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Cavalcanti-Dantas VDM, Fernandes B, Dantas PHLF, Uchoa GR, Mendes AF, Araújo Júnior WOD, Castellano LRC, Fernandes AIV, Goulart LR, Oliveira RADS, Assis PACD, Souza JRD, Morais CNLD. Differential epitope prediction across diverse circulating variants of SARS-COV-2 in Brazil. Comput Biol Chem 2024; 112:108139. [PMID: 38972100 DOI: 10.1016/j.compbiolchem.2024.108139] [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: 10/18/2023] [Revised: 06/09/2024] [Accepted: 06/22/2024] [Indexed: 07/09/2024]
Abstract
COVID-19, caused by the SARS-COV-2 virus, induces numerous immunological reactions linked to the severity of the clinical condition of those infected. The surface Spike protein (S protein) present in Sars-CoV-2 is responsible for the infection of host cells. This protein presents a high rate of mutations, which can increase virus transmissibility, infectivity, and immune evasion. Therefore, we propose to evaluate, using immunoinformatic techniques, the predicted epitopes for the S protein of seven variants of Sars-CoV-2. MHC class I and II epitopes were predicted and further assessed for their immunogenicity, interferon-gamma (IFN-γ) inducing capacity, and antigenicity. For B cells, linear and structural epitopes were predicted. For class I MHC epitopes, 40 epitopes were found for the clades of Wuhan, Clade 2, Clade 3, and 20AEU.1, Gamma, and Delta, in addition to 38 epitopes for Alpha and 44 for Omicron. For MHC II, there were differentially predicted epitopes for all variants and eight equally predicted epitopes. These were evaluated for differences in the MHC II alleles to which they would bind. Regarding B cell epitopes, 16 were found in the Wuhan variant, 14 in 22AEU.1 and in Clade 3, 15 in Clade 2, 11 in Alpha and Delta, 13 in Gamma, and 9 in Omicron. When compared, there was a reduction in the number of predicted epitopes concerning the Spike protein, mainly in the Delta and Omicron variants. These findings corroborate the need for updates seen today in bivalent mRNA vaccines against COVID-19 to promote a targeted immune response to the main circulating variant, Omicron, leading to more robust protection against this virus and avoiding cases of reinfection. When analyzing the specific epitopes for the RBD region of the spike protein, the Omicron variant did not present a B lymphocyte epitope from position 390, whereas the epitope at position 493 for MHC was predicted only for the Alpha, Gamma, and Omicron variants.
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Affiliation(s)
| | | | | | | | | | | | | | - Ana Isabel Vieira Fernandes
- Health Promotion Department of the Medical Sciences Center and Division for Infectious and Parasitic Diseases, Lauro Wanderley University Hospital, Federal University of Paraiba, Brazil
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2
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Fitzek S, Choi KEA. Shaping future practices: German-speaking medical and dental students' perceptions of artificial intelligence in healthcare. BMC MEDICAL EDUCATION 2024; 24:844. [PMID: 39107732 PMCID: PMC11304766 DOI: 10.1186/s12909-024-05826-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 07/26/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND The growing use of artificial intelligence (AI) in healthcare necessitates understanding the perspectives of future practitioners. This study investigated the perceptions of German-speaking medical and dental students regarding the role of artificial intelligence (AI) in their future practices. METHODS A 28-item survey adapted from the AI in Healthcare Education Questionnaire (AIHEQ) and the Medical Student's Attitude Toward AI in Medicine (MSATAIM) scale was administered to students in Austria, Germany, and Switzerland from April to July 2023. Participants were recruited through targeted advertisements on Facebook and Instagram and were required to be proficient in German and enrolled in medical or dental programs. The data analysis included descriptive statistics, correlations, t tests, and thematic analysis of the open-ended responses. RESULTS Of the 409 valid responses (mean age = 23.13 years), only 18.2% of the participants reported receiving formal training in AI. Significant positive correlations were found between self-reported tech-savviness and AI familiarity (r = 0.67) and between confidence in finding reliable AI information and positive attitudes toward AI (r = 0.72). While no significant difference in AI familiarity was found between medical and dental students, dental students exhibited slightly more positive attitudes toward the integration of AI into their future practices. CONCLUSION This study underscores the need for comprehensive AI education in medical and dental curricula to address knowledge gaps and prepare future healthcare professionals for the ethical and effective integration of AI in practice.
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Affiliation(s)
- Sebastian Fitzek
- Health Services Research, Faculty of Medicine/Dentistry, Danube Private University, Steiner Landstraße 124, Krems‑Stein, 3500, Austria.
| | - Kyung-Eun Anna Choi
- Health Services Research, Faculty of Medicine/Dentistry, Danube Private University, Steiner Landstraße 124, Krems‑Stein, 3500, Austria
- Center for Health Services Research, Brandenburg Medical School, Seebad 82/83, 15562 Rüdersdorf b. Berlin, Neuruppin, Germany
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3
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Yu B, Kaku A, Liu K, Parnandi A, Fokas E, Venkatesan A, Pandit N, Ranganath R, Schambra H, Fernandez-Granda C. Quantifying impairment and disease severity using AI models trained on healthy subjects. NPJ Digit Med 2024; 7:180. [PMID: 38969786 PMCID: PMC11226623 DOI: 10.1038/s41746-024-01173-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 06/21/2024] [Indexed: 07/07/2024] Open
Abstract
Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients to quantify their deviation from the healthy population. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients. The gold-standard Fugl-Meyer Assessment (FMA) requires in-person administration by a trained assessor for 30-45 minutes, which restricts monitoring frequency and precludes physicians from adapting rehabilitation protocols to the progress of each patient. The COBRA score, computed automatically in under one minute, is shown to be strongly correlated with the FMA on an independent test cohort for two different data modalities: wearable sensors (ρ = 0.814, 95% CI [0.700,0.888]) and video (ρ = 0.736, 95% C.I [0.584, 0.838]). To demonstrate the generalizability of the approach to other conditions, the COBRA score was also applied to quantify severity of knee osteoarthritis from magnetic-resonance imaging scans, again achieving significant correlation with an independent clinical assessment (ρ = 0.644, 95% C.I [0.585,0.696]).
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Affiliation(s)
- Boyang Yu
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA
| | - Aakash Kaku
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA
| | - Kangning Liu
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA
| | - Avinash Parnandi
- Department of Neurology, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
- Department of Rehabilitation Medicine, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Emily Fokas
- Department of Neurology, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Anita Venkatesan
- Department of Neurology, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Natasha Pandit
- Department of Rehabilitation Medicine, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Rajesh Ranganath
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA
- Courant Institute of Mathematical Sciences, New York University, 251 Mercer St, New York, NY, 10012, USA
| | - Heidi Schambra
- Department of Neurology, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA.
- Department of Rehabilitation Medicine, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA.
| | - Carlos Fernandez-Granda
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA.
- Courant Institute of Mathematical Sciences, New York University, 251 Mercer St, New York, NY, 10012, USA.
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4
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Asteris PG, Gandomi AH, Armaghani DJ, Kokoris S, Papandreadi AT, Roumelioti A, Papanikolaou S, Tsoukalas MZ, Triantafyllidis L, Koutras EI, Bardhan A, Mohammed AS, Naderpour H, Paudel S, Samui P, Ntanasis-Stathopoulos I, Dimopoulos MA, Terpos E. Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm. Eur J Intern Med 2024; 125:67-73. [PMID: 38458880 DOI: 10.1016/j.ejim.2024.02.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/23/2024] [Accepted: 02/29/2024] [Indexed: 03/10/2024]
Abstract
It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms. The optimal combination of hematological indicators that gives the best prediction consists of only four hematological indicators as follows: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin. The best prediction corresponds to a particularly high accuracy of 97.12 %. In conclusion, our novel approach provides a robust model based only on basic hematological parameters for predicting the risk for ICU admission and optimize COVID-19 patient management in the clinical practice.
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Affiliation(s)
- Panagiotis G Asteris
- Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
| | - Amir H Gandomi
- Faculty of Engineering & IT, University of Technology Sydney, Sydney, NSW 2007, Australia; University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
| | - Danial J Armaghani
- School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
| | - Styliani Kokoris
- Laboratory of Hematology and Hospital Blood Transfusion Department, University General Hospital "Attikon", National and Kapodistrian University of Athens, Medical School, Greece
| | - Anastasia T Papandreadi
- Software and Applications Department, University General Hospital "Attikon", National and Kapodistrian University of Athens, Medical School, Greece
| | - Anna Roumelioti
- Department of Hematology and Lymphoma BMTU, Evangelismos General Hospital, Athens, Greece
| | - Stefanos Papanikolaou
- NOMATEN Centre of Excellence, National Center for Nuclear Research, ulica A. Sołtana 7, 05-400 Swierk/Otwock, Poland
| | - Markos Z Tsoukalas
- Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
| | - Leonidas Triantafyllidis
- Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
| | - Evangelos I Koutras
- Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
| | - Abidhan Bardhan
- Civil Engineering Department, National Institute of Technology Patna, Bihar, India
| | - Ahmed Salih Mohammed
- Engineering Department, American University of Iraq, Sulaimani, Kurdistan-Region, Iraq
| | - Hosein Naderpour
- Institute of Industrial Science, University of Tokyo, Tokyo, Japan
| | - Satish Paudel
- Department of Civil and Environmental Engineering, University of Nevada, Reno, US
| | - Pijush Samui
- Civil Engineering Department, National Institute of Technology Patna, Bihar, India
| | - Ioannis Ntanasis-Stathopoulos
- Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece
| | - Meletios A Dimopoulos
- Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece
| | - Evangelos Terpos
- Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece.
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Dehdab R, Brendlin A, Werner S, Almansour H, Gassenmaier S, Brendel JM, Nikolaou K, Afat S. Evaluating ChatGPT-4V in chest CT diagnostics: a critical image interpretation assessment. Jpn J Radiol 2024:10.1007/s11604-024-01606-3. [PMID: 38867035 DOI: 10.1007/s11604-024-01606-3] [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: 04/02/2024] [Accepted: 05/28/2024] [Indexed: 06/14/2024]
Abstract
PURPOSE To assess the diagnostic accuracy of ChatGPT-4V in interpreting a set of four chest CT slices for each case of COVID-19, non-small cell lung cancer (NSCLC), and control cases, thereby evaluating its potential as an AI tool in radiological diagnostics. MATERIALS AND METHODS In this retrospective study, 60 CT scans from The Cancer Imaging Archive, covering COVID-19, NSCLC, and control cases were analyzed using ChatGPT-4V. A radiologist selected four CT slices from each scan for evaluation. ChatGPT-4V's interpretations were compared against the gold standard diagnoses and assessed by two radiologists. Statistical analyses focused on accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), along with an examination of the impact of pathology location and lobe involvement. RESULTS ChatGPT-4V showed an overall diagnostic accuracy of 56.76%. For NSCLC, sensitivity was 27.27% and specificity was 60.47%. In COVID-19 detection, sensitivity was 13.64% and specificity of 64.29%. For control cases, the sensitivity was 31.82%, with a specificity of 95.24%. The highest sensitivity (83.33%) was observed in cases involving all lung lobes. The chi-squared statistical analysis indicated significant differences in Sensitivity across categories and in relation to the location and lobar involvement of pathologies. CONCLUSION ChatGPT-4V demonstrated variable diagnostic performance in chest CT interpretation, with notable proficiency in specific scenarios. This underscores the challenges of cross-modal AI models like ChatGPT-4V in radiology, pointing toward significant areas for improvement to ensure dependability. The study emphasizes the importance of enhancing these models for broader, more reliable medical use.
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Affiliation(s)
- Reza Dehdab
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany.
| | - Andreas Brendlin
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany
| | - Sebastian Werner
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany
| | - Jan Michael Brendel
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany
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Suresh V, Singh KK, Vaish E, Gurjar M, Ambuli Nambi A, Khulbe Y, Muzaffar S. Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool. Cureus 2024; 16:e59797. [PMID: 38846182 PMCID: PMC11154024 DOI: 10.7759/cureus.59797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/07/2024] [Indexed: 06/09/2024] Open
Abstract
Artificial intelligence (AI) is a technique that attempts to replicate human intelligence, analytical behavior, and decision-making ability. This includes machine learning, which involves the use of algorithms and statistical techniques to enhance the computer's ability to make decisions more accurately. Due to AI's ability to analyze, comprehend, and interpret considerable volumes of data, it has been increasingly used in the field of healthcare. In critical care medicine, where most of the patient load requires timely interventions due to the perilous nature of the condition, AI's ability to monitor, analyze, and predict unfavorable outcomes is an invaluable asset. It can significantly improve timely interventions and prevent unfavorable outcomes, which, otherwise, is not always achievable owing to the constrained human ability to multitask with optimum efficiency. AI has been implicated in intensive care units over the past many years. In addition to its advantageous applications, this article discusses its disadvantages, prospects, and the changes needed to train future critical care professionals. A comprehensive search of electronic databases was performed using relevant keywords. Data from articles pertinent to the topic was assimilated into this review article.
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Affiliation(s)
- Vinay Suresh
- General Medicine and Surgery, King George's Medical University, Lucknow, IND
| | - Kaushal K Singh
- General Medicine, King George's Medical University, Lucknow, IND
| | - Esha Vaish
- Internal Medicine, Mount Sinai Morningside West, New York, USA
| | - Mohan Gurjar
- Critical Care Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, IND
| | | | - Yashita Khulbe
- General Medicine and Surgery, King George's Medical University, Lucknow, IND
| | - Syed Muzaffar
- Critical Care Medicine, King George's Medical University, Lucknow, IND
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7
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Lv C, Guo W, Yin X, Liu L, Huang X, Li S, Zhang L. Innovative applications of artificial intelligence during the COVID-19 pandemic. INFECTIOUS MEDICINE 2024; 3:100095. [PMID: 38586543 PMCID: PMC10998276 DOI: 10.1016/j.imj.2024.100095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/16/2023] [Accepted: 02/18/2024] [Indexed: 04/09/2024]
Abstract
The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.
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Affiliation(s)
- Chenrui Lv
- Huazhong Agricultural University, Wuhan 430070, China
| | - Wenqiang Guo
- Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Huazhong Agricultural University, Wuhan 430070, China
| | - Liu Liu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research, Shanghai 200001, China
| | - Xinlei Huang
- Huazhong Agricultural University, Wuhan 430070, China
| | - Shimin Li
- Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Huazhong Agricultural University, Wuhan 430070, China
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8
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Tenda ED, Henrina J, Setiadharma A, Aristy DJ, Romadhon PZ, Thahadian HF, Mahdi BA, Adhikara IM, Marfiani E, Suryantoro SD, Yunus RE, Yusuf PA. Derivation and validation of novel integrated inpatient mortality prediction score for COVID-19 (IMPACT) using clinical, laboratory, and AI-processed radiological parameter upon admission: a multicentre study. Sci Rep 2024; 14:2149. [PMID: 38272920 PMCID: PMC10810804 DOI: 10.1038/s41598-023-50564-9] [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: 07/31/2023] [Accepted: 12/21/2023] [Indexed: 01/27/2024] Open
Abstract
Limited studies explore the use of AI for COVID-19 prognostication. This study investigates the relationship between AI-aided radiographic parameters, clinical and laboratory data, and mortality in hospitalized COVID-19 patients. We conducted a multicentre retrospective study. The derivation and validation cohort comprised of 512 and 137 confirmed COVID-19 patients, respectively. Variable selection for constructing an in-hospital mortality scoring model was performed using the least absolute shrinkage and selection operator, followed by logistic regression. The accuracy of the scoring model was assessed using the area under the receiver operating characteristic curve. The final model included eight variables: anosmia (OR: 0.280; 95%CI 0.095-0.826), dyspnoea (OR: 1.684; 95%CI 1.049-2.705), loss of consciousness (OR: 4.593; 95%CI 1.702-12.396), mean arterial pressure (OR: 0.928; 95%CI 0.900-0.957), peripheral oxygen saturation (OR: 0.981; 95%CI 0.967-0.996), neutrophil % (OR: 1.034; 95%CI 1.013-1.055), serum urea (OR: 1.018; 95%CI 1.010-1.026), affected lung area score (OR: 1.026; 95%CI 1.014-1.038). The Integrated Inpatient Mortality Prediction Score for COVID-19 (IMPACT) demonstrated a predictive value of 0.815 (95% CI 0.774-0.856) in the derivation cohort. Internal validation resulted in an AUROC of 0.770 (95% CI 0.661-0.879). Our study provides valuable evidence of the real-world application of AI in clinical settings. However, it is imperative to conduct prospective validation of our findings, preferably utilizing a control group and extending the application to broader populations.
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Affiliation(s)
- Eric Daniel Tenda
- Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas Indonesia, Jl. Pangeran Diponegoro No. 71, RW. 5, Kenari, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta, 10430, Indonesia.
- Medical Technology Cluster of Indonesian Medical Research Institute (IMERI), Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia.
| | - Joshua Henrina
- Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas Indonesia, Jl. Pangeran Diponegoro No. 71, RW. 5, Kenari, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta, 10430, Indonesia
| | - Andry Setiadharma
- Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas Indonesia, Jl. Pangeran Diponegoro No. 71, RW. 5, Kenari, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta, 10430, Indonesia
| | - Dahliana Jessica Aristy
- Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas Indonesia, Jl. Pangeran Diponegoro No. 71, RW. 5, Kenari, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta, 10430, Indonesia
| | - Pradana Zaky Romadhon
- Hematology and Medical Oncology, Department of Internal Medicine, Universitas Airlangga Academic Hospital, Faculty of Medicine Universitas Airlangga, Surabaya, Indonesia
| | - Harik Firman Thahadian
- Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
| | - Bagus Aulia Mahdi
- Department of Internal Medicine, Faculty of Medicine Universitas Airlangga, Surabaya, Indonesia
| | - Imam Manggalya Adhikara
- Cardiology Division, Department of Internal Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
| | - Erika Marfiani
- Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Faculty of Medicine Universitas Airlangga, Universitas Airlangga Academic Hospital, Surabaya, Indonesia
| | - Satriyo Dwi Suryantoro
- Nephrology Division, Department of Internal Medicine, Faculty of Medicine Universitas Airlangga, Universitas Airlangga Academic Hospital, Surabaya, Indonesia
| | - Reyhan Eddy Yunus
- Medical Technology Cluster of Indonesian Medical Research Institute (IMERI), Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
- Department of Radiology, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
| | - Prasandhya Astagiri Yusuf
- Medical Technology Cluster of Indonesian Medical Research Institute (IMERI), Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
- Department of Medical Physiology and Biophysics, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
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9
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Hussain S, Songhua X, Aslam MU, Hussain F. Clinical predictions of COVID-19 patients using deep stacking neural networks. J Investig Med 2024; 72:112-127. [PMID: 37712431 DOI: 10.1177/10815589231201103] [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: 09/16/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic, which emerged in late 2019, has caused millions of infections and fatalities globally, disrupting various aspects of human society, including socioeconomic, political, and educational systems. One of the key challenges during the COVID-19 pandemic is accurately predicting the clinical development and outcome of the infected patients. In response, scientists and medical professionals globally have mobilized to develop prognostic strategies such as risk scores, biomarkers, and machine learning models to predict the clinical course and outcomes of COVID-19 patients. In this contribution, we deployed a mathematical approach called matrix factorization feature selection to select the most relevant features from the anonymized laboratory biomarkers and demographic data of COVID-19 patients. Based on these features, developed a model that leverages the deep stacking neural network (DSNN) to aid in clinical care by predicting patients' mortality risk. To gauge the performance of our suggested model, performed a comparative analysis with principal component analysis plus support vector machine, deep learning, and random forest, achieving outstanding performances. The DSNN model outperformed all the other models in terms of area under the curve (96.0%), F1-score (98.1%), recall (98.5%), accuracy (99.0%), precision (97.7%), specificity (97.0%), and maximum probability of correction decision (93.4%). Our model outperforms the clinical predictive models regarding patient mortality risk and classification in the literature. Therefore, we conclude that our robust model can help healthcare professionals to manage COVID-19 patients more effectively. We expect that early prediction of COVID-19 patients and preventive interventions can reduce the mortality risk of patients.
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Affiliation(s)
- Sajid Hussain
- School of Mathematics and Statistics XJTU, Xian, Shaanxi, China
| | - Xu Songhua
- School of Mathematics and Statistics XJTU, Xian, Shaanxi, China
| | | | - Fida Hussain
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Nuevo León, Mexico
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10
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Lashen H, St John TL, Almallah YZ, Sasidhar M, Shamout FE. Machine Learning Models Versus the National Early Warning Score System for Predicting Deterioration: Retrospective Cohort Study in the United Arab Emirates. JMIR AI 2023; 2:e45257. [PMID: 38875543 PMCID: PMC11041421 DOI: 10.2196/45257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 06/19/2023] [Accepted: 08/01/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Early warning score systems are widely used for identifying patients who are at the highest risk of deterioration to assist clinical decision-making. This could facilitate early intervention and consequently improve patient outcomes; for example, the National Early Warning Score (NEWS) system, which is recommended by the Royal College of Physicians in the United Kingdom, uses predefined alerting thresholds to assign scores to patients based on their vital signs. However, there is limited evidence of the reliability of such scores across patient cohorts in the United Arab Emirates. OBJECTIVE Our aim in this study was to propose a data-driven model that accurately predicts in-hospital deterioration in an inpatient cohort in the United Arab Emirates. METHODS We conducted a retrospective cohort study using a real-world data set that consisted of 16,901 unique patients associated with 26,073 inpatient emergency encounters and 951,591 observation sets collected between April 2015 and August 2021 at a large multispecialty hospital in Abu Dhabi, United Arab Emirates. The observation sets included routine measurements of heart rate, respiratory rate, systolic blood pressure, level of consciousness, temperature, and oxygen saturation, as well as whether the patient was receiving supplementary oxygen. We divided the data set of 16,901 unique patients into training, validation, and test sets consisting of 11,830 (70%; 18,319/26,073, 70.26% emergency encounters), 3397 (20.1%; 5206/26,073, 19.97% emergency encounters), and 1674 (9.9%; 2548/26,073, 9.77% emergency encounters) patients, respectively. We defined an adverse event as the occurrence of admission to the intensive care unit, mortality, or both if the patient was admitted to the intensive care unit first. On the basis of 7 routine vital signs measurements, we assessed the performance of the NEWS system in detecting deterioration within 24 hours using the area under the receiver operating characteristic curve (AUROC). We also developed and evaluated several machine learning models, including logistic regression, a gradient-boosting model, and a feed-forward neural network. RESULTS In a holdout test set of 2548 encounters with 95,755 observation sets, the NEWS system achieved an overall AUROC value of 0.682 (95% CI 0.673-0.690). In comparison, the best-performing machine learning models, which were the gradient-boosting model and the neural network, achieved AUROC values of 0.778 (95% CI 0.770-0.785) and 0.756 (95% CI 0.749-0.764), respectively. Our interpretability results highlight the importance of temperature and respiratory rate in predicting patient deterioration. CONCLUSIONS Although traditional early warning score systems are the dominant form of deterioration prediction models in clinical practice today, we strongly recommend the development and use of cohort-specific machine learning models as an alternative. This is especially important in external patient cohorts that were unseen during model development.
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Affiliation(s)
- Hazem Lashen
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | | | | | - Madhu Sasidhar
- Cleveland Clinic Tradition Hospital, Port St. Lucie, FL, United States
| | - Farah E Shamout
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
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11
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Schaudt D, von Schwerin R, Hafner A, Riedel P, Reichert M, von Schwerin M, Beer M, Kloth C. Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset. Sci Rep 2023; 13:18299. [PMID: 37880333 PMCID: PMC10600145 DOI: 10.1038/s41598-023-45532-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/20/2023] [Indexed: 10/27/2023] Open
Abstract
Since the beginning of the COVID-19 pandemic, many different machine learning models have been developed to detect and verify COVID-19 pneumonia based on chest X-ray images. Although promising, binary models have only limited implications for medical treatment, whereas the prediction of disease severity suggests more suitable and specific treatment options. In this study, we publish severity scores for the 2358 COVID-19 positive images in the COVIDx8B dataset, creating one of the largest collections of publicly available COVID-19 severity data. Furthermore, we train and evaluate deep learning models on the newly created dataset to provide a first benchmark for the severity classification task. One of the main challenges of this dataset is the skewed class distribution, resulting in undesirable model performance for the most severe cases. We therefore propose and examine different augmentation strategies, specifically targeting majority and minority classes. Our augmentation strategies show significant improvements in precision and recall values for the rare and most severe cases. While the models might not yet fulfill medical requirements, they serve as an appropriate starting point for further research with the proposed dataset to optimize clinical resource allocation and treatment.
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Affiliation(s)
- Daniel Schaudt
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany.
| | - Reinhold von Schwerin
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Alexander Hafner
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Pascal Riedel
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, James-Franck-Ring, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Marianne von Schwerin
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Meinrad Beer
- Department of Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Christopher Kloth
- Department of Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Baden-Wurttemberg, Germany
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12
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Xiao Y, Zhang J, Chi C, Ma Y, Song A. Criticality and clinical department prediction of ED patients using machine learning based on heterogeneous medical data. Comput Biol Med 2023; 165:107390. [PMID: 37659113 DOI: 10.1016/j.compbiomed.2023.107390] [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: 05/17/2023] [Revised: 07/27/2023] [Accepted: 08/25/2023] [Indexed: 09/04/2023]
Abstract
PROBLEM Emergency triage faces multiple challenges, including limited medical resources and inadequate manual triage nurses, which cause incorrect triage, overcrowding in the emergency department (ED), and long patient waiting time. OBJECTIVE This paper aims to propose and validate an accurate and efficient artificial intelligence-based method for effectively ED triage and alleviating the pressure on medical resources. METHODS We propose two novel machine learning models, TransNet and TextRNN, for predicting patient severity levels and clinical departments using heterogeneous medical data in ED triage. Our models employ a parallel structure for feature extraction and incorporate an attention mechanism to extract essential information from the fused features, enabling accurate predictions. The models analyze the triage data (2020-2022) from the ED of Beijing University People's Hospital, incorporating variables (demographics, triage vital signs, and chief complaints) to identify patient severity levels and clinical departments. We performed data cleaning, categorization, and encoding first. Then, we divided the available data into a training set (56%), a validation set (24%), and a test set (20%) by random sampling. Finally, our models underwent 5-fold cross-validation and were compared with other state-of-the-art models. RESULTS We comprehensively evaluated the proposed models against various Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Traditional Machine Learning (TML), and Transformer-based (TF) models, achieving excellent performance in predicting triage outcomes. Specifically, TextRNN achieved a prediction success rate of 86.23% [85.86-86.70] for severity levels and 94.30% [94.00-94.46] for clinical departments among 161,198 ED visits. Moreover, TransNet demonstrated higher sensitivities of 84.08% and 90.05% for severity levels and clinical departments, respectively, with specificities of 76.48% and 95.16%. The accuracy of our model is 0.87%, 0.18%, 4.29%, and 1.96%, higher than that of the above four family models on average. Furthermore, our method significantly reduced under-triage by 12.06% and over-triage by 17.92% compared to manual triage. CONCLUSIONS Experimental results demonstrated that the proposed models fuse heterogeneous medical data in the triage process, successfully predicting patients' triage outcomes. Our models can improve triage efficiency, reduce the under/over-triage rate, and provide physicians with valuable decision-making support.
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Affiliation(s)
- Yi Xiao
- The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Jun Zhang
- The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China.
| | - Cheng Chi
- Department of Emergency, Peking University People's Hospital, Beijing, 100044, China
| | - Yuqing Ma
- The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Aiguo Song
- The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
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13
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Roy S, Dutta P, Ghosh P. Hierarchical Vaccine Allocation Based on Epidemiological and Behavioral Considerations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2981-2991. [PMID: 37023164 DOI: 10.1109/tcbb.2023.3265317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Vaccines have proven useful in curbing contagion from new strains of the SARS-CoV-2 virus. However, equitable vaccine allocation continues to be a significant challenge worldwide, necessitating a comprehensive allocation strategy incorporating heterogeneity in epidemiological and behavioral considerations. In this paper, we present a hierarchical allocation strategy that assigns vaccines to zones and their constituent neighborhoods cost-effectively, based on their population density, susceptibility, infected count, and attitude towards vaccinations. Moreover, it includes a module that tackles vaccine shortages in certain zones by locally transferring vaccines from zones with surplus vaccines. We leverage the epidemiological, socio-demographic, and social media datasets from Chicago and Greece and their constituent community areas to show that the proposed allocation approach assigns vaccines based on the chosen criteria and captures the effects of disparate vaccine adoption rates. We conclude the paper with a lowdown on future efforts to extend this study to design models for effective public policies and vaccination strategies that curtail vaccine purchase costs.
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14
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John P, Vasa NJ, Zam A. Optical Biosensors for the Diagnosis of COVID-19 and Other Viruses-A Review. Diagnostics (Basel) 2023; 13:2418. [PMID: 37510162 PMCID: PMC10378272 DOI: 10.3390/diagnostics13142418] [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: 04/17/2023] [Revised: 07/12/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
The sudden outbreak of the COVID-19 pandemic led to a huge concern globally because of the astounding increase in mortality rates worldwide. The medical imaging computed tomography technique, whole-genome sequencing, and electron microscopy are the methods generally used for the screening and identification of the SARS-CoV-2 virus. The main aim of this review is to emphasize the capabilities of various optical techniques to facilitate not only the timely and effective diagnosis of the virus but also to apply its potential toward therapy in the field of virology. This review paper categorizes the potential optical biosensors into the three main categories, spectroscopic-, nanomaterial-, and interferometry-based approaches, used for detecting various types of viruses, including SARS-CoV-2. Various classifications of spectroscopic techniques such as Raman spectroscopy, near-infrared spectroscopy, and fluorescence spectroscopy are discussed in the first part. The second aspect highlights advances related to nanomaterial-based optical biosensors, while the third part describes various optical interferometric biosensors used for the detection of viruses. The tremendous progress made by lab-on-a-chip technology in conjunction with smartphones for improving the point-of-care and portability features of the optical biosensors is also discussed. Finally, the review discusses the emergence of artificial intelligence and its applications in the field of bio-photonics and medical imaging for the diagnosis of COVID-19. The review concludes by providing insights into the future perspectives of optical techniques in the effective diagnosis of viruses.
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Affiliation(s)
- Pauline John
- Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi 129188, United Arab Emirates
| | - Nilesh J Vasa
- Department of Engineering Design, Indian Institute of Technology Madras, Chennai 600036, India
| | - Azhar Zam
- Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi 129188, United Arab Emirates
- Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA
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15
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Zeng KG, Dutt T, Witowski J, Kranthi Kiran GV, Yeung F, Kim M, Kim J, Pleasure M, Moczulski C, Lopez LJL, Zhang H, Harbi MA, Shamout FE, Major VJ, Heacock L, Moy L, Schnabel F, Pak LM, Shen Y, Geras KJ. Improving Information Extraction from Pathology Reports using Named Entity Recognition. RESEARCH SQUARE 2023:rs.3.rs-3035772. [PMID: 37461545 PMCID: PMC10350195 DOI: 10.21203/rs.3.rs-3035772/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Pathology reports are considered the gold standard in medical research due to their comprehensive and accurate diagnostic information. Natural language processing (NLP) techniques have been developed to automate information extraction from pathology reports. However, existing studies suffer from two significant limitations. First, they typically frame their tasks as report classification, which restricts the granularity of extracted information. Second, they often fail to generalize to unseen reports due to variations in language, negation, and human error. To overcome these challenges, we propose a BERT (bidirectional encoder representations from transformers) named entity recognition (NER) system to extract key diagnostic elements from pathology reports. We also introduce four data augmentation methods to improve the robustness of our model. Trained and evaluated on 1438 annotated breast pathology reports, acquired from a large medical center in the United States, our BERT model trained with data augmentation achieves an entity F1-score of 0.916 on an internal test set, surpassing the BERT baseline (0.843). We further assessed the model's generalizability using an external validation dataset from the United Arab Emirates, where our model maintained satisfactory performance (F1-score 0.860). Our findings demonstrate that our NER systems can effectively extract fine-grained information from widely diverse medical reports, offering the potential for large-scale information extraction in a wide range of medical and AI research. We publish our code at https://github.com/nyukat/pathology_extraction.
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Affiliation(s)
| | - Tarun Dutt
- New York University Grossman School of Medicine, New York, NY, USA
| | - Jan Witowski
- New York University Grossman School of Medicine, New York, NY, USA
| | | | - Frank Yeung
- New York University Grossman School of Medicine, New York, NY, USA
| | - Michelle Kim
- New York University Grossman School of Medicine, New York, NY, USA
| | - Jesi Kim
- New York University Grossman School of Medicine, New York, NY, USA
| | | | | | | | - Hao Zhang
- New York University Grossman School of Medicine, New York, NY, USA
| | | | | | - Vincent J. Major
- New York University Grossman School of Medicine, New York, NY, USA
| | - Laura Heacock
- New York University Grossman School of Medicine, New York, NY, USA
| | - Linda Moy
- New York University Grossman School of Medicine, New York, NY, USA
| | - Freya Schnabel
- New York University Grossman School of Medicine, New York, NY, USA
| | - Linda M. Pak
- New York University Grossman School of Medicine, New York, NY, USA
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16
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Li H, Drukker K, Hu Q, Whitney HM, Fuhrman JD, Giger ML. Predicting intensive care need for COVID-19 patients using deep learning on chest radiography. J Med Imaging (Bellingham) 2023; 10:044504. [PMID: 37608852 PMCID: PMC10440543 DOI: 10.1117/1.jmi.10.4.044504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 07/12/2023] [Accepted: 08/01/2023] [Indexed: 08/24/2023] Open
Abstract
Purpose Image-based prediction of coronavirus disease 2019 (COVID-19) severity and resource needs can be an important means to address the COVID-19 pandemic. In this study, we propose an artificial intelligence/machine learning (AI/ML) COVID-19 prognosis method to predict patients' needs for intensive care by analyzing chest X-ray radiography (CXR) images using deep learning. Approach The dataset consisted of 8357 CXR exams from 5046 COVID-19-positive patients as confirmed by reverse transcription polymerase chain reaction (RT-PCR) tests for the SARS-CoV-2 virus with a training/validation/test split of 64%/16%/20% on a by patient level. Our model involved a DenseNet121 network with a sequential transfer learning technique employed to train on a sequence of gradually more specific and complex tasks: (1) fine-tuning a model pretrained on ImageNet using a previously established CXR dataset with a broad spectrum of pathologies; (2) refining on another established dataset to detect pneumonia; and (3) fine-tuning using our in-house training/validation datasets to predict patients' needs for intensive care within 24, 48, 72, and 96 h following the CXR exams. The classification performances were evaluated on our independent test set (CXR exams of 1048 patients) using the area under the receiver operating characteristic curve (AUC) as the figure of merit in the task of distinguishing between those COVID-19-positive patients who required intensive care following the imaging exam and those who did not. Results Our proposed AI/ML model achieved an AUC (95% confidence interval) of 0.78 (0.74, 0.81) when predicting the need for intensive care 24 h in advance, and at least 0.76 (0.73, 0.80) for 48 h or more in advance using predictions based on the AI prognostic marker derived from CXR images. Conclusions This AI/ML prediction model for patients' needs for intensive care has the potential to support both clinical decision-making and resource management.
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Affiliation(s)
- Hui Li
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Karen Drukker
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Qiyuan Hu
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Heather M. Whitney
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Jordan D. Fuhrman
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Maryellen L. Giger
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
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17
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Schaudt D, von Schwerin R, Hafner A, Riedel P, Späte C, Reichert M, Hinteregger A, Beer M, Kloth C. Leveraging human expert image annotations to improve pneumonia differentiation through human knowledge distillation. Sci Rep 2023; 13:9203. [PMID: 37280219 DOI: 10.1038/s41598-023-36148-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 05/30/2023] [Indexed: 06/08/2023] Open
Abstract
In medical imaging, deep learning models can be a critical tool to shorten time-to-diagnosis and support specialized medical staff in clinical decision making. The successful training of deep learning models usually requires large amounts of quality data, which are often not available in many medical imaging tasks. In this work we train a deep learning model on university hospital chest X-ray data, containing 1082 images. The data was reviewed, differentiated into 4 causes for pneumonia, and annotated by an expert radiologist. To successfully train a model on this small amount of complex image data, we propose a special knowledge distillation process, which we call Human Knowledge Distillation. This process enables deep learning models to utilize annotated regions in the images during the training process. This form of guidance by a human expert improves model convergence and performance. We evaluate the proposed process on our study data for multiple types of models, all of which show improved results. The best model of this study, called PneuKnowNet, shows an improvement of + 2.3% points in overall accuracy compared to a baseline model and also leads to more meaningful decision regions. Utilizing this implicit data quality-quantity trade-off can be a promising approach for many scarce data domains beyond medical imaging.
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Affiliation(s)
- Daniel Schaudt
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany.
| | - Reinhold von Schwerin
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Alexander Hafner
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Pascal Riedel
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Christian Späte
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, James-Franck-Ring, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Andreas Hinteregger
- Department of Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Meinrad Beer
- Department of Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Christopher Kloth
- Department of Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Baden-Wurttemberg, Germany
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18
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Azhari S, Banerjee D, Kotooka T, Usami Y, Tanaka H. Influence of junction resistance on spatiotemporal dynamics and reservoir computing performance arising from an SWNT/POM 3D network formed via a scaffold template technique. NANOSCALE 2023; 15:8169-8180. [PMID: 36892200 DOI: 10.1039/d2nr04619a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
For scientists in numerous fields, creating a physical device that can function like the human brain is an aspiration. It is believed that we may achieve brain-like spatiotemporal information processing by fabricating an in materio reservoir computing (RC) device because of a complex random network topology with nonlinear dynamics. One of the significant drawbacks of a two-dimensional physical reservoir system is the difficulty in controlling the network density. This work reports the use of a 3D porous template as a scaffold to fabricate a three-dimensional network of a single-walled carbon nanotube polyoxometalate nanocomposite. Although the three-dimensional system exhibits better nonlinear dynamics and spatiotemporal dynamics, and higher harmonics generation than a two-dimensional system, the results suggest a correlation between a higher number of resistive junctions and reservoir performance. We show that by increasing the spatial dimension of the device, the memory capacity improves, while the scale-free network exponent (γ) remains nearly unchanged. The three-dimensional device also displays improved performance in the well-known RC benchmark task of waveform generation. This study demonstrates the impact of an additional spatial dimension, network distribution and network density on in materio RC device performance and tries to shed some light on the reason behind such behavior.
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Affiliation(s)
- Saman Azhari
- Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu, Kitakyushu 8080196, Japan.
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu, Kitakyushu 8080196, Japan
| | - Deep Banerjee
- Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu, Kitakyushu 8080196, Japan.
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu, Kitakyushu 8080196, Japan
| | - Takumi Kotooka
- Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu, Kitakyushu 8080196, Japan.
| | - Yuki Usami
- Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu, Kitakyushu 8080196, Japan.
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu, Kitakyushu 8080196, Japan
| | - Hirofumi Tanaka
- Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu, Kitakyushu 8080196, Japan.
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu, Kitakyushu 8080196, Japan
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Wu Y, Dravid A, Wehbe RM, Katsaggelos AK. DeepCOVID-Fuse: A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels. Bioengineering (Basel) 2023; 10:bioengineering10050556. [PMID: 37237626 DOI: 10.3390/bioengineering10050556] [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: 04/11/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/28/2023] Open
Abstract
The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, highlighting the need for accurate and timely risk prediction models that can prioritize patient care and allocate resources effectively. This study presents DeepCOVID-Fuse, a deep learning fusion model that predicts risk levels in patients with confirmed COVID-19 by combining chest radiographs (CXRs) and clinical variables. The study collected initial CXRs, clinical variables, and outcomes (i.e., mortality, intubation, hospital length of stay, Intensive care units (ICU) admission) from February to April 2020, with risk levels determined by the outcomes. The fusion model was trained on 1657 patients (Age: 58.30 ± 17.74; Female: 807) and validated on 428 patients (56.41 ± 17.03; 190) from the local healthcare system and tested on 439 patients (56.51 ± 17.78; 205) from a different holdout hospital. The performance of well-trained fusion models on full or partial modalities was compared using DeLong and McNemar tests. Results show that DeepCOVID-Fuse significantly (p < 0.05) outperformed models trained only on CXRs or clinical variables, with an accuracy of 0.658 and an area under the receiver operating characteristic curve (AUC) of 0.842. The fusion model achieves good outcome predictions even when only one of the modalities is used in testing, demonstrating its ability to learn better feature representations across different modalities during training.
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Affiliation(s)
- Yunan Wu
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60201, USA
| | - Amil Dravid
- Department of Computer Science, Northwestern University, Evanston, IL 60201, USA
| | - Ramsey Michael Wehbe
- The Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60201, USA
- Department of Computer Science, Northwestern University, Evanston, IL 60201, USA
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20
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Li J, Chen J, Tang Y, Wang C, Landman BA, Zhou SK. Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives. Med Image Anal 2023; 85:102762. [PMID: 36738650 PMCID: PMC10010286 DOI: 10.1016/j.media.2023.102762] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 01/18/2023] [Accepted: 01/27/2023] [Indexed: 02/01/2023]
Abstract
Transformer, one of the latest technological advances of deep learning, has gained prevalence in natural language processing or computer vision. Since medical imaging bear some resemblance to computer vision, it is natural to inquire about the status quo of Transformers in medical imaging and ask the question: can the Transformer models transform medical imaging? In this paper, we attempt to make a response to the inquiry. After a brief introduction of the fundamentals of Transformers, especially in comparison with convolutional neural networks (CNNs), and highlighting key defining properties that characterize the Transformers, we offer a comprehensive review of the state-of-the-art Transformer-based approaches for medical imaging and exhibit current research progresses made in the areas of medical image segmentation, recognition, detection, registration, reconstruction, enhancement, etc. In particular, what distinguishes our review lies in its organization based on the Transformer's key defining properties, which are mostly derived from comparing the Transformer and CNN, and its type of architecture, which specifies the manner in which the Transformer and CNN are combined, all helping the readers to best understand the rationale behind the reviewed approaches. We conclude with discussions of future perspectives.
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Affiliation(s)
- Jun Li
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China
| | - Junyu Chen
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institutes, Baltimore, MD, USA
| | - Yucheng Tang
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Ce Wang
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China
| | - Bennett A Landman
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - S Kevin Zhou
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China; School of Biomedical Engineering & Suzhou Institute for Advanced Research, Center for Medical Imaging, Robotics, and Analytic Computing & Learning (MIRACLE), University of Science and Technology of China, Suzhou 215123, China.
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21
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Shen Y, Heacock L, Elias J, Hentel KD, Reig B, Shih G, Moy L. ChatGPT and Other Large Language Models Are Double-edged Swords. Radiology 2023; 307:e230163. [PMID: 36700838 DOI: 10.1148/radiol.230163] [Citation(s) in RCA: 226] [Impact Index Per Article: 226.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- Yiqiu Shen
- New York University, Center for Data Science, 60 5th Ave, New York, NY 10011
| | - Laura Heacock
- New York University School of Medicine, Department of Radiology, 160 E 34th St, New York, NY 10016
| | - Jonathan Elias
- Weill Cornell Medicine, Department of Primary Care, 525 East 68th Street, New York, NY 10065
| | - Keith D Hentel
- Weill Cornell Medicine, Department of Radiology, 525 East 68th Street, New York, NY 10065
| | - Beatriu Reig
- New York University School of Medicine, Department of Radiology, 160 E 34th St, New York, NY 10016
| | - George Shih
- Weill Cornell Medicine, Department of Radiology, 525 East 68th Street, New York, NY 10065
| | - Linda Moy
- New York University School of Medicine, Department of Radiology, 160 E 34th St, New York, NY 10016
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22
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Rehman AU, Mian SH, Usmani YS, Abidi MH, Mohammed MK. Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach. Healthcare (Basel) 2023; 11:healthcare11020260. [PMID: 36673628 PMCID: PMC9858678 DOI: 10.3390/healthcare11020260] [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: 12/20/2022] [Revised: 01/08/2023] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
In 2020, coronavirus (COVID-19) was declared a global pandemic and it remains prevalent today. A necessity to model the transmission of the virus has emerged as a result of COVID-19's exceedingly contagious characteristics and its rapid propagation throughout the world. Assessing the incidence of infection could enable policymakers to identify measures to halt the pandemic and gauge the required capacity of healthcare centers. Therefore, modeling the susceptibility, exposure, infection, and recovery in relation to the COVID-19 pandemic is crucial for the adoption of interventions by regulatory authorities. Fundamental factors, such as the infection rate, mortality rate, and recovery rate, must be considered in order to accurately represent the behavior of the pandemic using mathematical models. The difficulty in creating a mathematical model is in identifying the real model variables. Parameters might vary significantly across models, which can result in variations in the simulation results because projections primarily rely on a particular dataset. The purpose of this work was to establish a susceptible-exposed-infected-recovered (SEIR) model describing the propagation of the COVID-19 outbreak throughout the Kingdom of Saudi Arabia (KSA). The goal of this study was to derive the essential COVID-19 epidemiological factors from actual data. System dynamics modeling and design of experiment approaches were used to determine the most appropriate combination of epidemiological parameters and the influence of COVID-19. This study investigates how epidemiological variables such as seasonal amplitude, social awareness impact, and waning time can be adapted to correctly estimate COVID-19 scenarios such as the number of infected persons on a daily basis in KSA. This model can also be utilized to ascertain how stress (or hospital capacity) affects the percentage of hospitalizations and the number of deaths. Additionally, the results of this study can be used to establish policies or strategies for monitoring or restricting COVID-19 in Saudi Arabia.
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Affiliation(s)
- Ateekh Ur Rehman
- Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
- Correspondence:
| | - Syed Hammad Mian
- Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia
| | - Yusuf Siraj Usmani
- Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
| | - Mustufa Haider Abidi
- Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia
| | - Muneer Khan Mohammed
- Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia
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23
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Doheny EP, Flood M, Ryan S, McCarthy C, O'Carroll O, O'Seaghdha C, Mallon PW, Feeney ER, Keatings VM, Wilson M, Kennedy N, Gannon A, Edwards C, Lowery MM. Prediction of low pulse oxygen saturation in COVID-19 using remote monitoring post hospital discharge. Int J Med Inform 2023; 169:104911. [PMID: 36347139 PMCID: PMC9625852 DOI: 10.1016/j.ijmedinf.2022.104911] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/21/2022] [Accepted: 10/27/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND Monitoring systems have been developed during the COVID-19 pandemic enabling clinicians to remotely monitor physiological measures including pulse oxygen saturation (SpO2), heart rate (HR), and breathlessness in patients after discharge from hospital. These data may be leveraged to understand how symptoms vary over time in COVID-19 patients. There is also potential to use remote monitoring systems to predict clinical deterioration allowing early identification of patients in need of intervention. METHODS A remote monitoring system was used to monitor 209 patients diagnosed with COVID-19 in the period following hospital discharge. This system consisted of a patient-facing app paired with a Bluetooth-enabled pulse oximeter (measuring SpO2 and HR) linked to a secure portal where data were available for clinical review. Breathlessness score was entered manually to the app. Clinical teams were alerted automatically when SpO2 < 94 %. In this study, data recorded during the initial ten days of monitoring were retrospectively examined, and a random forest model was developed to predict SpO2 < 94 % on a given day using SpO2 and HR data from the two previous days and day of discharge. RESULTS Over the 10-day monitoring period, mean SpO2 and HR increased significantly, while breathlessness decreased. The coefficient of variation in SpO2, HR and breathlessness also decreased over the monitoring period. The model predicted SpO2 alerts (SpO2 < 94 %) with a mean cross-validated. sensitivity of 66 ± 18.57 %, specificity of 88.31 ± 10.97 % and area under the receiver operating characteristic of 0.80 ± 0.11. Patient age and sex were not significantly associated with the occurrence of asymptomatic SpO2 alerts. CONCLUSION Results indicate that SpO2 alerts (SpO2 < 94 %) on a given day can be predicted using SpO2 and heart rate data captured on the two preceding days via remote monitoring. The methods presented may help early identification of patients with COVID-19 at risk of clinical deterioration using remote monitoring.
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Affiliation(s)
- Emer P. Doheny
- School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland,Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland,Corresponding author
| | - Matthew Flood
- School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland
| | - Silke Ryan
- School of Medicine, University College Dublin, Dublin, Ireland,St. Vincent’s University Hospital, Dublin, Ireland
| | - Cormac McCarthy
- School of Medicine, University College Dublin, Dublin, Ireland,St. Vincent’s University Hospital, Dublin, Ireland
| | | | | | | | - Eoin R. Feeney
- School of Medicine, University College Dublin, Dublin, Ireland
| | | | | | | | - Avril Gannon
- Midland Regional Hospital at Tullamore, Tullamore, Ireland
| | | | - Madeleine M. Lowery
- School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland,Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
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24
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Aminu M, Yadav D, Hong L, Young E, Edelkamp P, Saad M, Salehjahromi M, Chen P, Sujit SJ, Chen MM, Sabloff B, Gladish G, de Groot PM, Godoy MCB, Cascone T, Vokes NI, Zhang J, Brock KK, Daver N, Woodman SE, Tawbi HA, Sheshadri A, Lee JJ, Jaffray D, Wu CC, Chung C, Wu J. Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19. Cancers (Basel) 2022; 15:275. [PMID: 36612278 PMCID: PMC9818576 DOI: 10.3390/cancers15010275] [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: 11/08/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVES Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population. METHODS We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction. RESULTS These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts. CONCLUSIONS Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population.
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Affiliation(s)
- Muhammad Aminu
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Divya Yadav
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Lingzhi Hong
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Elliana Young
- Department of Enterprise Data Engineering & Analytics, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Paul Edelkamp
- Department of Enterprise Data Engineering & Analytics, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Maliazurina Saad
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Morteza Salehjahromi
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Pingjun Chen
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Sheeba J. Sujit
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Melissa M. Chen
- Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Bradley Sabloff
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Gregory Gladish
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Patricia M. de Groot
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Myrna C. B. Godoy
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Tina Cascone
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Natalie I. Vokes
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Kristy K. Brock
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Naval Daver
- Department of Leukemia, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Scott E. Woodman
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Hussein A. Tawbi
- Department of Melanoma Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - David Jaffray
- Office of the Chief Technology and Digital Officer, MD Anderson Cancer Center, Houston, TX 77054, USA
| | | | - Carol C. Wu
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Caroline Chung
- Office of the Chief Data Officer, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
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25
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Chung J, Kim D, Choi J, Yune S, Song KD, Kim S, Chua M, Succi MD, Conklin J, Longo MGF, Ackman JB, Petranovic M, Lev MH, Do S. Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach. Sci Rep 2022; 12:21164. [PMID: 36476724 PMCID: PMC9729627 DOI: 10.1038/s41598-022-24721-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
Risk prediction requires comprehensive integration of clinical information and concurrent radiological findings. We present an upgraded chest radiograph (CXR) explainable artificial intelligence (xAI) model, which was trained on 241,723 well-annotated CXRs obtained prior to the onset of the COVID-19 pandemic. Mean area under the receiver operating characteristic curve (AUROC) for detection of 20 radiographic features was 0.955 (95% CI 0.938-0.955) on PA view and 0.909 (95% CI 0.890-0.925) on AP view. Coexistent and correlated radiographic findings are displayed in an interpretation table, and calibrated classifier confidence is displayed on an AI scoreboard. Retrieval of similar feature patches and comparable CXRs from a Model-Derived Atlas provides justification for model predictions. To demonstrate the feasibility of a fine-tuning approach for efficient and scalable development of xAI risk prediction models, we applied our CXR xAI model, in combination with clinical information, to predict oxygen requirement in COVID-19 patients. Prediction accuracy for high flow oxygen (HFO) and mechanical ventilation (MV) was 0.953 and 0.934 at 24 h and 0.932 and 0.836 at 72 h from the time of emergency department (ED) admission, respectively. Our CXR xAI model is auditable and captures key pathophysiological manifestations of cardiorespiratory diseases and cardiothoracic comorbidities. This model can be efficiently and broadly applied via a fine-tuning approach to provide fully automated risk and outcome predictions in various clinical scenarios in real-world practice.
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Affiliation(s)
- Joowon Chung
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA
| | - Doyun Kim
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA
| | - Jongmun Choi
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA
| | - Sehyo Yune
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA
| | - Kyoung Doo Song
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Republic of Korea
| | - Seonkyoung Kim
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA
| | - Michelle Chua
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA
| | - Marc D Succi
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA
| | - John Conklin
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA
| | - Maria G Figueiro Longo
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA
| | - Jeanne B Ackman
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA
| | - Milena Petranovic
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA
| | - Michael H Lev
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA
| | - Synho Do
- Department of Radiology, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA.
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26
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Walston SL, Matsumoto T, Miki Y, Ueda D. Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study. Br J Radiol 2022; 95:20220058. [PMID: 36193755 PMCID: PMC9733620 DOI: 10.1259/bjr.20220058] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVES The purpose of this study was to develop an artificial intelligence-based model to prognosticate COVID-19 patients at admission by combining clinical data and chest radiographs. METHODS This retrospective study used the Stony Brook University COVID-19 dataset of 1384 inpatients. After exclusions, 1356 patients were randomly divided into training (1083) and test datasets (273). We implemented three artificial intelligence models, which classified mortality, ICU admission, or ventilation risk. Each model had three submodels with different inputs: clinical data, chest radiographs, and both. We showed the importance of the variables using SHapley Additive exPlanations (SHAP) values. RESULTS The mortality prediction model was best overall with area under the curve, sensitivity, specificity, and accuracy of 0.79 (0.72-0.86), 0.74 (0.68-0.79), 0.77 (0.61-0.88), and 0.74 (0.69-0.79) for the clinical data-based model; 0.77 (0.69-0.85), 0.67 (0.61-0.73), 0.81 (0.67-0.92), 0.70 (0.64-0.75) for the image-based model, and 0.86 (0.81-0.91), 0.76 (0.70-0.81), 0.77 (0.61-0.88), 0.76 (0.70-0.81) for the mixed model. The mixed model had the best performance (p value < 0.05). The radiographs ranked fourth for prognostication overall, and first of the inpatient tests assessed. CONCLUSIONS These results suggest that prognosis models become more accurate if AI-derived chest radiograph features and clinical data are used together. ADVANCES IN KNOWLEDGE This AI model evaluates chest radiographs together with clinical data in order to classify patients as having high or low mortality risk. This work shows that chest radiographs taken at admission have significant COVID-19 prognostic information compared to clinical data other than age and sex.
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Affiliation(s)
| | | | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University,1-4-3 Asahi-machi, Abeno-ku, Osaka, Japan
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27
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Developing and validating a machine learning prognostic model for alerting to imminent deterioration of hospitalized patients with COVID-19. Sci Rep 2022; 12:19220. [PMID: 36357439 PMCID: PMC9648491 DOI: 10.1038/s41598-022-23553-7] [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: 05/26/2022] [Accepted: 11/02/2022] [Indexed: 11/12/2022] Open
Abstract
Our study was aimed at developing and validating a new approach, embodied in a machine learning-based model, for sequentially monitoring hospitalized COVID-19 patients and directing professional attention to patients whose deterioration is imminent. Model development employed real-world patient data (598 prediction events for 210 patients), internal validation (315 prediction events for 97 patients), and external validation (1373 prediction events for 307 patients). Results show significant divergence in longitudinal values of eight routinely collected blood parameters appearing several days before deterioration. Our model uses these signals to predict the personal likelihood of transition from non-severe to severe status within well-specified short time windows. Internal validation of the model's prediction accuracy showed ROC AUC of 0.8 and 0.79 for prediction scopes of 48 or 96 h, respectively; external validation showed ROC AUC of 0.7 and 0.73 for the same prediction scopes. Results indicate the feasibility of predicting the forthcoming deterioration of non-severe COVID-19 patients by eight routinely collected blood parameters, including neutrophil, lymphocyte, monocyte, and platelets counts, neutrophil-to-lymphocyte ratio, CRP, LDH, and D-dimer. A prospective clinical study and an impact assessment will allow implementation of this model in the clinic to improve care, streamline resources and ease hospital burden by timely focusing the medical attention on potentially deteriorating patients.
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28
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Healthcare Supply Chain Management under COVID-19 Settings: The Existing Practices in Hong Kong and the United States. Healthcare (Basel) 2022; 10:healthcare10081549. [PMID: 36011207 PMCID: PMC9408565 DOI: 10.3390/healthcare10081549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/11/2022] [Accepted: 08/15/2022] [Indexed: 11/18/2022] Open
Abstract
COVID-19 is recognized as an infectious disease generated by serious acute respiratory syndrome coronavirus 2. COVID-19 has rapidly spread all over the world within a short time period. Due to the coronavirus pandemic transmitting quickly worldwide, the impact on global healthcare systems and healthcare supply chain management has been profound. The COVID-19 outbreak has seriously influenced the routine and daily operations of healthcare facilities and the entire healthcare supply chain management and has brough about a public health crisis. As making sure the availability of healthcare facilities during COVID-19 is crucial, the debate on how to take resilience actions for sustaining healthcare supply chain management has gained new momentum. Apart from the logistics of handling human remains in some countries, supplies within the communities are urgently needed for emergency response. This study focuses on a comprehensive evaluation of the current practices of healthcare supply chain management in Hong Kong and the United States under COVID-19 settings. A wide range of different aspects associated with healthcare supply chain operations are considered, including the best practices for using respirators, transport of life-saving medical supplies, contingency healthcare strategies, blood distribution, and best practices for using disinfectants, as well as human remains handling and logistics. The outcomes of the conducted research identify the existing healthcare supply chain trends in two major Eastern and Western regions of the world, Hong Kong and the United States, and determine the key challenges and propose some strategies that can improve the effectiveness of healthcare supply chain management under COVID-19 settings. The study highlights how to build resilient healthcare supply chain management preparedness for future emergencies.
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29
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Chen X, Zhang Y, Cao G, Zhou J, Lin Y, Chen B, Nie K, Fu G, Su MY, Wang M. Dynamic change of COVID-19 lung infection evaluated using co-registration of serial chest CT images. Front Public Health 2022; 10:915615. [PMID: 36033815 PMCID: PMC9412202 DOI: 10.3389/fpubh.2022.915615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/18/2022] [Indexed: 01/22/2023] Open
Abstract
Purpose To evaluate the volumetric change of COVID-19 lesions in the lung of patients receiving serial CT imaging for monitoring the evolution of the disease and the response to treatment. Materials and methods A total of 48 patients, 28 males and 20 females, who were confirmed to have COVID-19 infection and received chest CT examination, were identified. The age range was 21-93 years old, with a mean of 54 ± 18 years. Of them, 33 patients received the first follow-up (F/U) scan, 29 patients received the second F/U scan, and 11 patients received the third F/U scan. The lesion region of interest (ROI) was manually outlined. A two-step registration method, first using the Affine alignment, followed by the non-rigid Demons algorithm, was developed to match the lung areas on the baseline and F/U images. The baseline lesion ROI was mapped to the F/U images using the obtained geometric transformation matrix, and the radiologist outlined the lesion ROI on F/U CT again. Results The median (interquartile range) lesion volume (cm3) was 30.9 (83.1) at baseline CT exam, 18.3 (43.9) at first F/U, 7.6 (18.9) at second F/U, and 0.6 (19.1) at third F/U, which showed a significant trend of decrease with time. The two-step registration could significantly decrease the mean squared error (MSE) between baseline and F/U images with p < 0.001. The method could match the lung areas and the large vessels inside the lung. When using the mapped baseline ROIs as references, the second-look ROI drawing showed a significantly increased volume, p < 0.05, presumably due to the consideration of all the infected areas at baseline. Conclusion The results suggest that the registration method can be applied to assist in the evaluation of longitudinal changes of COVID-19 lesions on chest CT.
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Affiliation(s)
- Xiao Chen
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yang Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States,Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Guoquan Cao
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiahuan Zhou
- Department of Radiology, Yuyao Hospital of Traditional Chinese Medicine, Ningbo, China
| | - Ya Lin
- The People's Hospital of Cangnan, Wenzhou, China
| | | | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Gangze Fu
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China,*Correspondence: Gangze Fu
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, CA, United States,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan,Min-Ying Su
| | - Meihao Wang
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China,Meihao Wang
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Gomes R, Kamrowski C, Langlois J, Rozario P, Dircks I, Grottodden K, Martinez M, Tee WZ, Sargeant K, LaFleur C, Haley M. A Comprehensive Review of Machine Learning Used to Combat COVID-19. Diagnostics (Basel) 2022; 12:1853. [PMID: 36010204 PMCID: PMC9406981 DOI: 10.3390/diagnostics12081853] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 12/19/2022] Open
Abstract
Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.
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Affiliation(s)
- Rahul Gomes
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Connor Kamrowski
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Jordan Langlois
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Papia Rozario
- Department of Geography and Anthropology, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA;
| | - Ian Dircks
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Keegan Grottodden
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Matthew Martinez
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Wei Zhong Tee
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Kyle Sargeant
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Corbin LaFleur
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Mitchell Haley
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
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Ren J, Liu D, Li G, Duan J, Dong J, Liu Z. Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients. Front Cardiovasc Med 2022; 9:923549. [PMID: 35811691 PMCID: PMC9263287 DOI: 10.3389/fcvm.2022.923549] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundDiabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them perform early intervention and improve personal health management.MethodsA retrospective cohort study was conducted to assess the risk of the occurrence of composite cardiovascular disease, which includes coronary heart disease, cerebrovascular diseases, congestive heart failure, and peripheral artery disease, in DKD patients. A least absolute shrinkage and selection operator (LASSO) regression was used to perform the variable selection. A deep learning-based survival model called DeepSurv, based on a feed-forward neural network was developed to predict CVD risk among DKD patients. We compared the model performance with the conventional Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), the area under the curve (AUC), and integrated Brier scores (IBS).ResultsWe recruited 890 patients diagnosed with DKD in this retrospective study. During a median follow-up of 10.4 months, there are 289 patients who sustained a subsequent CVD. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), chosen by LASSO regression were used to develop the predictive model. The DeepSurv model showed the best performance, achieved a C-index of 0.767(95% confidence intervals [CI]: 0.717–0.817), AUC of 0.780(95%CI: 0.721–0.839), and IBS of 0.067 in the validation set. Then we used the cut-off value determined by ROC (receiver operating characteristic) curve to divide the patients into different risk groups. Moreover, the DeepSurv model was also applied to develop an online calculation tool for patients to conduct risk monitoring.ConclusionA deep-learning-based predictive model using seven clinical variables can effectively predict CVD risk among DKD patients and perform risk stratification. An online calculator allows its easy implementation.
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Affiliation(s)
- Jingjing Ren
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
- Clinical Research Center of Big-data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dongwei Liu
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
| | - Guangpu Li
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
- Clinical Research Center of Big-data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jiayu Duan
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
- Clinical Research Center of Big-data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Jiayu Duan
| | - Jiancheng Dong
- Clinical Research Center of Big-data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Jiancheng Dong
| | - Zhangsuo Liu
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
- *Correspondence: Zhangsuo Liu
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Han X, Yu Z, Zhuo Y, Zhao B, Ren Y, Lamm L, Xue X, Feng J, Marr C, Shan F, Peng T, Zhang XY. The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system. iScience 2022; 25:104227. [PMID: 35434542 PMCID: PMC8989658 DOI: 10.1016/j.isci.2022.104227] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/10/2022] [Accepted: 04/05/2022] [Indexed: 01/09/2023] Open
Abstract
The respective value of clinical data and CT examinations in predicting COVID-19 progression is unclear, because the CT scans and clinical data previously used are not synchronized in time. To address this issue, we collected 119 COVID-19 patients with 341 longitudinal CT scans and paired clinical data, and we developed an AI system for the prediction of COVID-19 deterioration. By combining features extracted from CT and clinical data with our system, we can predict whether a patient will develop severe symptoms during hospitalization. Complementary to clinical data, CT examinations show significant add-on values for the prediction of COVID-19 progression in the early stage of COVID-19, especially in the 6th to 8th day after the symptom onset, indicating that this is the ideal time window for the introduction of CT examinations. We release our AI system to provide clinicians with additional assistance to optimize CT usage in the clinical workflow. COVID-19 patients with 341 longitudinal CT scans and paired clinical data included A new AI model for the prediction of COVID-19 progression was developed CT scans show significant add-on value over clinical data for the prediction Day 6–8 after the onset of COVID-19 symptoms is an ideal time window for a CT scan
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Affiliation(s)
- Xiaoyang Han
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
| | - Ziqi Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
| | - Yaoyao Zhuo
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Botao Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
| | - Yan Ren
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200433, China
| | - Lorenz Lamm
- Institute of AI for Health, Helmholtz Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany.,Helmholtz AI, Helmholtz Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Xiangyang Xue
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Tingying Peng
- Helmholtz AI, Helmholtz Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Xiao-Yong Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
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Negrini D, Danese E, Henry BM, Lippi G, Montagnana M. Artificial intelligence at the time of COVID-19: who does the lion's share? Clin Chem Lab Med 2022; 60:1881-1886. [PMID: 35470639 DOI: 10.1515/cclm-2022-0306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 04/13/2022] [Indexed: 01/06/2023]
Abstract
OBJECTIVES The development and use of artificial intelligence (AI) methodologies, especially machine learning (ML) and deep learning (DL), have been considerably fostered during the ongoing coronavirus disease 2019 (COVID-19) pandemic. Several models and algorithms have been developed and applied for both identifying COVID-19 cases and for assessing and predicting the risk of developing unfavourable outcomes. Our aim was to summarize how AI is being currently applied to COVID-19. METHODS We conducted a PubMed search using as query MeSH major terms "Artificial Intelligence" AND "COVID-19", searching for articles published until December 31, 2021, which explored the possible role of AI in COVID-19. The dataset origin (internal dataset or public datasets available online) and data used for training and testing the proposed ML/DL model(s) were retrieved. RESULTS Our analysis finally identified 292 articles in PubMed. These studies displayed large heterogeneity in terms of imaging test, laboratory parameters and clinical-demographic data included. Most models were based on imaging data, in particular CT scans or chest X-rays images. C-Reactive protein, leukocyte count, creatinine, lactate dehydrogenase, lymphocytes and platelets counts were found to be the laboratory biomarkers most frequently included in COVID-19 related AI models. CONCLUSIONS The lion's share of AI applied to COVID-19 seems to be played by diagnostic imaging. However, AI in laboratory medicine is also gaining momentum, especially with digital tools characterized by low cost and widespread applicability.
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Affiliation(s)
- Davide Negrini
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
| | - Elisa Danese
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
| | - Brandon M Henry
- Clinical Laboratory, Division of Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Giuseppe Lippi
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
| | - Martina Montagnana
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
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Speech as a Biomarker for COVID-19 Detection Using Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6093613. [PMID: 35444694 PMCID: PMC9014833 DOI: 10.1155/2022/6093613] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/07/2022] [Accepted: 03/21/2022] [Indexed: 11/30/2022]
Abstract
The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as “asymptomatic” COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the “recall” metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.
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35
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Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients. Sci Rep 2022; 12:6193. [PMID: 35418698 PMCID: PMC9007057 DOI: 10.1038/s41598-022-10136-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/25/2022] [Indexed: 12/15/2022] Open
Abstract
The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model.
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36
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A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data. Sci Rep 2022; 12:4329. [PMID: 35288579 PMCID: PMC8919158 DOI: 10.1038/s41598-022-07890-1] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 02/22/2022] [Indexed: 01/08/2023] Open
Abstract
AbstractCOVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for selection in a Boruta algorithm with SHAP game theoretical values. A classifier is built on the reduced feature space using CatBoost gradient boosting algorithm and reaching a probabilistic AUC of 0.949 on holdout test set. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance.
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37
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Khurana MP, Raaschou-Pedersen DE, Kurtzhals J, Bardram JE, Ostrowski SR, Bundgaard JS. Digital health competencies in medical school education: a scoping review and Delphi method study. BMC MEDICAL EDUCATION 2022; 22:129. [PMID: 35216611 PMCID: PMC8881190 DOI: 10.1186/s12909-022-03163-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 02/07/2022] [Indexed: 06/01/2023]
Abstract
INTRODUCTION In order to fulfill the enormous potential of digital health in the healthcare sector, digital health must become an integrated part of medical education. We aimed to investigate which knowledge, skills and attitudes should be included in a digital health curriculum for medical students through a scoping review and Delphi method study. METHODS We conducted a scoping review of the literature on digital health relevant for medical education. Key topics were split into three sub-categories: knowledge (facts, concepts, and information), skills (ability to carry out tasks) and attitudes (ways of thinking or feeling). Thereafter, we used a modified Delphi method where experts rated digital health topics over two rounds based on whether topics should be included in the curriculum for medical students on a scale from 1 (strongly disagree) to 5 (strongly agree). A predefined cut-off of ≥4 was used to identify topics that were critical to include in a digital health curriculum for medical students. RESULTS The scoping review resulted in a total of 113 included articles, with 65 relevant topics extracted and included in the questionnaire. The topics were rated by 18 experts, all of which completed both questionnaire rounds. A total of 40 (62%) topics across all three sub-categories met the predefined rating cut-off value of ≥4. CONCLUSION An expert panel identified 40 important digital health topics within knowledge, skills, and attitudes for medical students to be taught. These can help guide medical educators in the development of future digital health curricula.
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Affiliation(s)
- Mark P Khurana
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark.
| | - Daniel E Raaschou-Pedersen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark
| | - Jørgen Kurtzhals
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Microbiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Jakob E Bardram
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Johan S Bundgaard
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
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El-Sherif DM, Abouzid M, Elzarif MT, Ahmed AA, Albakri A, Alshehri MM. Telehealth and Artificial Intelligence Insights into Healthcare during the COVID-19 Pandemic. Healthcare (Basel) 2022; 10:385. [PMID: 35206998 PMCID: PMC8871559 DOI: 10.3390/healthcare10020385] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/13/2022] [Accepted: 02/15/2022] [Indexed: 02/06/2023] Open
Abstract
Soon after the coronavirus disease 2019 pandemic was proclaimed, digital health services were widely adopted to respond to this public health emergency, including comprehensive monitoring technologies, telehealth, creative diagnostic, and therapeutic decision-making methods. The World Health Organization suggested that artificial intelligence might be a valuable way of dealing with the crisis. Artificial intelligence is an essential technology of the fourth industrial revolution that is a critical nonmedical intervention for overcoming the present global health crisis, developing next-generation pandemic preparation, and regaining resilience. While artificial intelligence has much potential, it raises fundamental privacy, transparency, and safety concerns. This study seeks to address these issues and looks forward to an intelligent healthcare future based on best practices and lessons learned by employing telehealth and artificial intelligence during the COVID-19 pandemic.
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Affiliation(s)
- Dina M. El-Sherif
- National Institute of Oceanography and Fisheries (NIOF), Cairo 11516, Egypt
| | - Mohamed Abouzid
- Department of Physical Pharmacy and Pharmacokinetics, Poznan University of Medical Sciences, 60-781 Poznan, Poland;
- Doctoral School, Poznan University of Medical Sciences, 60-781 Poznan, Poland;
| | - Mohamed Tarek Elzarif
- Independent Digital Health Researcher and Entrepreneur, CEO Doctor Live Company, Cairo 12655, Egypt;
| | - Alhassan Ali Ahmed
- Doctoral School, Poznan University of Medical Sciences, 60-781 Poznan, Poland;
- Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 60-781 Poznan, Poland
| | - Ashwag Albakri
- Collage of Computer Science and Information Technology, Jazan University, Jizan 45142, Saudi Arabia;
| | - Mohammed M. Alshehri
- Medical Research Center, Jazan University, Jizan 45142, Saudi Arabia;
- Physical Therapy Department, Jazan University, Jizan 82412, Saudi Arabia
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Kamran F, Tang S, Otles E, McEvoy DS, Saleh SN, Gong J, Li BY, Dutta S, Liu X, Medford RJ, Valley TS, West LR, Singh K, Blumberg S, Donnelly JP, Shenoy ES, Ayanian JZ, Nallamothu BK, Sjoding MW, Wiens J. Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study. BMJ 2022; 376:e068576. [PMID: 35177406 PMCID: PMC8850910 DOI: 10.1136/bmj-2021-068576] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/12/2022] [Indexed: 12/13/2022]
Abstract
OBJECTIVE To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing. DESIGN Retrospective cohort study. SETTING One US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21. PARTICIPANTS 33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19. MAIN OUTCOME MEASURES An ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early. RESULTS 9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge. CONCLUSION A model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.
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Affiliation(s)
- Fahad Kamran
- Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA
- Joint first authors
| | - Shengpu Tang
- Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA
- Joint first authors
| | - Erkin Otles
- Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, MI, USA
- Medical Scientist Training Program, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Dustin S McEvoy
- Mass General Brigham Digital Health eCare, Somerville, MA, USA
| | - Sameh N Saleh
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jen Gong
- Center for Clinical Informatics and Improvement Research, University of California, San Francisco, CA, USA
| | - Benjamin Y Li
- Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA
- Medical Scientist Training Program, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Sayon Dutta
- Mass General Brigham Digital Health eCare, Somerville, MA, USA
- Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Xinran Liu
- Division of Hospital Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Richard J Medford
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Thomas S Valley
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Lauren R West
- Infection Control Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Karandeep Singh
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Seth Blumberg
- Division of Hospital Medicine, University of California, San Francisco, San Francisco, CA, USA
- Francis I Proctor Foundation, University of California, San Francisco, San Francisco, CA, USA
| | - John P Donnelly
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Erica S Shenoy
- Infection Control Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
| | - John Z Ayanian
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Brahmajee K Nallamothu
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Michael W Sjoding
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
- Joint senior authors
| | - Jenna Wiens
- Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
- Joint senior authors
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Machine Learning-Based COVID-19 Patients Triage Algorithm Using Patient-Generated Health Data from Nationwide Multicenter Database. Infect Dis Ther 2022; 11:787-805. [PMID: 35174469 PMCID: PMC8853007 DOI: 10.1007/s40121-022-00600-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 01/28/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction A prompt severity
assessment model of patients with confirmed infectious diseases could enable efficient diagnosis while alleviating burden on the medical system. This study aims to develop a SARS-CoV-2 severity assessment model and establish a medical system that allows patients to check the severity of their cases and informs them to visit the appropriate clinic center on the basis of past treatment data of other patients with similar severity levels. Methods This paper provides the development processes of a severity assessment model using machine learning techniques and its application on SARS-CoV-2-infected patients. The proposed model is trained on a nationwide data set provided by a Korean government agency and only requires patients’ basic personal data, allowing them to judge the severity of their own cases. After modeling, the boosting-based decision tree model was selected as the classifier while mortality rate was interpreted as the probability score. The data set was collected from all Korean citizens with confirmed COVID-19 between February 2020 and July 2021 (N = 149,471). Results The experiments achieved high model performance with an approximate precision of 0.923 and area under the curve of receiver operating characteristic (AUROC) score of 0.950 [95% tolerance interval (TI) 0.940–0.958, 95% confidence interval (CI) 0.949–0.950]. Moreover, our experiments identified the most important variables affecting the severity in the model via sensitivity analysis. Conclusion A prompt severity assessment model for managing infectious people has been attained through using a nationwide data set. It has demonstrated its superior performance by surpassing that of conventional risk assessments. With the model’s high performance and easily accessible features, the triage algorithm is expected to be particularly useful when patients monitor their health status by themselves through smartphone applications.
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41
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de Moura LV, Mattjie C, Dartora CM, Barros RC, Marques da Silva AM. Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography. Front Digit Health 2022; 3:662343. [PMID: 35112097 PMCID: PMC8801500 DOI: 10.3389/fdgth.2021.662343] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 11/29/2021] [Indexed: 12/18/2022] Open
Abstract
Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a defined set of radiological findings. Our work aims to investigate radiomic features and classification models to differentiate chest X-ray images of COVID-19-based pneumonia and other types of lung patterns. The goal is to provide grounds for understanding the distinctive COVID-19 radiographic texture features using supervised ensemble machine learning methods based on trees through the interpretable Shapley Additive Explanations (SHAP) approach. We use 2,611 COVID-19 chest X-ray images and 2,611 non-COVID-19 chest X-rays. After segmenting the lung in three zones and laterally, a histogram normalization is applied, and radiomic features are extracted. SHAP recursive feature elimination with cross-validation is used to select features. Hyperparameter optimization of XGBoost and Random Forest ensemble tree models is applied using random search. The best classification model was XGBoost, with an accuracy of 0.82 and a sensitivity of 0.82. The explainable model showed the importance of the middle left and superior right lung zones in classifying COVID-19 pneumonia from other lung patterns.
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Affiliation(s)
- Luís Vinícius de Moura
- Medical Image Computing Laboratory, School of Technology, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil
| | - Christian Mattjie
- Medical Image Computing Laboratory, School of Technology, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil
- Graduate Program in Biomedical Gerontology, School of Medicine, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil
| | - Caroline Machado Dartora
- Medical Image Computing Laboratory, School of Technology, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil
- Graduate Program in Biomedical Gerontology, School of Medicine, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil
| | - Rodrigo C. Barros
- Machine Learning Theory and Applications Lab, School of Technology, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil
| | - Ana Maria Marques da Silva
- Medical Image Computing Laboratory, School of Technology, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil
- Graduate Program in Biomedical Gerontology, School of Medicine, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil
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42
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Zhang Q, Gao J, Wu JT, Cao Z, Dajun Zeng D. Data science approaches to confronting the COVID-19 pandemic: a narrative review. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210127. [PMID: 34802267 PMCID: PMC8607150 DOI: 10.1098/rsta.2021.0127] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/22/2021] [Indexed: 05/07/2023]
Abstract
During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale 'big data' generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.
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Affiliation(s)
- Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Hong Kong
| | - Jianxi Gao
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Joseph T. Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Zhidong Cao
- The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, People’s Republic of China
| | - Daniel Dajun Zeng
- The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, People’s Republic of China
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43
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Vardas PE, Asselbergs FW, van Smeden M, Friedman P. The year in cardiovascular medicine 2021: digital health and innovation. Eur Heart J 2022; 43:271-279. [PMID: 34974610 DOI: 10.1093/eurheartj/ehab874] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/15/2021] [Accepted: 11/23/2021] [Indexed: 12/15/2022] Open
Abstract
This article presents some of the most important developments in the field of digital medicine that have appeared over the last 12 months and are related to cardiovascular medicine. The article consists of three main sections, as follows: (i) artificial intelligence-enabled cardiovascular diagnostic tools, techniques, and methodologies, (ii) big data and prognostic models for cardiovascular risk protection, and (iii) wearable devices in cardiovascular risk assessment, cardiovascular disease prevention, diagnosis, and management. To conclude the article, the authors present a brief further prospective on this new domain, highlighting existing gaps that are specifically related to artificial intelligence technologies, such as explainability, cost-effectiveness, and, of course, the importance of proper regulatory oversight for each clinical implementation.
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Affiliation(s)
- Panos E Vardas
- Heart Sector, Hygeia Hospitals Group, HHG, 5, Erithrou Stavrou, Marousi, Athens 15123, Greece.,European Heart Agency, ESC, Brussels, Belgium
| | - Folkert W Asselbergs
- Department of Cardiology, Division of Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Paul Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
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44
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Ahmad J, Saudagar AKJ, Malik KM, Ahmad W, Khan MB, Hasanat MHA, AlTameem A, AlKhathami M, Sajjad M. Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:480. [PMID: 35010740 PMCID: PMC8744904 DOI: 10.3390/ijerph19010480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/23/2021] [Accepted: 12/27/2021] [Indexed: 01/27/2023]
Abstract
The highly rapid spread of the current pandemic has quickly overwhelmed hospitals all over the world and motivated extensive research to address a wide range of emerging problems. The unforeseen influx of COVID-19 patients to hospitals has made it inevitable to deploy a rapid and accurate triage system, monitor progression, and predict patients at higher risk of deterioration in order to make informed decisions regarding hospital resource management. Disease detection in radiographic scans, severity estimation, and progression and prognosis prediction have been extensively studied with the help of end-to-end methods based on deep learning. The majority of recent works have utilized a single scan to determine severity or predict progression of the disease. In this paper, we present a method based on deep sequence learning to predict improvement or deterioration in successive chest X-ray scans and build a mathematical model to determine individual patient disease progression profile using successive scans. A deep convolutional neural network pretrained on a diverse lung disease dataset was used as a feature extractor to generate the sequences. We devised three strategies for sequence modeling in order to obtain both fine-grained and coarse-grained features and construct sequences of different lengths. We also devised a strategy to quantify positive or negative change in successive scans, which was then combined with age-related risk factors to construct disease progression profile for COVID-19 patients. The age-related risk factors allowed us to model rapid deterioration and slower recovery in older patients. Experiments conducted on two large datasets showed that the proposed method could accurately predict disease progression. With the best feature extractor, the proposed method was able to achieve AUC of 0.98 with the features obtained from radiographs. Furthermore, the proposed patient profiling method accurately estimated the health profile of patients.
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Affiliation(s)
- Jamil Ahmad
- Department of Computer Science, Islamia College Peshawar, Chartered University, Peshawar 25000, Pakistan; (J.A.); (M.S.)
| | - Abdul Khader Jilani Saudagar
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.B.K.); (M.H.A.H.); (A.A.); (M.A.)
| | - Khalid Mahmood Malik
- Department of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USA;
| | - Waseem Ahmad
- Lady Reading Hospital-Medical Teaching Institute, Peshawar 25000, Pakistan;
| | - Muhammad Badruddin Khan
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.B.K.); (M.H.A.H.); (A.A.); (M.A.)
| | - Mozaherul Hoque Abul Hasanat
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.B.K.); (M.H.A.H.); (A.A.); (M.A.)
| | - Abdullah AlTameem
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.B.K.); (M.H.A.H.); (A.A.); (M.A.)
| | - Mohammed AlKhathami
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.B.K.); (M.H.A.H.); (A.A.); (M.A.)
| | - Muhammad Sajjad
- Department of Computer Science, Islamia College Peshawar, Chartered University, Peshawar 25000, Pakistan; (J.A.); (M.S.)
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45
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Ibrahim ZM, Bean D, Searle T, Qian L, Wu H, Shek A, Kraljevic Z, Galloway J, Norton S, Teo JT, Dobson RJ. A Knowledge Distillation Ensemble Framework for Predicting Short- and Long-Term Hospitalization Outcomes From Electronic Health Records Data. IEEE J Biomed Health Inform 2022; 26:423-435. [PMID: 34129509 DOI: 10.1109/jbhi.2021.3089287] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The ability to perform accurate prognosis is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission and readmission from time-series of vital signs and laboratory results obtained within the first 24 hours of hospital admission. The stacked ensemble platform comprises two components: a) an unsupervised LSTM Autoencoder that learns an optimal representation of the time-series, using it to differentiate the less frequent patterns which conclude with an adverse event from the majority patterns that do not, and b) a gradient boosting model, which relies on the constructed representation to refine prediction by incorporating static features. The model is used to assess a patient's risk of adversity and provides visual justifications of its prediction. Results of three case studies show that the model outperforms existing platforms in ICU and general ward settings, achieving average Precision-Recall Areas Under the Curve (PR-AUCs) of 0.891 (95% CI: 0.878-0.939) for mortality and 0.908 (95% CI: 0.870-0.935) in predicting ICU admission and readmission.
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46
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Rauseo M, Perrini M, Gallo C, Mirabella L, Mariano K, Ferrara G, Santoro F, Tullo L, La Bella D, Vetuschi P, Cinnella G. Machine learning and predictive models: 2 years of Sars-CoV-2 pandemic in a single-center retrospective analysis. JOURNAL OF ANESTHESIA, ANALGESIA AND CRITICAL CARE 2022; 2:42. [PMCID: PMC9568961 DOI: 10.1186/s44158-022-00071-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background Methods Results Conclusion
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Affiliation(s)
- Michela Rauseo
- grid.10796.390000000121049995Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, 71122 Foggia, Italy
| | - Marco Perrini
- grid.10796.390000000121049995Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, 71122 Foggia, Italy
| | - Crescenzio Gallo
- Department of Clinical and Experimental Medicine “InfoLab” Bioinformatics Facility Head, University Hospital “Policlinico Riuniti”, Viale Pinto 1, 71122 Foggia, Italy
| | - Lucia Mirabella
- grid.10796.390000000121049995Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, 71122 Foggia, Italy
| | - Karim Mariano
- grid.10796.390000000121049995Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, 71122 Foggia, Italy
| | - Giuseppe Ferrara
- grid.10796.390000000121049995Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, 71122 Foggia, Italy
| | - Filomena Santoro
- grid.10796.390000000121049995Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, 71122 Foggia, Italy
| | - Livio Tullo
- grid.10796.390000000121049995Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, 71122 Foggia, Italy
| | - Daniela La Bella
- grid.10796.390000000121049995Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, 71122 Foggia, Italy
| | - Paolo Vetuschi
- grid.10796.390000000121049995Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, 71122 Foggia, Italy
| | - Gilda Cinnella
- grid.10796.390000000121049995Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, 71122 Foggia, Italy
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Ghosheh GO, Alamad B, Yang KW, Syed F, Hayat N, Iqbal I, Al Kindi F, Al Junaibi S, Al Safi M, Ali R, Zaher W, Al Harbi M, Shamout FE. Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic. INTELLIGENCE-BASED MEDICINE 2022; 6:100065. [PMID: 35721825 PMCID: PMC9188985 DOI: 10.1016/j.ibmed.2022.100065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 04/21/2022] [Accepted: 06/01/2022] [Indexed: 12/15/2022]
Abstract
Clinical evidence suggests that some patients diagnosed with coronavirus disease 2019 (COVID-19) experience a variety of complications associated with significant morbidity, especially in severe cases during the initial spread of the pandemic. To support early interventions, we propose a machine learning system that predicts the risk of developing multiple complications. We processed data collected from 3,352 patient encounters admitted to 18 facilities between April 1 and April 30, 2020, in Abu Dhabi (AD), United Arab Emirates. Using data collected during the first 24 h of admission, we trained machine learning models to predict the risk of developing any of three complications after 24 h of admission. The complications include Secondary Bacterial Infection (SBI), Acute Kidney Injury (AKI), and Acute Respiratory Distress Syndrome (ARDS). The hospitals were grouped based on geographical proximity to assess the proposed system's learning generalizability, AD Middle region and AD Western & Eastern regions, A and B, respectively. The overall system includes a data filtering criterion, hyperparameter tuning, and model selection. In test set A, consisting of 587 patient encounters (mean age: 45.5), the system achieved a good area under the receiver operating curve (AUROC) for the prediction of SBI (0.902 AUROC), AKI (0.906 AUROC), and ARDS (0.854 AUROC). Similarly, in test set B, consisting of 225 patient encounters (mean age: 42.7), the system performed well for the prediction of SBI (0.859 AUROC), AKI (0.891 AUROC), and ARDS (0.827 AUROC). The performance results and feature importance analysis highlight the system's generalizability and interpretability. The findings illustrate how machine learning models can achieve a strong performance even when using a limited set of routine input variables. Since our proposed system is data-driven, we believe it can be easily repurposed for different outcomes considering the changes in COVID-19 variants over time.
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48
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Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments. Symmetry (Basel) 2021. [DOI: 10.3390/sym14010016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.
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Cushnan D, Berka R, Bertolli O, Williams P, Schofield D, Joshi I, Favaro A, Halling-Brown M, Imreh G, Jefferson E, Sebire NJ, Reilly G, Rodrigues JCL, Robinson G, Copley S, Malik R, Bloomfield C, Gleeson F, Crotty M, Denton E, Dickson J, Leeming G, Hardwick HE, Baillie K, Openshaw PJ, Semple MG, Rubin C, Howlett A, Rockall AG, Bhayat A, Fascia D, Sudlow C, Jacob J. Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic. Digit Health 2021; 7:20552076211048654. [PMID: 34868617 PMCID: PMC8637703 DOI: 10.1177/20552076211048654] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 09/07/2021] [Indexed: 12/27/2022] Open
Abstract
The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the
unprecedented collection of health data to support research. Historically,
coordinating the collation of such datasets on a national scale has been
challenging to execute for several reasons, including issues with data privacy,
the lack of data reporting standards, interoperable technologies, and
distribution methods. The coronavirus SARS-CoV-2 disease pandemic has
highlighted the importance of collaboration between government bodies,
healthcare institutions, academic researchers and commercial companies in
overcoming these issues during times of urgency. The National COVID-19 Chest
Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey
NHS Foundation Trust and Faculty, is an example of such a national initiative.
Here, we summarise the experiences and challenges of setting up the National
COVID-19 Chest Imaging Database, and the implications for future ambitions of
national data curation in medical imaging to advance the safe adoption of
artificial intelligence in healthcare.
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Affiliation(s)
| | | | | | | | | | | | | | - Mark Halling-Brown
- Scientific Computing, Royal Surrey NHS Foundation Trust, UK.,CVSSP, University of Surrey, UK
| | | | - Emily Jefferson
- Health Data Research UK, UK.,Health Informatics Centre (HIC), School of Medicine, University of Dundee, UK
| | | | | | | | - Graham Robinson
- Department of Radiology, Royal United Hospitals Bath NHS Foundation Trust, UK
| | - Susan Copley
- Imaging Department, Hammersmith Hospital, Imperial College NHS Healthcare Trust, UK
| | - Rizwan Malik
- Department of Radiology, Bolton NHS Foundation Trust, UK
| | - Claire Bloomfield
- National Consortium of Intelligent Medical Imaging (NCIMI), The Big Data Institute, University of Oxford, UK.,Dept of Oncology, University of Oxford, UK
| | - Fergus Gleeson
- National Consortium of Intelligent Medical Imaging (NCIMI), The Big Data Institute, University of Oxford, UK.,Dept of Oncology, University of Oxford, UK
| | | | - Erika Denton
- Norfolk and Norwich University Hospital Foundation Trust, UK
| | | | - Gary Leeming
- Institute of Population Health, Faculty of Health and Life Sciences, University of Liverpool, UK
| | - Hayley E Hardwick
- National Institute of Health Research (NIHR) Health Protection Research Unit in Emerging and Zoonotic Infections, UK
| | | | | | - Malcolm G Semple
- NIHR Health Protection Research Unit, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, UK
| | - Caroline Rubin
- Department of Radiology, University Hospital Southampton NHS Foundation Trust, UK
| | | | - Andrea G Rockall
- Imaging Department, Hammersmith Hospital, Imperial College NHS Healthcare Trust, UK.,Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, UK
| | - Ayub Bhayat
- NHS Arden & Greater East Midlands Commissioning Support Unit, UK
| | | | - Cathie Sudlow
- British Heart Foundation Data Science Centre Led by Health Data Research UK, UK
| | | | - Joseph Jacob
- Department of Respiratory Medicine, University College London, UK.,Centre for Medical Image Computing, Department of Computer Science, University College London, UK
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Garcia Santa Cruz B, Bossa MN, Sölter J, Husch AD. Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem. Med Image Anal 2021. [PMID: 34597937 DOI: 10.1101/2021.02.15.21251775] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Computer-aided-diagnosis and stratification of COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. By adopting established tools for model evaluation to the task of evaluating datasets, this study provides a systematic appraisal of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias. Only 9 out of more than a hundred identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably most of the datasets utilised in 201 papers published in peer-reviewed journals, are not among these 9 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use. This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.
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Affiliation(s)
- Beatriz Garcia Santa Cruz
- Centre Hospitalier de Luxembourg, 4, Rue Ernest Barble, Luxembourg L-1210, Luxembourg; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts Fourneaux, Esch-sur-Alzette L-4362, Luxembourg.
| | - Matías Nicolás Bossa
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts Fourneaux, Esch-sur-Alzette L-4362, Luxembourg; Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Pleinlaan 2, Brussels B-1050, Belgium
| | - Jan Sölter
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts Fourneaux, Esch-sur-Alzette L-4362, Luxembourg
| | - Andreas Dominik Husch
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts Fourneaux, Esch-sur-Alzette L-4362, Luxembourg
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