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Youssef Ali Amer A, Wouters F, Vranken J, Dreesen P, de Korte-de Boer D, van Rosmalen F, van Bussel BCT, Smit-Fun V, Duflot P, Guiot J, van der Horst ICC, Mesotten D, Vandervoort P, Aerts JM, Vanrumste B. Vital Signs Prediction for COVID-19 Patients in ICU. SENSORS 2021; 21:s21238131. [PMID: 34884136 PMCID: PMC8662454 DOI: 10.3390/s21238131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 11/29/2021] [Accepted: 11/30/2021] [Indexed: 11/16/2022]
Abstract
This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time.
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Affiliation(s)
- Ahmed Youssef Ali Amer
- E-MEDIA, STADIUS, Department of Electrical Engineering (ESAT), Campus Group T, KU Leuven, 3000 Leuven, Belgium;
- Measure, Model & Manage Bioresponses (M3-BIORES), Department of Biosystems, KU Leuven, 3000 Leuven, Belgium;
| | - Femke Wouters
- Limburg Clinical Research Center/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium; (F.W.); (J.V.); (P.D.); (D.M.); (P.V.)
- Limburg Clinical Research Center/Mobile Health Unit, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
- Department of Anesthesiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
- Department of Cardiology and Future Health, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
| | - Julie Vranken
- Limburg Clinical Research Center/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium; (F.W.); (J.V.); (P.D.); (D.M.); (P.V.)
- Limburg Clinical Research Center/Mobile Health Unit, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
- Department of Anesthesiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
- Department of Cardiology and Future Health, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
| | - Pauline Dreesen
- Limburg Clinical Research Center/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium; (F.W.); (J.V.); (P.D.); (D.M.); (P.V.)
- Limburg Clinical Research Center/Mobile Health Unit, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
- Department of Anesthesiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
- Department of Cardiology and Future Health, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
| | - Dianne de Korte-de Boer
- Department of Anesthesiology and Pain Management, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (D.d.K.-d.B.); (V.S.-F.)
| | - Frank van Rosmalen
- Department of Intensive Care, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (F.v.R.); (B.C.T.v.B.); (I.C.C.v.d.H.)
| | - Bas C. T. van Bussel
- Department of Intensive Care, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (F.v.R.); (B.C.T.v.B.); (I.C.C.v.d.H.)
| | - Valérie Smit-Fun
- Department of Anesthesiology and Pain Management, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (D.d.K.-d.B.); (V.S.-F.)
| | - Patrick Duflot
- Service des Applications Informatiques, Centre Hospitalier Universitaire de Liège—CHU, 4000 Liège, Belgium;
| | - Julien Guiot
- Respiratory Medicine, Centre Hospitalier Universitaire de Liège—CHU, 4000 Liège, Belgium;
| | - Iwan C. C. van der Horst
- Department of Intensive Care, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands; (F.v.R.); (B.C.T.v.B.); (I.C.C.v.d.H.)
| | - Dieter Mesotten
- Limburg Clinical Research Center/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium; (F.W.); (J.V.); (P.D.); (D.M.); (P.V.)
- Limburg Clinical Research Center/Mobile Health Unit, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
- Department of Anesthesiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
- Department of Cardiology and Future Health, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
| | - Pieter Vandervoort
- Limburg Clinical Research Center/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium; (F.W.); (J.V.); (P.D.); (D.M.); (P.V.)
- Limburg Clinical Research Center/Mobile Health Unit, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
- Department of Anesthesiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
- Department of Cardiology and Future Health, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
| | - Jean-Marie Aerts
- Measure, Model & Manage Bioresponses (M3-BIORES), Department of Biosystems, KU Leuven, 3000 Leuven, Belgium;
| | - Bart Vanrumste
- E-MEDIA, STADIUS, Department of Electrical Engineering (ESAT), Campus Group T, KU Leuven, 3000 Leuven, Belgium;
- Correspondence:
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Li C, Zhang Z, Ren Y, Nie H, Lei Y, Qiu H, Xu Z, Pu X. Machine learning based early mortality prediction in the emergency department. Int J Med Inform 2021; 155:104570. [PMID: 34547624 DOI: 10.1016/j.ijmedinf.2021.104570] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/01/2021] [Accepted: 09/06/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND It is a great challenge for emergency physicians to early detect the patient's deterioration and prevent unexpected death through a large amount of clinical data, which requires sufficient experience and keen insight. OBJECTIVE To evaluate the performance of machine learning models in quantifying the severity of emergency department (ED) patients and identifying high-risk patients. METHODS Using routinely-available demographics, vital signs and laboratory tests extracted from electronic health records (EHRs), a framework based on machine learning and feature engineering was proposed for mortality prediction. Patients who had one complete record of vital signs and laboratory tests in ED were included. The following patients were excluded: pediatric patients aged < 18 years, pregnant woman, and patients died or were discharged or hospitalized within 12 h after admission. Based on 76 original features extracted, 9 machine learning models were adopted to validate our proposed framework. Their optimal hyper-parameters were fine-tuned using the grid search method. The prediction results were evaluated on performance metrics (i.e., accuracy, area under the curve (AUC), recall and precision) with repeated 5-fold cross-validation (CV). The time window from patient admission to the prediction was analyzed at 12 h, 24 h, 48 h, and entire stay. RESULTS We studied a total of 1114 ED patients with 71.54% (797/1114) survival and 28.46% (317/1114) death in the hospital. The results revealed a more complete time window leads to better prediction performance. Using the entire stay records, the LightGBM model with refined feature engineering demonstrated high discrimination and achieved 93.6% (±0.008) accuracy, 97.6% (±0.003) AUC, 97.1% (±0.008) recall, and 94.2% (±0.006) precision, even if no diagnostic information was utilized. CONCLUSIONS This study quantifies the criticality of ED patients and appears to have significant potential as a clinical decision support tool in assisting physicians in their clinical routine. While the model requires validation before use elsewhere, the same methodology could be used to create a strong model for the new hospital.
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Affiliation(s)
- Cong Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China; Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhuo Zhang
- Emergency Department, West China Hospital, Sichuan University, Chengdu, China
| | - Yazhou Ren
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
| | - Hu Nie
- Emergency Department, West China Hospital, Sichuan University, Chengdu, China.
| | - Yuqing Lei
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Hang Qiu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China; Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
| | - Zenglin Xu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China; Harbin Institute of Technology Shenzhen, Shenzhen, Guangdong, China
| | - Xiaorong Pu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China; Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
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Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project. J Clin Med 2021; 10:jcm10050992. [PMID: 33801207 PMCID: PMC7957866 DOI: 10.3390/jcm10050992] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/09/2021] [Accepted: 02/12/2021] [Indexed: 12/18/2022] Open
Abstract
Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.
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Investigating Health-Related Features and Their Impact on the Prediction of Diabetes Using Machine Learning. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11031173] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Diabetes Mellitus (DM) is one of the most common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and the cost of treatment. The World Health Organization (WHO) considers Saudi Arabia as one of the top 10 countries in diabetes prevalence across the world. Since most of its medical services are provided by the government, the cost of the treatment in terms of hospitals and clinical visits and lab tests represents a real burden due to the large scale of the disease. The ability to predict the diabetic status of a patient with only a handful of features can allow cost-effective, rapid, and widely-available screening of diabetes, thereby lessening the health and economic burden caused by diabetes alone. The goal of this paper is to investigate the prediction of diabetic patients and compare the role of HbA1c and FPG as input features. By using five different machine learning classifiers, and using feature elimination through feature permutation and hierarchical clustering, we established good performance for accuracy, precision, recall, and F1-score of the models on the dataset implying that our data or features are not bound to specific models. In addition, the consistent performance across all the evaluation metrics indicate that there was no trade-off or penalty among the evaluation metrics. Further analysis was performed on the data to identify the risk factors and their indirect impact on diabetes classification. Our analysis presented great agreement with the risk factors of diabetes and prediabetes stated by the American Diabetes Association (ADA) and other health institutions worldwide. We conclude that by performing analysis of the disease using selected features, important factors specific to the Saudi population can be identified, whose management can result in controlling the disease. We also provide some recommendations learned from this research.
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Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology. SENSORS 2020; 20:s20226593. [PMID: 33218084 PMCID: PMC7698871 DOI: 10.3390/s20226593] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/13/2020] [Accepted: 11/16/2020] [Indexed: 11/17/2022]
Abstract
In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients' vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.
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