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Wu H, Chen Z, Gu J, Jiang Y, Gao S, Chen W, Miao C. Predicting Chronic Pain and Treatment Outcomes Using Machine Learning Models Based on High-dimensional Clinical Data From a Large Retrospective Cohort. Clin Ther 2024; 46:490-498. [PMID: 38824080 DOI: 10.1016/j.clinthera.2024.04.012] [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: 08/29/2023] [Revised: 04/13/2024] [Accepted: 04/27/2024] [Indexed: 06/03/2024]
Abstract
PURPOSE To identify factors and indicators that affect chronic pain and pain relief, and to develop predictive models using machine learning. METHODS We analyzed the data of 67,028 outpatient cases and 11,310 valid samples with pain from a large retrospective cohort. We used decision tree, random forest, AdaBoost, neural network, and logistic regression to discover significant indicators and to predict pain and treatment relief. FINDINGS The random forest model had the highest accuracy, F1 value, precision, and recall rates for predicting pain relief. The main factors affecting pain and treatment relief included body mass index, blood pressure, age, body temperature, heart rate, pulse, and neutrophil/lymphocyte × platelet ratio. The logistic regression model had high sensitivity and specificity for predicting pain occurrence. IMPLICATIONS Machine learning models can be used to analyze the risk factors and predictors of chronic pain and pain relief, and to provide personalized and evidence-based pain management.
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Affiliation(s)
- Han Wu
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China
| | - Zhaoyuan Chen
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China
| | - Jiahui Gu
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China
| | - Yi Jiang
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China
| | - Shenjia Gao
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China
| | - Wankun Chen
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China.
| | - Changhong Miao
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China.
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Tyler S, Olis M, Aust N, Patel L, Simon L, Triantafyllidis C, Patel V, Lee DW, Ginsberg B, Ahmad H, Jacobs RJ. Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review. Cureus 2024; 16:e59906. [PMID: 38854295 PMCID: PMC11158416 DOI: 10.7759/cureus.59906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 05/08/2024] [Indexed: 06/11/2024] Open
Abstract
The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has become a major point of interest and raises the question of its impact on the emergency department (ED) triaging process. AI's capacity to emulate human cognitive processes coupled with advancements in computing has shown positive outcomes in various aspects of healthcare but little is known about the use of AI in triaging patients in ED. AI algorithms may allow for earlier diagnosis and intervention; however, overconfident answers may present dangers to patients. The purpose of this review was to explore comprehensively recently published literature regarding the effect of AI and ML in ED triage and identify research gaps. A systemized search was conducted in September 2023 using the electronic databases EMBASE, Ovid MEDLINE, and Web of Science. To meet inclusion criteria, articles had to be peer-reviewed, written in English, and based on primary data research studies published in US journals 2013-2023. Other criteria included 1) studies with patients needing to be admitted to hospital EDs, 2) AI must have been used when triaging a patient, and 3) patient outcomes must be represented. The search was conducted using controlled descriptors from the Medical Subject Headings (MeSH) that included the terms "artificial intelligence" OR "machine learning" AND "emergency ward" OR "emergency care" OR "emergency department" OR "emergency room" AND "patient triage" OR "triage" OR "triaging." The search initially identified 1,142 citations. After a rigorous, systemized screening process and critical appraisal of the evidence, 29 studies were selected for the final review. The findings indicated that 1) ML models consistently demonstrated superior discrimination abilities compared to conventional triage systems, 2) the integration of AI into the triage process yielded significant enhancements in predictive accuracy, disease identification, and risk assessment, 3) ML accurately determined the necessity of hospitalization for patients requiring urgent attention, and 4) ML improved resource allocation and quality of patient care, including predicting length of stay. The suggested superiority of ML models in prioritizing patients in the ED holds the potential to redefine triage precision.
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Affiliation(s)
- Samantha Tyler
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Matthew Olis
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Nicole Aust
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Love Patel
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Leah Simon
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Catherine Triantafyllidis
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Vijay Patel
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Dong Won Lee
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Brendan Ginsberg
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Hiba Ahmad
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Robin J Jacobs
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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Cai L, Zhao E, Niu H, Liu Y, Zhang T, Liu D, Zhang Z, Li J, Qiao P, Lv H, Ren P, Zheng W, Wang Z. A machine learning approach to predict cerebral perfusion status based on internal carotid artery blood flow. Comput Biol Med 2023; 164:107264. [PMID: 37481951 DOI: 10.1016/j.compbiomed.2023.107264] [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: 04/18/2023] [Revised: 06/20/2023] [Accepted: 07/16/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND AND OBJECTIVE Cerebral blood flow (CBF), or perfusion, is a prerequisite for maintaining brain metabolism and normal physiological functions. Diagnosing and evaluating cerebral perfusion status is crucial to managing brain disease. However, cerebral perfusion imaging devices are complicated to operate, should be controlled by specialized technicians, are often large, and are usually installed in fixed places such as hospitals. It is significantly difficult for clinicians to obtain the cerebral perfusion status in time. Considering that CBF is mainly supplied by the internal carotid artery (ICA), this study proposes a cerebral perfusion status prediction model that can automatically quantify the level of cerebral perfusion in patients by modeling the association between ICA blood flow and cerebral perfusion. MATERIALS AND METHODS Forty-eight participants were enrolled in the study after screening. We collected participants' ICA ultrasound and brain magnetic resonance imaging (MRI) data before and after dobutamine injection based on a rigorous experimental paradigm and built an ICA-cerebral perfusion datasetdd. Support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBOOST) were used for early prediction of cerebral perfusion status. The SHAP analysis was adopted to reveal the impact of interpretable predictions for each feature. RESULTS The XGBOOST model demonstrated the best overall classification performance with an accuracy of 78.01%, sensitivity of 96.67%, specificity of 98.23%, F1 score of 74.57%, Matthews correlation coefficient (MCC) of 62.17%, and area under the receiver operating characteristic curve (AUC) of 87.08%. Accelerated speed, peak systolic flow velocity, and resistance index of ICA blood flow are important factors for cerebral perfusion prediction. CONCLUSIONS The proposed method paves a new avenue for the study of predicting cerebral perfusion status automatically and providesv a noninvasive, real-time, and low-cost alternative to brain perfusion imaging. Moreover, this analysis identifies highly predictive features for the cerebral perfusion status and gives clinicians an intuitive understanding of the influence of key features. The prediction models can serve as an early warning tool that offers sufficient time for clinicians to take early intervention measures.
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Affiliation(s)
- Linkun Cai
- School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China.
| | - Erwei Zhao
- National Space Science Center, Chinese Academy of Sciences, 100190, Beijing, China.
| | - Haijun Niu
- School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
| | - Yawen Liu
- School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
| | - Tingting Zhang
- School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
| | - Dong Liu
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China
| | - Zhe Zhang
- China Astronaut Research and Training Center, Beijing, China
| | - Jing Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China
| | - Penggang Qiao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China
| | - Pengling Ren
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China.
| | - Wei Zheng
- National Space Science Center, Chinese Academy of Sciences, 100190, Beijing, China.
| | - Zhenchang Wang
- School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China.
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An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department. Sci Rep 2022; 12:17466. [PMID: 36261457 PMCID: PMC9580414 DOI: 10.1038/s41598-022-22233-w] [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: 08/02/2022] [Accepted: 10/11/2022] [Indexed: 01/12/2023] Open
Abstract
Emergency departments (EDs) are experiencing complex demands. An ED triage tool, the Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable machine learning framework. It achieved a good performance in the Singapore population. We aimed to externally validate the SERP in a Korean cohort for all ED patients and compare its performance with Korean triage acuity scale (KTAS). This retrospective cohort study included all adult ED patients of Samsung Medical Center from 2016 to 2020. The outcomes were 30-day and in-hospital mortality after the patients' ED visit. We used the area under the receiver operating characteristic curve (AUROC) to assess the performance of the SERP and other conventional scores, including KTAS. The study population included 285,523 ED visits, of which 53,541 were after the COVID-19 outbreak (2020). The whole cohort, in-hospital, and 30 days mortality rates were 1.60%, and 3.80%. The SERP achieved an AUROC of 0.821 and 0.803, outperforming KTAS of 0.679 and 0.729 for in-hospital and 30-day mortality, respectively. SERP was superior to other scores for in-hospital and 30-day mortality prediction in an external validation cohort. SERP is a generic, intuitive, and effective triage tool to stratify general patients who present to the emergency department.
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Zhou X, Wang H, Feng C, Xu R, He Y, Li L, Tu C. Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges. Front Oncol 2022; 12:908873. [PMID: 35928860 PMCID: PMC9345628 DOI: 10.3389/fonc.2022.908873] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/15/2022] [Indexed: 12/12/2022] Open
Abstract
Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed.
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Affiliation(s)
- Xiaowen Zhou
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Hua Wang
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Chengyao Feng
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Ruilin Xu
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yu He
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Lan Li
- Department of Pathology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Chao Tu
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Chao Tu,
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