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Ozrazgat-Baslanti T, Loftus TJ, Ren Y, Ruppert MM, Bihorac A. Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury. Curr Opin Crit Care 2021; 27:560-572. [PMID: 34757993 PMCID: PMC8783984 DOI: 10.1097/mcc.0000000000000887] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
PURPOSE OF REVIEW Acute kidney injury (AKI) affects nearly 60% of all patients admitted to ICUs. Large volumes of clinical, monitoring and laboratory data produced in ICUs allow the application of artificial intelligence analytics. The purpose of this article is to assimilate and critically evaluate recently published literature regarding artificial intelligence applications for predicting, diagnosing and subphenotyping AKI among critically ill patients. RECENT FINDINGS Among recent studies regarding artificial intelligence implementations for predicting, diagnosing and subphenotyping AKI among critically ill patients, there are many promising models, but few had external validation, clinical interpretability and high predictive performance. Deep learning techniques leveraging multimodal clinical data show great potential to provide continuous, accurate, early predictions of AKI risk, which could be implemented clinically to optimize preventive and early therapeutic management strategies. SUMMARY Use of consensus criteria, standard definitions and common data models could facilitate access to machine learning-ready data sets for external validation. The lack of interpretability, explainability, fairness and transparency of artificial intelligence models hinder their entrustment and clinical implementation; compliance with standardized reporting guidelines can mitigate these challenges.
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Affiliation(s)
- Tezcan Ozrazgat-Baslanti
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Tyler J. Loftus
- Department of Surgery, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Yuanfang Ren
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Matthew M. Ruppert
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Azra Bihorac
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
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Zanotto BS, Beck da Silva Etges AP, Dal Bosco A, Cortes EG, Ruschel R, De Souza AC, Andrade CMV, Viegas F, Canuto S, Luiz W, Ouriques Martins S, Vieira R, Polanczyk C, André Gonçalves M. Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers. JMIR Med Inform 2021; 9:e29120. [PMID: 34723829 PMCID: PMC8593798 DOI: 10.2196/29120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/27/2021] [Accepted: 08/05/2021] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND With the rapid adoption of electronic medical records (EMRs), there is an ever-increasing opportunity to collect data and extract knowledge from EMRs to support patient-centered stroke management. OBJECTIVE This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of clinical patient outcomes and the extraction of patient characteristics from EMRs. METHODS Our study addressed the computational problems of information extraction and automatic text classification. We identified essential tasks to be considered in an ischemic stroke value-based program. The 30 selected tasks were classified (manually labeled by specialists) according to the following value agenda: tier 1 (achieved health care status), tier 2 (recovery process), care related (clinical management and risk scores), and baseline characteristics. The analyzed data set was retrospectively extracted from the EMRs of patients with stroke from a private Brazilian hospital between 2018 and 2019. A total of 44,206 sentences from free-text medical records in Portuguese were used to train and develop 10 supervised computational machine learning methods, including state-of-the-art neural and nonneural methods, along with ontological rules. As an experimental protocol, we used a 5-fold cross-validation procedure repeated 6 times, along with subject-wise sampling. A heatmap was used to display comparative result analyses according to the best algorithmic effectiveness (F1 score), supported by statistical significance tests. A feature importance analysis was conducted to provide insights into the results. RESULTS The top-performing models were support vector machines trained with lexical and semantic textual features, showing the importance of dealing with noise in EMR textual representations. The support vector machine models produced statistically superior results in 71% (17/24) of tasks, with an F1 score >80% regarding care-related tasks (patient treatment location, fall risk, thrombolytic therapy, and pressure ulcer risk), the process of recovery (ability to feed orally or ambulate and communicate), health care status achieved (mortality), and baseline characteristics (diabetes, obesity, dyslipidemia, and smoking status). Neural methods were largely outperformed by more traditional nonneural methods, given the characteristics of the data set. Ontological rules were also effective in tasks such as baseline characteristics (alcoholism, atrial fibrillation, and coronary artery disease) and the Rankin scale. The complementarity in effectiveness among models suggests that a combination of models could enhance the results and cover more tasks in the future. CONCLUSIONS Advances in information technology capacity are essential for scalability and agility in measuring health status outcomes. This study allowed us to measure effectiveness and identify opportunities for automating the classification of outcomes of specific tasks related to clinical conditions of stroke victims, and thus ultimately assess the possibility of proactively using these machine learning techniques in real-world situations.
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Affiliation(s)
- Bruna Stella Zanotto
- National Institute of Health Technology Assessment - INCT/IATS (CNPQ 465518/2014-1), Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Graduate Program in Epidemiology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Ana Paula Beck da Silva Etges
- National Institute of Health Technology Assessment - INCT/IATS (CNPQ 465518/2014-1), Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,School of Technology, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | - Avner Dal Bosco
- School of Technology, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | - Eduardo Gabriel Cortes
- Graduate Program of Computer Science, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Renata Ruschel
- National Institute of Health Technology Assessment - INCT/IATS (CNPQ 465518/2014-1), Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Claudio M V Andrade
- Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Felipe Viegas
- Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sergio Canuto
- Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Washington Luiz
- Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Renata Vieira
- Centro Interdisciplinar de História, Culturas e Sociedades (CIDEHUS), Universidade de Évora, Évora, Portugal
| | - Carisi Polanczyk
- National Institute of Health Technology Assessment - INCT/IATS (CNPQ 465518/2014-1), Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Graduate Program in Epidemiology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Marcos André Gonçalves
- Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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Syed M, Syed S, Sexton K, Syeda HB, Garza M, Zozus M, Syed F, Begum S, Syed AU, Sanford J, Prior F. Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review. INFORMATICS-BASEL 2021; 8. [PMID: 33981592 DOI: 10.3390/informatics8010016] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.
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Affiliation(s)
- Mahanazuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Shorabuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Kevin Sexton
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
- Department of Surgery, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
- Department of Health Policy and Management, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Hafsa Bareen Syeda
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Maryam Garza
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Meredith Zozus
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas 78229, USA
| | - Farhanuddin Syed
- Shadan Institute of Medical Sciences, College of Medicine, Hyderabad, Telangana 500086, India
| | - Salma Begum
- Department of Information Technology, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Abdullah Usama Syed
- Department of Information Science, University of Arkansas at Little Rock (UALR), Little Rock, Arkansas 72205, USA
| | - Joseph Sanford
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
- Department of Anesthesiology, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
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