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Luo Y, Mao C, Sanchez‐Pinto LN, Ahmad FS, Naidech A, Rasmussen L, Pacheco JA, Schneider D, Mithal LB, Dresden S, Holmes K, Carson M, Shah SJ, Khan S, Clare S, Wunderink RG, Liu H, Walunas T, Cooper L, Yue F, Wehbe F, Fang D, Liebovitz DM, Markl M, Michelson KN, McColley SA, Green M, Starren J, Ackermann RT, D'Aquila RT, Adams J, Lloyd‐Jones D, Chisholm RL, Kho A. Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system. Learn Health Syst 2024; 8:e10417. [PMID: 39036530 PMCID: PMC11257059 DOI: 10.1002/lrh2.10417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 07/23/2024] Open
Abstract
Introduction The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare. Methods We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively. Results Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare. Conclusions Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.
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Affiliation(s)
- Yuan Luo
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Chengsheng Mao
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Lazaro N. Sanchez‐Pinto
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Division of Critical Care, Department of PediatricsNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Stanley Manne Children's Research InstituteAnn & Robert H. Lurie Children's Hospital of ChicagoChicagoIllinoisUSA
| | - Faraz S. Ahmad
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Cardiology, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Andrew Naidech
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Neurocritical Care, Department of NeurologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Luke Rasmussen
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Jennifer A. Pacheco
- Center for Genetic MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Daniel Schneider
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
| | - Leena B. Mithal
- Stanley Manne Children's Research InstituteAnn & Robert H. Lurie Children's Hospital of ChicagoChicagoIllinoisUSA
- Division of Infectious Diseases, Department of PediatricsNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Scott Dresden
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of Emergency MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Kristi Holmes
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Galter Health Sciences LibraryNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Matthew Carson
- Galter Health Sciences LibraryNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Sanjiv J. Shah
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Cardiology, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Seema Khan
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of SurgeryNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Susan Clare
- Department of SurgeryNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Richard G. Wunderink
- Division of Critical Care, Department of PediatricsNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Pulmonary and Critical Care Division, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Huiping Liu
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of PharmacologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Division of Hematology and Oncology, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Theresa Walunas
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Division of General Internal Medicine, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Center for Health Information PartnershipsInstitute for Public Health and Medicine, Northwestern UniversityChicagoIllinoisUSA
- Department of Microbiology‐ImmunologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Lee Cooper
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of PathologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Feng Yue
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of PathologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of Biochemistry and Molecular GeneticsNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Firas Wehbe
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of SurgeryNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Deyu Fang
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of PathologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - David M. Liebovitz
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Division of General Internal Medicine, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Center for Health Information PartnershipsInstitute for Public Health and Medicine, Northwestern UniversityChicagoIllinoisUSA
| | - Michael Markl
- Department of RadiologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Kelly N. Michelson
- Division of Critical Care, Department of PediatricsNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Stanley Manne Children's Research InstituteAnn & Robert H. Lurie Children's Hospital of ChicagoChicagoIllinoisUSA
- Center for Bioethics and Medical Humanities, Institute for Public Health and MedicineNorthwestern UniversityChicagoIllinoisUSA
| | - Susanna A. McColley
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Stanley Manne Children's Research InstituteAnn & Robert H. Lurie Children's Hospital of ChicagoChicagoIllinoisUSA
- Division of Pulmonary and Sleep Medicine, Department of PediatricsNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Marianne Green
- Division of General Internal Medicine, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Justin Starren
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Ronald T. Ackermann
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Division of General Internal Medicine, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Institute for Public Health and MedicineNorthwestern UniversityChicagoIllinoisUSA
| | - Richard T. D'Aquila
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Division of Infectious Diseases, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - James Adams
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of Emergency MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Donald Lloyd‐Jones
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Epidemiology, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Rex L. Chisholm
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of SurgeryNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Center for Health Information PartnershipsInstitute for Public Health and Medicine, Northwestern UniversityChicagoIllinoisUSA
| | - Abel Kho
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Division of General Internal Medicine, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Center for Health Information PartnershipsInstitute for Public Health and Medicine, Northwestern UniversityChicagoIllinoisUSA
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Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D, Vilovic M, Vrdoljak J, Martinovic D, Grahovac M, Bozic J. Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence. Microorganisms 2024; 12:842. [PMID: 38792673 PMCID: PMC11123121 DOI: 10.3390/microorganisms12050842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.
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Affiliation(s)
- Doris Rusic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marko Kumric
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Ana Seselja Perisin
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Dario Leskur
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Josipa Bukic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Darko Modun
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marino Vilovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Dinko Martinovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Department of Maxillofacial Surgery, University Hospital of Split, Spinciceva 1, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia;
| | - Josko Bozic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
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Pinto-de-Sá R, Sousa-Pinto B, Costa-de-Oliveira S. Brave New World of Artificial Intelligence: Its Use in Antimicrobial Stewardship-A Systematic Review. Antibiotics (Basel) 2024; 13:307. [PMID: 38666983 PMCID: PMC11047419 DOI: 10.3390/antibiotics13040307] [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: 02/20/2024] [Revised: 03/24/2024] [Accepted: 03/25/2024] [Indexed: 04/29/2024] Open
Abstract
Antimicrobial resistance (AMR) is a growing public health problem in the One Health dimension. Artificial intelligence (AI) is emerging in healthcare, since it is helpful to deal with large amounts of data and as a prediction tool. This systematic review explores the use of AI in antimicrobial stewardship programs (ASPs) and summarizes the predictive performance of machine learning (ML) algorithms, compared with clinical decisions, in inpatients and outpatients who need antimicrobial prescriptions. This review includes eighteen observational studies from PubMed, Scopus, and Web of Science. The exclusion criteria comprised studies conducted only in vitro, not addressing infectious diseases, or not referencing the use of AI models as predictors. Data such as study type, year of publication, number of patients, study objective, ML algorithms used, features, and predictors were extracted from the included publications. All studies concluded that ML algorithms were useful to assist antimicrobial stewardship teams in multiple tasks such as identifying inappropriate prescribing practices, choosing the appropriate antibiotic therapy, or predicting AMR. The most extracted performance metric was AUC, which ranged from 0.64 to 0.992. Despite the risks and ethical concerns that AI raises, it can play a positive and promising role in ASP.
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Affiliation(s)
- Rafaela Pinto-de-Sá
- Division of Microbiology, Department of Pathology, Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal;
| | - Bernardo Sousa-Pinto
- Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
- Center for Health Technology and Services Research—CINTESIS@RISE, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - Sofia Costa-de-Oliveira
- Division of Microbiology, Department of Pathology, Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal;
- Center for Health Technology and Services Research—CINTESIS@RISE, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
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Silva-Caso W, Pérez-Lazo G, Aguilar-Luis MA, Morales-Moreno A, Ballena-López J, Soto-Febres F, Martins-Luna J, Del Valle LJ, Kym S, Aguilar-Luis D, Denegri-Hinostroza D, Del Valle-Mendoza J. Identification and Clinical Characteristics of Community-Acquired Acinetobacter baumannii in Patients Hospitalized for Moderate or Severe COVID-19 in Peru. Antibiotics (Basel) 2024; 13:266. [PMID: 38534701 DOI: 10.3390/antibiotics13030266] [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: 11/08/2023] [Revised: 02/26/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024] Open
Abstract
Acinetobacter baumannii has been described as a cause of serious community-acquired infections in tropical countries. Currently, its implications when simultaneously identified with other pathogens are not yet adequately understood. A descriptive study was conducted on hospitalized patients with a diagnosis of moderate/severe SARS-CoV-2-induced pneumonia confirmed via real-time RT-PCR. Patients aged > 18 years who were admitted to a specialized COVID-19 treatment center in Peru were selected for enrollment. A. baumannii was detected via the PCR amplification of the blaOXA-51 gene obtained from nasopharyngeal swabs within 48 h of hospitalization. A total of 295 patients with COVID-19 who met the study inclusion criteria were enrolled. A. baumannii was simultaneously identified in 40/295 (13.5%) of COVID-19-hospitalized patients. Demographic data and comorbidities were comparable in both Acinetobacter-positive and -negative subgroups. However, patients identified as being infected with Acinetobacter were more likely to have received outpatient antibiotics prior to hospitalization, had a higher requirement for high-flow nasal cannula and a higher subjective incidence of fatigue, and were more likely to develop Acinetobacter-induced pneumonia during hospitalization. Conclusions: The group in which SARS-CoV-2 and A. baumannii were simultaneously identified had a higher proportion of fatigue, a higher frequency of requiring a high-flow cannula, and a higher proportion of superinfection with the same microorganism during hospitalization.
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Affiliation(s)
- Wilmer Silva-Caso
- School of Medicine, Research Center of the Faculty of Health Sciences, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru
| | - Giancarlo Pérez-Lazo
- Division of Infectious Diseases, Guillermo Almenara Irigoyen National Hospital-EsSalud, Lima 15033, Peru
| | - Miguel Angel Aguilar-Luis
- School of Medicine, Research Center of the Faculty of Health Sciences, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru
| | - Adriana Morales-Moreno
- Division of Infectious Diseases, Guillermo Almenara Irigoyen National Hospital-EsSalud, Lima 15033, Peru
| | - José Ballena-López
- Division of Infectious Diseases, Guillermo Almenara Irigoyen National Hospital-EsSalud, Lima 15033, Peru
| | - Fernando Soto-Febres
- Division of Infectious Diseases, Guillermo Almenara Irigoyen National Hospital-EsSalud, Lima 15033, Peru
| | - Johanna Martins-Luna
- School of Medicine, Research Center of the Faculty of Health Sciences, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru
- Facultad de Ciencias de la Salud, Universidad Tecnológica del Perú, Lima 15046, Peru
| | - Luis J Del Valle
- Barcelona Research Center for Multiscale Science and Engineering, Departament d'Enginyeria Química, EEBE, Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain
| | - Sungmin Kym
- Korea International Cooperation for Infectious Diseases, College of Medicine, Chungnam National University, Daejeon 35015, Republic of Korea
| | - Deysi Aguilar-Luis
- School of Medicine, Research Center of the Faculty of Health Sciences, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru
| | - Dayana Denegri-Hinostroza
- School of Medicine, Research Center of the Faculty of Health Sciences, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru
| | - Juana Del Valle-Mendoza
- School of Medicine, Research Center of the Faculty of Health Sciences, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru
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Eickelberg G, Sanchez-Pinto LN, Kline AS, Luo Y. Transportability of bacterial infection prediction models for critically ill patients. J Am Med Inform Assoc 2023; 31:98-108. [PMID: 37647884 PMCID: PMC10746321 DOI: 10.1093/jamia/ocad174] [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: 04/10/2023] [Revised: 07/20/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023] Open
Abstract
OBJECTIVE Bacterial infections (BIs) are common, costly, and potentially life-threatening in critically ill patients. Patients with suspected BIs may require empiric multidrug antibiotic regimens and therefore potentially be exposed to prolonged and unnecessary antibiotics. We previously developed a BI risk model to augment practices and help shorten the duration of unnecessary antibiotics to improve patient outcomes. Here, we have performed a transportability assessment of this BI risk model in 2 tertiary intensive care unit (ICU) settings and a community ICU setting. We additionally explored how simple multisite learning techniques impacted model transportability. METHODS Patients suspected of having a community-acquired BI were identified in 3 datasets: Medical Information Mart for Intensive Care III (MIMIC), Northwestern Medicine Tertiary (NM-T) ICUs, and NM "community-based" ICUs. ICU encounters from MIMIC and NM-T datasets were split into 70/30 train and test sets. Models developed on training data were evaluated against the NM-T and MIMIC test sets, as well as NM community validation data. RESULTS During internal validations, models achieved AUROCs of 0.78 (MIMIC) and 0.81 (NM-T) and were well calibrated. In the external community ICU validation, the NM-T model had robust transportability (AUROC 0.81) while the MIMIC model transported less favorably (AUROC 0.74), likely due to case-mix differences. Multisite learning provided no significant discrimination benefit in internal validation studies but offered more stability during transport across all evaluation datasets. DISCUSSION These results suggest that our BI risk models maintain predictive utility when transported to external cohorts. CONCLUSION Our findings highlight the importance of performing external model validation on myriad clinically relevant populations prior to implementation.
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Affiliation(s)
- Garrett Eickelberg
- Department of Preventive Medicine (Health & Biomedical Informatics), Feinberg School of Medicine, Chicago, IL 60611, United States
| | - Lazaro Nelson Sanchez-Pinto
- Department of Preventive Medicine (Health & Biomedical Informatics), Feinberg School of Medicine, Chicago, IL 60611, United States
- Departments of Pediatrics (Critical Care), Chicago, IL 60611, United States
| | - Adrienne Sarah Kline
- Department of Preventive Medicine (Health & Biomedical Informatics), Feinberg School of Medicine, Chicago, IL 60611, United States
| | - Yuan Luo
- Department of Preventive Medicine (Health & Biomedical Informatics), Feinberg School of Medicine, Chicago, IL 60611, United States
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Hernàndez-Carnerero À, Sànchez-Marrè M, Mora-Jiménez I, Soguero-Ruiz C, Martínez-Agüero S, Álvarez-Rodríguez J. Dimensionality reduction and ensemble of LSTMs for antimicrobial resistance prediction. Artif Intell Med 2023; 138:102508. [PMID: 36990585 DOI: 10.1016/j.artmed.2023.102508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 11/21/2022] [Accepted: 02/04/2023] [Indexed: 02/10/2023]
Abstract
Bacterial resistance to antibiotics has been rapidly increasing, resulting in low antibiotic effectiveness even treating common infections. The presence of resistant pathogens in environments such as a hospital Intensive Care Unit (ICU) exacerbates the critical admission-acquired infections. This work focuses on the prediction of antibiotic resistance in Pseudomonas aeruginosa nosocomial infections at the ICU, using Long Short-Term Memory (LSTM) artificial neural networks as the predictive method. The analyzed data were extracted from the Electronic Health Records (EHR) of patients admitted to the University Hospital of Fuenlabrada from 2004 to 2019 and were modeled as Multivariate Time Series. A data-driven dimensionality reduction method is built by adapting three feature importance techniques from the literature to the considered data and proposing an algorithm for selecting the most appropriate number of features. This is done using LSTM sequential capabilities so that the temporal aspect of features is taken into account. Furthermore, an ensemble of LSTMs is used to reduce the variance in performance. Our results indicate that the patient's admission information, the antibiotics administered during the ICU stay, and the previous antimicrobial resistance are the most important risk factors. Compared to other conventional dimensionality reduction schemes, our approach is able to improve performance while reducing the number of features for most of the experiments. In essence, the proposed framework achieve, in a computationally cost-efficient manner, promising results for supporting decisions in this clinical task, characterized by high dimensionality, data scarcity, and concept drift.
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Machine learning models to prognose 30-Day Mortality in Postoperative Disseminated Cancer Patients. Surg Oncol 2022; 44:101810. [DOI: 10.1016/j.suronc.2022.101810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/14/2022] [Accepted: 07/03/2022] [Indexed: 11/18/2022]
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Risk Management In Intensive Care Units With Artificial Intelligence Technologies: Systematic Review of Prediction Models Using Electronic Health Records. JOURNAL OF BASIC AND CLINICAL HEALTH SCIENCES 2022. [DOI: 10.30621/jbachs.993798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Background and aim: Clinical risk assessments should be made to protect patients from negative outcomes, and the definition, frequency and severity of the risk should be determined. The information contained in the electronic health records (EHRs) can use in different areas such as risk prediction, estimation of treatment effect ect. Many prediction models using artificial intelligence (AI) technologies that can be used in risk assessment have been developed. The aim of this study is to bring together the researches on prediction models developed with AI technologies using the EHRs of patients hospitalized in the intensive care unit (ICU) and to evaluate them in terms of risk management in healthcare.
Methods: The study restricted the search to the Web of Science, Pubmed, Science Direct, and Medline databases to retrieve research articles published in English in 2010 and after. Studies with a prediction model using data obtained from EHRs in the ICU are included. The study focused solely on research conducted in ICU to predict a health condition that poses a significant risk to patient safety using artificial intellegence (AI) technologies.
Results: Recognized prediction subcategories were mortality (n=6), sepsis (n=4), pressure ulcer (n=4), acute kidney injury (n=3), and other areas (n=10). It has been found that EHR-based prediction models are good risk management and decision support tools and adoption of such models in ICUs may reduce the prevalence of adverse conditions.
Conclusions: The article results remarks that developed models was found to have higher performance and better selectivity than previously developed risk models, so they are better at predicting risks and serious adverse events in ICU. It is recommended to use AI based prediction models developed using EHRs in risk management studies. Future work is still needed to researches to predict different health conditions risks.
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Eickelberg G, Luo Y, Sanchez-Pinto LN. Development and validation of MicrobEx: an open-source package for microbiology culture concept extraction. JAMIA Open 2022; 5:ooac026. [PMID: 35651524 PMCID: PMC9150069 DOI: 10.1093/jamiaopen/ooac026] [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: 01/14/2022] [Revised: 03/25/2022] [Accepted: 04/11/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Objective
Microbiology culture reports contain critical information for important clinical and public health applications. However, microbiology reports often have complex, semistructured, free-text data that present a barrier for secondary use. Here we present the development and validation of an open-source package designed to ingest free-text microbiology reports, determine whether the culture is positive, and return a list of Systemized Nomenclature of Medicine (SNOMED)-CT mapped bacteria.
Materials and Methods
Our concept extraction Python package, MicrobEx, is built upon a rule-based natural language processing algorithm and was developed using microbiology reports from 2 different electronic health record systems in a large healthcare organization, and then externally validated on the reports of 2 other institutions with manually reviewed results as a benchmark.
Results
MicrobEx achieved F1 scores >0.95 on all classification tasks across 2 independent validation sets with minimal customization. Additionally, MicrobEx matched or surpassed our MetaMap-based benchmark algorithm performance across positive culture classification and species capture classification tasks.
Discussion
Our results suggest that MicrobEx can be used to reliably estimate binary bacterial culture status, extract bacterial species, and map these to SNOMED organism observations when applied to semistructured, free-text microbiology reports from different institutions with relatively low customization.
Conclusion
MicrobEx offers an open-source software solution (available on both GitHub and PyPI) for bacterial culture status estimation and bacterial species extraction from free-text microbiology reports. The package was designed to be reused and adapted to individual institutions as an upstream process for other clinical applications such as: machine learning, clinical decision support, and disease surveillance systems.
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Affiliation(s)
- Garrett Eickelberg
- Department of Preventive Medicine (Health & Biomedical Informatics), Feinberg School of Medicine, Chicago, Illinois, USA
| | - Yuan Luo
- Department of Preventive Medicine (Health & Biomedical Informatics), Feinberg School of Medicine, Chicago, Illinois, USA
| | - L Nelson Sanchez-Pinto
- Department of Preventive Medicine (Health & Biomedical Informatics), Feinberg School of Medicine, Chicago, Illinois, USA
- Department of Pediatrics (Critical Care), Chicago, Illinois, USA
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10
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Abstract
This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2022. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2022. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901.
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Affiliation(s)
- Thomas De Corte
- Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium. .,Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium.
| | | | - Jan De Waele
- Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.,Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium
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11
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Abstract
PURPOSE OF REVIEW Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings. RECENT FINDINGS The COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts. SUMMARY As the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.
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12
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Abstract
Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-Layer Perceptron (MLP) models is the best model to predict the cure class.
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13
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Syed M, Syed S, Sexton K, Syeda HB, Garza M, Zozus M, Syed F, Begum S, Syed AU, Sanford J, Prior F. Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review. INFORMATICS-BASEL 2021; 8. [PMID: 33981592 DOI: 10.3390/informatics8010016] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.
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Affiliation(s)
- Mahanazuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Shorabuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Kevin Sexton
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
- Department of Surgery, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
- Department of Health Policy and Management, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Hafsa Bareen Syeda
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Maryam Garza
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Meredith Zozus
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas 78229, USA
| | - Farhanuddin Syed
- Shadan Institute of Medical Sciences, College of Medicine, Hyderabad, Telangana 500086, India
| | - Salma Begum
- Department of Information Technology, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Abdullah Usama Syed
- Department of Information Science, University of Arkansas at Little Rock (UALR), Little Rock, Arkansas 72205, USA
| | - Joseph Sanford
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
- Department of Anesthesiology, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
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