I. Loperto, A. Borrelli, Michele Sparano, M. Triassi
The CoViD-19 pandemic is an international public health emergency. The management of the hospitals found themselves facing an ever-increasing demand for beds, especially for intensive care, thus having to make critical decisions in a short time. A shared action was to block elective surgery deemed deferrable and to transfer resources to the management of the pandemic. The activity of the surgical departments has therefore undergone substantial alterations. In this study, logistic regression and statistical analysis was used to characterize this difference for the Urology Department in "San Giovanni di Dio and Ruggi d'Aragona" University Hospital of Salerno (Italy) by analyzing a set of variables and compared the data obtained in the year 2019, pre-pandemic, with that recorded the following year, in the height of the pandemic. The results show an increased number of emergency hospitalizations as well as scheduled hospitalizations with pre-admission.
{"title":"Hospital activities and CoViD-19: the case study of a Urology Department","authors":"I. Loperto, A. Borrelli, Michele Sparano, M. Triassi","doi":"10.1145/3502060.3503663","DOIUrl":"https://doi.org/10.1145/3502060.3503663","url":null,"abstract":"The CoViD-19 pandemic is an international public health emergency. The management of the hospitals found themselves facing an ever-increasing demand for beds, especially for intensive care, thus having to make critical decisions in a short time. A shared action was to block elective surgery deemed deferrable and to transfer resources to the management of the pandemic. The activity of the surgical departments has therefore undergone substantial alterations. In this study, logistic regression and statistical analysis was used to characterize this difference for the Urology Department in \"San Giovanni di Dio and Ruggi d'Aragona\" University Hospital of Salerno (Italy) by analyzing a set of variables and compared the data obtained in the year 2019, pre-pandemic, with that recorded the following year, in the height of the pandemic. The results show an increased number of emergency hospitalizations as well as scheduled hospitalizations with pre-admission.","PeriodicalId":193100,"journal":{"name":"2021 International Symposium on Biomedical Engineering and Computational Biology","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116560374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Skin plays an indispensable role in protection, sensation, temperature regulation and so on. Autologous skin grafting is the main method to treat skin wounds. However, patients with large-scale skin defects often face the problem of insufficient autologous skin, and the emergence of tissue engineering effectively compensates for the lack of skin sources. In recent years, with the efforts of scientists, skin tissue engineering technology has made some progress. In this paper, the development of skin tissue engineering in scaffold materials, seed cells and growth factors are reviewed, hoping to provide reference for the progress of skin tissue engineering.
{"title":"The evolution of skin tissue engineering: A review on recent trends and advances","authors":"Haohong Wu","doi":"10.1145/3502060.3502061","DOIUrl":"https://doi.org/10.1145/3502060.3502061","url":null,"abstract":"Skin plays an indispensable role in protection, sensation, temperature regulation and so on. Autologous skin grafting is the main method to treat skin wounds. However, patients with large-scale skin defects often face the problem of insufficient autologous skin, and the emergence of tissue engineering effectively compensates for the lack of skin sources. In recent years, with the efforts of scientists, skin tissue engineering technology has made some progress. In this paper, the development of skin tissue engineering in scaffold materials, seed cells and growth factors are reviewed, hoping to provide reference for the progress of skin tissue engineering.","PeriodicalId":193100,"journal":{"name":"2021 International Symposium on Biomedical Engineering and Computational Biology","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134370289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cristiana Giglio, C. Lauri, Antonio Della Vecchia, A. Borrelli, Giuseppe Russo, M. Triassi, G. Improta
Emergency Departments (EDs) overcrowding is an acknowledged critical issue affecting international public health in recent years, that arises from both the growth of the health care supply/demand imbalance and the lack of beds available in hospitals wards and EDs. Emergency department length of stay (ED-LOS) is identified as a valuable key measure of EDs bottlenecks, and specifically of the rapidity of access to care for patients and of the overcrowding. ED-LOS measures how long patients stay in the ED from their first registration and triage to their admittance to a hospital ward or their discharge. Prolonged ED-LOS has been associated with adverse outcomes, such as reduced level of quality of care and patient satisfaction, increased risk of mortality and financial loss. Understanding aspects affecting LOS is essential for the management of an ED and for implementing improvement interventions. The aim of this study is to determine the several factors affecting LOS in EDs and to build a model capable of predicting ED-LOS through different machine learning (ML) models. ML algorithms were performed considering data extracted from the ED database of the “San Giovanni di Dio e Ruggi d'Aragona” University Hospital (Salerno, Italy). The proposed prediction model shows promising outcomes and therefore it can be used for the prediction and governance of the ED-LOS, thus anticipating the occurrence of overcrowding and improving ED care and efficiency.
急诊科人满为患是近年来公认的影响国际公共卫生的一个关键问题，其原因是卫生保健供需失衡加剧以及医院病房和急诊科床位不足。急诊科住院时间(ED-LOS)被认为是衡量急诊科瓶颈的一个有价值的关键指标，特别是衡量病人获得护理的速度和过度拥挤的情况。ED- los衡量患者从首次登记和分诊到进入医院病房或出院在急诊室停留的时间。延长ED-LOS与不良后果相关，如护理质量和患者满意度降低、死亡风险增加和经济损失。了解影响LOS的各个方面对于ED的管理和实施改进干预措施至关重要。本研究的目的是确定影响ed中LOS的几个因素，并通过不同的机器学习(ML)模型建立一个能够预测ED-LOS的模型。ML算法考虑从“San Giovanni di Dio e Ruggi d'Aragona”大学医院(Salerno, Italy)的ED数据库中提取的数据。所提出的预测模型结果良好，可用于ED- los的预测和治理，从而预测过度拥挤的发生，提高ED的护理和效率。
{"title":"Investigation of factors increasing waiting times in the Emergency Departments of “San Giovanni di Dio e Ruggi d'Aragona” Hospital through machine learning","authors":"Cristiana Giglio, C. Lauri, Antonio Della Vecchia, A. Borrelli, Giuseppe Russo, M. Triassi, G. Improta","doi":"10.1145/3502060.3503628","DOIUrl":"https://doi.org/10.1145/3502060.3503628","url":null,"abstract":"Emergency Departments (EDs) overcrowding is an acknowledged critical issue affecting international public health in recent years, that arises from both the growth of the health care supply/demand imbalance and the lack of beds available in hospitals wards and EDs. Emergency department length of stay (ED-LOS) is identified as a valuable key measure of EDs bottlenecks, and specifically of the rapidity of access to care for patients and of the overcrowding. ED-LOS measures how long patients stay in the ED from their first registration and triage to their admittance to a hospital ward or their discharge. Prolonged ED-LOS has been associated with adverse outcomes, such as reduced level of quality of care and patient satisfaction, increased risk of mortality and financial loss. Understanding aspects affecting LOS is essential for the management of an ED and for implementing improvement interventions. The aim of this study is to determine the several factors affecting LOS in EDs and to build a model capable of predicting ED-LOS through different machine learning (ML) models. ML algorithms were performed considering data extracted from the ED database of the “San Giovanni di Dio e Ruggi d'Aragona” University Hospital (Salerno, Italy). The proposed prediction model shows promising outcomes and therefore it can be used for the prediction and governance of the ED-LOS, thus anticipating the occurrence of overcrowding and improving ED care and efficiency.","PeriodicalId":193100,"journal":{"name":"2021 International Symposium on Biomedical Engineering and Computational Biology","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121485304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. M. Ponsiglione, Massimo Majolo, G. Longo, Giuseppe Russo, M. Triassi, E. Raiola, G. Improta
The emergency department (ED) is the access point for urgent cases within the health facility. In recent years, however, several factors have led to a massive use of ED as a privileged access method to assistance even for patients who do not need timely treatment. This flow of so-called "non-urgent" cases generates pressure on the ED resources, leading to a considerable increase in waiting times which, in turn, generates an increase in patients who leave the ED before being visited from medical doctors. In this work, we investigate some of the factors that may lead to the decision to leave the ED before the first visit. Data were collected at the hospital A.O.R.N. “A. Cardarelli” of Naples (Italy) and then analyzed through traditional statistical tools and more advanced machine learning algorithms.
{"title":"Analysis of voluntary departures from the Emergency Department of the hospital AORN “A. Cardarelli”","authors":"A. M. Ponsiglione, Massimo Majolo, G. Longo, Giuseppe Russo, M. Triassi, E. Raiola, G. Improta","doi":"10.1145/3502060.3503630","DOIUrl":"https://doi.org/10.1145/3502060.3503630","url":null,"abstract":"The emergency department (ED) is the access point for urgent cases within the health facility. In recent years, however, several factors have led to a massive use of ED as a privileged access method to assistance even for patients who do not need timely treatment. This flow of so-called \"non-urgent\" cases generates pressure on the ED resources, leading to a considerable increase in waiting times which, in turn, generates an increase in patients who leave the ED before being visited from medical doctors. In this work, we investigate some of the factors that may lead to the decision to leave the ED before the first visit. Data were collected at the hospital A.O.R.N. “A. Cardarelli” of Naples (Italy) and then analyzed through traditional statistical tools and more advanced machine learning algorithms.","PeriodicalId":193100,"journal":{"name":"2021 International Symposium on Biomedical Engineering and Computational Biology","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122245587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Delong Yang, Dongnan Su, Zhaohui Luo, Peng Shang, Zhigang Hu
China has become a high-risk region of stroke. Most patients with stroke suffer regular bouts of post-stroke limb dyskinesia. Nowadays, there isn’t an effective treatment for these patients. Brain computer interface (BCI) establishes a new pathway to connect human brains and device, which provide an innovation method to repair the human brain nervous systems through rehabilitation training. However, one of the mainly brain activity recordings, Electroencephalogram (EEG), cannot be represented accurately by other algorithms. With the development of deep learning techniques, the topic of EEG signals’ representation by image generation technique has become an important research area. This paper we introduced the basic concepts of BCI systems first, then we give a survey of image generation techniques from EEG signals. At last, we proposed an experimental scheme of dataset establishment which is used for post-stroke patients with upper limb dyskinesia
{"title":"The Survey of Image Generation from EEG Signals based on Deep Learning","authors":"Delong Yang, Dongnan Su, Zhaohui Luo, Peng Shang, Zhigang Hu","doi":"10.1145/3502060.3502151","DOIUrl":"https://doi.org/10.1145/3502060.3502151","url":null,"abstract":"China has become a high-risk region of stroke. Most patients with stroke suffer regular bouts of post-stroke limb dyskinesia. Nowadays, there isn’t an effective treatment for these patients. Brain computer interface (BCI) establishes a new pathway to connect human brains and device, which provide an innovation method to repair the human brain nervous systems through rehabilitation training. However, one of the mainly brain activity recordings, Electroencephalogram (EEG), cannot be represented accurately by other algorithms. With the development of deep learning techniques, the topic of EEG signals’ representation by image generation technique has become an important research area. This paper we introduced the basic concepts of BCI systems first, then we give a survey of image generation techniques from EEG signals. At last, we proposed an experimental scheme of dataset establishment which is used for post-stroke patients with upper limb dyskinesia","PeriodicalId":193100,"journal":{"name":"2021 International Symposium on Biomedical Engineering and Computational Biology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128108081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Teresa Angela Trunfio, Lucia De Coppi, R. Alfano, A. Borrelli, Giuseppe Ferrucci, P. Gargiulo
CoViD-19 caused a significant alteration of the normal activity of hospital facilities. In particular, the surgical departments have been converted and reprogrammed to cope with the emergency, giving priority to urgent procedures that cannot be deferred. To this must be added the actions implemented by governments, such as the lockdown, which forced people to stay in their homes. In this study it was analyzed how the pandemic and the actions taken to counter the spread of the infection have influenced the activity of the Department of Emergency Surgery of "San Giovanni di Dio and Ruggi d'Aragona" University Hospital in Salerno (Italy). Logistic regression and statistical analysis were used to compare the pre-pandemic data (year 2019) with that recorded at the height of the pandemic (year 2020). The results show that Diagnostic Related Group (DRG) weight and urgent hospitalization increased significantly in 2020.
{"title":"The effect of CoViD-19 pandemic on the hospitalization of a department of Emergency Surgery","authors":"Teresa Angela Trunfio, Lucia De Coppi, R. Alfano, A. Borrelli, Giuseppe Ferrucci, P. Gargiulo","doi":"10.1145/3502060.3503657","DOIUrl":"https://doi.org/10.1145/3502060.3503657","url":null,"abstract":"CoViD-19 caused a significant alteration of the normal activity of hospital facilities. In particular, the surgical departments have been converted and reprogrammed to cope with the emergency, giving priority to urgent procedures that cannot be deferred. To this must be added the actions implemented by governments, such as the lockdown, which forced people to stay in their homes. In this study it was analyzed how the pandemic and the actions taken to counter the spread of the infection have influenced the activity of the Department of Emergency Surgery of \"San Giovanni di Dio and Ruggi d'Aragona\" University Hospital in Salerno (Italy). Logistic regression and statistical analysis were used to compare the pre-pandemic data (year 2019) with that recorded at the height of the pandemic (year 2020). The results show that Diagnostic Related Group (DRG) weight and urgent hospitalization increased significantly in 2020.","PeriodicalId":193100,"journal":{"name":"2021 International Symposium on Biomedical Engineering and Computational Biology","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122232602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Alfano, I. Loperto, Teresa Angela Trunfio, Cristiana Giglio, Giovanni Rossi, A. Borrelli, A. Scala, P. Gargiulo
Due to the Sars-Cov-2 pandemic, the entire health sector in all countries around the world had to reorganize. It was not only a question of increasing the number of intensive care places, but all other medical specialties also had to rewrite their protocols. In this context, it is interesting to investigate whether these new measures have changed the volume of activity and the way hospital departments work. The aim of this study is precisely to verify what has happened to the Transplantation and Related Surgery Centre of the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno. For this reason, data were collected on patients who were admitted in 2019 (pre-pendemic) and 2020. The statistical analysis carried out showed that nothing had changed in this department in 2020.
{"title":"Using Statistical Analysis and Logistic Regression to study the effect of CoViD-19 on hospital activities of the C.O.U. General Surgery and Kidney Transplants","authors":"R. Alfano, I. Loperto, Teresa Angela Trunfio, Cristiana Giglio, Giovanni Rossi, A. Borrelli, A. Scala, P. Gargiulo","doi":"10.1145/3502060.3503655","DOIUrl":"https://doi.org/10.1145/3502060.3503655","url":null,"abstract":"Due to the Sars-Cov-2 pandemic, the entire health sector in all countries around the world had to reorganize. It was not only a question of increasing the number of intensive care places, but all other medical specialties also had to rewrite their protocols. In this context, it is interesting to investigate whether these new measures have changed the volume of activity and the way hospital departments work. The aim of this study is precisely to verify what has happened to the Transplantation and Related Surgery Centre of the \"San Giovanni di Dio e Ruggi d'Aragona\" University Hospital of Salerno. For this reason, data were collected on patients who were admitted in 2019 (pre-pendemic) and 2020. The statistical analysis carried out showed that nothing had changed in this department in 2020.","PeriodicalId":193100,"journal":{"name":"2021 International Symposium on Biomedical Engineering and Computational Biology","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127146767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Lauri, Teresa Angela Trunfio, Ylenia Colella, A. Lombardi, A. Borrelli, P. Gargiulo
Endarterectomy is a commonly performed surgical procedure for reducing long-term stroke risks. Due to the prolonged Length of Stay (LOS) experienced by patients undergoing endarterectomy, predicting this parameter has become increasingly important for both costs savings and the improvement of the management of beds. This study aims to develop a prediction model of LOS value starting from the clinical data related to patients undergoing endarterectomy, exploiting the potential of several Machine Learning algorithms. Data extracted from the information system of the “San Giovanni di Dio and Ruggi d'Aragona” University Hospital (Salerno, Italy) were considered to perform the analysis. The proposed prediction model shows promising outcomes in estimating the LOS and therefore it can be a significant tool for enhancing the planning of endarterectomy procedures.
{"title":"Investigating the impact of age, gender, and comorbid conditions on the prolonged length of stay after endarterectomy","authors":"C. Lauri, Teresa Angela Trunfio, Ylenia Colella, A. Lombardi, A. Borrelli, P. Gargiulo","doi":"10.1145/3502060.3503636","DOIUrl":"https://doi.org/10.1145/3502060.3503636","url":null,"abstract":"Endarterectomy is a commonly performed surgical procedure for reducing long-term stroke risks. Due to the prolonged Length of Stay (LOS) experienced by patients undergoing endarterectomy, predicting this parameter has become increasingly important for both costs savings and the improvement of the management of beds. This study aims to develop a prediction model of LOS value starting from the clinical data related to patients undergoing endarterectomy, exploiting the potential of several Machine Learning algorithms. Data extracted from the information system of the “San Giovanni di Dio and Ruggi d'Aragona” University Hospital (Salerno, Italy) were considered to perform the analysis. The proposed prediction model shows promising outcomes in estimating the LOS and therefore it can be a significant tool for enhancing the planning of endarterectomy procedures.","PeriodicalId":193100,"journal":{"name":"2021 International Symposium on Biomedical Engineering and Computational Biology","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126795979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Scala, Lucia De Coppi, I. Loperto, A. Borrelli, A. Lombardi, M. Triassi
CoViD-19 has placed the health systems of many countries in further crisis. The elective surgeries were canceled and the staff of several departments, including general medicine, underwent a reallocation to deal with the health emergency. In a context of economic fragility, in recent years the use of indicators for measuring health quality and performance has acquired more and more importance. Access limited only to emergency-urgency cases within hospitals has however produced a benefit in improving the appropriateness of admissions. In this study the value of parameter sets obtained in year 2019 (pre-pandemic) and year 2020 (during the pandemic), including Length of Stay and Diagnostic Related Group (DRG) Weight, of the Department of General Medicine of the University Hospital of Salerno 'San Giovanni di Dio e Ruggi D'Aragona' in Salerno (Italy) were compared using statistical analysis and logistic regression. The statistical analysis shows an increase in the DRG Weight in 2020, so an increase of the complexity of the cases treated and a greater appropriateness of hospitalizations.
{"title":"Investigating the impact of CoViD-19 on the activities of a Department of General Medicine","authors":"A. Scala, Lucia De Coppi, I. Loperto, A. Borrelli, A. Lombardi, M. Triassi","doi":"10.1145/3502060.3503662","DOIUrl":"https://doi.org/10.1145/3502060.3503662","url":null,"abstract":"CoViD-19 has placed the health systems of many countries in further crisis. The elective surgeries were canceled and the staff of several departments, including general medicine, underwent a reallocation to deal with the health emergency. In a context of economic fragility, in recent years the use of indicators for measuring health quality and performance has acquired more and more importance. Access limited only to emergency-urgency cases within hospitals has however produced a benefit in improving the appropriateness of admissions. In this study the value of parameter sets obtained in year 2019 (pre-pandemic) and year 2020 (during the pandemic), including Length of Stay and Diagnostic Related Group (DRG) Weight, of the Department of General Medicine of the University Hospital of Salerno 'San Giovanni di Dio e Ruggi D'Aragona' in Salerno (Italy) were compared using statistical analysis and logistic regression. The statistical analysis shows an increase in the DRG Weight in 2020, so an increase of the complexity of the cases treated and a greater appropriateness of hospitalizations.","PeriodicalId":193100,"journal":{"name":"2021 International Symposium on Biomedical Engineering and Computational Biology","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124041100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Antonella Fiorillo, Ilaria Picone, I. Latessa, A. Cuocolo
Healthcare Associated Infections are among the world's leading public health problems and the most serious complications for hospitalized patients that can impact length of stay (LOS). In this work, medical record data of 24365 patients admitted to general surgery and clinical medicine wards were used collectively with the aim of creating models capable of predicting overall LOS, measured in days, considering clinical information. Multiple linear regression analysis was performed with IBM SPSS, the coefficient of determination (R2) was equal to 0,288. A regression analysis with ML algorithms was performed with the Knime Analysis Platform. The R2 were quite low for both multiple linear regression and ML regression analyses. The use of these techniques showed that there is a relationship between clinical variables and overall LOS. The results constitute a valid support tool for decision makers to provide the turnover index for the benefit of health policy in the management of departments.
{"title":"Modelling the length of hospital stay in medicine and surgical departments","authors":"Antonella Fiorillo, Ilaria Picone, I. Latessa, A. Cuocolo","doi":"10.1145/3502060.3503639","DOIUrl":"https://doi.org/10.1145/3502060.3503639","url":null,"abstract":"Healthcare Associated Infections are among the world's leading public health problems and the most serious complications for hospitalized patients that can impact length of stay (LOS). In this work, medical record data of 24365 patients admitted to general surgery and clinical medicine wards were used collectively with the aim of creating models capable of predicting overall LOS, measured in days, considering clinical information. Multiple linear regression analysis was performed with IBM SPSS, the coefficient of determination (R2) was equal to 0,288. A regression analysis with ML algorithms was performed with the Knime Analysis Platform. The R2 were quite low for both multiple linear regression and ML regression analyses. The use of these techniques showed that there is a relationship between clinical variables and overall LOS. The results constitute a valid support tool for decision makers to provide the turnover index for the benefit of health policy in the management of departments.","PeriodicalId":193100,"journal":{"name":"2021 International Symposium on Biomedical Engineering and Computational Biology","volume":"2020 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121019491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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