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Suresh V, Singh KK, Vaish E, Gurjar M, Ambuli Nambi A, Khulbe Y, Muzaffar S. Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool. Cureus 2024; 16:e59797. [PMID: 38846182 PMCID: PMC11154024 DOI: 10.7759/cureus.59797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/07/2024] [Indexed: 06/09/2024] Open
Abstract
Artificial intelligence (AI) is a technique that attempts to replicate human intelligence, analytical behavior, and decision-making ability. This includes machine learning, which involves the use of algorithms and statistical techniques to enhance the computer's ability to make decisions more accurately. Due to AI's ability to analyze, comprehend, and interpret considerable volumes of data, it has been increasingly used in the field of healthcare. In critical care medicine, where most of the patient load requires timely interventions due to the perilous nature of the condition, AI's ability to monitor, analyze, and predict unfavorable outcomes is an invaluable asset. It can significantly improve timely interventions and prevent unfavorable outcomes, which, otherwise, is not always achievable owing to the constrained human ability to multitask with optimum efficiency. AI has been implicated in intensive care units over the past many years. In addition to its advantageous applications, this article discusses its disadvantages, prospects, and the changes needed to train future critical care professionals. A comprehensive search of electronic databases was performed using relevant keywords. Data from articles pertinent to the topic was assimilated into this review article.
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Affiliation(s)
- Vinay Suresh
- General Medicine and Surgery, King George's Medical University, Lucknow, IND
| | - Kaushal K Singh
- General Medicine, King George's Medical University, Lucknow, IND
| | - Esha Vaish
- Internal Medicine, Mount Sinai Morningside West, New York, USA
| | - Mohan Gurjar
- Critical Care Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, IND
| | | | - Yashita Khulbe
- General Medicine and Surgery, King George's Medical University, Lucknow, IND
| | - Syed Muzaffar
- Critical Care Medicine, King George's Medical University, Lucknow, IND
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Olender RT, Roy S, Nishtala PS. Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis. BMC Geriatr 2023; 23:561. [PMID: 37710210 PMCID: PMC10503191 DOI: 10.1186/s12877-023-04246-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 08/19/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care. DESIGN Systematic review and meta-analyses. PARTICIPANTS Older adults (≥ 65 years) in any setting. INTERVENTION Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months. OUTCOME MEASURES Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric. RESULTS Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power. CONCLUSION The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.
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Affiliation(s)
- Robert T Olender
- Department of Life Sciences, University of Bath, Bath, BA2 7AY, UK.
| | - Sandipan Roy
- Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, UK
| | - Prasad S Nishtala
- Department of Life Sciences & Centre for Therapeutic Innovation, University of Bath, Bath, BA2 7AY, UK
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Maslov GO, Zabegalov KN, Demin KA, Kolesnikova TO, Kositsyn YM, de Abreu MS, Petersen EV, Kalueff AV. Towards experimental models of delirium utilizing zebrafish. Behav Brain Res 2023; 453:114607. [PMID: 37524203 DOI: 10.1016/j.bbr.2023.114607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/28/2023] [Accepted: 07/29/2023] [Indexed: 08/02/2023]
Abstract
Delirium is an acute neuropsychiatric condition characterized by impaired behavior and cognition. Although the syndrome has been known for millennia, its CNS mechanisms and risk factors remain poorly understood. Experimental animal models, especially rodent-based, are commonly used to probe various pathogenetic aspects of delirium. Complementing rodents, the zebrafish (Danio rerio) emerges as a promising novel model organism to study delirium. Zebrafish demonstrate high genetic and physiological homology to mammals, easy maintenance, robust behaviors in various sensitive behavioral tests, and the potential to screen for pharmacological agents relevant to delirium. Here, we critically discuss recent developments in the field, and emphasize the developing utility of zebrafish models for translational studies of delirium and deliriant drugs. Overall, the zebrafish represents a valuable and promising aquatic model species whose use may help understand delirium etiology, as well as develop novel therapies for this severely debilitating disorder.
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Affiliation(s)
- Gleb O Maslov
- Neurobiology Program, Sirius University of Science and Technology, Sochi, Russia; Ural Federal University, Ekaterinburg, Russia
| | | | - Konstantin A Demin
- Institute of Experimental Medicine, Almazov National Medical Research Centre, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia; Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Tatiana O Kolesnikova
- Neurobiology Program, Sirius University of Science and Technology, Sochi, Russia; Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Yuriy M Kositsyn
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Murilo S de Abreu
- Laboratory of Cell and Molecular Biology and Neurobiology, Moscow Institute of Physics and Technology, Moscow, Russia.
| | - Elena V Petersen
- Laboratory of Cell and Molecular Biology and Neurobiology, Moscow Institute of Physics and Technology, Moscow, Russia
| | - Allan V Kalueff
- Neurobiology Program, Sirius University of Science and Technology, Sochi, Russia; Institute of Experimental Medicine, Almazov National Medical Research Centre, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia; Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia; Novosibirsk State University, Novosibirsk, Russia; Laboratory of Preclinical Bioscreening, Granov Russian Research Center of Radiology and Surgical Technologies, Ministry of Healthcare of Russian Federation, Pesochny, Russia; Ural Federal University, Ekaterinburg, Russia.
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Xie Q, Wang XL, Pei JH, Wu YP, Guo Q, Su YJ, Yan H, Nan RL, Chen HX, Dou XM. Machine Learning-Based Prediction Models for Delirium: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc 2022; 23:1655-1668.e6. [DOI: 10.1016/j.jamda.2022.06.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 05/22/2022] [Accepted: 06/18/2022] [Indexed: 10/16/2022]
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McCombe N, Joshi A, Finn DP, McClean PL, Roberts G, O'Brien JT, Thomas AJ, Kane JPM, Wong-Lin K. Distinguishing Lewy Body Dementia from Alzheimer's Disease using Machine Learning on Heterogeneous Data: A Feasibility Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4929-4933. [PMID: 36085984 DOI: 10.1109/embc48229.2022.9871714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Dementia with Lewy Bodies (DLB) is the second most common form of dementia, but diagnostic markers for DLB can be expensive and inaccessible, and many cases of DLB are undiagnosed. This work applies machine learning techniques to determine the feasibility of distinguishing DLB from Alzheimer's Disease (AD) using heterogeneous data features. The Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was first applied using a Leave-One-Out Cross-Validation protocol to a dataset comprising DLB and AD cases. Then, interpretable association rule-based diagnostic classifiers were obtained for distinguishing DLB from AD. The various diagnostic classifiers generated by this process had high accuracy over the whole dataset (mean accuracy of 94%). The mean accuracy in classifying their out-of-sample case was 80.5%. Every classifier generated consisted of very simple structure, each using 1-2 classification rules and 1-3 data features. As a group, the classifiers were heterogeneous and used several different data features. In particular, some of the classifiers used very simple and inexpensive diagnostic features, yet with high diagnostic accuracy. This work suggests that opportunities may exist for incorporating accessible diagnostic assessments while improving diagnostic rate for DLB. Clinical Relevance- Simple and interpretable high-performing machine learning algorithms identified a variety of readily available clinical assessments for differential diagnosis of dementia offering the opportunities to incorporate various simple and inexpensive screening tests for DLB and addressing the problem of DLB underdiagnosis.
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Cano-Escalera G, Graña M, Irazusta J, Labayen I, Besga A. Survival of Frail Elderly with Delirium. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19042247. [PMID: 35206439 PMCID: PMC8872606 DOI: 10.3390/ijerph19042247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 02/01/2023]
Abstract
This study aims to determine when frailty increases the risks of delirium mortality. Hospital patients falling into the elderly frail or pre-frail category were recruited, some without delirium, some with delirium at admission, and some who developed delirium during admission. We screened for frailty, cognitive status, and co-morbidities whenever possible and extracted drug information and mortality data from electronic health records. Kaplan–Meier estimates of survival probability functions were computed at four times, comparing delirium versus non delirium patients. Differences in survival were assessed by a log-rank test. Independent Cox’s regression was carried out to identify significant hazard risks (HR) at 1 month, 6 months, 1 year, and 2 years. Delirium predicted mortality (log-rank test, p < 0.0001) at all four censoring points. Variables with significant HRs were frailty indicators, comorbidities, polypharmacy, and the use of specific drugs. For the delirium cohort, variables with the most significant 2-year hazard risks (HR(95%CI)) were: male gender (0.43 20 (0.26,0.69)), weight loss (0.45 (0.26,0.74)), sit and stand up test (0.67 (0.49,0.92)), readmission within 30 days of discharge (0.50 (0.30,0.80)), cerebrovascular disease (0.45 (0.27,0.76)), head trauma (0.54 22 (0.29,0.98)), number of prescribed drugs (1.10 (1.03,1.18)), and the use of diuretics (0.57 (0.34,0.96)). These results suggest that polypharmacy and the use of diuretics increase mortality in frail elderly patients with delirium.
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Affiliation(s)
- Guillermo Cano-Escalera
- Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Paseo Manuel de Lardizabal, 1, 20018 Donostia-San Sebastian, Spain;
- Computational Intelligence Group, University of the Basque Country (UPV/EHU), 20018 Donostia-San Sebastian, Spain
- Correspondence:
| | - Manuel Graña
- Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Paseo Manuel de Lardizabal, 1, 20018 Donostia-San Sebastian, Spain;
- Computational Intelligence Group, University of the Basque Country (UPV/EHU), 20018 Donostia-San Sebastian, Spain
| | - Jon Irazusta
- Department of Physiology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain;
- BioCruces Health Research Institute, 48903 Barakaldo, Spain
| | - Idoia Labayen
- Institute for Innovation & Sustainable Development in Food Chain (IS-FOOD), Public University of Navarra, 31006 Pamplona, Spain;
| | - Ariadna Besga
- BioAraba, Health Research Institute, Hospital Universitario de Araba, Department of Medicine, 01004 Vitoria, Spain;
- Biomedical Research Centre in Mental Health Network (CIBERSAM) G10, Spain
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