1
|
Gao Z, Cheng S, Wittrup E, Gryak J, Najarian K. Learning using privileged information with logistic regression on acute respiratory distress syndrome detection. Artif Intell Med 2024; 156:102947. [PMID: 39208711 DOI: 10.1016/j.artmed.2024.102947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 07/02/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024]
Abstract
The advanced learning paradigm, learning using privileged information (LUPI), leverages information in training that is not present at the time of prediction. In this study, we developed privileged logistic regression (PLR) models under the LUPI paradigm to detect acute respiratory distress syndrome (ARDS), with mechanical ventilation variables or chest x-ray image features employed in the privileged domain and electronic health records in the base domain. In model training, the objective of privileged logistic regression was designed to incorporate data from the privileged domain and encourage knowledge transfer across the privileged and base domains. An asymptotic analysis was also performed, yielding sufficient conditions under which the addition of privileged information increases the rate of convergence in the proposed model. Results for ARDS detection show that PLR models achieve better classification performances than logistic regression models trained solely on the base domain, even when privileged information is partially available. Furthermore, PLR models demonstrate performance on par with or superior to state-of-the-art models under the LUPI paradigm. As the proposed models are effective, easy to interpret, and highly explainable, they are ideal for other clinical applications where privileged information is at least partially available.
Collapse
Affiliation(s)
- Zijun Gao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48109, MI, USA.
| | - Shuyang Cheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48109, MI, USA.
| | - Emily Wittrup
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48109, MI, USA.
| | - Jonathan Gryak
- Queens College, City University of New York, New York, 11367, NY, USA.
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48109, MI, USA; Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, 48109, MI, USA; Department of Emergency Medicine, University of Michigan, Ann Arbor, 48109, MI, USA; Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, 48109, MI, USA; Queens College, City University of New York, New York, 11367, NY, USA.
| |
Collapse
|
2
|
Tran TK, Tran MC, Joseph A, Phan PA, Grau V, Farmery AD. A systematic review of machine learning models for management, prediction and classification of ARDS. Respir Res 2024; 25:232. [PMID: 38834976 DOI: 10.1186/s12931-024-02834-x] [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: 02/13/2024] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
AIM Acute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promising solutions for classification, event detection and predictive models in the management of ARDS. METHOD In this review, we provide systematic description of different studies in the application of Machine Learning (ML) and artificial intelligence for management, prediction, and classification of ARDS. We searched the following databases: Google Scholar, PubMed, and EBSCO from 2009 to 2023. A total of 243 studies was screened, in which, 52 studies were included for review and analysis. We integrated knowledge of previous work providing the state of art and overview of explainable decision models in machine learning and have identified areas for future research. RESULTS Gradient boosting is the most common and successful method utilised in 12 (23.1%) of the studies. Due to limitation of data size available, neural network and its variation is used by only 8 (15.4%) studies. Whilst all studies used cross validating technique or separated database for validation, only 1 study validated the model with clinician input. Explainability methods were presented in 15 (28.8%) of studies with the most common method is feature importance which used 14 times. CONCLUSION For databases of 5000 or fewer samples, extreme gradient boosting has the highest probability of success. A large, multi-region, multi centre database is required to reduce bias and take advantage of neural network method. A framework for validating with and explaining ML model to clinicians involved in the management of ARDS would be very helpful for development and deployment of the ML model.
Collapse
Affiliation(s)
- Tu K Tran
- Department of Engineering and Science, University of Oxford, Oxford, UK.
- Nuffield Department of Clinical Neurosciences, Oxford Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
| | - Minh C Tran
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Arun Joseph
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Phi A Phan
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Vicente Grau
- Department of Engineering and Science, University of Oxford, Oxford, UK
| | - Andrew D Farmery
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, Oxford Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| |
Collapse
|
3
|
Warner E, Lee J, Hsu W, Syeda-Mahmood T, Kahn Jr. CE, Gevaert O, Rao A. Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects. Int J Comput Vis 2024; 132:3753-3769. [PMID: 39211895 PMCID: PMC11349845 DOI: 10.1007/s11263-024-02032-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 02/09/2024] [Indexed: 09/04/2024]
Abstract
Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning models. This survey navigates the current landscape of multimodal ML, focusing on its profound impact on medical image analysis and clinical decision support systems. Emphasizing challenges and innovations in addressing multimodal representation, fusion, translation, alignment, and co-learning, the paper explores the transformative potential of multimodal models for clinical predictions. It also highlights the need for principled assessments and practical implementation of such models, bringing attention to the dynamics between decision support systems and healthcare providers and personnel. Despite advancements, challenges such as data biases and the scarcity of "big data" in many biomedical domains persist. We conclude with a discussion on principled innovation and collaborative efforts to further the mission of seamless integration of multimodal ML models into biomedical practice.
Collapse
Affiliation(s)
- Elisa Warner
- Department of Computational Medicine and Bioinformatics, University of Michigan Ann Arbor, 100 Washtenaw Ave., Ann Arbor, MI 48109 USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan Ann Arbor, 100 Washtenaw Ave., Ann Arbor, MI 48109 USA
| | - William Hsu
- Department of Medical and Imaging Informatics, University of California Los Angeles, 924 Westwood Blvd Ste 420, Los Angeles, CA 90024 USA
| | | | - Charles E. Kahn Jr.
- Department of Radiology, University of Pennsylvania, 3400 Spruce St., Philadelphia, PA 19104 USA
| | - Olivier Gevaert
- Center for Biomedical Informatics Research, Stanford, 1265 Welch Road, Stanford, CA 94305 USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan Ann Arbor, 100 Washtenaw Ave., Ann Arbor, MI 48109 USA
| |
Collapse
|
4
|
Alge OP, Gryak J, VanEpps JS, Najarian K. Sepsis Trajectory Prediction Using Privileged Information and Continuous Physiological Signals. Diagnostics (Basel) 2024; 14:234. [PMID: 38337750 PMCID: PMC10854680 DOI: 10.3390/diagnostics14030234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024] Open
Abstract
The aim of this research is to apply the learning using privileged information paradigm to sepsis prognosis. We used signal processing of electrocardiogram and electronic health record data to construct support vector machines with and without privileged information to predict an increase in a given patient's quick-Sequential Organ Failure Assessment score, using a retrospective dataset. We applied this to both a small, critically ill cohort and a broader cohort of patients in the intensive care unit. Within the smaller cohort, privileged information proved helpful in a signal-informed model, and across both cohorts, electrocardiogram data proved to be informative to creating the prediction. Although learning using privileged information did not significantly improve results in this study, it is a paradigm worth studying further in the context of using signal processing for sepsis prognosis.
Collapse
Affiliation(s)
- Olivia P. Alge
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jonathan Gryak
- Department of Computer Science, Queens College, The City University of New York, Flushing, NY 11367, USA
| | - J. Scott VanEpps
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Macromolecular Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- The Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- The Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA
- Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|
5
|
Bickenbach J. [Potential of AI for the Treatment of Acute Respiratory Distress Syndrome (ARDS)]. Anasthesiol Intensivmed Notfallmed Schmerzther 2024; 59:34-44. [PMID: 38190824 DOI: 10.1055/a-2043-8644] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Acute respiratory distress syndrome (ARDS) is still associated with high mortality rates and poses a significant, vital threat to ICU patients because this syndrome is often detected too late (or not at all), and timely therapy and the fastest possible elimination of the underlying causes thus fail to materialize. Artificial Intelligence (AI) solutions can enable clinicians to make every minute in the ICU work for the patient by processing and analyzing all relevant data, thus supporting early diagnosis, adhering to clinical guidelines, and even providing a prognosis for the course of the ICU. This article shows what is already possible and where further challenges lie in this field of digital medicine.
Collapse
|
6
|
Jang W, Choi YS, Kim JY, Yon DK, Lee YJ, Chung SH, Kim CY, Yeo SG, Lee J. Artificial Intelligence-Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study. J Med Internet Res 2023; 25:e47612. [PMID: 37428525 PMCID: PMC10366668 DOI: 10.2196/47612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/04/2023] [Accepted: 06/14/2023] [Indexed: 07/11/2023] Open
Abstract
BACKGROUND Respiratory distress syndrome (RDS) is a disease that commonly affects premature infants whose lungs are not fully developed. RDS results from a lack of surfactant in the lungs. The more premature the infant is, the greater is the likelihood of having RDS. However, even though not all premature infants have RDS, preemptive treatment with artificial pulmonary surfactant is administered in most cases. OBJECTIVE We aimed to develop an artificial intelligence model to predict RDS in premature infants to avoid unnecessary treatment. METHODS In this study, 13,087 very low birth weight infants who were newborns weighing less than 1500 grams were assessed in 76 hospitals of the Korean Neonatal Network. To predict RDS in very low birth weight infants, we used basic infant information, maternity history, pregnancy/birth process, family history, resuscitation procedure, and test results at birth such as blood gas analysis and Apgar score. The prediction performances of 7 different machine learning models were compared, and a 5-layer deep neural network was proposed in order to enhance the prediction performance from the selected features. An ensemble approach combining multiple models from the 5-fold cross-validation was subsequently developed. RESULTS Our proposed ensemble 5-layer deep neural network consisting of the top 20 features provided high sensitivity (83.03%), specificity (87.50%), accuracy (84.07%), balanced accuracy (85.26%), and area under the curve (0.9187). Based on the model that we developed, a public web application that enables easy access for the prediction of RDS in premature infants was deployed. CONCLUSIONS Our artificial intelligence model may be useful for preparations for neonatal resuscitation, particularly in cases involving the delivery of very low birth weight infants, as it can aid in predicting the likelihood of RDS and inform decisions regarding the administration of surfactant.
Collapse
Affiliation(s)
- Woocheol Jang
- Biomedical Engineering, Kyung Hee University, Yongin-si, Republic of Korea
| | - Yong Sung Choi
- Department of Pediatrics, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
| | - Ji Yoo Kim
- Department of Pediatrics, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
| | - Dong Keon Yon
- Department of Pediatrics, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
| | - Young Joo Lee
- Department of Obstetrics and Gynecology, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
| | - Sung-Hoon Chung
- Department of Pediatrics, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, Republic of Korea
| | - Chae Young Kim
- Department of Pediatrics, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, Republic of Korea
| | - Seung Geun Yeo
- Department of Otorhinolaryngology Head and Neck Surgery, Kyung Hee University Medical Center, Kyung Hee University School of Medicine, Seoul, Republic of Korea
| | - Jinseok Lee
- Biomedical Engineering, Kyung Hee University, Yongin-si, Republic of Korea
| |
Collapse
|
7
|
Zhang Y, Gao Z, Wittrup E, Gryak J, Najarian K. Increasing efficiency of SVMp+ for handling missing values in healthcare prediction. PLOS DIGITAL HEALTH 2023; 2:e0000281. [PMID: 37384608 DOI: 10.1371/journal.pdig.0000281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/29/2023] [Indexed: 07/01/2023]
Abstract
Missing data presents a challenge for machine learning applications specifically when utilizing electronic health records to develop clinical decision support systems. The lack of these values is due in part to the complex nature of clinical data in which the content is personalized to each patient. Several methods have been developed to handle this issue, such as imputation or complete case analysis, but their limitations restrict the solidity of findings. However, recent studies have explored how using some features as fully available privileged information can increase model performance including in SVM. Building on this insight, we propose a computationally efficient kernel SVM-based framework (l2-SVMp+) that leverages partially available privileged information to guide model construction. Our experiments validated the superiority of l2-SVMp+ over common approaches for handling missingness and previous implementations of SVMp+ in both digit recognition, disease classification and patient readmission prediction tasks. The performance improves as the percentage of available privileged information increases. Our results showcase the capability of l2-SVMp+ to handle incomplete but important features in real-world medical applications, surpassing traditional SVMs that lack privileged information. Additionally, l2-SVMp+ achieves comparable or superior model performance compared to imputed privileged features.
Collapse
Affiliation(s)
- Yufeng Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Zijun Gao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Emily Wittrup
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Computer Science, Queens College, City University of New York, New York, United States of America
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, United States of America
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| |
Collapse
|
8
|
Rashid M, Ramakrishnan M, Chandran VP, Nandish S, Nair S, Shanbhag V, Thunga G. Artificial intelligence in acute respiratory distress syndrome: A systematic review. Artif Intell Med 2022; 131:102361. [DOI: 10.1016/j.artmed.2022.102361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 07/01/2022] [Accepted: 07/11/2022] [Indexed: 11/02/2022]
|
9
|
Warner E, Al-Turkestani N, Bianchi J, Gurgel ML, Cevidanes L, Rao A. Predicting Osteoarthritis of the Temporomandibular Joint Using Random Forest with Privileged Information. ETHICAL AND PHILOSOPHICAL ISSUES IN MEDICAL IMAGING, MULTIMODAL LEARNING AND FUSION ACROSS SCALES FOR CLINICAL DECISION SUPPORT, AND TOPOLOGICAL DATA ANALYSIS FOR BIOMEDICAL IMAGING : 1ST INTERNATIONAL WORKSHOP, EPIMI 2022, 12TH INTERNA... 2022; 13755:77-86. [PMID: 37416761 PMCID: PMC10323493 DOI: 10.1007/978-3-031-23223-7_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
Osteoarthritis of the temporomandibular joint (TMJ OA) is the most common disorder of the TMJ. A clinical decision support (CDS) system designed to detect TMJ OA could function as a useful screening tool as part of regular check-ups to detect early onset. This study implements a CDS concept model based on Random Forest and dubbed RF+ to predict TMJ OA with the hypothesis that a model which leverages high-resolution radiological and biomarker data in training only can improve predictions compared with a baseline model which does not use privileged information. We found that the RF+ model can outperform the baseline model even when privileged features are not of gold standard quality. Additionally, we introduce a novel method for post-hoc feature analysis, finding shortRunHighGreyLevelEmphasis of the lateral condyles and joint distance to be the most important features from the privileged modalities for predicting TMJ OA.
Collapse
Affiliation(s)
- Elisa Warner
- University of Michigan, Ann Arbor, MI 48109, USA
| | - Najla Al-Turkestani
- University of Michigan, Ann Arbor, MI 48109, USA
- King Abdulaziz University, Jeddah 22254, Saudi Arabia
| | - Jonas Bianchi
- University of the Pacific, San Francisco, CA 94103, USA
| | | | | | - Arvind Rao
- University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|
10
|
Safi K, Aly WHF, AlAkkoumi M, Kanj H, Ghedira M, Hutin E. EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data. Bioengineering (Basel) 2022; 9:bioengineering9070283. [PMID: 35877334 PMCID: PMC9311556 DOI: 10.3390/bioengineering9070283] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/22/2022] [Accepted: 06/22/2022] [Indexed: 11/24/2022] Open
Abstract
There has recently been increasing interest in postural stability aimed at gaining a better understanding of the human postural system. This system controls human balance in quiet standing and during locomotion. Parkinson’s disease (PD) is the most common degenerative movement disorder that affects human stability and causes falls and injuries. This paper proposes a novel methodology to differentiate between healthy individuals and those with PD through the empirical mode decomposition (EMD) method. EMD enables the breaking down of a complex signal into several elementary signals called intrinsic mode functions (IMFs). Three temporal parameters and three spectral parameters are extracted from each stabilometric signal as well as from its IMFs. Next, the best five features are selected using the feature selection method. The classification task is carried out using four known machine-learning methods, KNN, decision tree, Random Forest and SVM classifiers, over 10-fold cross validation. The used dataset consists of 28 healthy subjects (14 young adults and 14 old adults) and 32 PD patients (12 young adults and 20 old adults). The SVM method has a performance of 92% and the Dempster–Sahfer formalism method has an accuracy of 96.51%.
Collapse
Affiliation(s)
- Khaled Safi
- Computer Science Department, Strasbourg University, 67081 Strasbourg, France
- Correspondence:
| | - Wael Hosny Fouad Aly
- College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait; (W.H.F.A.); (M.A.); (H.K.)
| | - Mouhammad AlAkkoumi
- College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait; (W.H.F.A.); (M.A.); (H.K.)
| | - Hassan Kanj
- College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait; (W.H.F.A.); (M.A.); (H.K.)
| | - Mouna Ghedira
- Laboratory ARM, EA BIOTN, UPEC, CHU Henri Mondor, 94000 Cŕeteil, France; (M.G.); (E.H.)
| | - Emilie Hutin
- Laboratory ARM, EA BIOTN, UPEC, CHU Henri Mondor, 94000 Cŕeteil, France; (M.G.); (E.H.)
| |
Collapse
|
11
|
Zhang Z, Navarese EP, Zheng B, Meng Q, Liu N, Ge H, Pan Q, Yu Y, Ma X. Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome. J Evid Based Med 2020; 13:301-312. [PMID: 33185950 DOI: 10.1111/jebm.12418] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 10/21/2020] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.
Collapse
Affiliation(s)
- Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Eliano Pio Navarese
- Interventional Cardiology and Cardiovascular Medicine Research, Department of Cardiology and Internal Medicine, Nicolaus Copernicus University, Bydgoszcz, Poland
- Faculty of Medicine, University of Alberta, Edmonton, Canada
| | - Bin Zheng
- Department of Surgery, 2D, Walter C Mackenzie Health Sciences Centre, University of Alberta, Edmonton, Alberta, Canada
| | - Qinghe Meng
- Department of Surgery, State University of New York Upstate Medical University, Syracuse, New York
| | - Nan Liu
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore
| | - Huiqing Ge
- Department of Respiratory Care, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qing Pan
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Yuetian Yu
- Department of Critical Care Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xuelei Ma
- Department of biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|