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Wieland-Jorna Y, van Kooten D, Verheij RA, de Man Y, Francke AL, Oosterveld-Vlug MG. Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review. JAMIA Open 2024; 7:ooae044. [PMID: 38798774 PMCID: PMC11126158 DOI: 10.1093/jamiaopen/ooae044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/21/2024] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Objective Natural language processing (NLP) can enhance research on activities of daily living (ADL) by extracting structured information from unstructured electronic health records (EHRs) notes. This review aims to give insight into the state-of-the-art, usability, and performance of NLP systems to extract information on ADL from EHRs. Materials and Methods A systematic review was conducted based on searches in Pubmed, Embase, Cinahl, Web of Science, and Scopus. Studies published between 2017 and 2022 were selected based on predefined eligibility criteria. Results The review identified 22 studies. Most studies (65%) used NLP for classifying unstructured EHR data on 1 or 2 ADL. Deep learning, combined with a ruled-based method or machine learning, was the approach most commonly used. NLP systems varied widely in terms of the pre-processing and algorithms. Common performance evaluation methods were cross-validation and train/test datasets, with F1, precision, and sensitivity as the most frequently reported evaluation metrics. Most studies reported relativity high overall scores on the evaluation metrics. Discussion NLP systems are valuable for the extraction of unstructured EHR data on ADL. However, comparing the performance of NLP systems is difficult due to the diversity of the studies and challenges related to the dataset, including restricted access to EHR data, inadequate documentation, lack of granularity, and small datasets. Conclusion This systematic review indicates that NLP is promising for deriving information on ADL from unstructured EHR notes. However, what the best-performing NLP system is, depends on characteristics of the dataset, research question, and type of ADL.
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Affiliation(s)
- Yvonne Wieland-Jorna
- Netherlands Institute for Health Services Research (Nivel), Utrecht, Postbus 1568, 3500 BN, The Netherlands
- Tranzo, School of Social Sciences and Behavioural Research, Tilburg University, Tilburg, Postbus 90153, 5000 LE, The Netherlands
| | - Daan van Kooten
- Netherlands Institute for Health Services Research (Nivel), Utrecht, Postbus 1568, 3500 BN, The Netherlands
| | - Robert A Verheij
- Netherlands Institute for Health Services Research (Nivel), Utrecht, Postbus 1568, 3500 BN, The Netherlands
- Tranzo, School of Social Sciences and Behavioural Research, Tilburg University, Tilburg, Postbus 90153, 5000 LE, The Netherlands
| | - Yvonne de Man
- Netherlands Institute for Health Services Research (Nivel), Utrecht, Postbus 1568, 3500 BN, The Netherlands
| | - Anneke L Francke
- Netherlands Institute for Health Services Research (Nivel), Utrecht, Postbus 1568, 3500 BN, The Netherlands
- Department of Public and Occupational Health, Location Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Postbus 7057, 1007 MB, The Netherlands
| | - Mariska G Oosterveld-Vlug
- Netherlands Institute for Health Services Research (Nivel), Utrecht, Postbus 1568, 3500 BN, The Netherlands
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Benavent D, Benavent-Núñez M, Marin-Corral J, Arias-Manjón J, Navarro-Compán V, Taberna M, Salcedo I, Peiteado D, Carmona L, de Miguel E. Natural language processing to identify and characterize spondyloarthritis in clinical practice. RMD Open 2024; 10:e004302. [PMID: 38796183 PMCID: PMC11129039 DOI: 10.1136/rmdopen-2024-004302] [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: 03/05/2024] [Accepted: 05/07/2024] [Indexed: 05/28/2024] Open
Abstract
OBJECTIVE This study aims to use a novel technology based on natural language processing (NLP) to extract clinical information from electronic health records (EHRs) to characterise the clinical profile of patients diagnosed with spondyloarthritis (SpA) at a large-scale hospital. METHODS An observational, retrospective analysis was conducted on EHR data from all patients with SpA (including psoriatic arthritis (PsA)) at Hospital Universitario La Paz, between 2020 and 2022. Data were collected using Savana Manager, an NLP-based system, enabling the extraction of information from unstructured, free-text EHRs. Variables analysed included demographic data, SpA subtypes, comorbidities and treatments. The performance of the technology in detecting SpA clinical entities was evaluated through precision, recall and F-1 score metrics. RESULTS From a hospital population of 639 474 patients, 4337 (0.7%) patients had a diagnosis of SpA or their subtypes in their EHR. The population predominantly comprised men (55.3%) with a mean age of 50.9 years. Peripheral SpA (including PsA) was reported in 31.6%, axial SpA in 20.9%, both axial and peripheral SpA in 3.7%, while 43.7% of patients did not have the SpA subtype reported. Common comorbidities included hypertension (25.0%), dyslipidaemia (22.2%) and diabetes mellitus (15.5%). The use of conventional disease-modifying antirheumatic drugs (csDMARDs) and biological DMARDs (bDMARDs) was documented, with methotrexate (25.3% of patients) being the most used csDMARDs and adalimumab (10.6% of patients) the most used bDMARD. The NLP technology demonstrated high precision and recall, with all the assessed F-1 score values over 0.80, indicating reliable data extraction. CONCLUSION The application of NLP technology facilitated the characterisation of the SpA patient profile, including demographics, clinical features, comorbidities and treatments. This study supports the utility of NLP in enhancing the understanding of SpA and suggests its potential for improving patient management by extracting meaningful information from unstructured EHR data.
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Affiliation(s)
- Diego Benavent
- Savana Research S.L, Madrid, Spain
- Rheumatology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain
| | - María Benavent-Núñez
- Savana Research S.L, Madrid, Spain
- Nutrition Department, CEU San Pablo Monteprincipe School, Madrid, Spain
| | | | | | | | | | | | - Diana Peiteado
- Rheumatology, Hospital Universitario La Paz, Madrid, Spain
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Mickley JP, Grove AF, Rouzrokh P, Yang L, Larson AN, Sanchez-Sotello J, Maradit Kremers H, Wyles CC. A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence. Arthritis Care Res (Hoboken) 2024; 76:590-599. [PMID: 37849415 DOI: 10.1002/acr.25260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/27/2023] [Accepted: 10/13/2023] [Indexed: 10/19/2023]
Abstract
The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.
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Chandwar K, Prasanna Misra D. What does artificial intelligence mean in rheumatology? Arch Rheumatol 2024; 39:1-9. [PMID: 38774703 PMCID: PMC11104749 DOI: 10.46497/archrheumatol.2024.10664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 01/29/2024] [Indexed: 05/24/2024] Open
Abstract
Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician's diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential.
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Affiliation(s)
- Kunal Chandwar
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, India
| | - Durga Prasanna Misra
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, India
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Román Ivorra JA, Trallero-Araguas E, Lopez Lasanta M, Cebrián L, Lojo L, López-Muñíz B, Fernández-Melon J, Núñez B, Silva-Fernández L, Veiga Cabello R, Ahijado P, De la Morena Barrio I, Costas Torrijo N, Safont B, Ornilla E, Restrepo J, Campo A, Andreu JL, Díez E, López Robles A, Bollo E, Benavent D, Vilanova D, Luján Valdés S, Castellanos-Moreira R. Prevalence and clinical characteristics of patients with rheumatoid arthritis with interstitial lung disease using unstructured healthcare data and machine learning. RMD Open 2024; 10:e003353. [PMID: 38296310 PMCID: PMC10836356 DOI: 10.1136/rmdopen-2023-003353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 01/03/2024] [Indexed: 02/05/2024] Open
Abstract
OBJECTIVES Real-world data regarding rheumatoid arthritis (RA) and its association with interstitial lung disease (ILD) is still scarce. This study aimed to estimate the prevalence of RA and ILD in patients with RA (RAILD) in Spain, and to compare clinical characteristics of patients with RA with and without ILD using natural language processing (NLP) on electronic health records (EHR). METHODS Observational case-control, retrospective and multicentre study based on the secondary use of unstructured clinical data from patients with adult RA and RAILD from nine hospitals between 2014 and 2019. NLP was used to extract unstructured clinical information from EHR and standardise it into a SNOMED-CT terminology. Prevalence of RA and RAILD were calculated, and a descriptive analysis was performed. Characteristics between patients with RAILD and RA patients without ILD (RAnonILD) were compared. RESULTS From a source population of 3 176 165 patients and 64 241 683 EHRs, 13 958 patients with RA were identified. Of those, 5.1% patients additionally had ILD (RAILD). The overall age-adjusted prevalence of RA and RAILD were 0.53% and 0.02%, respectively. The most common ILD subtype was usual interstitial pneumonia (29.3%). When comparing RAILD versus RAnonILD patients, RAILD patients were older and had more comorbidities, notably concerning infections (33.6% vs 16.5%, p<0.001), malignancies (15.9% vs 8.5%, p<0.001) and cardiovascular disease (25.8% vs 13.9%, p<0.001) than RAnonILD. RAILD patients also had higher inflammatory burden reflected in more pharmacological prescriptions and higher inflammatory parameters and presented a higher in-hospital mortality with a higher risk of death (HR 2.32; 95% CI 1.59 to 2.81, p<0.001). CONCLUSIONS We found an estimated age-adjusted prevalence of RA and RAILD by analysing real-world data through NLP. RAILD patients were more vulnerable at the time of inclusion with higher comorbidity and inflammatory burden than RAnonILD, which correlated with higher mortality.
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Affiliation(s)
- Jose A Román Ivorra
- Reumathology Department, Hospital Politécnico y Universitario La Fe, Valencia, Spain
| | | | - Maria Lopez Lasanta
- Rheumatology Department, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Laura Cebrián
- Rheumatology Department, Hospital Infanta Leonor, Madrid, Spain
| | - Leticia Lojo
- Rheumatology Department, Hospital Infanta Leonor, Madrid, Spain
| | | | | | - Belén Núñez
- Pneumology Department, Hospital Universitario Son Espases, Palma, Spain
| | | | - Raúl Veiga Cabello
- Rheumatology Department, Hospital Universitario Central de la Defensa Gómez Ulla, Madrid, Spain
| | - Pilar Ahijado
- Rheumatology, Hospital Universitario Fuenlabrada, Madrid, Spain
| | | | | | - Belén Safont
- Pneumology Department, Hospital Clinico Universitario, Valencia, Spain
| | - Enrique Ornilla
- Rheumatology Department, Clínica Universidad de Navarra, Pamplona, Spain
| | - Juliana Restrepo
- Rheumatology Department, Clinica Universidad de Navarra, Pamplona, Spain
| | - Arantxa Campo
- Pneumology Department, Clinica Universidad de Navarra, Pamplona, Spain
| | - Jose L Andreu
- Rheumatology Department, Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain
| | - Elvira Díez
- Rheumatology Department, Complejo Asistencial Universitario de Leon, León, Spain
| | | | - Elena Bollo
- Pneumology Department, Complejo Asistencial Universitario de Leon, Leon, Spain
| | | | - David Vilanova
- Health Economics and Outcomes Research, Bristol-Myers Squibb Company, Madrid, Spain
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Benavent D, Muñoz-Fernández S, De la Morena I, Fernández-Nebro A, Marín-Corral J, Castillo Rosa E, Taberna M, Sanabra C, Sastre C. Using natural language processing to explore characteristics and management of patients with axial spondyloarthritis and psoriatic arthritis treated under real-world conditions in Spain: SpAINET study. Ther Adv Musculoskelet Dis 2023; 15:1759720X231220818. [PMID: 38146537 PMCID: PMC10749530 DOI: 10.1177/1759720x231220818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 11/28/2023] [Indexed: 12/27/2023] Open
Abstract
Background Spondyloarthritis (SpA) is a group of related but phenotypically distinct inflammatory disorders that include axial SpA (axSpA) and psoriatic arthritis (PsA). Information on the characteristics and management of these patients in the real world remains scarce. Objectives To explore the characteristics and management [disease activity assessment and treatment with secukinumab (SEC) or other biologic disease-modifying antirheumatic drugs (bDMARDs)] of axSpA and PsA patients using natural language processing (NLP) in Electronic Health Records (EHRs). Design National, multicenter, observational, and retrospective study. Methods We analyzed free-text and structured clinical information from EHR at three hospitals. All adult patients with axSpA, PsA or non-classified SpA from 2018 to 2021 with minimum follow-up of three months were included when starting SEC or other bDMARDs. Clinical variables were extracted using EHRead® technology based on Systemized Nomenclature of Medicine-Clinical Terms (SNOMED CT) terminology. Results Out of 887,735 patients, 758 were included, of which 328 had axSpA [58.5% male; mean (SD) age of 50.7 (12.7) years], 365 PsA [54.8% female, 53.9 (12.4) years], and 65 non-classified SpA. Mean (SD) time since diagnosis was 36.8 (61.0) and 24.1 (35.2) months for axSpA and PsA, respectively. Only 116 axSpA patients (35.3%) had available Ankylosing Spondylitis Disease Activity Score (ASDAS) or Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) at bDMARD onset, of which 61 presented active disease. Disease Activity in PSoriatic Arthritis (DAPSA) or Disease Assessment Score - 28 joints (DAS-28) values at bDMARD onset were available for only 61 PsA (16.7%) patients, with 23 of them having active disease. The number of patients with available tender joint count or swollen joint count assessment was 68 (20.7%) and 59 (18%) for axSpA, and 115 (31.5%) and 119 (32.6%) for PsA, respectively. SEC was used in 63 (19.2%) axSpA patients and in 63 (17.3%) PsA patients. Conclusion Using NLP, the study showed that around one-third of axSpA and one-sixth of PsA patients have disease activity assessments with ASDAS/BASDAI or DAPSA/DAS-28, respectively, highlighting an area of improvement in these patients' management.
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Affiliation(s)
- Diego Benavent
- SAVANA Research S.L., Calle de Larra 12, Madrid 28013, Spain
| | - Santiago Muñoz-Fernández
- Hospital Universitario Infanta Sofía, Universidad Europea de Madrid, San Sebastián de los Reyes, Madrid, Spain
| | - Isabel De la Morena
- Department of Rheumatology, Hospital Clínico Universitario de Valencia, Valencia, Valencia, Spain
| | - Antonio Fernández-Nebro
- Instituto de Investigación Biomédica de Málaga (IBIMA)-Plataforma Bionand, Málaga, Spain
- UGC de Reumatología, Hospital Regional Universitario de Málaga, Málaga, Spain
- Departamento de Medicina, Universidad de Málaga, Málaga, Spain
| | | | | | | | | | - Carlos Sastre
- Medical Department, Novartis Farmacéutica SA., Barcelona, Spain
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Wang J, Tian Y, Zhou T, Tong D, Ma J, Li J. A survey of artificial intelligence in rheumatoid arthritis. RHEUMATOLOGY AND IMMUNOLOGY RESEARCH 2023; 4:69-77. [PMID: 37485476 PMCID: PMC10362600 DOI: 10.2478/rir-2023-0011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/14/2023] [Indexed: 07/25/2023]
Abstract
The article offers a survey of currently notable artificial intelligence methods (released between 2019-2023), with a particular emphasis on the latest advancements in detecting rheumatoid arthritis (RA) at an early stage, providing early treatment, and managing the disease. We discussed challenges in these areas followed by specific artificial intelligence (AI) techniques and summarized advances, relevant strengths, and obstacles. Overall, the application of AI in the fields of RA has the potential to enable healthcare professionals to detect RA at an earlier stage, thereby facilitating timely intervention and better disease management. However, more research is required to confirm the precision and dependability of AI in RA, and several problems such as technological and ethical concerns related to these approaches must be resolved before their widespread adoption.
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Affiliation(s)
- Jiaqi Wang
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou310027, Zhejiang Province, China
| | - Tianshu Zhou
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Danyang Tong
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Jing Ma
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Jingsong Li
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou310027, Zhejiang Province, China
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Houssein EH, Mohamed RE, Ali AA. Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques. Sci Rep 2023; 13:7173. [PMID: 37138014 PMCID: PMC10156668 DOI: 10.1038/s41598-023-34294-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 04/27/2023] [Indexed: 05/05/2023] Open
Abstract
Heart disease remains the major cause of death, despite recent improvements in prediction and prevention. Risk factor identification is the main step in diagnosing and preventing heart disease. Automatically detecting risk factors for heart disease in clinical notes can help with disease progression modeling and clinical decision-making. Many studies have attempted to detect risk factors for heart disease, but none have identified all risk factors. These studies have proposed hybrid systems that combine knowledge-driven and data-driven techniques, based on dictionaries, rules, and machine learning methods that require significant human effort. The National Center for Informatics for Integrating Biology and Beyond (i2b2) proposed a clinical natural language processing (NLP) challenge in 2014, with a track (track2) focused on detecting risk factors for heart disease risk factors in clinical notes over time. Clinical narratives provide a wealth of information that can be extracted using NLP and Deep Learning techniques. The objective of this paper is to improve on previous work in this area as part of the 2014 i2b2 challenge by identifying tags and attributes relevant to disease diagnosis, risk factors, and medications by providing advanced techniques of using stacked word embeddings. The i2b2 heart disease risk factors challenge dataset has shown significant improvement by using the approach of stacking embeddings, which combines various embeddings. Our model achieved an F1 score of 93.66% by using BERT and character embeddings (CHARACTER-BERT Embedding) stacking. The proposed model has significant results compared to all other models and systems that we developed for the 2014 i2b2 challenge.
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Affiliation(s)
- Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Rehab E Mohamed
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Abdelmgeid A Ali
- Faculty of Computers and Information, Minia University, Minia, Egypt
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New developments in electronic health record analysis. Nat Rev Rheumatol 2023; 19:74-75. [PMID: 36536118 PMCID: PMC9761741 DOI: 10.1038/s41584-022-00894-1] [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: 12/23/2022]
Abstract
Consolidation of electronic health record (EHR) databases can improve our understanding of real-world patient journeys2. A newly developed natural language processing (NLP) pipeline can automatically extract outcome measures from EHR databases and has potential for EHR data analysis for both clinical and research purposes4. Although an automated phenotyping algorithm has potential for diagnosing patients with certain subtypes of rheumatoid arthritis using EHR data, design choices (such as the definition of what key elements are used for phenotyping) and missing data currently limit its clinical utility3.
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