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Taylor N, Kormilitzin A, Lorge I, Nevado-Holgado A, Cipriani A, Joyce DW. Model development for bespoke large language models for digital triage assistance in mental health care. Artif Intell Med 2024; 157:102988. [PMID: 39383705 DOI: 10.1016/j.artmed.2024.102988] [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: 03/28/2024] [Revised: 08/24/2024] [Accepted: 09/18/2024] [Indexed: 10/11/2024]
Abstract
Contemporary large language models (LLMs) may have utility for processing unstructured, narrative free-text clinical data contained in electronic health records (EHRs) - a particularly important use-case for mental health where a majority of routinely-collected patient data lacks structured, machine-readable content. A significant problem for the United Kingdom's National Health Service (NHS) are the long waiting lists for specialist mental healthcare. According to NHS data (NHS Digital, 2024), in each month of 2023, there were between 370,000 and 470,000 individual new referrals into secondary mental healthcare services. Referrals must be triaged by clinicians, using clinical information contained in the patient's EHR to arrive at a decision about the most appropriate mental healthcare team to assess and potentially treat these patients. The ability to efficiently recommend a relevant team by ingesting potentially voluminous clinical notes could help services both reduce referral waiting times and with the right technology, improve the evidence available to justify triage decisions. We present and evaluate three different approaches for LLM-based, end-to-end ingestion of variable-length clinical EHR data to assist clinicians when triaging referrals. Our model is able to deliver triage recommendations consistent with existing clinical practices and its architecture was implemented on a single GPU, making it practical for implementation in resource-limited NHS environments where private implementations of LLM technology will be necessary to ensure confidential clinical data are appropriately controlled and governed. Code available at: https://github.com/NtaylorOX/BespokeLLM_Triage.
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Affiliation(s)
- Niall Taylor
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom.
| | | | - Isabelle Lorge
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | | | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical research Centre, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Dan W Joyce
- Department of Primary Care and Mental Health, University of Liverpool, Liverpool, United Kingdom; Civic Health Innovation Labs, University of Liverpool, Liverpool, United Kingdom; Mental health Research for Innovation Centre (M-RIC), Mersey Care NHS Foundation Trust, Prescot, Merseyside, United Kingdom
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2
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Newby D, Taylor N, Joyce DW, Winchester LM. Optimising the use of electronic medical records for large scale research in psychiatry. Transl Psychiatry 2024; 14:232. [PMID: 38824136 PMCID: PMC11144247 DOI: 10.1038/s41398-024-02911-1] [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: 02/17/2023] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 06/03/2024] Open
Abstract
The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.
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Affiliation(s)
- Danielle Newby
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Niall Taylor
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Dan W Joyce
- Department of Primary Care and Mental Health and Civic Health, Innovation Labs, Institute of Population Health, University of Liverpool, Liverpool, UK
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Kraljevic Z, Bean D, Shek A, Bendayan R, Hemingway H, Yeung JA, Deng A, Baston A, Ross J, Idowu E, Teo JT, Dobson RJB. Foresight-a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study. Lancet Digit Health 2024; 6:e281-e290. [PMID: 38519155 PMCID: PMC11220626 DOI: 10.1016/s2589-7500(24)00025-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 12/20/2023] [Accepted: 02/05/2024] [Indexed: 03/24/2024]
Abstract
BACKGROUND An electronic health record (EHR) holds detailed longitudinal information about a patient's health status and general clinical history, a large portion of which is stored as unstructured, free text. Existing approaches to model a patient's trajectory focus mostly on structured data and a subset of single-domain outcomes. This study aims to evaluate the effectiveness of Foresight, a generative transformer in temporal modelling of patient data, integrating both free text and structured formats, to predict a diverse array of future medical outcomes, such as disorders, substances (eg, to do with medicines, allergies, or poisonings), procedures, and findings (eg, relating to observations, judgements, or assessments). METHODS Foresight is a novel transformer-based pipeline that uses named entity recognition and linking tools to convert EHR document text into structured, coded concepts, followed by providing probabilistic forecasts for future medical events, such as disorders, substances, procedures, and findings. The Foresight pipeline has four main components: (1) CogStack (data retrieval and preprocessing); (2) the Medical Concept Annotation Toolkit (structuring of the free-text information from EHRs); (3) Foresight Core (deep-learning model for biomedical concept modelling); and (4) the Foresight web application. We processed the entire free-text portion from three different hospital datasets (King's College Hospital [KCH], South London and Maudsley [SLaM], and the US Medical Information Mart for Intensive Care III [MIMIC-III]), resulting in information from 811 336 patients and covering both physical and mental health institutions. We measured the performance of models using custom metrics derived from precision and recall. FINDINGS Foresight achieved a precision@10 (ie, of 10 forecasted candidates, at least one is correct) of 0·68 (SD 0·0027) for the KCH dataset, 0·76 (0·0032) for the SLaM dataset, and 0·88 (0·0018) for the MIMIC-III dataset, for forecasting the next new disorder in a patient timeline. Foresight also achieved a precision@10 value of 0·80 (0·0013) for the KCH dataset, 0·81 (0·0026) for the SLaM dataset, and 0·91 (0·0011) for the MIMIC-III dataset, for forecasting the next new biomedical concept. In addition, Foresight was validated on 34 synthetic patient timelines by five clinicians and achieved a relevancy of 33 (97% [95% CI 91-100]) of 34 for the top forecasted candidate disorder. As a generative model, Foresight can forecast follow-on biomedical concepts for as many steps as required. INTERPRETATION Foresight is a general-purpose model for biomedical concept modelling that can be used for real-world risk forecasting, virtual trials, and clinical research to study the progression of disorders, to simulate interventions and counterfactuals, and for educational purposes. FUNDING National Health Service Artificial Intelligence Laboratory, National Institute for Health and Care Research Biomedical Research Centre, and Health Data Research UK.
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Affiliation(s)
- Zeljko Kraljevic
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; National Institute for Health and Care Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - Dan Bean
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; National Institute for Health and Care Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - Anthony Shek
- Department of Neurology, King's College Hospital National Health Service (NHS) Foundation Trust, London, UK; Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Rebecca Bendayan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; National Institute for Health and Care Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - Harry Hemingway
- Health Data Research UK London and Institute of Health Informatics, University College London, London, UK; NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK
| | - Joshua Au Yeung
- Department of Neurology, King's College Hospital National Health Service (NHS) Foundation Trust, London, UK; Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - Alfred Baston
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Jack Ross
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Esther Idowu
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - James T Teo
- Department of Neurology, King's College Hospital National Health Service (NHS) Foundation Trust, London, UK; Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Health Data Research UK London and Institute of Health Informatics, University College London, London, UK; National Institute for Health and Care Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK; NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK.
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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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Peng C, Yang X, Chen A, Smith KE, PourNejatian N, Costa AB, Martin C, Flores MG, Zhang Y, Magoc T, Lipori G, Mitchell DA, Ospina NS, Ahmed MM, Hogan WR, Shenkman EA, Guo Y, Bian J, Wu Y. A study of generative large language model for medical research and healthcare. NPJ Digit Med 2023; 6:210. [PMID: 37973919 PMCID: PMC10654385 DOI: 10.1038/s41746-023-00958-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 11/01/2023] [Indexed: 11/19/2023] Open
Abstract
There are enormous enthusiasm and concerns in applying large language models (LLMs) to healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT, which are not developed for medical use. This study develops a generative clinical LLM, GatorTronGPT, using 277 billion words of text including (1) 82 billion words of clinical text from 126 clinical departments and approximately 2 million patients at the University of Florida Health and (2) 195 billion words of diverse general English text. We train GatorTronGPT using a GPT-3 architecture with up to 20 billion parameters and evaluate its utility for biomedical natural language processing (NLP) and healthcare text generation. GatorTronGPT improves biomedical natural language processing. We apply GatorTronGPT to generate 20 billion words of synthetic text. Synthetic NLP models trained using synthetic text generated by GatorTronGPT outperform models trained using real-world clinical text. Physicians' Turing test using 1 (worst) to 9 (best) scale shows that there are no significant differences in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p < 0.001). This study provides insights into the opportunities and challenges of LLMs for medical research and healthcare.
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Affiliation(s)
- Cheng Peng
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Xi Yang
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
- Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, FL, USA
| | - Aokun Chen
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
- Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, FL, USA
| | | | | | | | | | | | - Ying Zhang
- Research Computing, University of Florida, Gainesville, FL, USA
| | - Tanja Magoc
- Integrated Data Repository Research Services, University of Florida, Gainesville, FL, USA
| | - Gloria Lipori
- Integrated Data Repository Research Services, University of Florida, Gainesville, FL, USA
- Lillian S. Wells Department of Neurosurgery, Clinical and Translational Science Institute, University of Florida, Gainesville, FL, USA
| | - Duane A Mitchell
- Lillian S. Wells Department of Neurosurgery, Clinical and Translational Science Institute, University of Florida, Gainesville, FL, USA
| | - Naykky S Ospina
- Division of Endocrinology, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Mustafa M Ahmed
- Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
| | - William R Hogan
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Elizabeth A Shenkman
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
- Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
- Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, FL, USA
| | - Yonghui Wu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
- Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, FL, USA.
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6
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Searle T, Ibrahim Z, Teo J, Dobson RJB. Discharge summary hospital course summarisation of in patient Electronic Health Record text with clinical concept guided deep pre-trained Transformer models. J Biomed Inform 2023; 141:104358. [PMID: 37023846 DOI: 10.1016/j.jbi.2023.104358] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 03/29/2023] [Accepted: 04/02/2023] [Indexed: 04/08/2023]
Abstract
Brief Hospital Course (BHC) summaries are succinct summaries of an entire hospital encounter, embedded within discharge summaries, written by senior clinicians responsible for the overall care of a patient. Methods to automatically produce summaries from inpatient documentation would be invaluable in reducing clinician manual burden of summarising documents under high time-pressure to admit and discharge patients. Automatically producing these summaries from the inpatient course, is a complex, multi-document summarisation task, as source notes are written from various perspectives (e.g. nursing, doctor, radiology), during the course of the hospitalisation. We demonstrate a range of methods for BHC summarisation demonstrating the performance of deep learning summarisation models across extractive and abstractive summarisation scenarios. We also test a novel ensemble extractive and abstractive summarisation model that incorporates a medical concept ontology (SNOMED) as a clinical guidance signal and shows superior performance in 2 real-world clinical data sets.
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Affiliation(s)
- Thomas Searle
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Zina Ibrahim
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - James Teo
- King's College Hospital NHS Foundation Trust, London, UK
| | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Institute of Health Informatics, University College London, London, UK
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Fu S, Vassilaki M, Ibrahim OA, Petersen RC, Pagali S, St Sauver J, Moon S, Wang L, Fan JW, Liu H, Sohn S. Quality assessment of functional status documentation in EHRs across different healthcare institutions. Front Digit Health 2022; 4:958539. [PMID: 36238199 PMCID: PMC9552292 DOI: 10.3389/fdgth.2022.958539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/05/2022] [Indexed: 11/29/2022] Open
Abstract
The secondary use of electronic health records (EHRs) faces challenges in the form of varying data quality-related issues. To address that, we retrospectively assessed the quality of functional status documentation in EHRs of persons participating in Mayo Clinic Study of Aging (MCSA). We used a convergent parallel design to collect quantitative and qualitative data and independently analyzed the findings. We discovered a heterogeneous documentation process, where the care practice teams, institutions, and EHR systems all play an important role in how text data is documented and organized. Four prevalent instrument-assisted documentation (iDoc) expressions were identified based on three distinct instruments: Epic smart form, questionnaire, and occupational therapy and physical therapy templates. We found strong differences in the usage, information quality (intrinsic and contextual), and naturality of language among different type of iDoc expressions. These variations can be caused by different source instruments, information providers, practice settings, care events and institutions. In addition, iDoc expressions are context specific and thus shall not be viewed and processed uniformly. We recommend conducting data quality assessment of unstructured EHR text prior to using the information.
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Affiliation(s)
- Sunyang Fu
- Department of AI and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Maria Vassilaki
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Omar A. Ibrahim
- Department of AI and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Ronald C. Petersen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Sandeep Pagali
- Department of Medicine, Mayo Clinic, Rochester, MN, United States
| | - Jennifer St Sauver
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Sungrim Moon
- Department of AI and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Liwei Wang
- Department of AI and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Jungwei W. Fan
- Department of AI and Informatics, Mayo Clinic, Rochester, MN, United States
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Hongfang Liu
- Department of AI and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Sunghwan Sohn
- Department of AI and Informatics, Mayo Clinic, Rochester, MN, United States
- Correspondence: Sunghwan Sohn
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Liu J, Capurro D, Nguyen A, Verspoor K. "Note Bloat" impacts deep learning-based NLP models for clinical prediction tasks. J Biomed Inform 2022; 133:104149. [PMID: 35878821 DOI: 10.1016/j.jbi.2022.104149] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/28/2022] [Accepted: 07/19/2022] [Indexed: 10/17/2022]
Abstract
One unintended consequence of the Electronic Health Records (EHR) implementation is the overuse of content-importing technology, such as copy-and-paste, that creates "bloated" notes containing large amounts of textual redundancy. Despite the rising interest in applying machine learning models to learn from real-patient data, it is unclear how the phenomenon of note bloat might affect the Natural Language Processing (NLP) models derived from these notes. Therefore, in this work we examine the impact of redundancy on deep learning-based NLP models, considering four clinical prediction tasks using a publicly available EHR database. We applied two deduplication methods to the hospital notes, identifying large quantities of redundancy, and found that removing the redundancy usually has little negative impact on downstream performances, and can in certain circumstances assist models to achieve significantly better results. We also showed it is possible to attack model predictions by simply adding note duplicates, causing changes of correct predictions made by trained models into wrong predictions. In conclusion, we demonstrated that EHR text redundancy substantively affects NLP models for clinical prediction tasks, showing that the awareness of clinical contexts and robust modeling methods are important to create effective and reliable NLP systems in healthcare contexts.
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Affiliation(s)
- Jinghui Liu
- School of Computing and Information Systems, The University of Melbourne, Victoria, Australia; Australian e-Health Research Centre, CSIRO, Brisbane, Australia.
| | - Daniel Capurro
- School of Computing and Information Systems, The University of Melbourne, Victoria, Australia; Centre for Digital Transformation of Health, Melbourne Medical School, The University of Melbourne, Victoria, Australia.
| | - Anthony Nguyen
- Australian e-Health Research Centre, CSIRO, Brisbane, Australia.
| | - Karin Verspoor
- School of Computing and Information Systems, The University of Melbourne, Victoria, Australia; Centre for Digital Transformation of Health, Melbourne Medical School, The University of Melbourne, Victoria, Australia; School of Computing Technologies, RMIT University, Victoria, Australia.
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