.18±2.78 (kg/m^2). The median reporting times of the seven major symptoms were 21, 26, 57, 2, 18, 30, and 49 days. Results showed that patients with dysmenorrhea were younger(mean 38.26 (SD 7.05), P=.004) and slimmer (mean 22.37 (SD 3.81), P=.04), with lower fertility and parity (P<.05), and tended to stay longer in the hospital (P<.05). Logistic regression models identified preoperative menstrual duration (OR 1.14, 95% CI 5.86-6.37; P= .009), age of menophania (OR -1.02 ,95%CI 11.96-13.47,P=.03), and the number(OR 2.34,95% CI 1.45-1.83,P=.04) and size of fibroids(OR 0.12,95% CI 2.43-3.51,P=.04) as significant risk factors for postoperative symptoms.
CONCLUSIONS
Unstructured free texts from social media platforms extracted by NLP technology can be used for analysis, extracting the conceptual information about patients' HRQol,adopt personalized treatment for patients at different stages of recovery to improve the quality of life of patients. Python-based text mining of free-text data can accurately extract symptom burden administered and save considerable time compared to manual review, maximizing the utility of the extant information in population-based electronic health records for comparative effectiveness research.
CLINICALTRIAL
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Carrillo-Larco RM, Castillo-Cara M, Lovón-Melgarejo J. Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters. Wellcome Open Res 2022. [DOI: 10.12688/wellcomeopenres.16867.5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, 26 terms appeared with a frequency of 0.08 or greater, while in 2021 27 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.
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Ali SR, Strafford H, Dobbs TD, Fonferko-Shadrach B, Lacey AS, Pickrell WO, Hutchings HA, Whitaker IS. Development and validation of an automated basal cell carcinoma histopathology information extraction system using natural language processing. Front Surg 2022; 9:870494. [PMID: 36439548 PMCID: PMC9683031 DOI: 10.3389/fsurg.2022.870494] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 07/11/2022] [Indexed: 01/26/2024] Open
Abstract
Introduction Routinely collected healthcare data are a powerful research resource, but often lack detailed disease-specific information that is collected in clinical free text such as histopathology reports. We aim to use natural Language Processing (NLP) techniques to extract detailed clinical and pathological information from histopathology reports to enrich routinely collected data. Methods We used the general architecture for text engineering (GATE) framework to build an NLP information extraction system using rule-based techniques. During validation, we deployed our rule-based NLP pipeline on 200 previously unseen, de-identified and pseudonymised basal cell carcinoma (BCC) histopathological reports from Swansea Bay University Health Board, Wales, UK. The results of our algorithm were compared with gold standard human annotation by two independent and blinded expert clinicians involved in skin cancer care. Results We identified 11,224 items of information with a mean precision, recall, and F1 score of 86.0% (95% CI: 75.1-96.9), 84.2% (95% CI: 72.8-96.1), and 84.5% (95% CI: 73.0-95.1), respectively. The difference between clinician annotator F1 scores was 7.9% in comparison with 15.5% between the NLP pipeline and the gold standard corpus. Cohen's Kappa score on annotated tokens was 0.85. Conclusion Using an NLP rule-based approach for named entity recognition in BCC, we have been able to develop and validate a pipeline with a potential application in improving the quality of cancer registry data, supporting service planning, and enhancing the quality of routinely collected data for research.
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Affiliation(s)
- Stephen R. Ali
- Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, United Kingdom
- Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, United Kingdom
| | - Huw Strafford
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom
- Health Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | - Thomas D. Dobbs
- Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, United Kingdom
- Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, United Kingdom
| | - Beata Fonferko-Shadrach
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | - Arron S. Lacey
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom
- Health Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | - William Owen Pickrell
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom
- Department of Neurology, Morriston Hospital, Swansea, United Kingdom
| | - Hayley A. Hutchings
- Patient and Population Health and Informatics Research, Swansea University Medical School, Swansea, United Kingdom
| | - Iain S. Whitaker
- Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, United Kingdom
- Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, United Kingdom
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Kooragayala K, Lou J, Hong YK. ASO Author Reflections: Natural Language Processing Aids in the Detection of Incidental Pancreatic Lesions in Cross-Sectional Imaging. Ann Surg Oncol 2022; 29:8520-8521. [PMID: 35969301 DOI: 10.1245/s10434-022-12407-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 11/18/2022]
Affiliation(s)
| | - Johanna Lou
- Department of Surgery, Cooper University Hospital, Camden, NJ, USA
| | - Young Ki Hong
- Department of Surgery, Cooper University Hospital, Camden, NJ, USA
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30
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Kooragayala K, Crudeli C, Kalola A, Bhat V, Lou J, Sensenig R, Atabek U, Echeverria K, Hong Y. Utilization of Natural Language Processing Software to Identify Worrisome Pancreatic Lesions. Ann Surg Oncol 2022; 29:8513-8519. [PMID: 35969302 DOI: 10.1245/s10434-022-12391-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 07/23/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Computed tomography (CT) imaging is routinely obtained for diagnostics, especially in trauma and emergency rooms, often identifying incidental findings. We utilized a natural language processing (NLP) algorithm to quantify the incidence of clinically relevant pancreatic lesions in CT imaging. PATIENTS AND METHODS We utilized the electronic medical record to perform a retrospective chart review of adult patients admitted for trauma to a level 1 tertiary care center between 2010 and 2020 who underwent abdominal CT imaging. An open-source NLP software was used to identify patients with intrapapillary mucinous neoplasms (IPMN), pancreatic cysts, pancreatic ductal dilation, or pancreatic masses after optimizing the algorithm using a test group of patients who underwent pancreatic surgery. RESULTS The algorithm identified pancreatic lesions in 27 of 28 patients who underwent pancreatic surgery and excluded 1 patient who had a pure ampullary mass. The study cohort consisted of 18,769 patients who met our inclusion criteria admitted to the hospital. Of this population, 232 were found to have pancreatic lesions of interest. There were 48 (20.7%) patients with concern for IPMN, pancreatic cysts in 36 (15.5%), concerning masses in 30 (12.9%), traumatic findings in 44 (19.0%), pancreatitis in 41 (17.7%), and ductal abnormalities in 19 (18.2%) patients. Prior pancreatic surgery and other findings were identified in 14 (6.0%) patients. CONCLUSIONS In this study, we propose a novel use of NLP software to identify potentially malignant pancreatic lesions annotated in CT imaging performed for other purposes. This methodology can significantly increase the screening and automated referral for the management of precancerous lesions.
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Affiliation(s)
| | - Connor Crudeli
- Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Ami Kalola
- Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Vipul Bhat
- Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Johanna Lou
- Department Surgery, Cooper University Hospital, Camden, NJ, USA
| | | | - Umur Atabek
- Department Surgery, Cooper University Hospital, Camden, NJ, USA
| | - Karla Echeverria
- Department of Trauma, Cooper University Hospital, Camden, NJ, USA
| | - Young Hong
- Department Surgery, Cooper University Hospital, Camden, NJ, USA.
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Linna N, Kahn CE. Applications of Natural Language Processing in Radiology: A Systematic Review. Int J Med Inform 2022; 163:104779. [DOI: 10.1016/j.ijmedinf.2022.104779] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/28/2022] [Accepted: 04/21/2022] [Indexed: 12/27/2022]
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Ren G, Yu K, Xie Z, Liu L, Wang P, Zhang W, Wang Y, Wu X. Differentiation of lumbar disc herniation and lumbar spinal stenosis using natural language processing–based machine learning based on positive symptoms. Neurosurg Focus 2022; 52:E7. [DOI: 10.3171/2022.1.focus21561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 01/20/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE
The purpose of this study was to develop natural language processing (NLP)–based machine learning algorithms to automatically differentiate lumbar disc herniation (LDH) and lumbar spinal stenosis (LSS) based on positive symptoms in free-text admission notes. The secondary purpose was to compare the performance of the deep learning algorithm with the ensemble model on the current task.
METHODS
In total, 1921 patients whose principal diagnosis was LDH or LSS between June 2013 and June 2020 at Zhongda Hospital, affiliated with Southeast University, were retrospectively analyzed. The data set was randomly divided into a training set and testing set at a 7:3 ratio. Long Short-Term Memory (LSTM) and extreme gradient boosting (XGBoost) models were developed in this study. NLP algorithms were assessed on the testing set by the following metrics: receiver operating characteristic (ROC) curve, area under the curve (AUC), accuracy score, recall score, F1 score, and precision score.
RESULTS
In the testing set, the LSTM model achieved an AUC of 0.8487, accuracy score of 0.7818, recall score of 0.9045, F1 score of 0.8108, and precision score of 0.7347. In comparison, the XGBoost model achieved an AUC of 0.7565, accuracy score of 0.6961, recall score of 0.7387, F1 score of 0.7153, and precision score of 0.6934.
CONCLUSIONS
NLP-based machine learning algorithms were a promising auxiliary to the electronic health record in spine disease diagnosis. LSTM, the deep learning model, showed better capacity compared with the widely used ensemble model, XGBoost, in differentiation of LDH and LSS using positive symptoms. This study presents a proof of concept for the application of NLP in prediagnosis of spine disease.
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Affiliation(s)
- GuanRui Ren
- Zhongda Hospital, Medical College, Southeast University, Nanjing; and
| | - Kun Yu
- Nanjing Jiangbei Hospital, Nanjing, Jiangsu, China
| | - ZhiYang Xie
- Zhongda Hospital, Medical College, Southeast University, Nanjing; and
| | - Lei Liu
- Zhongda Hospital, Medical College, Southeast University, Nanjing; and
| | - PeiYang Wang
- Zhongda Hospital, Medical College, Southeast University, Nanjing; and
| | - Wei Zhang
- Zhongda Hospital, Medical College, Southeast University, Nanjing; and
| | - YunTao Wang
- Zhongda Hospital, Medical College, Southeast University, Nanjing; and
| | - XiaoTao Wu
- Zhongda Hospital, Medical College, Southeast University, Nanjing; and
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A Natural Language Interface for an Autonomous Camera Control System on the da Vinci Surgical Robot. ROBOTICS 2022. [DOI: 10.3390/robotics11020040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Positioning a camera during laparoscopic and robotic procedures is challenging and essential for successful operations. During surgery, if the camera view is not optimal, surgery becomes more complex and potentially error-prone. To address this need, we have developed a voice interface to an autonomous camera system that can trigger behavioral changes and be more of a partner to the surgeon. Similarly to a human operator, the camera can take cues from the surgeon to help create optimized surgical camera views. It has the advantage of nominal behavior that is helpful in most general cases and has a natural language interface that makes it dynamically customizable and on-demand. It permits the control of a camera with a higher level of abstraction. This paper shows the implementation details and usability of a voice-activated autonomous camera system. A voice activation test on a limited set of practiced key phrases was performed using both online and offline voice recognition systems. The results show an on-average greater than 94% recognition accuracy for the online system and 86% accuracy for the offline system. However, the response time of the online system was greater than 1.5 s, whereas the local system was 0.6 s. This work is a step towards cooperative surgical robots that will effectively partner with human operators to enable more robust surgeries. A video link of the system in operation is provided in this paper.
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Carrillo-Larco RM, Castillo-Cara M, Lovón-Melgarejo J. Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters. Wellcome Open Res 2022. [DOI: 10.12688/wellcomeopenres.16867.4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, 22 terms appeared with a frequency of 0.05 or greater, while in 2021 27 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.
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35
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Dagli MM, Rajesh A, Asaad M, Butler CE. The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review. Am Surg 2021:31348211065101. [PMID: 34958252 DOI: 10.1177/00031348211065101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Interest in the use of artificial intelligence (AI) and machine learning (ML) in medicine has grown exponentially over the last few years. With its ability to enhance speed, precision, and efficiency, AI has immense potential, especially in the field of surgery. This article aims to provide a comprehensive literature review of artificial intelligence as it applies to surgery and discuss practical examples, current applications, and challenges to the adoption of this technology. Furthermore, we elaborate on the utility of natural language processing and computer vision in improving surgical outcomes, research, and patient care.
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Affiliation(s)
| | - Aashish Rajesh
- Department of Surgery, 14742University of Texas Health Science Center, San Antonio, TX, USA
| | - Malke Asaad
- Department of Plastic & Reconstructive Surgery, 571198the University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles E Butler
- Department of Plastic & Reconstructive Surgery, 571198the University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Carrillo-Larco RM, Castillo-Cara M, Lovón-Melgarejo J. Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters. Wellcome Open Res 2021. [DOI: 10.12688/wellcomeopenres.16867.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, 22 terms appeared with a frequency of 0.05 or greater, while in 2021 27 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.
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Carrillo-Larco RM, Castillo-Cara M, Lovón-Melgarejo J. Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters. Wellcome Open Res 2021. [DOI: 10.12688/wellcomeopenres.16867.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, nine terms appeared with a frequency of 0.10 or greater, while in 2021 43 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.
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Gumbs AA, Frigerio I, Spolverato G, Croner R, Illanes A, Chouillard E, Elyan E. Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery? SENSORS (BASEL, SWITZERLAND) 2021; 21:5526. [PMID: 34450976 PMCID: PMC8400539 DOI: 10.3390/s21165526] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/03/2021] [Accepted: 08/11/2021] [Indexed: 12/30/2022]
Abstract
Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.
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Affiliation(s)
- Andrew A. Gumbs
- Centre Hospitalier Intercommunal de POISSY/SAINT-GERMAIN-EN-LAYE 10, Rue Champ de Gaillard, 78300 Poissy, France;
| | - Isabella Frigerio
- Department of Hepato-Pancreato-Biliary Surgery, Pederzoli Hospital, 37019 Peschiera del Garda, Italy;
| | - Gaya Spolverato
- Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, 35122 Padova, Italy;
| | - Roland Croner
- Department of General-, Visceral-, Vascular- and Transplantation Surgery, University of Magdeburg, Haus 60a, Leipziger Str. 44, 39120 Magdeburg, Germany;
| | - Alfredo Illanes
- INKA–Innovation Laboratory for Image Guided Therapy, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany;
| | - Elie Chouillard
- Centre Hospitalier Intercommunal de POISSY/SAINT-GERMAIN-EN-LAYE 10, Rue Champ de Gaillard, 78300 Poissy, France;
| | - Eyad Elyan
- School of Computing, Robert Gordon University, Aberdeen AB10 7JG, UK;
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Carrillo-Larco RM, Castillo-Cara M, Lovón-Melgarejo J. Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters. Wellcome Open Res 2021. [DOI: 10.12688/wellcomeopenres.16867.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, nine terms appeared with a frequency of 0.10 or greater, while in 2021 43 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.
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Comment on "Natural Language Processing in Surgery: A Systematic Review and Meta-analysis". Ann Surg 2021; 274:e941-e942. [PMID: 34016811 DOI: 10.1097/sla.0000000000004939] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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