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Rashidi HH, Ikram A, Dang LT, Bashir A, Zohra T, Ali A, Tanvir H, Mudassar M, Ravindran R, Akhtar N, Sikandar RI, Umer M, Akhter N, Butt R, Fennell BD, Khan IH. Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings. Sci Rep 2024; 14:14892. [PMID: 38937503 PMCID: PMC11211475 DOI: 10.1038/s41598-024-63707-3] [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: 02/01/2024] [Accepted: 05/31/2024] [Indexed: 06/29/2024] Open
Abstract
Accurate screening of COVID-19 infection status for symptomatic patients is a critical public health task. Although molecular and antigen tests now exist for COVID-19, in resource-limited settings, screening tests are often not available. Furthermore, during the early stages of the pandemic tests were not available in any capacity. We utilized an automated machine learning (ML) approach to train and evaluate thousands of models on a clinical dataset consisting of commonly available clinical and laboratory data, along with cytokine profiles for patients (n = 150). These models were then further tested for generalizability on an out-of-sample secondary dataset (n = 120). We were able to develop a ML model for rapid and reliable screening of patients as COVID-19 positive or negative using three approaches: commonly available clinical and laboratory data, a cytokine profile, and a combination of the common data and cytokine profile. Of the tens of thousands of models automatically tested for the three approaches, all three approaches demonstrated > 92% sensitivity and > 88 specificity while our highest performing model achieved 95.6% sensitivity and 98.1% specificity. These models represent a potential effective deployable solution for COVID-19 status classification for symptomatic patients in resource-limited settings and provide proof-of-concept for rapid development of screening tools for novel emerging infectious diseases.
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Affiliation(s)
- Hooman H Rashidi
- Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh Medical Center, and University of Pittsburgh School of Medicine, Pittsburgh, USA.
| | - Aamer Ikram
- National Institutes of Health, Islamabad, Pakistan
| | - Luke T Dang
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA
| | - Adnan Bashir
- Health Information Systems Program (HISP), Islamabad, Pakistan
| | | | - Amna Ali
- National Institutes of Health, Islamabad, Pakistan
| | - Hamza Tanvir
- National Institutes of Health, Islamabad, Pakistan
| | | | - Resmi Ravindran
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA
| | - Nasim Akhtar
- Pakistan Institute of Medical Sciences, Islamabad, Pakistan
| | | | - Mohammed Umer
- Rawalpindi Medical University-Rawalpindi, Rawalpindi, Pakistan
| | - Naeem Akhter
- Rawalpindi Medical University-Rawalpindi, Rawalpindi, Pakistan
| | - Rafi Butt
- Isolation Hospital and Infectious Treatment Centre, Islamabad, Pakistan
| | - Brandon D Fennell
- Department of Medicine, University of California, San Francisco, USA
| | - Imran H Khan
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA.
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2
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Vakili Ojarood M, Torabi H, Soltani A, Farzan R, Farhadi B. Machine learning as a hopeful indicator for prediction of complications and mortality in burn patients. Burns 2024:S0305-4179(24)00152-9. [PMID: 38821726 DOI: 10.1016/j.burns.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Accepted: 05/02/2024] [Indexed: 06/02/2024]
Affiliation(s)
| | - Hossein Torabi
- Department of General Surgery, Poursina Medical and Educational Center, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
| | - Azadeh Soltani
- Department of Information Technology Engineering, Mehrastan University, Astaneh Ashrafieh, Iran.
| | - Ramyar Farzan
- Department of Plastic & Reconstructive Surgery, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
| | - Bahar Farhadi
- School of Medicine, Islamic Azad University, Mashhad Branch, Mashhad, Iran.
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3
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Perschinka F, Peer A, Joannidis M. [Artificial intelligence and acute kidney injury]. Med Klin Intensivmed Notfmed 2024; 119:199-207. [PMID: 38396124 PMCID: PMC10995052 DOI: 10.1007/s00063-024-01111-5] [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: 01/15/2024] [Accepted: 01/17/2024] [Indexed: 02/25/2024]
Abstract
Digitalization is increasingly finding its way into intensive care units and with it artificial intelligence (AI) for critically ill patients. One promising area for the use of AI is in the field of acute kidney injury (AKI). The use of AI is primarily focused on the prediction of AKI, but further approaches are also being used to classify existing AKI into different phenotypes. Different AI models are used for prediction. The area under the receiver operating characteristic curve values (AUROC) achieved with these models vary and are influenced by several factors, such as the prediction time and the definition of AKI. Most models have an AUROC between 0.650 and 0.900, with lower values for predictions further into the future and when applying Acute Kidney Injury Network (AKIN) instead of KDIGO criteria. Classification into phenotypes already makes it possible to categorize patients into groups with different risks of mortality or requirement of renal replacement therapy (RRT), but the etiologies or therapeutic consequences derived from this are still lacking. However, all the models suffer from AI-specific shortcomings. The use of large databases does not make it possible to promptly include recent changes in therapy and the implementation of new biomarkers in a relevant proportion. For this reason, serum creatinine and urinary output, with their known limitations, dominate current AI models for prediction impairing the performance of the current models. On the other hand, the increasingly complex models no longer allow physicians to understand the basis on which the warning of a threatening AKI is calculated and subsequent initiation of therapy should take place. The successful use of AIs in routine clinical practice will be highly determined by the trust of the physicians in the systems and overcoming the aforementioned weaknesses. However, the clinician will remain irreplaceable as the decisive authority for critically ill patients by combining measurable and nonmeasurable parameters.
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Affiliation(s)
| | | | - Michael Joannidis
- Gemeinsame Einrichtung für Internistische Notfall- und Intensivmedizin, Department Innere Medizin, Medizinische Universität Innsbruck, Anichstraße 35, 6020, Innsbruck, Österreich.
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4
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Parry IS. Remembering the Past, Transforming the Future: Perspectives for the Burn Team. J Burn Care Res 2024; 45:265-272. [PMID: 37448190 DOI: 10.1093/jbcr/irad100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/15/2023]
Affiliation(s)
- Ingrid S Parry
- Department of Clinical Research Shriners Hospitals for Children, Northern California Sacramento, CA, USA
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5
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Legrand M, Clark AT, Neyra JA, Ostermann M. Acute kidney injury in patients with burns. Nat Rev Nephrol 2024; 20:188-200. [PMID: 37758939 DOI: 10.1038/s41581-023-00769-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2023] [Indexed: 09/29/2023]
Abstract
Burn injury is associated with a high risk of acute kidney injury (AKI) with a prevalence of AKI among patients with burns of 9-50%. Despite an improvement in burn injury survival in the past decade, AKI in patients with burns is associated with an extremely poor short-term and long-term prognosis, with a mortality of >80% among those with severe AKI. Factors that contribute to the development of AKI in patients with burns include haemodynamic alterations, burn-induced systemic inflammation and apoptosis, haemolysis, rhabdomyolysis, smoke inhalation injury, drug nephrotoxicity and sepsis. Early and late AKI after burn injury differ in their aetiologies and outcomes. Sepsis is the main driver of late AKI in patients with burns and late AKI has been associated with higher mortality than early AKI. Prevention of early AKI involves correction of hypovolaemia and avoidance of nephrotoxic drugs (for example, hydroxocobalamin), whereas prevention of late AKI involves prevention and early recognition of sepsis as well as avoidance of nephrotoxins. Treatment of AKI in patients with burns remains supportive, including prevention of fluid overload, treatment of electrolyte disturbance and use of kidney replacement therapy when indicated.
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Affiliation(s)
- Matthieu Legrand
- Department of Anaesthesia and Perioperative Care, Division of Critical Care Medicine, University of California San Francisco, San Francisco, CA, USA.
| | - Audra T Clark
- Department of General Surgery, Division of Burn, Trauma, Critical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Javier A Neyra
- Department of Internal Medicine, Division of Nephrology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Marlies Ostermann
- Department of Critical Care & Nephrology, King's College London, Guy's & St Thomas' Hospital, London, UK
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Begum M F, Narayan S. A Pattern mixture model with long short-term memory network for oliguric acute kidney injury prediction. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2023. [DOI: 10.1016/j.jksuci.2023.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
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7
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Yu X, Ji Y, Huang M, Feng Z. Machine learning for acute kidney injury: Changing the traditional disease prediction mode. Front Med (Lausanne) 2023; 10:1050255. [PMID: 36817768 PMCID: PMC9935708 DOI: 10.3389/fmed.2023.1050255] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/17/2023] [Indexed: 02/05/2023] Open
Abstract
Acute kidney injury (AKI) is a serious clinical comorbidity with clear short-term and long-term prognostic implications for inpatients. The diversity of risk factors for AKI has been recognized in previous studies, and a series of predictive models have been developed using traditional statistical methods in conjunction with its preventability, but they have failed to meet the expectations in limited clinical applications, the rapid spread of electronic health records and artificial intelligence machine learning technology has brought new hope for the construction of AKI prediction models. In this article, we systematically review the definition and classification of machine learning methods, modeling ideas and evaluation methods, and the characteristics and current status of modeling studies. According to the modeling objectives, we subdivided them into critical care medical setting models, all medical environment models, special surgery models, special disease models, and special nephrotoxin exposure models. As the first review article to comprehensively summarize and analyze machine learning prediction models for AKI, we aim to objectively describe the advantages and disadvantages of machine learning approaches to modeling, and help other researchers more quickly and intuitively understand the current status of modeling research, inspire ideas and learn from experience, so as to guide and stimulate more research and more in-depth exploration in the future, which will ultimately provide greater help to improve the overall status of AKI diagnosis and treatment.
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Taib BG, Karwath A, Wensley K, Minku L, Gkoutos GV, Moiemen N. Artificial intelligence in the management and treatment of burns: A systematic review and meta-analyses. J Plast Reconstr Aesthet Surg 2023; 77:133-161. [PMID: 36571960 DOI: 10.1016/j.bjps.2022.11.049] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 10/17/2022] [Accepted: 11/17/2022] [Indexed: 11/24/2022]
Abstract
INTRODUCTION AND AIM Artificial Intelligence (AI) is already being successfully employed to aid the interpretation of multiple facets of burns care. In the light of the growing influence of AI, this systematic review and diagnostic test accuracy meta-analyses aim to appraise and summarise the current direction of research in this field. METHOD A systematic literature review was conducted of relevant studies published between 1990 and 2021, yielding 35 studies. Twelve studies were suitable for a Diagnostic Test Meta-Analyses. RESULTS The studies generally focussed on burn depth (Accuracy 68.9%-95.4%, Sensitivity 90.8% and Specificity 84.4%), burn segmentation (Accuracy 76.0%-99.4%, Sensitivity 97.9% and specificity 97.6%) and burn related mortality (Accuracy >90%-97.5% Sensitivity 92.9% and specificity 93.4%). Neural networks were the most common machine learning (ML) algorithm utilised in 69% of the studies. The QUADAS-2 tool identified significant heterogeneity between studies. DISCUSSION The potential application of AI in the management of burns patients is promising, especially given its propitious results across a spectrum of dimensions, including burn depth, size, mortality, related sepsis and acute kidney injuries. The accuracy of the results analysed within this study is comparable to current practices in burns care. CONCLUSION The application of AI in the treatment and management of burns patients, as a series of point of care diagnostic adjuncts, is promising. Whilst AI is a potentially valuable tool, a full evaluation of its current utility and potential is limited by significant variations in research methodology and reporting.
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Affiliation(s)
- Bilal Gani Taib
- Burns and Plastic Surgery Department, Queen Elizabeth Hospital, Mindelsohn Way, Birmingham B15 2TH, United Kingdom.
| | - A Karwath
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom; Health Data Research UK Midlands Site, Birmingham, United Kingdom; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, United Kingdom
| | - K Wensley
- Burns and Plastic Surgery Department, Queen Elizabeth Hospital, Mindelsohn Way, Birmingham B15 2TH, United Kingdom
| | - L Minku
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - G V Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom; Health Data Research UK Midlands Site, Birmingham, United Kingdom; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, United Kingdom; NIHR Surgical Reconstruction and Microbiology Research Centre, Birmingham, United Kingdom
| | - N Moiemen
- College of Medical and Dental Sciences, University of Birmingham, United Kingdom; Centre for Conflict Wound Research, Scar Free Foundation, Birmingham, United Kingdom; NIHR Surgical Reconstruction and Microbiology Research Centre, Birmingham, United Kingdom
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9
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Chandrashekar K, Setlur AS, Sabhapathi C A, Raiker SS, Singh S, Niranjan V. Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications. Cancer Inform 2023; 22:11769351221147244. [PMID: 36714384 PMCID: PMC9880585 DOI: 10.1177/11769351221147244] [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: 10/29/2022] [Accepted: 12/06/2022] [Indexed: 01/24/2023] Open
Abstract
Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.
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Affiliation(s)
| | | | | | | | | | - Vidya Niranjan
- Vidya Niranjan, Department of
Biotechnology, R V College of Engineering, Bengaluru, Karnataka 560059, India.
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10
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Vagliano I, Chesnaye NC, Leopold JH, Jager KJ, Abu-Hanna A, Schut MC. Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal. Clin Kidney J 2022; 15:2266-2280. [PMID: 36381375 PMCID: PMC9664575 DOI: 10.1093/ckj/sfac181] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND The number of studies applying machine learning (ML) to predict acute kidney injury (AKI) has grown steadily over the past decade. We assess and critically appraise the state of the art in ML models for AKI prediction, considering performance, methodological soundness, and applicability. METHODS We searched PubMed and ArXiv, extracted data, and critically appraised studies based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD), Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Prediction Model Risk of Bias Assessment Tool (PROBAST) guidelines. RESULTS Forty-six studies from 3166 titles were included. Thirty-eight studies developed a model, five developed and externally validated one, and three studies externally validated one. Flexible ML methods were used more often than deep learning, although the latter was common with temporal variables and text as predictors. Predictive performance showed an area under receiver operating curves ranging from 0.49 to 0.99. Our critical appraisal identified a high risk of bias in 39 studies. Some studies lacked internal validation, whereas external validation and interpretability of results were rarely considered. Fifteen studies focused on AKI prediction in the intensive care setting, and the US-derived Medical Information Mart for Intensive Care (MIMIC) data set was commonly used. Reproducibility was limited as data and code were usually unavailable. CONCLUSIONS Flexible ML methods are popular for the prediction of AKI, although more complex models based on deep learning are emerging. Our critical appraisal identified a high risk of bias in most models: Studies should use calibration measures and external validation more often, improve model interpretability, and share data and code to improve reproducibility.
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Affiliation(s)
- Iacopo Vagliano
- Deptartment of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Nicholas C Chesnaye
- ERA Registry, Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jan Hendrik Leopold
- Deptartment of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Kitty J Jager
- ERA Registry, Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ameen Abu-Hanna
- Deptartment of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Martijn C Schut
- Deptartment of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
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11
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Filler G, Sharma AP. Biologic sex and the estimation of GFR in pediatric and young adult patients with acute kidney injury. Pediatr Nephrol 2022; 37:2971-2974. [PMID: 35725966 DOI: 10.1007/s00467-022-05654-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/03/2022] [Accepted: 06/03/2022] [Indexed: 01/10/2023]
Affiliation(s)
- Guido Filler
- Department of Pediatrics, Schulich School of Medicine & Dentistry, Western University, London, ON, N6A 5W9, Canada. .,Department of Pathology and Laboratory Medicine, Schulich School of Medicine & Dentistry, Western University, London, ON, N5A 5A5, Canada. .,Department of Medicine, Schulich School of Medicine & Dentistry, Western University, London, ON, N6A 5W9, Canada. .,The Lilibeth Caberto Kidney Clinical Research Unit, London Health Sciences Centre, London, ON, Canada. .,Department of Pediatrics, Children's Hospital, London Health Science Centre, Western University, 800 Commissioners Road East, London, ON, N6A 5W9, Canada.
| | - Ajay P Sharma
- Department of Pediatrics, Schulich School of Medicine & Dentistry, Western University, London, ON, N6A 5W9, Canada
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12
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Bajaj T, Koyner JL. Artificial Intelligence in Acute Kidney Injury Prediction. Adv Chronic Kidney Dis 2022; 29:450-460. [PMID: 36253028 PMCID: PMC10259199 DOI: 10.1053/j.ackd.2022.07.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/20/2022] [Accepted: 07/27/2022] [Indexed: 01/25/2023]
Abstract
The use of artificial intelligence (AI) in nephrology and its associated clinical research is growing. Recent years have seen increased interest in utilizing AI to predict the development of hospital-based acute kidney injury (AKI). Several AI techniques have been employed to improve the ability to detect AKI across a variety of hospitalized settings. This review discusses the evolutions of AKI risk prediction discussing the static risk assessment models of yesteryear as well as the more recent trend toward AI and advanced learning techniques. We discuss the relative improvement in AKI detection as well as the relative dearth of data around the clinical implementation and patient outcomes using these models. The use of AI for AKI detection and clinical care is in its infancy, and this review describes how we arrived at our current position and hints at the promise of the future.
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Affiliation(s)
- Tushar Bajaj
- Section of Nephrology, Department of Medicine University of Chicago, Chicago, IL, USA
| | - Jay L Koyner
- Section of Nephrology, Department of Medicine University of Chicago, Chicago, IL, USA.
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13
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Ejmalian A, Aghaei A, Nabavi S, Abedzadeh Darabad M, Tajbakhsh A, Abin AA, Ebrahimi Moghaddam M, Dabbagh A, Jahangirifard A, Memary E, Sayyadi S. Prediction of Acute Kidney Injury After Cardiac Surgery Using Interpretable Machine Learning. Anesth Pain Med 2022; 12:e127140. [PMID: 36937087 PMCID: PMC10016126 DOI: 10.5812/aapm-127140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 08/16/2022] [Accepted: 09/04/2022] [Indexed: 11/16/2022] Open
Abstract
Background Acute kidney injury (AKI) is a complication that occurs for various reasons after surgery, especially cardiac surgery. This complication can lead to a prolonged treatment process, increased costs, and sometimes death. Prediction of postoperative AKI can help anesthesiologists to implement preventive and early treatment strategies to reduce the risk of AKI. Objectives This study tries to predict postoperative AKI using interpretable machine learning models. Methods For this study, the information of 1435 patients was collected from multiple centers. The gathered data are in six categories: demographic characteristics and type of surgery, past medical history (PMH), drug history (DH), laboratory information, anesthesia and surgery information, and postoperative variables. Machine learning methods, including support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), random forest (RF), logistic regression, XGBoost, and AdaBoost, were used to predict postoperative AKI. Local interpretable model-agnostic explanations (LIME) and the Shapley methods were then leveraged to check the interpretability of models. Results Comparing the area under the curves (AUCs) obtained for different machine learning models show that the RF and XGBoost methods with values of 0.81 and 0.80 best predict postoperative AKI. The interpretations obtained for the machine learning models show that creatinine (Cr), cardiopulmonary bypass time (CPB time), blood sugar (BS), and albumin (Alb) have the most significant impact on predictions. Conclusions The treatment team can be informed about the possibility of postoperative AKI before cardiac surgery using machine learning models such as RF and XGBoost and adjust the treatment procedure accordingly. Interpretability of predictions for each patient ensures the validity of obtained predictions.
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Affiliation(s)
- Azar Ejmalian
- Deptartment of Anesthesiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Atefe Aghaei
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | - Shahabedin Nabavi
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | | | - Ardeshir Tajbakhsh
- Anesthesiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Ali Abin
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | | | - Ali Dabbagh
- Anesthesiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Jahangirifard
- Lung Transplantation Research Center, National Research Institute of Tuberculosis and Lung Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Elham Memary
- Anesthesiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Shahram Sayyadi
- Anesthesiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Corresponding Author: Anesthesiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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14
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Feizkhah A, Mobayen M, Ghazanfari MJ, Bagheri Toolaroud P, Ghorbani Vajargah P, Mollaei A, Falakdami A, Takasi P, Karkhah S. Machine learning for burned wound management. Burns 2022; 48:1261-1262. [PMID: 35501174 DOI: 10.1016/j.burns.2022.04.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 03/25/2022] [Accepted: 04/02/2022] [Indexed: 12/15/2022]
Affiliation(s)
- Alireza Feizkhah
- Burn and Regenerative Medicine Research Center, Guilan University of Medical Sciences, Rasht, Iran; Department of Medical Physics, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
| | - Mohammadreza Mobayen
- Burn and Regenerative Medicine Research Center, Guilan University of Medical Sciences, Rasht, Iran.
| | - Mohammad Javad Ghazanfari
- Burn and Regenerative Medicine Research Center, Guilan University of Medical Sciences, Rasht, Iran; Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Kashan University of Medical Sciences, Kashan, Iran.
| | | | - Pooyan Ghorbani Vajargah
- Burn and Regenerative Medicine Research Center, Guilan University of Medical Sciences, Rasht, Iran; Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran.
| | - Amirabbas Mollaei
- Burn and Regenerative Medicine Research Center, Guilan University of Medical Sciences, Rasht, Iran; Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran.
| | - Atefeh Falakdami
- Burn and Regenerative Medicine Research Center, Guilan University of Medical Sciences, Rasht, Iran; Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran.
| | - Poorya Takasi
- Burn and Regenerative Medicine Research Center, Guilan University of Medical Sciences, Rasht, Iran; Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran.
| | - Samad Karkhah
- Burn and Regenerative Medicine Research Center, Guilan University of Medical Sciences, Rasht, Iran; Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran; Quchan School of Nursing, Mashhad University of Medical Sciences, Mashhad, Iran.
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Magherini R, Mussi E, Volpe Y, Furferi R, Buonamici F, Servi M. Machine Learning for Renal Pathologies: An Updated Survey. SENSORS 2022; 22:s22134989. [PMID: 35808481 PMCID: PMC9269842 DOI: 10.3390/s22134989] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/22/2022] [Accepted: 06/29/2022] [Indexed: 12/04/2022]
Abstract
Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.
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Birkelo BC, Pannu N, Siew ED. Overview of Diagnostic Criteria and Epidemiology of Acute Kidney Injury and Acute Kidney Disease in the Critically Ill Patient. Clin J Am Soc Nephrol 2022; 17:717-735. [PMID: 35292532 PMCID: PMC9269585 DOI: 10.2215/cjn.14181021] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Since the description ischuria renalis by William Heberden (1), AKI has remained a prominent complication of critical illness. Beyond KRT, treatment has been limited by the capacity to phenotype this condition. Here, we chronicle the evolution of attempts to classify AKI, including the adoption of consensus definitions, the expansion of diagnosis and prognosis with novel biomarkers, and emerging tools such as artificial intelligence (AI).
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Affiliation(s)
- Bethany C. Birkelo
- Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for Acute Kidney Injury Research (VIP-AKI), Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Neesh Pannu
- Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Edward D. Siew
- Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for Acute Kidney Injury Research (VIP-AKI), Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee
- Health Services Research and Development, Veterans Affairs Tennessee Valley, Nashville, Tennessee
- Veterans Affairs Geriatrics Research Education and Clinical Center (GRECC), Tennessee Valley Health System (THVS), Veteran’s Health Administration, Nashville, Tennessee
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17
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Machine learning for burns clinical care: Opportunities & challenges. Burns 2022; 48:734-735. [PMID: 35177281 DOI: 10.1016/j.burns.2022.01.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 01/07/2022] [Indexed: 12/15/2022]
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18
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Artificial intelligence-enabled decision support in nephrology. Nat Rev Nephrol 2022; 18:452-465. [PMID: 35459850 DOI: 10.1038/s41581-022-00562-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2022] [Indexed: 12/12/2022]
Abstract
Kidney pathophysiology is often complex, nonlinear and heterogeneous, which limits the utility of hypothetical-deductive reasoning and linear, statistical approaches to diagnosis and treatment. Emerging evidence suggests that artificial intelligence (AI)-enabled decision support systems - which use algorithms based on learned examples - may have an important role in nephrology. Contemporary AI applications can accurately predict the onset of acute kidney injury before notable biochemical changes occur; can identify modifiable risk factors for chronic kidney disease onset and progression; can match or exceed human accuracy in recognizing renal tumours on imaging studies; and may augment prognostication and decision-making following renal transplantation. Future AI applications have the potential to make real-time, continuous recommendations for discrete actions and yield the greatest probability of achieving optimal kidney health outcomes. Realizing the clinical integration of AI applications will require cooperative, multidisciplinary commitment to ensure algorithm fairness, overcome barriers to clinical implementation, and build an AI-competent workforce. AI-enabled decision support should preserve the pre-eminence of wisdom and augment rather than replace human decision-making. By anchoring intuition with objective predictions and classifications, this approach should favour clinician intuition when it is honed by experience.
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Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence-An Updated Overview. J Clin Med 2022; 11:jcm11082265. [PMID: 35456357 PMCID: PMC9031863 DOI: 10.3390/jcm11082265] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.
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Affiliation(s)
- Ștefan Busnatu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
| | - Alexandra Bolocan
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - George E. D. Petrescu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Dan Nicolae Păduraru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Iulian Năstasă
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Mircea Lupușoru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Marius Geantă
- Centre for Innovation in Medicine, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Octavian Andronic
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 50044 Bucharest, Romania
- Correspondence:
| | - Henrique Martins
- Faculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilha, Portugal;
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Dagli MM, Rajesh A, Asaad M, Butler CE. The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review. Am Surg 2021:31348211065101. [PMID: 34958252 DOI: 10.1177/00031348211065101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Interest in the use of artificial intelligence (AI) and machine learning (ML) in medicine has grown exponentially over the last few years. With its ability to enhance speed, precision, and efficiency, AI has immense potential, especially in the field of surgery. This article aims to provide a comprehensive literature review of artificial intelligence as it applies to surgery and discuss practical examples, current applications, and challenges to the adoption of this technology. Furthermore, we elaborate on the utility of natural language processing and computer vision in improving surgical outcomes, research, and patient care.
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Affiliation(s)
| | - Aashish Rajesh
- Department of Surgery, 14742University of Texas Health Science Center, San Antonio, TX, USA
| | - Malke Asaad
- Department of Plastic & Reconstructive Surgery, 571198the University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles E Butler
- Department of Plastic & Reconstructive Surgery, 571198the University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Does Artificial Intelligence Make Clinical Decision Better? A Review of Artificial Intelligence and Machine Learning in Acute Kidney Injury Prediction. Healthcare (Basel) 2021; 9:healthcare9121662. [PMID: 34946388 PMCID: PMC8701097 DOI: 10.3390/healthcare9121662] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/19/2021] [Accepted: 11/26/2021] [Indexed: 02/06/2023] Open
Abstract
Acute kidney injury (AKI) is a common complication of hospitalization that greatly and negatively affects the short-term and long-term outcomes of patients. Current guidelines use serum creatinine level and urine output rate for defining AKI and as the staging criteria of AKI. However, because they are not sensitive or specific markers of AKI, clinicians find it difficult to predict the occurrence of AKI and prescribe timely treatment. Advances in computing technology have led to the recent use of machine learning and artificial intelligence in AKI prediction, recent research reported that by using electronic health records (EHR) the AKI prediction via machine-learning models can reach AUROC over 0.80, in some studies even reach 0.93. Our review begins with the background and history of the definition of AKI, and the evolution of AKI risk factors and prediction models is also appraised. Then, we summarize the current evidence regarding the application of e-alert systems and machine-learning models in AKI prediction.
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Soranno DE, Bihorac A, Goldstein SL, Kashani KB, Menon S, Nadkarni GN, Neyra JA, Pannu NI, Singh K, Cerda J, Koyner JL. Artificial Intelligence for AKI!Now: Let's Not Await Plato's Utopian Republic. KIDNEY360 2021; 3:376-381. [PMID: 35373136 PMCID: PMC8967630 DOI: 10.34067/kid.0003472021] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/17/2021] [Indexed: 01/10/2023]
Affiliation(s)
- Danielle E. Soranno
- Departments of Pediatrics, Bioengineering and Medicine, University of Colorado, Aurora, Colorado
| | - Azra Bihorac
- Department of Medicine and Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, Florida
| | - Stuart L. Goldstein
- University of Cincinnati College of Medicine and Cincinnati Children’s Hospital, Cincinnati, Ohio
| | - Kianoush B. Kashani
- Division of Nephrology and Hypertension, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota
| | - Shina Menon
- University of Washington and Seattle Children’s Hospital, Seattle, Washington
| | - Girish N. Nadkarni
- Division of Data Driven and Digital Medicine (D3M) and Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Javier A. Neyra
- Division of Nephrology, Bone and Mineral Metabolism, University of Kentucky Medical Center, Lexington, Kentucky
| | | | - Karandeep Singh
- Department of Internal Medicine and School of Information, University of Michigan, Ann Arbor, Michigan
| | - Jorge Cerda
- Department of Medicine, Albany Medical Center, Albany, New York
| | - Jay L. Koyner
- Section of Nephrology, Department of Medicine, University of Chicago, Chicago, Illinois
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23
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Rashidi HH, Dang LT, Albahra S, Ravindran R, Khan IH. Automated machine learning for endemic active tuberculosis prediction from multiplex serological data. Sci Rep 2021; 11:17900. [PMID: 34504228 PMCID: PMC8429671 DOI: 10.1038/s41598-021-97453-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 08/25/2021] [Indexed: 11/09/2022] Open
Abstract
Serological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies due to variable immune responses among patients. Clinical interpretation of these complex datasets requires development of suitable algorithms, a time consuming and tedious undertaking addressed by the automated machine learning platform MILO (Machine Intelligence Learning Optimizer). MILO seamlessly integrates data processing, feature selection, model training, and model validation to simultaneously generate and evaluate thousands of models. These models were then further tested for generalizability on out-of-sample secondary and tertiary datasets. Out of 31 antigens evaluated, a 23-antigen model was the most robust on both the secondary dataset (TB vs healthy) and the tertiary dataset (TB vs COPD) with sensitivity of 90.5% and respective specificities of 100.0% and 74.6%. MILO represents a user-friendly, end-to-end solution for automated generation and deployment of optimized models, ideal for applications where rapid clinical implementation is critical such as emerging infectious diseases.
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Affiliation(s)
- Hooman H Rashidi
- Department of Pathology and Laboratory Medicine, University of California Davis, 4400 V Street, Sacramento, CA, 95817, USA.
| | - Luke T Dang
- Department of Pathology and Laboratory Medicine, University of California Davis, 4400 V Street, Sacramento, CA, 95817, USA
| | - Samer Albahra
- Department of Pathology and Laboratory Medicine, University of California Davis, 4400 V Street, Sacramento, CA, 95817, USA
| | - Resmi Ravindran
- Department of Pathology and Laboratory Medicine, University of California Davis, 4400 V Street, Sacramento, CA, 95817, USA
| | - Imran H Khan
- Department of Pathology and Laboratory Medicine, University of California Davis, 4400 V Street, Sacramento, CA, 95817, USA.
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E Moura FS, Amin K, Ekwobi C. Artificial intelligence in the management and treatment of burns: a systematic review. BURNS & TRAUMA 2021; 9:tkab022. [PMID: 34423054 PMCID: PMC8375569 DOI: 10.1093/burnst/tkab022] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 03/08/2021] [Accepted: 04/30/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Artificial intelligence (AI) is an innovative field with potential for improving burn care. This article provides an updated review on machine learning in burn care and discusses future challenges and the role of healthcare professionals in the successful implementation of AI technologies. METHODS A systematic search was carried out on MEDLINE, Embase and PubMed databases for English-language articles studying machine learning in burns. Articles were reviewed quantitatively and qualitatively for clinical applications, key features, algorithms, outcomes and validation methods. RESULTS A total of 46 observational studies were included for review. Assessment of burn depth (n = 26), support vector machines (n = 19) and 10-fold cross-validation (n = 11) were the most common application, algorithm and validation tool used, respectively. CONCLUSION AI should be incorporated into clinical practice as an adjunct to the experienced burns provider once direct comparative analysis to current gold standards outlining its benefits and risks have been studied. Future considerations must include the development of a burn-specific common framework. Authors should use common validation tools to allow for effective comparisons. Level I/II evidence is required to produce robust proof about clinical and economic impacts.
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Affiliation(s)
| | - Kavit Amin
- Department of Plastic Surgery, Manchester University NHS Foundation Trust, UK
- Department of Plastic Surgery, Lancashire Teaching Hospitals NHS Foundation Trust, Royal Preston Hospital, Preston, UK
| | - Chidi Ekwobi
- Department of Plastic Surgery, Lancashire Teaching Hospitals NHS Foundation Trust, Royal Preston Hospital, Preston, UK
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25
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Rashidi HH, Tran N, Albahra S, Dang LT. Machine learning in health care and laboratory medicine: General overview of supervised learning and Auto-ML. Int J Lab Hematol 2021; 43 Suppl 1:15-22. [PMID: 34288435 DOI: 10.1111/ijlh.13537] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/17/2021] [Accepted: 03/25/2021] [Indexed: 11/27/2022]
Abstract
Artificial Intelligence (AI) and machine learning (ML) have now spawned a new field within health care and health science research. These new predictive analytics tools are starting to change various facets of our clinical care domains including the practice of laboratory medicine. Many of these ML tools and studies are also starting to populate our literature landscape as we know it but unfamiliarity of the average reader to the basic knowledge and critical concepts within AI/ML is now demanding a need to better prepare our audience to such relatively unfamiliar concepts. A fundamental knowledge of such platforms will inevitably enhance cross-disciplinary literacy and ultimately lead to enhanced integration and understanding of such tools within our discipline. In this review, we provide a general outline of AI/ML along with an overview of the fundamental concepts of ML categories, specifically supervised, unsupervised, and reinforcement learning. Additionally, since the vast majority of our current approaches within ML in laboratory medicine and health care involve supervised algorithms, we will predominantly concentrate on such platforms. Finally, the need for making such tools more accessible to the average investigator is becoming a major driving force for the need of automation within these ML platforms. This has now given rise to the automated ML (Auto-ML) world which will undoubtedly help shape the future of ML within health care. Hence, an overview of Auto-ML is also covered within this manuscript which will hopefully enrich the reader's understanding, appreciation, and the need for embracing such tools.
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Affiliation(s)
- Hooman H Rashidi
- Department of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Sacramento, CA, USA
| | - Nam Tran
- Department of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Sacramento, CA, USA
| | - Samer Albahra
- Department of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Sacramento, CA, USA
| | - Luke T Dang
- Department of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Sacramento, CA, USA
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26
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Sharma M, Taweesedt PT, Surani S. Utilizing Artificial Intelligence in Critical Care: Adding A Handy Tool to Our Armamentarium. Cureus 2021; 13:e15531. [PMID: 34268051 PMCID: PMC8266146 DOI: 10.7759/cureus.15531] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/08/2021] [Indexed: 11/06/2022] Open
Abstract
We have witnessed rapid advancement in technology over the last few decades. With the advent of artificial intelligence (AI), newer avenues have opened for researchers. AI has added an entirely new dimension to this technological boom. Researchers in medical science have been excited about the tantalizing prospect of utilizing AI for the benefit of patient care. Lately, we have come across studies trying to test and validate various models based on AI to improve patient care strategies in critical care medicine as well. Thus, in this review, we will attempt to succinctly review current literature discussing AI in critical care medicine and analyze its future utility based on prevailing evidence.
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Affiliation(s)
- Munish Sharma
- Internal Medicine, Corpus Christi Medical Center, Corpus Christi, USA
| | | | - Salim Surani
- Internal Medicine, Corpus Christi Medical Center, Corpus Christi, USA.,Internal Medicine, University of North Texas, Dallas, USA
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27
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Mantelakis A, Assael Y, Sorooshian P, Khajuria A. Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery. PLASTIC AND RECONSTRUCTIVE SURGERY-GLOBAL OPEN 2021; 9:e3638. [PMID: 34235035 PMCID: PMC8225366 DOI: 10.1097/gox.0000000000003638] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 04/14/2021] [Indexed: 01/25/2023]
Abstract
INTRODUCTION Machine learning (ML) is a set of models and methods that can detect patterns in vast amounts of data and use this information to perform various kinds of decision-making under uncertain conditions. This review explores the current role of this technology in plastic surgery by outlining the applications in clinical practice, diagnostic and prognostic accuracies, and proposed future direction for clinical applications and research. METHODS EMBASE, MEDLINE, CENTRAL and ClinicalTrials.gov were searched from 1990 to 2020. Any clinical studies (including case reports) which present the diagnostic and prognostic accuracies of machine learning models in the clinical setting of plastic surgery were included. Data collected were clinical indication, model utilised, reported accuracies, and comparison with clinical evaluation. RESULTS The database identified 1181 articles, of which 51 articles were included in this review. The clinical utility of these algorithms was to assist clinicians in diagnosis prediction (n=22), outcome prediction (n=21) and pre-operative planning (n=8). The mean accuracy is 88.80%, 86.11% and 80.28% respectively. The most commonly used models were neural networks (n=31), support vector machines (n=13), decision trees/random forests (n=10) and logistic regression (n=9). CONCLUSIONS ML has demonstrated high accuracies in diagnosis and prognostication of burn patients, congenital or acquired facial deformities, and in cosmetic surgery. There are no studies comparing ML to clinician's performance. Future research can be enhanced using larger datasets or utilising data augmentation, employing novel deep learning models, and applying these to other subspecialties of plastic surgery.
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Affiliation(s)
| | | | | | - Ankur Khajuria
- Kellogg College, University of Oxford
- Department of Surgery and Cancer, Imperial College London, UK
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28
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Song X, Liu X, Liu F, Wang C. Comparison of machine learning and logistic regression models in predicting acute kidney injury: A systematic review and meta-analysis. Int J Med Inform 2021; 151:104484. [PMID: 33991886 DOI: 10.1016/j.ijmedinf.2021.104484] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 04/10/2021] [Accepted: 05/06/2021] [Indexed: 12/16/2022]
Abstract
INTRODUCTION We aimed to assess whether machine learning models are superior at predicting acute kidney injury (AKI) compared to logistic regression (LR), a conventional prediction model. METHODS Eligible studies were identified using PubMed and Embase. A total of 24 studies consisting of 84 prediction models met inclusion criteria. Independent samples t-test was performed to detect mean differences in area under the curve (AUC) between ML and LR models. One-way ANOVA and post-hoc t-tests were performed to assess mean differences in AUC between ML methods. RESULTS AUC data were similar between ML (0.736 ± 0.116) and LR (0.748 ± 0.057) models (p = 0.538). However, specific ML models, such as gradient boosting (0.838 ± 0.077), exhibited superior performance at predicting AKI as compared to other ML models in the literature (p < 0.05). Creatinine and urine output, standard variables assessed for AKI staging, were classified as significant predictors across multiple ML models, although the majority of significant predictors were unique and study specific. CONCLUSIONS These data suggest that ML models perform equally to that of LR, however ML models exhibit variable performance with some ML models displaying exceptional performance. The variability in ML prediction of AKI can be attributed, in part, to the specific ML model utilized, variable selection and processing, study and subject characteristics, and the steps associated with model training, validation, testing, and calibration.
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Affiliation(s)
- Xuan Song
- ICU, DongE Hospital Affiliated to Shandong First Medical University, Shandong, China
| | - Xinyan Liu
- ICU, DongE Hospital Affiliated to Shandong First Medical University, Shandong, China
| | - Fei Liu
- Urology Department, Tai'an Traditional Chinese Medicine Hospital Affiliated to Shandong University of Traditional Chinese Medicine, Shandong, China
| | - Chunting Wang
- ICU, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China.
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29
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Kim K, Yang H, Yi J, Son HE, Ryu JY, Kim YC, Jeong JC, Chin HJ, Na KY, Chae DW, Han SS, Kim S. Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation. J Med Internet Res 2021; 23:e24120. [PMID: 33861200 PMCID: PMC8087972 DOI: 10.2196/24120] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 02/26/2021] [Accepted: 03/15/2021] [Indexed: 12/23/2022] Open
Abstract
Background Acute kidney injury (AKI) is commonly encountered in clinical practice and is associated with poor patient outcomes and increased health care costs. Despite it posing significant challenges for clinicians, effective measures for AKI prediction and prevention are lacking. Previously published AKI prediction models mostly have a simple design without external validation. Furthermore, little is known about the process of linking model output and clinical decisions due to the black-box nature of neural network models. Objective We aimed to present an externally validated recurrent neural network (RNN)–based continuous prediction model for in-hospital AKI and show applicable model interpretations in relation to clinical decision support. Methods Study populations were all patients aged 18 years or older who were hospitalized for more than 48 hours between 2013 and 2017 in 2 tertiary hospitals in Korea (Seoul National University Bundang Hospital and Seoul National University Hospital). All demographic data, laboratory values, vital signs, and clinical conditions of patients were obtained from electronic health records of each hospital. We developed 2-stage hierarchical prediction models (model 1 and model 2) using RNN algorithms. The outcome variable for model 1 was the occurrence of AKI within 7 days from the present. Model 2 predicted the future trajectory of creatinine values up to 72 hours. The performance of each developed model was evaluated using the internal and external validation data sets. For the explainability of our models, different model-agnostic interpretation methods were used, including Shapley Additive Explanations, partial dependence plots, individual conditional expectation, and accumulated local effects plots. Results We included 69,081 patients in the training, 7675 in the internal validation, and 72,352 in the external validation cohorts for model development after excluding cases with missing data and those with an estimated glomerular filtration rate less than 15 mL/min/1.73 m2 or end-stage kidney disease. Model 1 predicted any AKI development with an area under the receiver operating characteristic curve (AUC) of 0.88 (internal validation) and 0.84 (external validation), and stage 2 or higher AKI development with an AUC of 0.93 (internal validation) and 0.90 (external validation). Model 2 predicted the future creatinine values within 3 days with mean-squared errors of 0.04-0.09 for patients with higher risks of AKI and 0.03-0.08 for those with lower risks. Based on the developed models, we showed AKI probability according to feature values in total patients and each individual with partial dependence, accumulated local effects, and individual conditional expectation plots. We also estimated the effects of feature modifications such as nephrotoxic drug discontinuation on future creatinine levels. Conclusions We developed and externally validated a continuous AKI prediction model using RNN algorithms. Our model could provide real-time assessment of future AKI occurrences and individualized risk factors for AKI in general inpatient cohorts; thus, we suggest approaches to support clinical decisions based on prediction models for in-hospital AKI.
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Affiliation(s)
- Kipyo Kim
- Division of Nephrology and Hypertension, Department of Internal Medicine, Inha University College of Medicine, Incheon, Republic of Korea
| | - Hyeonsik Yang
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jinyeong Yi
- Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Hyung-Eun Son
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Ji-Young Ryu
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Yong Chul Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jong Cheol Jeong
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Ho Jun Chin
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Ki Young Na
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Dong-Wan Chae
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Seung Seok Han
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sejoong Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.,Center for Artificial Intelligence in Healthcare, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
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30
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Rashidi HH, Makley A, Palmieri TL, Albahra S, Loegering J, Fang L, Yamaguchi K, Gerlach T, Rodriquez D, Tran NK. Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction Through an Automated Machine Learning Platform and Point-of-Care Testing. Arch Pathol Lab Med 2021; 145:320-326. [PMID: 33635951 DOI: 10.5858/arpa.2020-0110-oa] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/30/2020] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Delayed recognition of acute kidney injury (AKI) results in poor outcomes in military and civilian burn-trauma care. Poor predictive ability of urine output (UOP) and creatinine contribute to the delayed recognition of AKI. OBJECTIVE.— To determine the impact of point-of-care (POC) AKI biomarker enhanced by machine learning (ML) algorithms in burn-injured and trauma patients. DESIGN.— We conducted a 2-phased study to develop and validate a novel POC device for measuring neutrophil gelatinase-associated lipocalin (NGAL) and creatinine from blood samples. In phase I, 40 remnant plasma samples were used to evaluate the analytic performance of the POC device. Next, phase II enrolled 125 adults with either burns that were 20% or greater of total body surface area or nonburn trauma with suspicion of AKI for clinical validation. We applied an automated ML approach to develop models predicting AKI, using a combination of NGAL, creatinine, and/or UOP as features. RESULTS.— Point-of-care NGAL (mean [SD] bias: 9.8 [38.5] ng/mL, P = .10) and creatinine results (mean [SD] bias: 0.28 [0.30] mg/dL, P = .18) were comparable to the reference method. NGAL was an independent predictor of AKI (odds ratio, 1.6; 95% CI, 0.08-5.20; P = .01). The optimal ML model achieved an accuracy, sensitivity, and specificity of 96%, 92.3%, and 97.7%, respectively, with NGAL, creatinine, and UOP as features. Area under the receiver operator curve was 0.96. CONCLUSIONS.— Point-of-care NGAL testing is feasible and produces results comparable to reference methods. Machine learning enhanced the predictive performance of AKI biomarkers including NGAL and was superior to the current techniques.
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Affiliation(s)
- Hooman H Rashidi
- From the Department of Pathology and Laboratory Medicine (Rashidi, Albahra, Loegering, Tran), University of California, Davis, Sacramento
| | - Amy Makley
- The Department of Surgery, University of Cincinnati, Cincinnati, Ohio (Makley)
| | - Tina L Palmieri
- Department of Surgery (Palmieri), University of California, Davis, Sacramento
| | - Samer Albahra
- From the Department of Pathology and Laboratory Medicine (Rashidi, Albahra, Loegering, Tran), University of California, Davis, Sacramento
| | - Julia Loegering
- From the Department of Pathology and Laboratory Medicine (Rashidi, Albahra, Loegering, Tran), University of California, Davis, Sacramento
| | - Lei Fang
- Nanomix, Inc, Emeryville, California (Fang, Yamaguchi)
| | | | - Travis Gerlach
- The Department of Surgery, David Grant Medical Center, Travis Air Force Base, Fairfield, California (Gerlach)
| | - Dario Rodriquez
- The Department of Surgery, 711th Human Performance Wing, Wright-Patterson Air Force Base, Cincinnati, Ohio (Rodriquez Jr)
| | - Nam K Tran
- From the Department of Pathology and Laboratory Medicine (Rashidi, Albahra, Loegering, Tran), University of California, Davis, Sacramento
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31
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Scott IA. Demystifying machine learning: a primer for physicians. Intern Med J 2021; 51:1388-1400. [PMID: 33462882 DOI: 10.1111/imj.15200] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/16/2021] [Accepted: 01/16/2021] [Indexed: 01/17/2023]
Abstract
Machine learning is a tool for analysing digitised data sets and formulating predictions that can optimise clinical decision-making. It aims to identify complex patterns in large data sets and encode them into models that can then classify new unseen cases or make predictions on new data. Machine learning methods take several forms and individual models can be of many different types. More than 50 models have been approved for use in routine healthcare, and the numbers continue to grow exponentially. The reliability and robustness of any model depends on multiple factors, including the quality and quantity of the data used to develop the models, and the selection of features in the data considered most important to maximising accuracy. In ensuring models are safe, effective and reproducible in routine care, physicians need to have some understanding of how these models are developed and evaluated, and to collaborate with data and computer scientists in their design and validation. This narrative review introduces principles, methods and examples of machine learning in a way that does not require mastery of highly complex statistical and computational concepts.
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Affiliation(s)
- Ian A Scott
- Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, Queensland, Australia.,School of Clinical Medicine, University of Queensland, Brisbane, Queensland, Australia
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32
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Artificial intelligence to guide management of acute kidney injury in the ICU: a narrative review. Curr Opin Crit Care 2021; 26:563-573. [PMID: 33027147 DOI: 10.1097/mcc.0000000000000775] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
PURPOSE OF REVIEW Acute kidney injury (AKI) frequently complicates hospital admission, especially in the ICU or after major surgery, and is associated with high morbidity and mortality. The risk of developing AKI depends on the presence of preexisting comorbidities and the cause of the current disease. Besides, many other parameters affect the kidney function, such as the state of other vital organs, the host response, and the initiated treatment. Advancements in the field of informatics have led to the opportunity to store and utilize the patient-related data to train and validate models to detect specific patterns and, as such, predict disease states or outcomes. RECENT FINDINGS Machine-learning techniques have also been applied to predict AKI, as well as the patients' outcomes related to their AKI, such as mortality or the need for kidney replacement therapy. Several models have recently been developed, but only a few of them have been validated in external cohorts. SUMMARY In this article, we provide an overview of the machine-learning prediction models for AKI and its outcomes in critically ill patients and individuals undergoing major surgery. We also discuss the pitfalls and the opportunities related to the implementation of these models in clinical practices.
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Yao L, Zhang H, Zhang M, Chen X, Zhang J, Huang J, Zhang L. Application of artificial intelligence in renal disease. CLINICAL EHEALTH 2021. [DOI: 10.1016/j.ceh.2021.11.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
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34
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Lo Vercio L, Amador K, Bannister JJ, Crites S, Gutierrez A, MacDonald ME, Moore J, Mouches P, Rajasheka D, Schimert S, Subbanna N, Tuladhar A, Wang N, Wilms M, Winder A, Forkert ND. Supervised machine learning tools: a tutorial for clinicians. J Neural Eng 2020; 17. [PMID: 33036008 DOI: 10.1088/1741-2552/abbff2] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 10/09/2020] [Indexed: 12/13/2022]
Abstract
In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no exception to this. Machine learning aims to identify complex patterns in multi-dimensional data and use these uncovered patterns to classify new unseen cases or make data-driven predictions. In recent years, deep neural networks have shown to be capable of producing results that considerably exceed those of conventional machine learning methods for various classification and regression tasks. In this paper, we provide an accessible tutorial of the most important supervised machine learning concepts and methods, including deep learning, which are potentially the most relevant for the medical domain. We aim to take some of the mystery out of machine learning and depict how machine learning models can be useful for medical applications. Finally, this tutorial provides a few practical suggestions for how to properly design a machine learning model for a generic medical problem.
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Affiliation(s)
| | | | | | | | | | | | - Jasmine Moore
- Radiology, University of Calgary, Calgary, Alberta, CANADA
| | | | | | | | | | - Anup Tuladhar
- Radiology, University of Calgary, Calgary, Alberta, CANADA
| | - Nanjia Wang
- Radiology, University of Calgary, Calgary, Alberta, CANADA
| | - Matthias Wilms
- Radiology, University of Calgary, Calgary, Alberta, CANADA
| | - Anthony Winder
- Radiology, University of Calgary, Calgary, Alberta, CANADA
| | - Nils Daniel Forkert
- Radiology, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta, T2N 1N4, CANADA
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35
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Qu C, Gao L, Yu XQ, Wei M, Fang GQ, He J, Cao LX, Ke L, Tong ZH, Li WQ. Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients. Gastroenterol Res Pract 2020; 2020:3431290. [PMID: 33061958 PMCID: PMC7542489 DOI: 10.1155/2020/3431290] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 08/19/2020] [Accepted: 09/06/2020] [Indexed: 12/20/2022] Open
Abstract
Background. Acute kidney injury (AKI) has long been recognized as a common and important complication of acute pancreatitis (AP). In the study, machine learning (ML) techniques were used to establish predictive models for AKI in AP patients during hospitalization. This is a retrospective review of prospectively collected data of AP patients admitted within one week after the onset of abdominal pain to our department from January 2014 to January 2019. Eighty patients developed AKI after admission (AKI group) and 254 patients did not (non-AKI group) in the hospital. With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR). XGBoost performed best in predicting AKI with an AUC of 91.93% among the machine learning models. The AUC of logistic regression analysis was 87.28%. Present findings suggest that compared to the classical logistic regression model, machine learning models using features that can be easily obtained at admission had a better performance in predicting AKI in the AP patients.
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Affiliation(s)
- Cheng Qu
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Lin Gao
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xian-qiang Yu
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Clinical Medical College of Southeast University, Nanjing, China
| | - Mei Wei
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Guo-quan Fang
- Electrical Engineering School of Southeast University, China
| | - Jianing He
- Institute for Hospital Management of Tsinghua University, Shenzhen, China
| | - Long-xiang Cao
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Lu Ke
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Zhi-hui Tong
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wei-qin Li
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
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36
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Predicting Acute Kidney Injury: A Machine Learning Approach Using Electronic Health Records. INFORMATION 2020. [DOI: 10.3390/info11080386] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Acute kidney injury (AKI) is a common complication in hospitalized patients and can result in increased hospital stay, health-related costs, mortality and morbidity. A number of recent studies have shown that AKI is predictable and avoidable if early risk factors can be identified by analyzing Electronic Health Records (EHRs). In this study, we employ machine learning techniques to identify older patients who have a risk of readmission with AKI to the hospital or emergency department within 90 days after discharge. One million patients’ records are included in this study who visited the hospital or emergency department in Ontario between 2014 and 2016. The predictor variables include patient demographics, comorbid conditions, medications and diagnosis codes. We developed 31 prediction models based on different combinations of two sampling techniques, three ensemble methods, and eight classifiers. These models were evaluated through 10-fold cross-validation and compared based on the AUROC metric. The performances of these models were consistent, and the AUROC ranged between 0.61 and 0.88 for predicting AKI among 31 prediction models. In general, the performances of ensemble-based methods were higher than the cost-sensitive logistic regression. We also validated features that are most relevant in predicting AKI with a healthcare expert to improve the performance and reliability of the models. This study predicts the risk of AKI for a patient after being discharged, which provides healthcare providers enough time to intervene before the onset of AKI.
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37
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Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept. Sci Rep 2020; 10:12354. [PMID: 32704168 PMCID: PMC7378181 DOI: 10.1038/s41598-020-69433-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 07/03/2020] [Indexed: 11/22/2022] Open
Abstract
Sepsis is the primary cause of burn-related mortality and morbidity. Traditional indicators of sepsis exhibit poor performance when used in this unique population due to their underlying hypermetabolic and inflammatory response following burn injury. To address this challenge, we developed the Machine Intelligence Learning Optimizer (MILO), an automated machine learning (ML) platform, to automatically produce ML models for predicting burn sepsis. We conducted a retrospective analysis of 211 adult patients (age ≥ 18 years) with severe burn injury (≥ 20% total body surface area) to generate training and test datasets for ML applications. The MILO approach was compared against an exhaustive “non-automated” ML approach as well as standard statistical methods. For this study, traditional multivariate logistic regression (LR) identified seven predictors of burn sepsis when controlled for age and burn size (OR 2.8, 95% CI 1.99–4.04, P = 0.032). The area under the ROC (ROC-AUC) when using these seven predictors was 0.88. Next, the non-automated ML approach produced an optimal model based on LR using 16 out of the 23 features from the study dataset. Model accuracy was 86% with ROC-AUC of 0.96. In contrast, MILO identified a k-nearest neighbor-based model using only five features to be the best performer with an accuracy of 90% and a ROC-AUC of 0.96. Machine learning augments burn sepsis prediction. MILO identified models more quickly, with less required features, and found to be analytically superior to traditional ML approaches. Future studies are needed to clinically validate the performance of MILO-derived ML models for sepsis prediction.
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38
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Zhu K, Song H, Zhang Z, Ma B, Bao X, Zhang Q, Jin J. Acute kidney injury in solitary kidney patients after partial nephrectomy: incidence, risk factors and prediction. Transl Androl Urol 2020; 9:1232-1243. [PMID: 32676406 PMCID: PMC7354300 DOI: 10.21037/tau.2020.03.45] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Background To analyze the incidence and risk factors of acute kidney injury (AKI) after partial nephrectomy (PN) in patients with solitary kidney, and to build AKI prediction models using logistic regression and machine learning (ML) approaches. Methods Clinical data of 87 solitary kidney patients with renal mass who received PN from January 2003 to March 2019 were collected. The diagnosis of AKI was based on KDIGO criteria. Logistic regression analysis and ML method were used to build prediction models. Results AKI developed in 52 (59.8%) patients. The logistic regression model had three variables: ischemia time (P=0.003), surgery time (P=0.001) and preoperative fasted blood glucose level (FBG) (P=0.049). The area under curve (AUC) was 0.826, with the specificity and sensitivity of optimal threshold value 82.9% and 69.2%. The ML model had the following variables: ischemia time, surgery time, age, FBG, mean arterial pressure (MAP), colloid, crystalloid, etc. XGBoost model has the best prediction performance. The AUC was 0.749, lower than that of the logistic regression model with no statistical difference (P=0.258), with the specificity and sensitivity 62.9% and 84.6%, respectively. Conclusions The incidence of AKI after PN in patients with a solitary kidney was relatively high, it was associated with longer ischemia time, surgery time and higher FBG level, etc. The performance of ML model had no significant difference with logistic regression model. Prospective studies with larger sample sizes are awaited to test and verify our research findings.
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Affiliation(s)
- Kun Zhu
- Department of Urology, Peking University First Hospital, Beijing 100034, China.,Institute of Urology, Peking University, Beijing 100034, China.,National Research Center for Genitourinary Oncology, Beijing 100034, China
| | - Haifeng Song
- Department of Urology, Peking University First Hospital, Beijing 100034, China.,Institute of Urology, Peking University, Beijing 100034, China.,National Research Center for Genitourinary Oncology, Beijing 100034, China
| | - Zhenan Zhang
- Department of Urology, Peking University First Hospital, Beijing 100034, China.,Institute of Urology, Peking University, Beijing 100034, China.,National Research Center for Genitourinary Oncology, Beijing 100034, China
| | - Binglei Ma
- Department of Urology, Peking University First Hospital, Beijing 100034, China.,Institute of Urology, Peking University, Beijing 100034, China.,National Research Center for Genitourinary Oncology, Beijing 100034, China
| | - Xiaoyuan Bao
- Medical Informatics Center, Peking University Health Science Center, Beijing 100191, China
| | - Qian Zhang
- Department of Urology, Peking University First Hospital, Beijing 100034, China.,Institute of Urology, Peking University, Beijing 100034, China.,National Research Center for Genitourinary Oncology, Beijing 100034, China
| | - Jie Jin
- Department of Urology, Peking University First Hospital, Beijing 100034, China.,Institute of Urology, Peking University, Beijing 100034, China.,National Research Center for Genitourinary Oncology, Beijing 100034, China
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39
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Thongprayoon C, Hansrivijit P, Bathini T, Vallabhajosyula S, Mekraksakit P, Kaewput W, Cheungpasitporn W. Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches. J Clin Med 2020; 9:jcm9061767. [PMID: 32517295 PMCID: PMC7355827 DOI: 10.3390/jcm9061767] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 06/04/2020] [Indexed: 02/08/2023] Open
Abstract
Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| | - Panupong Hansrivijit
- Department of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USA;
| | - Tarun Bathini
- Department of Internal Medicine, University of Arizona, Tucson, AZ 85724, USA;
| | | | - Poemlarp Mekraksakit
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79424, USA;
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Wisit Cheungpasitporn
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
- Correspondence: ; Tel.: +1-601-984-5670; Fax: +1-601-984-5765
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40
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Abstract
This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2020. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2020. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.
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Affiliation(s)
- Guillermo Gutierrez
- Pulmonary, Critical Care and Sleep Medicine Division, The George Washington University, Washington, DC, USA.
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41
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Gameiro J, Branco T, Lopes JA. Artificial Intelligence in Acute Kidney Injury Risk Prediction. J Clin Med 2020; 9:jcm9030678. [PMID: 32138284 PMCID: PMC7141311 DOI: 10.3390/jcm9030678] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 02/26/2020] [Accepted: 02/28/2020] [Indexed: 12/23/2022] Open
Abstract
Acute kidney injury (AKI) is a frequent complication in hospitalized patients, which is associated with worse short and long-term outcomes. It is crucial to develop methods to identify patients at risk for AKI and to diagnose subclinical AKI in order to improve patient outcomes. The advances in clinical informatics and the increasing availability of electronic medical records have allowed for the development of artificial intelligence predictive models of risk estimation in AKI. In this review, we discussed the progress of AKI risk prediction from risk scores to electronic alerts to machine learning methods.
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Affiliation(s)
- Joana Gameiro
- Division of Nephrology and Renal Transplantation, Department of Medicine, Centro Hospitalar Lisboa Norte, EPE, Av. Prof. Egas Moniz, 1649-035 Lisboa, Portugal;
- Correspondence:
| | - Tiago Branco
- Department of Medicine, Centro Hospitalar Lisboa Norte, EPE, Av. Prof. Egas Moniz, 1649-035 Lisboa, Portugal;
| | - José António Lopes
- Division of Nephrology and Renal Transplantation, Department of Medicine, Centro Hospitalar Lisboa Norte, EPE, Av. Prof. Egas Moniz, 1649-035 Lisboa, Portugal;
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42
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Lei L, Wang Y, Xue Q, Tong J, Zhou CM, Yang JJ. A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection. PeerJ 2020; 8:e8583. [PMID: 32140301 PMCID: PMC7047869 DOI: 10.7717/peerj.8583] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 01/16/2020] [Indexed: 12/24/2022] Open
Abstract
Objective Machine learning methods may have better or comparable predictive ability than traditional analysis. We explore machine learning methods to predict the likelihood of acute kidney injury after liver cancer resection. Methods This is a secondary analysis cohort study. We reviewed data from patients who had undergone resection of primary hepatocellular carcinoma between January 2008 and October 2015. Results The analysis included 1,173 hepatectomy patients, 77 (6.6%) of whom had AKI and 1,096 (93.4%) who did not. The importance matrix for the Gbdt algorithm model shows that age, cholesterol, tumor size, surgery duration and PLT were the five most important parameters. Figure 1 shows that Age, tumor size and surgery duration had weak positive correlations with AKI. Cholesterol and PLT also had weak negative correlations with AKI. The models constructed by the four machine learning algorithms in the training group were compared. Among the four machine learning algorithms, random forest and gbm had the highest accuracy, 0.989 and 0.970 respectively. The precision of four of the five algorithms was 1, random forest being the exception. Among the test group, gbm had the highest accuracy (0.932). Random forest and gbm had the highest precision, both being 0.333. The AUC values for the four algorithms were: Gbdt (0.772), gbm (0.725), forest (0.662) and DecisionTree (0.628). Conclusions Machine learning technology can predict acute kidney injury after hepatectomy. Age, cholesterol, tumor size, surgery duration and PLT influence the likelihood and development of postoperative acute kidney injury.
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Affiliation(s)
- Lei Lei
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ying Wang
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qiong Xue
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianhua Tong
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Cheng-Mao Zhou
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-Jun Yang
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Rashidi HH, Sen S, Palmieri TL, Blackmon T, Wajda J, Tran NK. Early Recognition of Burn- and Trauma-Related Acute Kidney Injury: A Pilot Comparison of Machine Learning Techniques. Sci Rep 2020; 10:205. [PMID: 31937795 PMCID: PMC6959341 DOI: 10.1038/s41598-019-57083-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 12/16/2019] [Indexed: 12/30/2022] Open
Abstract
Severely burned and non-burned trauma patients are at risk for acute kidney injury (AKI). The study objective was to assess the theoretical performance of artificial intelligence (AI)/machine learning (ML) algorithms to augment AKI recognition using the novel biomarker, neutrophil gelatinase associated lipocalin (NGAL), combined with contemporary biomarkers such as N-terminal pro B-type natriuretic peptide (NT-proBNP), urine output (UOP), and plasma creatinine. Machine learning approaches including logistic regression (LR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and deep neural networks (DNN) were used in this study. The AI/ML algorithm helped predict AKI 61.8 (32.5) hours faster than the Kidney Disease and Improving Global Disease Outcomes (KDIGO) criteria for burn and non-burned trauma patients. NGAL was analytically superior to traditional AKI biomarkers such as creatinine and UOP. With ML, the AKI predictive capability of NGAL was further enhanced when combined with NT-proBNP or creatinine. The use of AI/ML could be employed with NGAL to accelerate detection of AKI in at-risk burn and non-burned trauma patients.
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Affiliation(s)
- Hooman H Rashidi
- Department of Pathology and Laboratory Medicine, 4400 V Street, Sacramento, CA, 95817, USA.
| | - Soman Sen
- Division of Burn Surgery, Department of Surgery, 2221 Stockton Blvd., Sacramento, CA, 95817, USA
| | - Tina L Palmieri
- Division of Burn Surgery, Department of Surgery, 2221 Stockton Blvd., Sacramento, CA, 95817, USA
| | - Thomas Blackmon
- Department of Pathology and Laboratory Medicine, 4400 V Street, Sacramento, CA, 95817, USA
| | - Jeffery Wajda
- UC Davis Health (2315 Stockton Blvd, Sacramento, CA, 95817, USA
| | - Nam K Tran
- Department of Pathology and Laboratory Medicine, 4400 V Street, Sacramento, CA, 95817, USA.
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Zhang Z. Machine learning method for the management of acute kidney injury: more than just treating biomarkers individually. Biomark Med 2019; 13:1251-1253. [PMID: 31559832 DOI: 10.2217/bmm-2019-0363] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Affiliation(s)
- Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, PR China
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Rashidi HH, Tran NK, Betts EV, Howell LP, Green R. Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods. Acad Pathol 2019; 6:2374289519873088. [PMID: 31523704 PMCID: PMC6727099 DOI: 10.1177/2374289519873088] [Citation(s) in RCA: 138] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Revised: 07/15/2019] [Accepted: 07/26/2019] [Indexed: 12/28/2022] Open
Abstract
Increased interest in the opportunities provided by artificial intelligence and machine learning has spawned a new field of health-care research. The new tools under development are targeting many aspects of medical practice, including changes to the practice of pathology and laboratory medicine. Optimal design in these powerful tools requires cross-disciplinary literacy, including basic knowledge and understanding of critical concepts that have traditionally been unfamiliar to pathologists and laboratorians. This review provides definitions and basic knowledge of machine learning categories (supervised, unsupervised, and reinforcement learning), introduces the underlying concept of the bias-variance trade-off as an important foundation in supervised machine learning, and discusses approaches to the supervised machine learning study design along with an overview and description of common supervised machine learning algorithms (linear regression, logistic regression, Naive Bayes, k-nearest neighbor, support vector machine, random forest, convolutional neural networks).
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Affiliation(s)
- Hooman H. Rashidi
- Department of Pathology and Laboratory Medicine, University of California Davis, School of Medicine, Davis, CA, USA
| | - Nam K. Tran
- Department of Pathology and Laboratory Medicine, University of California Davis, School of Medicine, Davis, CA, USA
| | - Elham Vali Betts
- Department of Pathology and Laboratory Medicine, University of California Davis, School of Medicine, Davis, CA, USA
| | - Lydia P. Howell
- Department of Pathology and Laboratory Medicine, University of California Davis, School of Medicine, Davis, CA, USA
| | - Ralph Green
- Department of Pathology and Laboratory Medicine, University of California Davis, School of Medicine, Davis, CA, USA
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