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Laffafchi S, Ebrahimi A, Kafan S. Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data. Health Inf Sci Syst 2024; 12:17. [PMID: 38464464 PMCID: PMC10917730 DOI: 10.1007/s13755-024-00276-9] [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: 12/15/2022] [Accepted: 01/17/2024] [Indexed: 03/12/2024] Open
Abstract
Pulmonary Embolism (PE) is a life-threatening clinical disease with no specific clinical symptoms and Computed Tomography Angiography (CTA) is used for diagnosis. Clinical decision support scoring systems like Wells and rGeneva based on PE risk factors have been developed to estimate the pre-test probability but are underused, leading to continuous overuse of CTA imaging. This diagnostic study aimed to propose a novel approach for efficient management of PE diagnosis using a two-step interconnected machine learning framework directly by analyzing patients' Electronic Health Records data. First, we performed feature importance analysis according to the result of LightGBM superiority for PE prediction, then four state-of-the-art machine learning methods were applied for PE prediction based on the feature importance results, enabling swift and accurate pre-test diagnosis. Throughout the study patients' data from different departments were collected from Sina educational hospital, affiliated with the Tehran University of medical sciences in Iran. Generally, the Ridge classification method obtained the best performance with an F1 score of 0.96. Extensive experimental findings showed the effectiveness and simplicity of this diagnostic process of PE in comparison with the existing scoring systems. The main strength of this approach centered on PE disease management procedures, which would reduce avoidable invasive CTA imaging and be applied as a primary prognosis of PE, hence assisting the healthcare system, clinicians, and patients by reducing costs and promoting treatment quality and patient satisfaction.
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Affiliation(s)
- Soroor Laffafchi
- Department of Business Administration and Entrepreneurship, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Ahmad Ebrahimi
- Department of Industrial and Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Samira Kafan
- Department of Pulmonary Medicine, Sina Hospital, International Relations Office, Medical School, Tehran University of Medical Sciences, PourSina St., Tehran, 1417613151 Iran
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2
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Bartenschlager CC, Grieger M, Erber J, Neidel T, Borgmann S, Vehreschild JJ, Steinbrecher M, Rieg S, Stecher M, Dhillon C, Ruethrich MM, Jakob CEM, Hower M, Heller AR, Vehreschild M, Wyen C, Messmann H, Piepel C, Brunner JO, Hanses F, Römmele C. Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways. Health Care Manag Sci 2023; 26:412-429. [PMID: 37428304 PMCID: PMC10485125 DOI: 10.1007/s10729-023-09647-2] [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: 10/08/2021] [Accepted: 06/01/2023] [Indexed: 07/11/2023]
Abstract
The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results.
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Affiliation(s)
- Christina C Bartenschlager
- Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159, Augsburg, Germany
- Professor of Applied Data Science in Health Care, Nürnberg School of Health, Ohm University of Applied Sciences Nuremberg, Nuremberg, Germany
- Anaesthesiology and Operative Intensive Care Medicine, Faculty of Medicine, University of Augsburg, Stenglinstrasse 2, 86156, Augsburg, Germany
| | - Milena Grieger
- Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159, Augsburg, Germany
| | - Johanna Erber
- Department of Internal Medicine II, Technical University of Munich, School of Medicine, University Hospital Rechts Der Isar, Munich, Germany
| | - Tobias Neidel
- Anaesthesiology and Operative Intensive Care Medicine, Faculty of Medicine, University of Augsburg, Stenglinstrasse 2, 86156, Augsburg, Germany
| | - Stefan Borgmann
- Hygiene and Infectiology, Klinikum Ingolstadt, Ingolstadt, Germany
| | - Jörg J Vehreschild
- Department of Internal Medicine, Hematology and Oncology, Goethe University Frankfurt, Frankfurt Am Main, Germany
- Department I of Internal Medicine, University of Cologne, University Hospital of Cologne, Cologne, Germany
- German Center for Infection Research, Partner Site Bonn-Cologne, Cologne, Germany
| | - Markus Steinbrecher
- Clinic for Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| | - Siegbert Rieg
- Clinic for Internal Medicine II - Infectiology, University Hospital Freiburg, Freiburg, Germany
| | - Melanie Stecher
- Department I of Internal Medicine, University of Cologne, University Hospital of Cologne, Cologne, Germany
- German Center for Infection Research, Partner Site Bonn-Cologne, Cologne, Germany
| | - Christine Dhillon
- COVID-19 Task Force, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| | - Maria M Ruethrich
- Hematology and Internal Oncology, University Hospital Jena, Jena, Germany
| | - Carolin E M Jakob
- Department I of Internal Medicine, University of Cologne, University Hospital of Cologne, Cologne, Germany
- German Center for Infection Research, Partner Site Bonn-Cologne, Cologne, Germany
| | - Martin Hower
- Pneumology, Infectiology and Internal Intensive Care Medicine, Klinikum Dortmund, Germany
| | - Axel R Heller
- Anaesthesiology and Operative Intensive Care Medicine, Faculty of Medicine, University of Augsburg, Stenglinstrasse 2, 86156, Augsburg, Germany
| | - Maria Vehreschild
- Department of Internal Medicine, Infectious Diseases, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt Am Main, Germany
| | - Christoph Wyen
- Praxis am Ebertplatz, Cologne, Germany
- Department of Medicine I, University Hospital of Cologne, Cologne, Germany
| | - Helmut Messmann
- Clinic for Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| | - Christiane Piepel
- Department of Hemato-Oncology and Infectious Diseases, Klinikum Bremen-Mitte, Bremen, Germany
| | - Jens O Brunner
- Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159, Augsburg, Germany.
- Department of Technology, Management, and Economics, Technical University of Denmark, Hovedstaden, Denmark.
- Data and Development Support, Region Zealand, Denmark.
| | - Frank Hanses
- Internal Medicine and Infectious Diseases, University Hospital Regensburg, Regensburg, Germany
| | - Christoph Römmele
- Clinic for Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
- COVID-19 Task Force, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
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3
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du Toit C, Tran TQB, Deo N, Aryal S, Lip S, Sykes R, Manandhar I, Sionakidis A, Stevenson L, Pattnaik H, Alsanosi S, Kassi M, Le N, Rostron M, Nichol S, Aman A, Nawaz F, Mehta D, Tummala R, McCallum L, Reddy S, Visweswaran S, Kashyap R, Joe B, Padmanabhan S. Survey and Evaluation of Hypertension Machine Learning Research. J Am Heart Assoc 2023; 12:e027896. [PMID: 37119074 PMCID: PMC10227215 DOI: 10.1161/jaha.122.027896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 03/27/2023] [Indexed: 04/30/2023]
Abstract
Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension-related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real-time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.
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Affiliation(s)
- Clea du Toit
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Tran Quoc Bao Tran
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Neha Deo
- Mayo Clinic Alix School of MedicineRochesterMN
| | - Sachin Aryal
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | - Stefanie Lip
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Robert Sykes
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Ishan Manandhar
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | | | - Leah Stevenson
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | | | - Safaa Alsanosi
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
- Department of Pharmacology and Toxicology, Faculty of MedicineUmm Al Qura UniversityMakkahSaudi Arabia
| | - Maria Kassi
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Ngoc Le
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Maggie Rostron
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Sarah Nichol
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Alisha Aman
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Faisal Nawaz
- College of MedicineMohammed Bin Rashid University of Medicine and Health SciencesDubaiUAE
| | - Dhruven Mehta
- Department of Internal MedicineTriStar Centennial Medical Center, HCA HealthcareNashvilleTN
| | - Ramakumar Tummala
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | - Linsay McCallum
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | | | - Shyam Visweswaran
- Department of Biomedical InformaticsUniversity of PittsburghPittsburghPA
| | - Rahul Kashyap
- Department of Anesthesiology and Critical Care MedicineMayo ClinicRochesterMN
| | - Bina Joe
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | - Sandosh Padmanabhan
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
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4
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Hu Y, Huerta J, Cordella N, Mishuris RG, Paschalidis IC. Personalized hypertension treatment recommendations by a data-driven model. BMC Med Inform Decis Mak 2023; 23:44. [PMID: 36859187 PMCID: PMC9979505 DOI: 10.1186/s12911-023-02137-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 02/09/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND Hypertension is a prevalent cardiovascular disease with severe longer-term implications. Conventional management based on clinical guidelines does not facilitate personalized treatment that accounts for a richer set of patient characteristics. METHODS Records from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used, selecting patients with either a hypertension diagnosis or meeting diagnostic criteria (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 42,752). Models were developed to recommend a class of antihypertensive medications for each patient based on their characteristics. Regression immunized against outliers was combined with a nearest neighbor approach to associate with each patient an affinity group of other patients. This group was then used to make predictions of future Systolic Blood Pressure (SBP) under each prescription type. For each patient, we leveraged these predictions to select the class of medication that minimized their future predicted SBP. RESULTS The proposed model, built with a distributionally robust learning procedure, leads to a reduction of 14.28 mmHg in SBP, on average. This reduction is 70.30% larger than the reduction achieved by the standard-of-care and 7.08% better than the corresponding reduction achieved by the 2nd best model which uses ordinary least squares regression. All derived models outperform following the previous prescription or the current ground truth prescription in the record. We randomly sampled and manually reviewed 350 patient records; 87.71% of these model-generated prescription recommendations passed a sanity check by clinicians. CONCLUSION Our data-driven approach for personalized hypertension treatment yielded significant improvement compared to the standard-of-care. The model implied potential benefits of computationally deprescribing and can support situations with clinical equipoise.
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Affiliation(s)
- Yang Hu
- Department of Electrical and Computer Engineering, Division of Systems Engineering, Boston University, 8 Saint Mary's St., Boston, MA, 02215, USA
| | - Jasmine Huerta
- Department of Medicine, Boston Medical Center, School of Medicine, Boston University, Boston, MA, USA
| | - Nicholas Cordella
- Department of Medicine, Boston Medical Center, School of Medicine, Boston University, Boston, MA, USA
| | - Rebecca G Mishuris
- Department of Medicine, Boston Medical Center, School of Medicine, Boston University, Boston, MA, USA
| | - Ioannis Ch Paschalidis
- Department of Electrical and Computer Engineering, Division of Systems Engineering, Boston University, 8 Saint Mary's St., Boston, MA, 02215, USA.
- Department of Biomedical Engineering, Faculty of Computing & Data Sciences, Hariri Institute for Computing and Computational Science & Engineering, Boston University, 8 Saint Mary's St., Boston, MA, 02215, USA.
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5
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Labandeira-Garcia JL, Labandeira CM, Valenzuela R, Pedrosa MA, Quijano A, Rodriguez-Perez AI. Drugs Modulating Renin-Angiotensin System in COVID-19 Treatment. Biomedicines 2022; 10:biomedicines10020502. [PMID: 35203711 PMCID: PMC8962306 DOI: 10.3390/biomedicines10020502] [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: 01/29/2022] [Revised: 02/16/2022] [Accepted: 02/17/2022] [Indexed: 02/07/2023] Open
Abstract
A massive worldwide vaccination campaign constitutes the main tool against the COVID-19 pandemic. However, drug treatments are also necessary. Antivirals are the most frequently considered treatments. However, strategies targeting mechanisms involved in disease aggravation may also be effective. A major role of the tissue renin-angiotensin system (RAS) in the pathophysiology and severity of COVID-19 has been suggested. The main link between RAS and COVID-19 is angiotensin-converting enzyme 2 (ACE2), a central RAS component and the primary binding site for SARS-CoV-2 that facilitates the virus entry into host cells. An initial suggestion that the susceptibility to infection and disease severity may be enhanced by angiotensin type-1 receptor blockers (ARBs) and ACE inhibitors (ACEIs) because they increase ACE2 levels, led to the consideration of discontinuing treatments in thousands of patients. More recent experimental and clinical data indicate that ACEIs and, particularly, ARBs can be beneficial for COVID-19 outcome, both by reducing inflammatory responses and by triggering mechanisms (such as ADAM17 inhibition) counteracting viral entry. Strategies directly activating RAS anti-inflammatory components such as soluble ACE2, Angiotensin 1-7 analogues, and Mas or AT2 receptor agonists may also be beneficial. However, while ACEIs and ARBs are cheap and widely used, the second type of strategies are currently under study.
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Affiliation(s)
- Jose L. Labandeira-Garcia
- Research Center for Molecular Medicine and Chronic Diseases (CIMUS), IDIS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; (C.M.L.); (R.V.); (M.A.P.); (A.Q.)
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain
- Correspondence: (J.L.L.-G.); (A.I.R.-P.)
| | - Carmen M. Labandeira
- Research Center for Molecular Medicine and Chronic Diseases (CIMUS), IDIS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; (C.M.L.); (R.V.); (M.A.P.); (A.Q.)
- Neurology Service, Hospital Alvaro Cunqueiro, University Hospital Complex, 36213 Vigo, Spain
| | - Rita Valenzuela
- Research Center for Molecular Medicine and Chronic Diseases (CIMUS), IDIS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; (C.M.L.); (R.V.); (M.A.P.); (A.Q.)
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain
| | - Maria A. Pedrosa
- Research Center for Molecular Medicine and Chronic Diseases (CIMUS), IDIS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; (C.M.L.); (R.V.); (M.A.P.); (A.Q.)
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain
| | - Aloia Quijano
- Research Center for Molecular Medicine and Chronic Diseases (CIMUS), IDIS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; (C.M.L.); (R.V.); (M.A.P.); (A.Q.)
| | - Ana I. Rodriguez-Perez
- Research Center for Molecular Medicine and Chronic Diseases (CIMUS), IDIS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; (C.M.L.); (R.V.); (M.A.P.); (A.Q.)
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain
- Correspondence: (J.L.L.-G.); (A.I.R.-P.)
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6
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Morton A, Bish E, Megiddo I, Zhuang W, Aringhieri R, Brailsford S, Deo S, Geng N, Higle J, Hutton D, Janssen M, Kaplan EH, Li J, Oliveira MD, Prinja S, Rauner M, Silal S, Song J. Introduction to the special issue: Management Science in the Fight Against Covid-19. Health Care Manag Sci 2021; 24:251-252. [PMID: 34129134 PMCID: PMC8204067 DOI: 10.1007/s10729-021-09569-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/19/2021] [Indexed: 01/29/2023]
Affiliation(s)
- Alec Morton
- University of Strathclyde London, 16 Richmond St, Glasgow, G1 1XQ, UK.
| | - Ebru Bish
- The University of Alabama, AL, Tuscaloosa, 35487, USA
| | - Itamar Megiddo
- University of Strathclyde London, 16 Richmond St, Glasgow, G1 1XQ, UK
| | - Weifen Zhuang
- Xiamen University, 422 Siming S Rd, Siming District, Fujian, Xiamen, China
| | - Roberto Aringhieri
- Computer Science Dept, University of Turin, Corso Svizzera. 185, TO, 10149, Torino, Italy
| | - Sally Brailsford
- University of Southampton, SO17 1BJ, Southampton, United Kingdom
| | - Sarang Deo
- Indian School of Business, Telangana, 500111, Hyderabad, India
| | - Na Geng
- Shanghai Jiao Tong University, Minhang District, 200240, Shanghai, China
| | - Julie Higle
- University of Southern California, CA, 90007, Los Angeles, United States
| | - David Hutton
- University of Michigan, 500 S State St, MI, 48109, Ann Arbor, United States
| | | | - Edward H Kaplan
- Yale School of Management, Yale School of Public Health, Yale School of Engineering and Applied Science, 165 Whitney Avenue, New Haven, CT, 06511, New Haven, United States
| | - Jianbin Li
- Huazhong University of Science and Technology, 1037 Luoyu Rd, Hubei, 430074, Wuhan, China
| | - Mónica D Oliveira
- Centre for Management Studies of Instituto Superior Técnico (CEG-IST), Institute for Bioengineering and Biosciences, and Institute for Health and Bioeconomy - i4HB Associate Laboratory, University to Lisboa, Institute for Bioengineering and Biosciences, and Institute for Health and Bioeconomy - i4HB Associate Laboratory, 1649-001, Lisboa, Portugal
| | - Shankar Prinja
- Post Graduate Institute of Medical Education & Research (PGIMER), Madhya Marg, Sector 12, 160012, Chandigarh, India
| | - Marion Rauner
- University of Vienna, Faculty of Business, Economics, and Statistics Department of Business Decisions and Analytics Oskar-Morgenstern-Platz 1, A-1090, Vienna, Austria
| | - Sheetal Silal
- Modelling and Simulation Hub, Africa (MASHA), University of Cape Town, Cape Town, 7700, Rondebosch, South Africa
| | - Jie Song
- Peking University, 5 Yiheyuan Rd, Haidian District, 100871, Beijing, China
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