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Laffafchi S, Ebrahimi A, Kafan S. Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data. Health Inf Sci Syst 2024; 12:17. [PMID: 38464464 PMCID: PMC10917730 DOI: 10.1007/s13755-024-00276-9] [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: 12/15/2022] [Accepted: 01/17/2024] [Indexed: 03/12/2024] Open
Abstract
Pulmonary Embolism (PE) is a life-threatening clinical disease with no specific clinical symptoms and Computed Tomography Angiography (CTA) is used for diagnosis. Clinical decision support scoring systems like Wells and rGeneva based on PE risk factors have been developed to estimate the pre-test probability but are underused, leading to continuous overuse of CTA imaging. This diagnostic study aimed to propose a novel approach for efficient management of PE diagnosis using a two-step interconnected machine learning framework directly by analyzing patients' Electronic Health Records data. First, we performed feature importance analysis according to the result of LightGBM superiority for PE prediction, then four state-of-the-art machine learning methods were applied for PE prediction based on the feature importance results, enabling swift and accurate pre-test diagnosis. Throughout the study patients' data from different departments were collected from Sina educational hospital, affiliated with the Tehran University of medical sciences in Iran. Generally, the Ridge classification method obtained the best performance with an F1 score of 0.96. Extensive experimental findings showed the effectiveness and simplicity of this diagnostic process of PE in comparison with the existing scoring systems. The main strength of this approach centered on PE disease management procedures, which would reduce avoidable invasive CTA imaging and be applied as a primary prognosis of PE, hence assisting the healthcare system, clinicians, and patients by reducing costs and promoting treatment quality and patient satisfaction.
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Affiliation(s)
- Soroor Laffafchi
- Department of Business Administration and Entrepreneurship, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Ahmad Ebrahimi
- Department of Industrial and Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Samira Kafan
- Department of Pulmonary Medicine, Sina Hospital, International Relations Office, Medical School, Tehran University of Medical Sciences, PourSina St., Tehran, 1417613151 Iran
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Bahrami S, Hajian-Tilaki K, Bayani M, Chehrazi M, Mohamadi-Pirouz Z, Amoozadeh A. Bayesian model averaging for predicting factors associated with length of COVID-19 hospitalization. BMC Med Res Methodol 2023; 23:163. [PMID: 37415112 PMCID: PMC10326965 DOI: 10.1186/s12874-023-01981-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 06/18/2023] [Indexed: 07/08/2023] Open
Abstract
INTRODUCTION The length of hospital stay (LOHS) caused by COVID-19 has imposed a financial burden, and cost on the healthcare service system and a high psychological burden on patients and health workers. The purpose of this study is to adopt the Bayesian model averaging (BMA) based on linear regression models and to determine the predictors of the LOHS of COVID-19. METHODS In this historical cohort study, from 5100 COVID-19 patients who had registered in the hospital database, 4996 patients were eligible to enter the study. The data included demographic, clinical, biomarkers, and LOHS. Factors affecting the LOHS were fitted in six models, including the stepwise method, AIC, BIC in classical linear regression models, two BMA using Occam's Window and Markov Chain Monte Carlo (MCMC) methods, and GBDT algorithm, a new method of machine learning. RESULTS The average length of hospitalization was 6.7 ± 5.7 days. In fitting classical linear models, both stepwise and AIC methods (R 2 = 0.168 and adjusted R 2 = 0.165) performed better than BIC (R 2 = 0.160 and adjusted = 0.158). In fitting the BMA, Occam's Window model has performed better than MCMC with R 2 = 0.174. The GBDT method with the value of R 2 = 0.64, has performed worse than the BMA in the testing dataset but not in the training dataset. Based on the six fitted models, hospitalized in ICU, respiratory distress, age, diabetes, CRP, PO2, WBC, AST, BUN, and NLR were associated significantly with predicting LOHS of COVID-19. CONCLUSION The BMA with Occam's Window method has a better fit and better performance in predicting affecting factors on the LOHS in the testing dataset than other models.
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Affiliation(s)
- Shabnam Bahrami
- Student Research Center, Research Institute, Babol University of Medical Sciences, Babol, Iran
| | - Karimollah Hajian-Tilaki
- Department of Biostatistics and Epidemiology, School of Public Health, Babol University of Medical Sciences, Babol, Iran.
- Social Determinants of Health Research Center, Research Institute, Babol University of Medical Sciences, Babol, Iran.
| | - Masomeh Bayani
- Department of Infectious Diseases, Ayatollah Rohani Hospital, Babol University of Medical Sciences, Babol, Iran
| | - Mohammad Chehrazi
- Department of Biostatistics and Epidemiology, School of Public Health, Babol University of Medical Sciences, Babol, Iran
- Neonatal Research Unit, Imperial College London, Exhibition Rd, South Kensington, London, SW7 2BX, UK
| | - Zahra Mohamadi-Pirouz
- Student Research Center, Research Institute, Babol University of Medical Sciences, Babol, Iran
| | - Abazar Amoozadeh
- Social Determinants of Health Research Center, Research Institute, Babol University of Medical Sciences, Babol, Iran
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Zhang Z, Zeng T, Wang Y, Su Y, Tian X, Ma G, Luan Z, Li F. Prediction Model of hospitalization time of COVID-19 patients based on Gradient Boosted Regression Trees. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:10444-10458. [PMID: 37322941 DOI: 10.3934/mbe.2023459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
When an outbreak of COVID-19 occurs, it will cause a shortage of medical resources and the surge of demand for hospital beds. Predicting the length of stay (LOS) of COVID-19 patients is helpful to the overall coordination of hospital management and improves the utilization rate of medical resources. The purpose of this paper is to predict LOS for patients with COVID-19, so as to provide hospital management with auxiliary decision-making of medical resource scheduling. We collected the data of 166 COVID-19 patients in a hospital in Xinjiang from July 19, 2020, to August 26, 2020, and carried out a retrospective study. The results showed that the median LOS was 17.0 days, and the average of LOS was 18.06 days. Demographic data and clinical indicators were included as predictive variables to construct a model for predicting the LOS using gradient boosted regression trees (GBRT). The MSE, MAE and MAPE of the model are 23.84, 4.12 and 0.76 respectively. The importance of all the variables involved in the prediction of the model was analyzed, and the clinical indexes creatine kinase-MB (CK-MB), C-reactive protein (CRP), creatine kinase (CK), white blood cell count (WBC) and the age of patients had a higher contribution to the LOS. We found our GBRT model can accurately predict the LOS of COVID-19 patients, which will provide good assistant decision-making for medical management.
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Affiliation(s)
- Zhihao Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
| | - Ting Zeng
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
- School of Public Health, Xinjiang Medical University, Urumqi 830017, China
| | - Yijia Wang
- College of Mathematics and System Science, Xinjiang University, Urumqi 830017, China
| | - Yinxia Su
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
| | - Xianghua Tian
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
| | - Guoxiang Ma
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
| | - Zemin Luan
- School of Public Health, Xinjiang Medical University, Urumqi 830017, China
| | - Fengjun Li
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
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Keller MS, Qureshi N, Albertson E, Pevnick J, Brandt N, Bui A, Sarkisian CA. Comparing risk prediction models aimed at predicting hospitalizations for adverse drug events in community dwelling older adults: a protocol paper. RESEARCH SQUARE 2023:rs.3.rs-2429369. [PMID: 36711695 PMCID: PMC9882666 DOI: 10.21203/rs.3.rs-2429369/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Background The objective of this paper is to describe the creation, validation, and comparison of two risk prediction modeling approaches for community-dwelling older adults to identify individuals at highest risk for adverse drug event-related hospitalizations. One approach will use traditional statistical methods, the second will use a machine learning approach. Methods We will construct medication, clinical, health care utilization, and other variables known to be associated with adverse drug event-related hospitalizations. To create the cohort, we will include older adults (≥ 65 years of age) empaneled to a primary care physician within the Cedars-Sinai Health System primary care clinics with polypharmacy (≥ 5 medications) or at least 1 medication commonly implicated in ADEs (certain oral hypoglycemics, anti-coagulants, anti-platelets, and insulins). We will use a Fine-Gray Cox proportional hazards model for one risk modeling approach and DataRobot, a data science and analytics platform, to run and compare several widely used supervised machine learning algorithms, including Random Forest, Support Vector Machine, Extreme Gradient Boosting (XGBoost), Decision Tree, Naïve Bayes, and K-Nearest Neighbors. We will use a variety of metrics to compare model performance and to assess the risk of algorithmic bias. Discussion In conclusion, we hope to develop a pragmatic model that can be implemented in the primary care setting to risk stratify older adults to further optimize medication management.
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Affiliation(s)
| | | | | | | | | | - Alex Bui
- David Geffen School of Medicine: University of California Los Angeles David Geffen School of Medicine
| | - Catherine A Sarkisian
- David Geffen School of Medicine: University of California Los Angeles David Geffen School of Medicine
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Amiri P, Montazeri M, Ghasemian F, Asadi F, Niksaz S, Sarafzadeh F, Khajouei R. Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms. Digit Health 2023; 9:20552076231170493. [PMID: 37312960 PMCID: PMC10259141 DOI: 10.1177/20552076231170493] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/31/2023] [Indexed: 06/15/2023] Open
Abstract
Background The severity of coronavirus (COVID-19) in patients with chronic comorbidities is much higher than in other patients, which can lead to their death. Machine learning (ML) algorithms as a potential solution for rapid and early clinical evaluation of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. Objective The objective of this study was to predict the mortality risk and length of stay (LoS) of patients with COVID-19 and history of chronic comorbidities using ML algorithms. Methods This retrospective study was conducted by reviewing the medical records of COVID-19 patients with a history of chronic comorbidities from March 2020 to January 2021 in Afzalipour Hospital in Kerman, Iran. The outcome of patients, hospitalization was recorded as discharge or death. The filtering technique used to score the features and well-known ML algorithms were applied to predict the risk of mortality and LoS of patients. Ensemble Learning methods is also used. To evaluate the performance of the models, different measures including F1, precision, recall, and accuracy were calculated. The TRIPOD guideline assessed transparent reporting. Results This study was performed on 1291 patients, including 900 alive and 391 dead patients. Shortness of breath (53.6%), fever (30.1%), and cough (25.3%) were the three most common symptoms in patients. Diabetes mellitus(DM) (31.3%), hypertension (HTN) (27.3%), and ischemic heart disease (IHD) (14.2%) were the three most common chronic comorbidities of patients. Twenty-six important factors were extracted from each patient's record. Gradient boosting model with 84.15% accuracy was the best model for predicting mortality risk and multilayer perceptron (MLP) with rectified linear unit function (MSE = 38.96) was the best model for predicting the LoS. The most common chronic comorbidities among these patients were DM (31.3%), HTN (27.3%), and IHD (14.2%). The most important factors in predicting the risk of mortality were hyperlipidemia, diabetes, asthma, and cancer, and in predicting LoS was shortness of breath. Conclusion The results of this study showed that the use of ML algorithms can be a good tool to predict the risk of mortality and LoS of patients with COVID-19 and chronic comorbidities based on physiological conditions, symptoms, and demographic information of patients. The Gradient boosting and MLP algorithms can quickly identify patients at risk of death or long-term hospitalization and notify physicians to do appropriate interventions.
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Affiliation(s)
- Parastoo Amiri
- Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran
| | - Mahdieh Montazeri
- Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Fahimeh Ghasemian
- Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Fatemeh Asadi
- Student Research Committee, School of Management and Medical Information, Kerman University of Medical Sciences, Kerman, Iran
| | - Saeed Niksaz
- Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Farhad Sarafzadeh
- Infectious and Internal Medicine Department, Afzalipour Hospital, Kerman University of Medical Sciences, Kerman, Iran
| | - Reza Khajouei
- Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
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Jaotombo F, Pauly V, Fond G, Orleans V, Auquier P, Ghattas B, Boyer L. Machine-learning prediction for hospital length of stay using a French medico-administrative database. JOURNAL OF MARKET ACCESS & HEALTH POLICY 2022; 11:2149318. [PMID: 36457821 PMCID: PMC9707380 DOI: 10.1080/20016689.2022.2149318] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 10/17/2022] [Accepted: 11/16/2022] [Indexed: 06/17/2023]
Abstract
INTRODUCTION Prolonged Hospital Length of Stay (PLOS) is an indicator of deteriorated efficiency in Quality of Care. One goal of public health management is to reduce PLOS by identifying its most relevant predictors. The objective of this study is to explore Machine Learning (ML) models that best predict PLOS. METHODS Our dataset was collected from the French Medico-Administrative database (PMSI) as a retrospective cohort study of all discharges in the year 2015 from a large university hospital in France (APHM). The study outcomes were LOS transformed into a binary variable (long vs. short LOS) according to the 90th percentile (14 days). Logistic regression (LR), classification and regression trees (CART), random forest (RF), gradient boosting (GB) and neural networks (NN) were applied to the collected data. The predictive performance of the models was evaluated using the area under the ROC curve (AUC). RESULTS Our analysis included 73,182 hospitalizations, of which 7,341 (10.0%) led to PLOS. The GB classifier was the most performant model with the highest AUC (0.810), superior to all the other models (all p-values <0.0001). The performance of the RF, GB and NN models (AUC ranged from 0.808 to 0.810) was superior to that of the LR model (AUC = 0.795); all p-values <0.0001. In contrast, LR was superior to CART (AUC = 0.786), p < 0.0001. The variable most predictive of the PLOS was the destination of the patient after hospitalization to other institutions. The typical clinical profile of these patients (17.5% of the sample) was the elderly patient, admitted in emergency, for a trauma, a neurological or a cardiovascular pathology, more often institutionalized, with more comorbidities notably mental health problems, dementia and hemiplegia. DISCUSSION The integration of ML, particularly the GB algorithm, may be useful for health-care professionals and bed managers to better identify patients at risk of PLOS. These findings underscore the need to strengthen hospitals through targeted allocation to meet the needs of an aging population.
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Affiliation(s)
- Franck Jaotombo
- Aix-Marseille University, EA 3279 - Public Health, Chronic Diseases and Quality of Life - Research Unit, La Timone Medical University, Marseille, France
- I2M, CNRS, UMR, Aix-Marseille University, Marseille, France
- Operations Data and Artificial Intelligence, EM Lyon Business School, Ecully, France
| | - Vanessa Pauly
- Aix-Marseille University, EA 3279 - Public Health, Chronic Diseases and Quality of Life - Research Unit, La Timone Medical University, Marseille, France
- Service d’Information Médicale, Public Health Department, La Conception Hospital, Assistance Publique - Hôpitaux de Marseille, Marseille, France
| | - Guillaume Fond
- Aix-Marseille University, EA 3279 - Public Health, Chronic Diseases and Quality of Life - Research Unit, La Timone Medical University, Marseille, France
| | - Veronica Orleans
- Service d’Information Médicale, Public Health Department, La Conception Hospital, Assistance Publique - Hôpitaux de Marseille, Marseille, France
| | - Pascal Auquier
- Aix-Marseille University, EA 3279 - Public Health, Chronic Diseases and Quality of Life - Research Unit, La Timone Medical University, Marseille, France
| | - Badih Ghattas
- I2M, CNRS, UMR, Aix-Marseille University, Marseille, France
| | - Laurent Boyer
- Aix-Marseille University, EA 3279 - Public Health, Chronic Diseases and Quality of Life - Research Unit, La Timone Medical University, Marseille, France
- Service d’Information Médicale, Public Health Department, La Conception Hospital, Assistance Publique - Hôpitaux de Marseille, Marseille, France
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Saadatmand S, Salimifard K, Mohammadi R, Kuiper A, Marzban M, Farhadi A. Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients. ANNALS OF OPERATIONS RESEARCH 2022; 328:1-29. [PMID: 36196268 PMCID: PMC9521862 DOI: 10.1007/s10479-022-04984-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/06/2022] [Indexed: 05/19/2023]
Abstract
The recent COVID-19 pandemic has affected health systems across the world. Especially, Intensive Care Units (ICUs) have played a pivotal role in the treatment of critically-ill patients. At the same time however, the increasing number of admissions due to the vast prevalence of the virus have caused several problems for ICU wards such as overburdening of staff and shortages of medical resources. These issues might have affected the quality of healthcare services provided directly impacting a patient's survival. The objective of this research is to leverage Machine Learning (ML) on hospital data in order to support hospital managers and practitioners with the treatment of COVID-19 patients. This is accomplished by providing more detailed inference about a patient's likelihood of ICU admission, mortality and in case of hospitalization the length of stay (LOS). In this pursuit, the outcome variables are in three separate models predicted by five different ML algorithms: eXtreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), Random Forest (RF), bagged-CART (b-CART), and LogitBoost (LB). With the exception of KNN, the studied models show good predictive capabilities when evaluating relevant accuracy scores, such as area under the curve. By implementing an ensemble stacking approach (either a Neural Net or a General Linear Model) on top of the aforementioned ML algorithms the performance is further boosted. Ultimately, for the prediction of admission to the ICU, the ensemble stacking via a Neural Net achieved the best result with an accuracy of over 95%. For mortality at the ICU, the vanilla XGB performed slightly better (1% difference with the meta-model). To predict large length of stays both ensemble stacking approaches yield comparable results. Besides it direct implications for managing COVID-19 patients, the approach presented serves as an example how data can be employed in future pandemics or crises.
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Affiliation(s)
- Sara Saadatmand
- Computational Intelligence and Intelligent Optimization Research Group, Persian Gulf University, Bushehr, 75169 Iran
| | - Khodakaram Salimifard
- Computational Intelligence and Intelligent Optimization Research Group, Persian Gulf University, Bushehr, 75169 Iran
| | - Reza Mohammadi
- Section Business Analytics, Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands
| | - Alex Kuiper
- Section Business Analytics, Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands
| | - Maryam Marzban
- Department of Public Health, School of Public Health, Bushehr University of Medical Science, Bushehr, Iran
| | - Akram Farhadi
- The Persian Gulf Tropical Medicine Research Center, The Persian Gulf Biomedical Science Research Institute, Bushehr University of Medical Science, Bushehr, Iran
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Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning—A Scoping Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19138195. [PMID: 35805855 PMCID: PMC9266736 DOI: 10.3390/ijerph19138195] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/28/2022] [Accepted: 06/29/2022] [Indexed: 12/17/2022]
Abstract
The SARS-CoV-2 pandemic has put unprecedented pressure on the hospital sector around the world. It has shown the importance of preparing and planning in the future for an outbreak that overwhelms every aspect of a hospital on a rapidly expanding scale. We conducted a scoping review to identify, map, and systemize existing knowledge about the relationships between COVID-19 and hospital infrastructure adaptation and capacity planning worldwide. We searched the Web of Science, Scopus, and PubMed and hand-searched gray papers published in English between December 2019 and December 2021. A total of 106 papers were included: 102 empirical studies and four technical reports. Empirical studies entailed five reviews, 40 studies focusing on hospital infrastructure adaptation and planning during the pandemics, and 57 studies on modeling the hospital capacity needed, measured mostly by the number of beds. The majority of studies were conducted in high-income countries and published within the first year of the pandemic. The strategies adopted by hospitals can be classified into short-term (repurposing medical and non-medical buildings, remote adjustments, and establishment of de novo structures) and long-term (architectural and engineering modifications, hospital networks, and digital approaches). More research is needed, focusing on specific strategies and the quality assessment of the evidence.
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Liuzzi P, Campagnini S, Fanciullacci C, Arienti C, Patrini M, Carrozza MC, Mannini A. Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution. Med Biol Eng Comput 2022; 60:459-470. [PMID: 34993693 PMCID: PMC8739354 DOI: 10.1007/s11517-021-02479-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 11/24/2021] [Indexed: 11/25/2022]
Abstract
COVID-19 cases are increasing around the globe with almost 5 million of deaths. We propose here a deep learning model capable of predicting the duration of the infection by means of information available at hospital admission. A total of 222 patients were enrolled in our observational study. Anagraphical and anamnestic data, COVID-19 signs and symptoms, COVID-19 therapy, hematochemical test results, and prior therapies administered to patients are used as predictors. A set of 55 features, all of which can be taken in the first hours of the patient’s hospitalization, was considered. Different solutions were compared achieving the best performance with a sequential convolutional neural network-based model merged in an ensemble with two different meta-learners linked in cascade. We obtained a median absolute error of 2.7 days (IQR = 3.0) in predicting the duration of the infection; the error was equally distributed in the infection duration range. This tool could preemptively give an outlook of the COVID-19 patients’ expected path and the associated hospitalization effort. The proposed solution could be viable in tackling the huge burden and the logistics complexity of hospitals or rehabilitation centers during the pandemic waves.
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Affiliation(s)
- Piergiuseppe Liuzzi
- Scuola Superiore Sant'Anna, The BioRobotics Institute, Viale Rinaldo Piaggio 34, 56025, Pontedera, PI, Italy.,IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, 50143, Firenze, FI, Italy
| | - Silvia Campagnini
- Scuola Superiore Sant'Anna, The BioRobotics Institute, Viale Rinaldo Piaggio 34, 56025, Pontedera, PI, Italy. .,IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, 50143, Firenze, FI, Italy.
| | - Chiara Fanciullacci
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, 50143, Firenze, FI, Italy
| | - Chiara Arienti
- IRCCS Fondazione Don Carlo Gnocchi, via Alfonso Capecelatro 66, 20148, Milano, FI, Italy
| | - Michele Patrini
- IRCCS Fondazione Don Carlo Gnocchi, via Alfonso Capecelatro 66, 20148, Milano, FI, Italy
| | - Maria Chiara Carrozza
- Scuola Superiore Sant'Anna, The BioRobotics Institute, Viale Rinaldo Piaggio 34, 56025, Pontedera, PI, Italy
| | - Andrea Mannini
- Scuola Superiore Sant'Anna, The BioRobotics Institute, Viale Rinaldo Piaggio 34, 56025, Pontedera, PI, Italy.,IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, 50143, Firenze, FI, Italy
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