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AB1435 CLINICAL PREDICTION MODELS FOR METHOTREXATE OUTCOMES IN PATIENTS WITH RHEUMATOID ARTHRITIS: SYSTEMATIC REVIEW AND CRITICAL APPRAISAL

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Annals of the Rheumatic Diseases 2022年 81卷 1823-1824页

作者： C. Gehringer G. Martin K. Hyrich S. Verstappen J. Sergeant University of Manchester Centre for Epidemiology Versus Arthritis Manchester United Kingdom University of Manchester Centre for Health Informatics Division of Informatics Imaging and Data Science Manchester United Kingdom Manchester University NHS Foundation Trust NIHR Manchester Biomedical Research Centre Manchester United Kingdom University of Manchester Centre for Biostatistics Manchester Academic Health Science Centre Manchester United Kingdom

Background Methotrexate (MTX) is the preferred first line therapy for rheumatoid arthritis (RA). MTX has several advantages over other treatments including effectiveness and low cost; however, around 40% of patients are classed as non-responders after 6 months (1). Therefore, there is a clinical need to identify patients at high-risk of poor outcomes, such that patients could potentially be fast tracked onto alternative therapies to improve their clinical outcomes and quality of life. Such risk stratification is possible through prognostic prediction models, although models which have previously been developed appear to have had little impact on practice. This may be in part due to methodological features of their development and validation but, to date, no review has collated the evidence in this field. Objectives This systematic review aimed to (i) identify and summarise multivariable prediction models of MTX treatment outcomes in biologic-naïve adult RA patients, and (ii) critically appraise their methodological properties. Methods The electronic databases Medline and Embase were searched to identify studies developing or validating prediction models of MTX outcomes in the population of interest, including demographic, disease-specific or treatment-related covariates, published after 2005. Models were stratified by outcome definition, and information on participants, predictors, model performance, handling of missing data and model validation were extracted. A risk of bias (ROB) assessment using PROBAST (prediction model risk of bias assessment tool) was carried out. Two reviewers were independently involved in screening, data extraction, and ROB stages. Results The included studies used three main outcome definitions: a state of disease activity, such as low disease activity or remission; the EULAR response criteria; or discontinuation due to adverse events (AEs). Some studies incorporated AEs into a composite outcome with disease activity and few accounted for

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Are dogs not susceptible to retroviral infections?

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Animal Diseases 2024年第3期4卷 217-223页

作者： Jorge Casseb João Henrique Campos Luciano Rodrigo Lopes Laboratory of Investigation in Dermatology and Immunodefciencies Department of DermatologyUniversity of São Paulo Medical SchoolSão PauloSPBrazil Igen-Institute of Immunogenetics São PauloSPBrazil Bioinformatics and Biomedical Data Science Division Health Informatics DepartmentUniversidade Federal de São Paulo–UNIFESP.Rua Botucatu 862Prédio Leal PradotérreoVila ClementinoCEP 04023-062São PauloSPBrazil

Retroviruses have been proven to cause infections and diseases in a series of mammalian hosts but not in ***,this letter discussed the dog susceptibility to retrovirus infection,encompassing arguments to understand why dogs may have not been infected by retroviruses thus *** potential resistance of retrovirus in dogs enables this provocative short communication to discuss this question,looking at some evolutive *** lineage of canids has shown,throughout its evolutionary history,a smaller accumulation of retroviruses in canid genomes,classifed as endogenous *** this context,the genomes of canids seem to ofer obstacles,which have been evolutionarily conserved,in the face of retroviral infection.

关键词： Retrovirus dogs Endogenous retrovirus

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Distinct virologic trajectories in chronic hepatitis B identify heterogeneity in response to nucleos(t)ide analogue therapy

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JHEP Reports 2025年第1期7卷 101229-101229页

作者： Wang, Tingyan Campbell, Cori Stockdale, Alexander J. Todd, Stacy McIntyre, Karl Frankland, Andrew Jaworski, Jakub Glampson, Ben Papadimitriou, Dimitri Mercuri, Luca Mayer, Erik Jones, Christopher R. Salih, Hizni Roadknight, Gail Little, Stephanie Noble, Theresa Várnai, Kinga A. Davis, Cai Heinson, Ashley I. George, Michael Borca, Florina English, Louise Romão, Luis Ramlakhan, David Barnes, Eleanor Matthews, Philippa C. Gelson, William Cooke, Graham S. Khakoo, Salim I. Nastouli, Eleni Davies, Jim Woods, Kerrie Ryder, Stephen Elsharkawy, Ahmed Easom, Nicholas Bernal, William Ahmad, Shazaad Macdonald, Douglas Stanworth, Simon Maynard, Suzanne MacNaughton, Heidi Kim, Yun Meneses, Josune Olza Roberts, Timothy Rogers, Heather Roberts, Lara Higgins, Finola Vilar, Javier Norris, Ruth Tilston, George Serafimova, Ilina Montague, Sarah Verheyden, Juliette Juurlink, Irene Jack, Kathryn Waldren-Glenn, Alex Poole, Lizzie Day, Victoria Reglar, Berit NIHR Oxford Biomedical Research Centre Oxford United Kingdom Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Clinical Infection Microbiology and Immunology Institute of Infection Veterinary and Ecological Sciences University of Liverpool Liverpool United Kingdom Tropical Infectious Diseases Unit Royal Liverpool Hospital Liverpool University Hospitals NHS Trust Liverpool United Kingdom Liverpool Clinical Laboratories Liverpool University Hospitals NHS Trust Liverpool United Kingdom Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom iCARE Secure Data Environment Digital Collaboration Space Imperial College Healthcare NHS Trust London United Kingdom Department of Surgery and Cancer Imperial College London London United Kingdom Department of Infectious Disease Imperial College London London United Kingdom NIHR Health Informatics Collaborative Oxford University Hospitals NHS Foundation Trust Oxford United Kingdom Southampton Emerging Therapies and Technologies Centre University Hospital Southampton NHS Foundation Trust Southampton United Kingdom Clinical Informatics Research Unit Faculty of Medicine University of Southampton Southampton United Kingdom NIHR University College London Hospitals Biomedical Research Centre London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom Department of Infection Immunity and Inflammation UCL Great Ormond Street Institute of Child Health London United Kingdom Department of Virology UCLH London United Kingdom School of Clinical and Experimental Sciences Faculty of Medicine University of Southampton Southampton United Kingdom Cambridge Liver Unit Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom Faculty of Medicine Department of Infectious Disease Imperial College London United Kingdom The Francis Crick Institute London United Kingdom Division of Infection and Immunity University College Lon

Background & Aims: The dynamics of HBV viral load (VL) in patients with chronic hepatitis B (CHB) on nucleos(t)ide analogue (NA) treatment and its relationship with liver disease are poorly understood. We aimed to study longitudinal VL patterns and their associations with CHB clinical outcomes. Methods: Utilising large scale, routinely collected electronic health records from six centres in England, collated by the National Institute for Health and Care Research Health informatics Collaborative (NIHR HIC), we applied latent class mixed models to investigate VL trajectory patterns in adults receiving NA treatment. We assessed associations of VL trajectory with alanine transaminase, and with liver fibrosis/cirrhosis. Results: We retrieved data from 1,885 adults on NA treatment (median follow-up 6.2 years, IQR 3.7–9.3 years), with 21,691 VL measurements (median 10 per patient, IQR 5–17). Five VL classes were identified from the derivation cohort (n = 1,367, discrimination: 0.93, entropy: 0.90): class 1 ‘long term suppression’ (n = 827, 60.5%), class 2 ‘timely virological suppression’ (n = 254, 18.6%), class 3 ‘persistent moderate viraemia’ (n = 140, 10.2%), class 4 ‘persistent high-level viraemia’ (n = 44, 3.2%), and class 5 ‘slow virological suppression’ (n = 102, 7.5%). The model demonstrated a discrimination of 0.93 and entropy of 0.88 for the validation cohort (n = 518). Alanine transaminase decreased variably over time in VL-suppressed groups (classes 1, 2, 5;all p <0.001), but did not significantly improve in those with persistent viraemia (classes 3, 4). Patients in class 5 had twofold increased hazards of fibrosis/cirrhosis compared with class 1 (adjusted hazard ratio, 2.00;95% CI, 1.33–3.02). Conclusions: Heterogeneity exists in virological response to NA therapy in CHB patients, with over 20% showing potentially suboptimal responses. Slow virological suppression is associated with liver disease progression. Impact and implications: Treatment recommendations f

关键词： Antiviral treatment Cirrhosis HBV Health informatics Collaborative (HIC) Latent class mixed models Liver fibrosis Longitudinal Nucleotide analogue Viral load

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Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach

Adverse Drug Reaction Posts Detection with a Bi-LSTM based a...

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International Conference on Big data and Smart Computing (BIGCOMP)

作者： Chung-Chun Lee Seunghee Lee Mi Hwa Song Suehyun Lee Jong-Yeop Kim Department of Biomedical Informatics College of Medicine Konyang University Daejeon Republic of Korea Healthcare Data Science Center Konyang Univ. Medical Center Daejeon Republic of Korea Division of Information and Communication Sciences Semyung University Jecheon Republic of Korea

Social Network Services (SNS) is currently the most active communication platform, generating big data every day. In the field of pharmacovigilance, it is also an interesting research topic to extract meaningful information from accumulated data from SNS data. In this work, we propose a Recurrent Neural Network (RNN) based classification model to extract Adver Drug Reactions (ADR) posts from social network service (SNS) data. For Ketoprofen drugs with high prescription frequency and high number of posts, posts from Naver Blog and Cafe (2005-2020) were secured, and the final 3,828 cases were analyzed. As a result, three types of lexicons (drugs name, ADR, and stop words) were defined for Ketoprofen, and based on this, 87% accuracy was obtained based on the Bi-LSTM classification model. Extra drug was also verified through the entire process above, with an accuracy of 80%. It is expected to provide convenience in extracting ADR posts through the developed Bi-LSTM classification model.

关键词： Drugs Recurrent neural networks Social networking (online) Filtering Computational modeling Blogs Big data

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Repurposing Drugs for Alzheimer's Diseases through Link Prediction on biomedical Literature 11

Repurposing Drugs for Alzheimer's Diseases through Link Pred...

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11th IEEE International Conference on Healthcare informatics, ICHI 2023

作者： Xiao, Yongkang Hou, Yu Zhou, Huixue Diallo, Gayo Fiszman, Marcelo Wolfson, Julian Kilicoglu, Halil Chen, You Xu, Hua Mantyh, William G. Zhang, Rui University of Minnesota Institute for Health Informatics MinneapolisMN United States University of Minnesota Department of Surgery MinneapolisMN United States University of Bordeaux Inria Sistm Team AHeaD Inserm 1219 Bordeaux Population Health F-33000 France Pontifical Catholic University of Rio de Janeiro Nites - Núcleo de Inovação e Tecnologia em Saúde Brazil University of Minnesota Division of Biostatistics School of Public Health MinneapolisMN United States University of Illinois Urbana-Champaign School of Information Sciences ChampaignIL United States Vanderbilt University Medical Center Department of Biomedical Informatics NashvilleTN United States Yale University Section of Biomedical Informatics and Data Science New HavenCT United States University of Minnesota Department of Neurology MinneapolisMN United States

ISBN: (纸本)9798350302639

Recently, computational drug repurposing has emerged as a promising method for identifying new interventions for diseases. This study predicts novel drugs for Alzheimer's disease (AD) through link prediction on our developed biomedical knowledge graph. We constructed a comprehensive knowledge graph containing AD concepts and various potential interventions, called ADInt, by integrating a dietary supplement (DS) domain knowledge graph, SuppKG, with semantic triples from SemMedDB database. Four knowledge graph embedding models (TransE, RotatE, DistMult and ComplEX) and two graph convolutional network models (R-GCN and CompGCN) were compared to learn the representation of ADInt. R-GCN outperformed other models by evaluating on the time slice test set and the clinical trial test set, and was used to generate the score tables for the link prediction task. According to the results of link prediction, we proposed candidate drugs for AD. In conclusion, we presented a novel methodology to extend an existing knowledge graph and discover novel drugs for AD. Our method can potentially be applied to other clinical problems, such as discovering drug adverse reactions and drug-drug interactions. © 2023 IEEE.

关键词： Knowledge graph

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Heightened COVID-19 Mortality in People With Severe Mental Illness Persists After Vaccination: A Cohort Study of Greater Manchester Residents (vol 49, pg 275, 2023)

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SCHIZOPHRENIA BULLETIN 2023年第5期49卷 1399-1403页

作者： Hassan, Lamiece Sawyer, Chelsea Peek, Niels Lovell, Karina Carvalho, Andre F. Solmi, Marco Tilston, George Sperrin, Matthew Firth, Joseph Division of Psychology and Mental Health The University of Manchester Manchester Academic Health Science Centre Manchester M13 9PL UK Centre for Health Informatics Division of Informatics Imaging and Data Sciences The University of Manchester M13 9PL UK NIHR Greater Manchester Patient Safety Translational Research Centre The University of Manchester Manchester UK Manchester Academic Health Science Centre National Institute for Health Research Manchester Biomedical Research Centre The University of Manchester Manchester M13 9PL UK Division of Nursing Midwifery and Social Work University of Manchester Manchester Academic Health Science Centre Manchester M13 9PL UK IMPACT (Innovation in Mental and Physical Health and Clinical Treatment) Strategic Research Centre School of Medicine Barwon Health Deakin University Geelong Victoria Australia Psychiatry Department University of Ottawa Ottawa ON Canada The Ottawa Hospital University of Ottawa Ottawa ON Canada Clinical Epidemiology Program Ottawa Hospital Research Institute (OHRI) University of Ottawa Ottawa ON Canada Greater Manchester Mental Health NHS Foundation Trust Manchester Academic Health Science Centre Manchester M13 9PL UK

Background and hypothesis Previous studies show that people with severe mental illness (SMI) are at higher risk of COVID-19 mortality, however limited evidence exists regarding risk post-vaccination. We investigated COVID-19 mortality among people with schizophrenia and other SMIs before, during and after the UK vaccine roll-out. Study Design Using the Greater Manchester (GM) Care Record to access routinely collected health data linked with death records, we plotted COVID-19 mortality rates over time in GM residents with schizophrenia/psychosis, bipolar disorder (BD) and/or recurrent major depressive disorder (MDD) from February 2020 to September 2021. Multivariable logistic regression was used to compare mortality risk (risk ratios;RRs) between people with SMI (N=190,188) and age-sex matched controls (N=760,752), adjusted for sociodemographic factors, pre-existing comorbidities and vaccination *** Results Mortality risks were significantly higher among people with SMI compared with matched controls, particularly among people with schizophrenia/psychosis (RR 3.14, CI 2.66-3.71) and/or BD (RR 3.17, CI 2.15-4.67). In adjusted models, the relative risk of COVID-19 mortality decreased, though remained significantly higher than matched controls for people with schizophrenia (RR 1.53, CI 1.24-1.88) and BD (RR 2.28, CI 1.49-3.49), but not recurrent MDD (RR 0.92, CI 0.78-1.09). People with SMI continued to show higher mortality rate ratios relative to controls throughout 2021, during vaccination roll-out. Conclusions People with SMI, notably schizophrenia and bipolar disorder, were at greater risk of COVID-19 mortality compared to matched controls. Despite population vaccination efforts that have prioritised people with SMI, disparities still remain in COVID-19 mortality for people with SMI.

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Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection

arXiv

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arXiv 2022年

作者： Corbin, Conor K. Baiocchi, Michael Chen, Jonathan H. Department of Biomedical Data Science StanfordCA United States Department of Epidemiology and Population Health StanfordCA United States Center for Biomedical Informatics Research StanfordCA United States Division of Hospital Medicine StanfordCA United States Clinical Excellence Research Center StanfordCA United States

When evaluating the performance of clinical machine learning models, one must consider the deployment population. When the population of patients with observed labels is only a subset of the deployment population (label selection), standard model performance estimates on the observed population may be misleading. In this study we describe three classes of label selection and simulate five causally distinct scenarios to assess how particular selection mechanisms bias a suite of commonly reported binary machine learning model performance metrics. Simulations reveal that when selection is affected by observed features, naive estimates of model discrimination may be misleading. When selection is affected by labels, naive estimates of calibration fail to reflect reality. We borrow traditional weighting estimators from causal inference literature and find that when selection probabilities are properly specified, they recover full population estimates. We then tackle the real-world task of monitoring the performance of deployed machine learning models whose interactions with clinicians feed-back and affect the selection mechanism of the labels. We train three machine learning models to flag low-yield laboratory diagnostics, and simulate their intended consequence of reducing wasteful laboratory utilization. We find that naive estimates of AUROC on the observed population undershoot actual performance by up to 20%. Such a disparity could be large enough to lead to the wrongful termination of a successful clinical decision support tool. We propose an altered deployment procedure, one that combines injected randomization with traditional weighted estimates, and find it recovers true model performance. © 2022, CC BY.

关键词： Diagnosis

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Fitness assessment in acute myeloid leukemia: recommendations from an expert panel on behalf of the European LeukemiaNet

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Blood Advances 2025年第9期9卷 2207-2220页

作者： Venditti, Adriano Palmieri, Raffaele Maurillo, Luca Röllig, Christoph Wierzbowska, Agnieszka de Leeuw, David Efficace, Fabio Curti, Antonio Lam Ngai, Lok Tettero, Jesse Adès, Lionel Almeida, Antonio Bullinger, Lars Dennis, Mike Esteve, Jordi Ferrara, Felicetto Heuser, Michael Huls, Gerwin Lübbert, Michael Mehta, Priyanka Montesinos, Pau Pabst, Thomas Récher, Christian Rossi, Giuseppe Russell, Nigel Sierra, Jorge Stauder, Reinhard Vey, Norbert Walter, Roland B. Wang, Eunice Nier, Samantha Garcez Martins, Carolina Ossenkoppele, Gert Dipartimento di Biomedicina e Prevenzione Università Tor Vergata Rome Italy Department of Onco-Hematology Fondazione Policlinico Tor Vergata Rome Italy Medizinische Klinik und Poliklinik 1 Medizinische Fakultät und Universitätsklinikum Carl Gustav Carus Technische Universität Dresden Dresden Germany Department of Hematology Medical University of Lodz Lodz Poland Department of Hematology and Transplantology Provincial Multispecialized Oncology Trauma Center Lodz Poland Department of Hematology Amsterdam University Medical Center Location Vrije University Medical Center Cancer Center Amsterdam Amsterdam Netherlands Data Center and Health Outcomes Research Unit Gruppo Italiano Malattie Ematologiche Maligne dell’Adulto Rome Italy Dipartimento delle “Malattie oncologiche ed ematologiche ” Istituto di Ricovero e Cura a Carattere Scientifico Azienda Ospedaliero-Universitaria di Bologna Istituto di Ematologia “Seràgnoli” Bologna Italy Service Hématologie Séniors Hopital Saint-Louis (Assistance Publique–Hopitaux de Paris) Paris Cité University INSERM U944 Paris France Department of Hematology Hospital da Luz Lisbon Portugal and Faculdade de Medicina Universidade Católica Portuguesa Lisbon Portugal Department of Hematology Oncology and Cancer Immunology Campus Virchow Charité-Universitätsmedizin Berlin Freie Universität Berlin Humboldt-Universität Zu Berlin Germany The Christie National Health Service Foundation Trust Manchester United Kingdom Servicio de Hematología Hospital Clínic of Barcelona Institut d’Investigacions Biomèdiques August Pi i Sunyer Barcelona Spain Division of Hematology Cardarelli Hospital Naples Italy Department of Hematology Hemostasis Oncology and Stem Cell Transplantation Hannover Medical School Hannover Germany Department of Hematology University Medical Center Groningen University of Groningen Groningen Netherlands Department of Hematology Oncology and Stem Cell Transplantation Faculty of Medicine University Medical Center Freib

Fitness assessment in patients with acute myeloid leukemia (AML) is critical to deliver the right therapy to the right patient. Although several scoring systems are available to aid in determining fitness, the absence of validation studies has resulted in the lack of universally accepted assessment procedures. This limitation, combined with the increasing availability of novel agents expanding the spectrum of less-intensive options, has introduced additional complexity to the fitness assessment process. In this evolving context, fitness should reflect eligibility for a specific treatment among the several available, rather than a generic binary classification of eligibility for intensive chemotherapy. Moreover, the growing emphasis on patient-centered care, further highlights the importance of integrating quality of life, patient preferences, patient self-reported physical and social functioning status, social support, and early integration of palliative care into the assessment framework. A modern interpretation of fitness assessment should incorporate a comprehensive evaluation that extends beyond traditional clinical and biological disease characteristics. Thus, fitness assessment in patients with AML represents only 1 piece of a larger puzzle, encompassing the patient’s overall capacity to sustain and benefit from a specific therapeutic program. © 2025 American Society of Hematology.

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Disease diagnostics using machine learning of B cell and T cell receptor sequences

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science (New York, N.Y.) 2025年第6736期387卷 eadp2407页

作者： Zaslavsky, Maxim E. Craig, Erin Michuda, Jackson K. Sehgal, Nidhi Ram-Mohan, Nikhil Lee, Ji-Yeun Nguyen, Khoa D. Hoh, Ramona A. Pham, Tho D. Röltgen, Katharina Lam, Brandon Parsons, Ella S. Macwana, Susan R. DeJager, Wade Drapeau, Elizabeth M. Roskin, Krishna M. Cunningham-Rundles, Charlotte Moody, M Anthony Haynes, Barton F. Goldman, Jason D. Heath, James R. Chinthrajah, R Sharon Nadeau, Kari C. Pinsky, Benjamin A. Blish, Catherine A. Hensley, Scott E. Jensen, Kent Meyer, Everett Balboni, Imelda Utz, Paul J. Merrill, Joan T. Guthridge, Joel M. James, Judith A. Yang, Samuel Tibshirani, Robert Kundaje, Anshul Boyd, Scott D. Department of Computer Science Stanford University Stanford CA United States Department of Biomedical Data Science Stanford University Stanford CA United States Department of Genetics Stanford University Stanford CA United States Department of Pathology Stanford University Stanford CA United States Department of Emergency Medicine Stanford University Stanford CA United States Stanford Blood Center Stanford CA United States Department of Medical Parasitology and Infection Biology Swiss Tropical and Public Health Institute Allschwil Switzerland University of Basel Basel Switzerland Sean N. Parker Center for Allergy and Asthma Research Stanford University Stanford CA United States Department of Arthritis and Clinical Immunology Oklahoma Medical Research Foundation Oklahoma City OK United States Department of Microbiology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Pediatrics University of Cincinnati College of Medicine Cincinnati OH United States Divisions of Biomedical Informatics and Immunobiology Cincinnati Children's Hospital Medical Center Cincinnati OH United States Icahn School of Medicine at Mount Sinai New York NY USA Department of Pediatrics Duke University Durham NC United States Duke Human Vaccine Institute Duke University Durham NC United States Department of Immunology Duke University Durham NC United States Department of Medicine Duke University Durham NC United States Swedish Center for Research and Innovation Swedish Medical Center Seattle WA United States Division of Allergy and Infectious Diseases University of Washington Seattle WA United States Institute for Systems Biology Seattle WA United States Department of Bioengineering University of Washington Seattle WA United States Department of Environmental Health Harvard T.H. Chan School of Public Health Boston MA United States Division of Allergy and Inflammation Beth Israel Deaconess Medical Center Bosto

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to severe acute respiratory syndrome coronavirus 2, influenza, and human immunodeficiency virus, highlight antigen-specific receptors, and reveal distinct characteristics of systemic lupus erythematosus and type-1 diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of immune responses.

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A deep learning approach to Lung Nodule Growth Prediction using CT image combined with Demographic and image features 23

A deep learning approach to Lung Nodule Growth Prediction us...

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7th International Conference on Medical and Health informatics, ICMHI 2023

作者： Li, Ai-Hsien Adams Nguyen, Duc-Khanh Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Yang, Pan-Chyr Division of Cardiology Far Eastern Memorial Hospital Taipei220 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Information Management Yuan Ze University Taoyuan320 Taiwan Department of Radiology Far Eastern Memorial Hospital Taipei220 Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital Taipei Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan320 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei106 Taiwan Department of Internal Medicine College of Medicine National Taiwan University Taipei100 Taiwan Center of Genomic Medicine National Taiwan University Taipei100 Taiwan

ISBN: (纸本)9798400700712

Lung cancer remains a significant global public health concern, being the leading cause of cancer-related deaths worldwide. Despite recent medical advancements, the disease still has a high mortality rate, making early detection and treatment critical for improving patient outcomes. Accurate prediction of nodule growth is key to early lung cancer treatment, but current assessments offer unclear indications of future growth, leading physicians to recommend costly and anxiety-provoking follow-up appointments every 3 to 12 months. To address this need, this study develops an ensemble deep learning approach that combines demographic data with existing CT images to predict whether lung nodules will grow, potentially reducing unnecessary examinations and easing the burden on patients. Our study, conducted on 862 patients with 1004 nodules, produced promising preliminary results with Accuracy, Sensitivity, Precision, F1 Score, and AUC of 0.66, 0.66, 0.67, 0.66, and 0.71, respectively. The proposed method provides a promising support system to empower patients to make informed decisions about seeking medical attention and helps physicians facilitate early treatment regimens, leading to improved patient outcomes and potentially saving lives. © 2023 ACM.

关键词： Forecasting

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