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Choi A, Choi SY, Chung K, Chung HS, Song T, Choi B, Kim JH. Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department. Sci Rep 2023; 13:8561. [PMID: 37237057 DOI: 10.1038/s41598-023-35617-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 05/21/2023] [Indexed: 05/28/2023] Open
Abstract
This study aimed to develop a machine learning-based clinical decision support system for emergency departments based on the decision-making framework of physicians. We extracted 27 fixed and 93 observation features using data on vital signs, mental status, laboratory results, and electrocardiograms during emergency department stay. Outcomes included intubation, admission to the intensive care unit, inotrope or vasopressor administration, and in-hospital cardiac arrest. eXtreme gradient boosting algorithm was used to learn and predict each outcome. Specificity, sensitivity, precision, F1 score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve were assessed. We analyzed 303,345 patients with 4,787,121 input data, resampled into 24,148,958 1 h-units. The models displayed a discriminative ability to predict outcomes (AUROC > 0.9), and the model with lagging 6 and leading 0 displayed the highest value. The AUROC curve of in-hospital cardiac arrest had the smallest change, with increased lagging for all outcomes. With inotropic use, intubation, and intensive care unit admission, the range of AUROC curve change with the leading 6 was the highest according to different amounts of previous information (lagging). In this study, a human-centered approach to emulate the clinical decision-making process of emergency physicians has been adopted to enhance the use of the system. Machine learning-based clinical decision support systems customized according to clinical situations can help improve the quality of care.
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Affiliation(s)
- Arom Choi
- Department of Emergency Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
- Institute for Innovation in Digital Healthcare, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - So Yeon Choi
- Department of Emergency Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Kyungsoo Chung
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
- Institute for Innovation in Digital Healthcare, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Hyun Soo Chung
- Department of Emergency Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Taeyoung Song
- LG Electronics, 128 Yeoui-daero, Yeongdeungpo-gu, Seoul, 07336, Republic of Korea
| | - Byunghun Choi
- LG Electronics, 128 Yeoui-daero, Yeongdeungpo-gu, Seoul, 07336, Republic of Korea
| | - Ji Hoon Kim
- Department of Emergency Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
- Institute for Innovation in Digital Healthcare, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
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Utaibi KA, Ahmad U, Sait SM, Iqbal S. Medical imaging and nano-engineering advances with artificial intelligence. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS, PART N: JOURNAL OF NANOMATERIALS, NANOENGINEERING AND NANOSYSTEMS 2023. [DOI: 10.1177/23977914231161443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Medical imaging is a broad field of research and artificial intelligence used to explore such images is termed as AI-Imaging. AI-imaging is further divided into sub-branches including the computational, theoretical and practical experiments in wet and dry labs. The current research focuses on the background of medical imaging, recent advances in the field of medical imaging for oncology, challenges and possible solutions. During this research, some computational and programing tools are outlined. The process of image segmentation is important as it can help to explore the medical images in more detail. During this research, the steps involved in image segmentation are outlined and the numerical experiments are performed on a set of breast cancer medical images. It is concluded during this research that the achievements in this domain are always credited by the smart programing & computational tools and computer vision. The current research also outlines the step-wise protocols of deep learning, designed for different types of medical imaging such as X-rays, CT-scan and MRI are documented to provide a comprehensive understanding, that can help in bridging the two domains of medicine and computer vision, in a reliable and fruitful manner.
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Affiliation(s)
- Khalid Al Utaibi
- Computer Science and Software Engineering Department, University of Ha’il, Ha’il, Saudi Arabia
| | - Usama Ahmad
- Department of Mathematics, Comsats University Islamabad, Islamabad,Pakistan
| | - Sadiq M Sait
- Center for Communications and IT Research, Research Institute, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
| | - Sohail Iqbal
- Department of General Medicine, Shahdara Hospital, Lahore, Pakistan
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Khan A, Hazart A, Galarraga O, Garcia-Salicetti S, Vigneron V. Treatment Outcome Prediction Using Multi-Task Learning: Application to Botulinum Toxin in Gait Rehabilitation. SENSORS (BASEL, SWITZERLAND) 2022; 22:8452. [PMID: 36366149 PMCID: PMC9654854 DOI: 10.3390/s22218452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 10/22/2022] [Accepted: 10/28/2022] [Indexed: 06/16/2023]
Abstract
We propose a framework for optimizing personalized treatment outcomes for patients with neurological diseases. A typical consequence of such diseases is gait disorders, partially explained by command and muscle tone problems associated with spasticity. Intramuscular injection of botulinum toxin type A is a common treatment for spasticity. According to the patient's profile, offering the optimal treatment combined with the highest possible benefit-risk ratio is important. For the prediction of knee and ankle kinematics after botulinum toxin type A (BTX-A) treatment, we propose: (1) a regression strategy based on a multi-task architecture composed of LSTM models; (2) to introduce medical treatment data (MTD) for context modeling; and (3) a gating mechanism to model treatment interaction more efficiently. The proposed models were compared with and without metadata describing treatments and with serial models. Multi-task learning (MTL) achieved the lowest root-mean-squared error (RMSE) (5.60°) for traumatic brain injury (TBI) patients on knee trajectories and the lowest RMSE (3.77°) for cerebral palsy (CP) patients on ankle trajectories, with only a difference of 5.60° between actual and predicted. Overall, the best RMSE ranged from 5.24° to 6.24° for the MTL models. To the best of our knowledge, this is the first time that MTL has been used for post-treatment gait trajectory prediction. The MTL models outperformed the serial models, particularly when introducing treatment metadata. The gating mechanism is efficient in modeling treatment interaction and improving trajectory prediction.
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Affiliation(s)
- Adil Khan
- Informatique, Bio-Informatique et Systèmes Complexes (IBISC) EA 4526, Univ Evry, Université Paris-Saclay, 91020 Evry, France
- Department of Computer Science, Sukkur IBA University, Sukkur 65200, Sindh, Pakistan
| | - Antoine Hazart
- Informatique, Bio-Informatique et Systèmes Complexes (IBISC) EA 4526, Univ Evry, Université Paris-Saclay, 91020 Evry, France
| | - Omar Galarraga
- UGECAM Ile-de-France, Movement Analysis Laboratory, 77170 Coubert, France
| | | | - Vincent Vigneron
- Informatique, Bio-Informatique et Systèmes Complexes (IBISC) EA 4526, Univ Evry, Université Paris-Saclay, 91020 Evry, France
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Chen YP, Lo YH, Lai F, Huang CH. Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study. J Med Internet Res 2021; 23:e25113. [PMID: 33502324 PMCID: PMC7875703 DOI: 10.2196/25113] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Revised: 11/19/2020] [Accepted: 01/15/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The electronic health record (EHR) contains a wealth of medical information. An organized EHR can greatly help doctors treat patients. In some cases, only limited patient information is collected to help doctors make treatment decisions. Because EHRs can serve as a reference for this limited information, doctors' treatment capabilities can be enhanced. Natural language processing and deep learning methods can help organize and translate EHR information into medical knowledge and experience. OBJECTIVE In this study, we aimed to create a model to extract concept embeddings from EHRs for disease pattern retrieval and further classification tasks. METHODS We collected 1,040,989 emergency department visits from the National Taiwan University Hospital Integrated Medical Database and 305,897 samples from the National Hospital and Ambulatory Medical Care Survey Emergency Department data. After data cleansing and preprocessing, the data sets were divided into training, validation, and test sets. We proposed a Transformer-based model to embed EHRs and used Bidirectional Encoder Representations from Transformers (BERT) to extract features from free text and concatenate features with structural data as input to our proposed model. Then, Deep InfoMax (DIM) and Simple Contrastive Learning of Visual Representations (SimCLR) were used for the unsupervised embedding of the disease concept. The pretrained disease concept-embedding model, named EDisease, was further finetuned to adapt to the critical care outcome prediction task. We evaluated the performance of embedding using t-distributed stochastic neighbor embedding (t-SNE) to perform dimension reduction for visualization. The performance of the finetuned predictive model was evaluated against published models using the area under the receiver operating characteristic (AUROC). RESULTS The performance of our model on the outcome prediction had the highest AUROC of 0.876. In the ablation study, the use of a smaller data set or fewer unsupervised methods for pretraining deteriorated the prediction performance. The AUROCs were 0.857, 0.870, and 0.868 for the model without pretraining, the model pretrained by only SimCLR, and the model pretrained by only DIM, respectively. On the smaller finetuning set, the AUROC was 0.815 for the proposed model. CONCLUSIONS Through contrastive learning methods, disease concepts can be embedded meaningfully. Moreover, these methods can be used for disease retrieval tasks to enhance clinical practice capabilities. The disease concept model is also suitable as a pretrained model for subsequent prediction tasks.
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Affiliation(s)
- Yen-Pin Chen
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei City, Taiwan
- Department of Emergency Medicine, National Taiwan University BioMedical Park Hospital, Hsinchu County, Taiwan
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei City, Taiwan
| | - Yuan-Hsun Lo
- Department of Applied Mathematics, National Pingtung University, Pingtung City, Taiwan
| | - Feipei Lai
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei City, Taiwan
| | - Chien-Hua Huang
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei City, Taiwan
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Fernandes M, Mendes R, Vieira SM, Leite F, Palos C, Johnson A, Finkelstein S, Horng S, Celi LA. Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing. PLoS One 2020; 15:e0230876. [PMID: 32240233 PMCID: PMC7117713 DOI: 10.1371/journal.pone.0230876] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 03/10/2020] [Indexed: 12/23/2022] Open
Abstract
Emergency department triage is the first point in time when a patient's acuity level is determined. The time to assign a priority at triage is short and it is vital to accurately stratify patients at this stage, since under-triage can lead to increased morbidity, mortality and costs. Our aim was to present a model that can assist healthcare professionals in triage decision making, namely in the stratification of patients through the risk prediction of a composite critical outcome-mortality and cardiopulmonary arrest. Our study cohort consisted of 235826 adult patients triaged at a Portuguese Emergency Department from 2012 to 2016. Patients were assigned to emergent, very urgent or urgent priorities of the Manchester Triage System (MTS). Demographics, clinical variables routinely collected at triage and the patients' chief complaint were used. Logistic regression, random forests and extreme gradient boosting were developed using all available variables. The term frequency-inverse document frequency (TF-IDF) natural language processing weighting factor was applied to vectorize the chief complaint. Stratified random sampling was used to split the data into train (70%) and test (30%) data sets. Ten-fold cross validation was performed in train to optimize model hyper-parameters. The performance obtained with the best model was compared against the reference model-a regularized logistic regression trained using only triage priorities. Extreme gradient boosting exhibited good calibration properties and yielded areas under the receiver operating characteristic and precision-recall curves of 0.96 (95% CI 0.95-0.97) and 0.31 (95% CI 0.26-0.36), respectively. The predictors ranked with higher importance by this model were the Glasgow coma score, the patients' age, pulse oximetry and arrival mode. Compared to the reference, the extreme gradient boosting model using clinical variables and the chief complaint presented higher recall for patients assigned MTS-3 and can identify those who are at risk of the composite outcome.
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Affiliation(s)
- Marta Fernandes
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
- * E-mail:
| | - Rúben Mendes
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Susana M. Vieira
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | | | - Carlos Palos
- Hospital Beatriz Ângelo, Luz Saúde, Lisbon, Portugal
| | - Alistair Johnson
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Stan Finkelstein
- Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Steven Horng
- Department of Emergency Medicine / Division of Clinical Informatics / Center for Healthcare Delivery Science, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
| | - Leo Anthony Celi
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
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Fernandes M, Vieira SM, Leite F, Palos C, Finkelstein S, Sousa JM. Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review. Artif Intell Med 2020; 102:101762. [DOI: 10.1016/j.artmed.2019.101762] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 08/29/2019] [Accepted: 11/07/2019] [Indexed: 12/23/2022]
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