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Viderman D, Kotov A, Popov M, Abdildin Y. Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review. Int J Med Inform 2024; 182:105308. [PMID: 38091862 DOI: 10.1016/j.ijmedinf.2023.105308] [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: 09/15/2023] [Revised: 11/20/2023] [Accepted: 12/03/2023] [Indexed: 01/07/2024]
Abstract
INTRODUCTION Since the beginning of the COVID-19 pandemic, numerous machine and deep learning (MDL) methods have been proposed in the literature to analyze patient physiological data. The objective of this review is to summarize various aspects of these methods and assess their practical utility for predicting various clinical outcomes. METHODS We searched PubMed, Scopus, and Cochrane Library, screened and selected the studies matching the inclusion criteria. The clinical analysis focused on the characteristics of the patient cohorts in the studies included in this review, the specific tasks in the context of the COVID-19 pandemic that machine and deep learning methods were used for, and their practical limitations. The technical analysis focused on the details of specific MDL methods and their performance. RESULTS Analysis of the 48 selected studies revealed that the majority (∼54 %) of them examined the application of MDL methods for the prediction of survival/mortality-related patient outcomes, while a smaller fraction (∼13 %) of studies also examined applications to the prediction of patients' physiological outcomes and hospital resource utilization. 21 % of the studies examined the application of MDL methods to multiple clinical tasks. Machine and deep learning methods have been shown to be effective at predicting several outcomes of COVID-19 patients, such as disease severity, complications, intensive care unit (ICU) transfer, and mortality. MDL methods also achieved high accuracy in predicting the required number of ICU beds and ventilators. CONCLUSION Machine and deep learning methods have been shown to be valuable tools for predicting disease severity, organ dysfunction and failure, patient outcomes, and hospital resource utilization during the COVID-19 pandemic. The discovered knowledge and our conclusions and recommendations can also be useful to healthcare professionals and artificial intelligence researchers in managing future pandemics.
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Affiliation(s)
- Dmitriy Viderman
- Department of Surgery, School of Medicine, Nazarbayev University, Astana, Kazakhstan; Department of Anesthesiology, Intensive Care, and Pain Medicine, National Research Oncology Center, Astana, Kazakhstan.
| | - Alexander Kotov
- Department of Computer Science, College of Engineering, Wayne State University, Detroit, USA.
| | - Maxim Popov
- Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
| | - Yerkin Abdildin
- Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
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Okada Y, Shahidah N, Ng YY, Chia MYC, Gan HN, Leong BSH, Mao DR, Ng WM, Irisawa T, Yamada T, Nishimura T, Kiguchi T, Kishimoto M, Matsuyama T, Nishioka N, Kiyohara K, Kitamura T, Iwami T, Ong MEH. Outcome assessment for out-of-hospital cardiac arrest patients in Singapore and Japan with initial shockable rhythm. Crit Care 2023; 27:351. [PMID: 37700335 PMCID: PMC10496207 DOI: 10.1186/s13054-023-04636-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/04/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Singapore and Osaka in Japan have comparable population sizes and prehospital management; however, the frequency of ECPR differs greatly for out-of-hospital cardiac arrest (OHCA) patients with initial shockable rhythm. Given this disparity, we hypothesized that the outcomes among the OHCA patients with initial shockable rhythm in Singapore were different from those in Osaka. The aim of this study was to evaluate the outcomes of OHCA patients with initial shockable rhythm in Singapore compared to the expected outcomes derived from Osaka data using machine learning-based prediction models. METHODS This was a secondary analysis of two OHCA databases: the Singapore PAROS database (SG-PAROS) and the Osaka-CRITICAL database from Osaka, Japan. This study included adult (18-74 years) OHCA patients with initial shockable rhythm. A machine learning-based prediction model was derived and validated using data from the Osaka-CRITICAL database (derivation data 2012-2017, validation data 2018-2019), and applied to the SG-PAROS database (2010-2016 data), to predict the risk-adjusted probability of favorable neurological outcomes. The observed and expected outcomes were compared using the observed-expected ratio (OE ratio) with 95% confidence intervals (CI). RESULTS From the SG-PAROS database, 1,789 patients were included in the analysis. For OHCA patients who achieved return of spontaneous circulation (ROSC) on hospital arrival, the observed favorable neurological outcome was at the same level as expected (OE ratio: 0.905 [95%CI: 0.784-1.036]). On the other hand, for those who had continued cardiac arrest on hospital arrival, the outcomes were lower than expected (shockable rhythm on hospital arrival, OE ratio: 0.369 [95%CI: 0.258-0.499], and nonshockable rhythm, OE ratio: 0.137 [95%CI: 0.065-0.235]). CONCLUSION This observational study found that the outcomes for patients with initial shockable rhythm but who did not obtain ROSC on hospital arrival in Singapore were lower than expected from Osaka. We hypothesize this is mainly due to differences in the use of ECPR.
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Affiliation(s)
- Yohei Okada
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Nur Shahidah
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
| | - Yih Yng Ng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Department of Preventive and Population Medicine, Tan Tock Seng Hospital, Singapore, Singapore
| | - Michael Y C Chia
- Emergency Department, Tan Tock Seng Hospital, Singapore, Singapore
| | - Han Nee Gan
- Accident & Emergency, Changi General Hospital, Singapore, Singapore
| | - Benjamin S H Leong
- Emergency Medicine Department, National University Hospital, Singapore, Singapore
| | - Desmond R Mao
- Department of Acute and Emergency Care, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Wei Ming Ng
- Emergency Medicine Department, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Taro Irisawa
- Department of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, Suita, Japan
| | - Tomoki Yamada
- Emergency and Critical Care Medical Center, Osaka Police Hospital, Osaka, Japan
| | - Tetsuro Nishimura
- Department of Critical Care Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Takeyuki Kiguchi
- Critical Care and Trauma Center, Osaka General Medical Center, Osaka, Japan
| | - Masafumi Kishimoto
- Osaka Prefectural Nakakawachi Medical Center of Acute Medicine, Higashi-Osaka, Japan
| | - Tasuku Matsuyama
- Department of Emergency Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Norihiro Nishioka
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kosuke Kiyohara
- Department of Food Science Faculty of Home Economics, Otsuma Women's University, Tokyo, Japan
| | - Tetsuhisa Kitamura
- Division of Environmental Medicine and Population Sciences, Department of Social and Environmental Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Taku Iwami
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Marcus Eng Hock Ong
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
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