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Article: Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy.

Shaban, Warda M / Rabie, Asmaa H / Saleh, Ahmed I / Abo-Elsoud, M A

2021 Volume 119, Page(s) 108110

Abstract: COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Early detection of COVID-19 patients is an important issue for treating and controlling the disease from spreading. In this paper, a new ... ...

Abstract	COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Early detection of COVID-19 patients is an important issue for treating and controlling the disease from spreading. In this paper, a new strategy for detecting COVID-19 infected patients will be introduced, which is called Distance Biased Naïve Bayes (DBNB). The novelty of DBNB as a proposed classification strategy is concentrated in two contributions. The first is a new feature selection technique called Advanced Particle Swarm Optimization (APSO) which elects the most informative and significant features for diagnosing COVID-19 patients. APSO is a hybrid method based on both filter and wrapper methods to provide accurate and significant features for the next classification phase. The considered features are extracted from Laboratory findings for different cases of people, some of whom are COVID-19 infected while some are not. APSO consists of two sequential feature selection stages, namely; Initial Selection Stage (IS
Language	English
Publishing date	2021-06-16
Publishing country	England
Document type	Journal Article
ZDB-ID	1466343-0
ISSN	0031-3203
ISSN	0031-3203
DOI	10.1016/j.patcog.2021.108110
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier.

Shaban, Warda M / Rabie, Asmaa H / Saleh, Ahmed I / Abo-Elsoud, M A

Knowledge-based systems

2020 Volume 205, Page(s) 106270

Abstract: COVID-19 infection is growing in a rapid rate. Due to unavailability of specific drugs, early detection of (COVID-19) patients is essential for disease cure and control. There is a vital need to detect the disease at early stage and instantly quarantine ... ...

Abstract	COVID-19 infection is growing in a rapid rate. Due to unavailability of specific drugs, early detection of (COVID-19) patients is essential for disease cure and control. There is a vital need to detect the disease at early stage and instantly quarantine the infected people. Many research have been going on, however, none of them introduces satisfactory results yet. In spite of its simplicity, K-Nearest Neighbor (KNN) classifier has proven high flexibility in complex classification problems. However, it can be easily trapped. In this paper, a new COVID-19 diagnose strategy is introduced, which is called COVID-19 Patients Detection Strategy (CPDS). The novelty of CPDS is concentrated in two contributions. The first is a new hybrid feature selection Methodology (HFSM), which elects the most informative features from those extracted from chest Computed Tomography (CT) images for COVID-19 patients and non COVID-19 peoples. HFSM is a hybrid methodology as it combines evidence from both wrapper and filter feature selection methods. It consists of two stages, namely; Fast Selection Stage (FS
Keywords	covid19
Language	English
Publishing date	2020-07-18
Publishing country	Netherlands
Document type	Journal Article
ISSN	0950-7051
ISSN	0950-7051
DOI	10.1016/j.knosys.2020.106270
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network.

Shaban, Warda M / Rabie, Asmaa H / Saleh, Ahmed I / Abo-Elsoud, M A

Applied soft computing

2020 Volume 99, Page(s) 106906

Abstract: COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Recently, the detection of coronavirus (COVID-19) is a critical task for the medical practitioner. Unfortunately, COVID-19 spreads so quickly ... ...

Abstract	COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Recently, the detection of coronavirus (COVID-19) is a critical task for the medical practitioner. Unfortunately, COVID-19 spreads so quickly between people and approaches millions of people worldwide in few months. It is very much essential to quickly and accurately identify the infected people so that prevention of spread can be taken. Although several medical tests have been used to detect certain injuries, the hopefully detection efficiency has not been accomplished yet. In this paper, a new Hybrid Diagnose Strategy (HDS) has been introduced. HDS relies on a novel technique for ranking selected features by projecting them into a proposed Patient Space (PS). A Feature Connectivity Graph (FCG) is constructed which indicates both the weight of each feature as well as the binding degree to other features. The rank of a feature is determined based on two factors; the first is the feature weight, while the second is its binding degree to its neighbors in PS. Then, the ranked features are used to derive the classification model that can classify new persons to decide whether they are infected or not. The classification model is a hybrid model that consists of two classifiers; fuzzy inference engine and Deep Neural Network (DNN). The proposed HDS has been compared against recent techniques. Experimental results have shown that the proposed HDS outperforms the other competitors in terms of the average value of accuracy, precision, recall, and F-measure in which it provides about of 97.658%, 96.756%, 96.55%, and 96.615% respectively. Additionally, HDS provides the lowest error value of 2.342%. Further, the results were validated statistically using Wilcoxon Signed Rank Test and Friedman Test.
Keywords	covid19
Language	English
Publishing date	2020-11-12
Publishing country	United States
Document type	Journal Article
ISSN	1568-4946
ISSN	1568-4946
DOI	10.1016/j.asoc.2020.106906
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network

Shaban, Warda M. / Rabie, Asmaa H. / Saleh, Ahmed I. / Abo-Elsoud, M.A.

Applied Soft Computing

2020 , Page(s) 106906

Keywords	Software ; covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ISSN	1568-4946
DOI	10.1016/j.asoc.2020.106906
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

Shaban, Warda M. / Rabie, Asmaa H. / Saleh, Ahmed I. / Abo-Elsoud, M.A.

Knowledge-Based Systems

2020 Volume 205, Page(s) 106270

Keywords	Software ; Information Systems and Management ; Management Information Systems ; Artificial Intelligence ; covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ISSN	0950-7051
DOI	10.1016/j.knosys.2020.106270
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network

Shaban, Warda M / Rabie, Asmaa H / Saleh, Ahmed I / Abo-Elsoud, M A

Appl Soft Comput

Abstract	COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Recently, the detection of coronavirus (COVID-19) is a critical task for the medical practitioner. Unfortunately, COVID-19 spreads so quickly between people and approaches millions of people worldwide in few months. It is very much essential to quickly and accurately identify the infected people so that prevention of spread can be taken. Although several medical tests have been used to detect certain injuries, the hopefully detection efficiency has not been accomplished yet. In this paper, a new Hybrid Diagnose Strategy (HDS) has been introduced. HDS relies on a novel technique for ranking selected features by projecting them into a proposed Patient Space (PS). A Feature Connectivity Graph (FCG) is constructed which indicates both the weight of each feature as well as the binding degree to other features. The rank of a feature is determined based on two factors; the first is the feature weight, while the second is its binding degree to its neighbors in PS. Then, the ranked features are used to derive the classification model that can classify new persons to decide whether they are infected or not. The classification model is a hybrid model that consists of two classifiers; fuzzy inference engine and Deep Neural Network (DNN). The proposed HDS has been compared against recent techniques. Experimental results have shown that the proposed HDS outperforms the other competitors in terms of the average value of accuracy, precision, recall, and F-measure in which it provides about of 97.658%, 96.756%, 96.55%, and 96.615% respectively. Additionally, HDS provides the lowest error value of 2.342%. Further, the results were validated statistically using Wilcoxon Signed Rank Test and Friedman Test.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #919734
Database	COVID19

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Article: A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

Shaban, Warda M. / Rabie, Asmaa H. / Saleh, Ahmed I. / Abo-Elsoud, M. A.

Knowl Based Syst

Abstract	COVID-19 infection is growing in a rapid rate. Due to unavailability of specific drugs, early detection of (COVID-19) patients is essential for disease cure and control. There is a vital need to detect the disease at early stage and instantly quarantine the infected people. Many research have been going on, however, none of them introduces satisfactory results yet. In spite of its simplicity, K-Nearest Neighbor (KNN) classifier has proven high flexibility in complex classification problems. However, it can be easily trapped. In this paper, a new COVID-19 diagnose strategy is introduced, which is called COVID-19 Patients Detection Strategy (CPDS). The novelty of CPDS is concentrated in two contributions. The first is a new hybrid feature selection Methodology (HFSM), which elects the most informative features from those extracted from chest Computed Tomography (CT) images for COVID-19 patients and non COVID-19 peoples. HFSM is a hybrid methodology as it combines evidence from both wrapper and filter feature selection methods. It consists of two stages, namely; Fast Selection Stage (FS 2) and Accurate Selection Stage (AS 2). FS 2relies on filter, while AS 2uses Genetic Algorithm (GA) as a wrapper method. As a hybrid methodology, HFSM elects the significant features for the next detection phase. The second contribution is an enhanced K-Nearest Neighbor (EKNN) classifier, which avoids the trapping problem of the traditional KNN by adding solid heuristics in choosing the neighbors of the tested item. EKNN depends on measuring the degree of both closeness and strength of each neighbor of the tested item, then elects only the qualified neighbors for classification. Accordingly, EKNN can accurately detect infected patients with the minimum time penalty based on those significant features selected by HFSM technique. Extensive experiments have been done considering the proposed detection strategy as well as recent competitive techniques on the chest CT images. Experimental results have shown that the proposed detection strategy outperforms recent techniques as it introduces the maximum accuracy rate.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #653292
Database	COVID19

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Article: A Huge Subcapsular Splenic Cyst Like Hematoma in Sickle Cell Anemia.

Odeh, Ahmad M / Boumarah, Kawthar A / Alsumaien, Wejdan A / Al-Abbad, Mohmmed T / Al-Ali, Aminah H / Alammar, Zainab A / Alsuqair, Hesham / Albeladi, Abdulqader M / Alsuwaigh, Abdulmohsen / Omrani, Ammar / Almuhanna, Mohammed M / Busbaih, Zaki / Al-Shaban, Hussain R / Aldhameen, Abrar A

Cureus

2022 Volume 14, Issue 2, Page(s) e22582

Abstract: ... was admitted to the intensive care unit (ICU) and stabilized. He was transferred to the regular ward ...

Abstract	Nontraumatic splenic rupture and hematoma are rare in sickle cell disease. We present a case of a 22-year-old Saudi male with sickle cell disease. He presented to our hospital with a history of nontraumatic abdominal pain, hemodynamic instability, and abdominal tenderness, with a large mass extending to the umbilicus. A computed tomography (CT) examination showed splenomegaly and a spleen infarction. The patient was admitted to the intensive care unit (ICU) and stabilized. He was transferred to the regular ward and discharged against medical advice (DAMA). Later on, he presented again with persistent abdominal pain. He underwent splenectomy with cholecystectomy. The patient did well postoperatively and was discharged in good condition. While conservative management is common, operative management should be considered in patient with persistent pain. Splenic rupture has a high mortality rate.
Language	English
Publishing date	2022-02-24
Publishing country	United States
Document type	Case Reports
ZDB-ID	2747273-5
ISSN	2168-8184
ISSN	2168-8184
DOI	10.7759/cureus.22582
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Readiness of health facilities to manage individuals infected with COVID-19, Uganda, June 2021.

BMC health services research

2023 Volume 23, Issue 1, Page(s) 441

Abstract: ... additional COVID-19 wards in hospitals and deliver medicines and PPE to referral hospitals. Adequate ...

Abstract	Background: The COVID-19 pandemic overwhelmed the capacity of health facilities globally, emphasizing the need for readiness to respond to rapid increases in cases. The first wave of COVID-19 in Uganda peaked in late 2020 and demonstrated challenges with facility readiness to manage cases. The second wave began in May 2021. In June 2021, we assessed the readiness of health facilities in Uganda to manage the second wave of COVID-19. Methods: Referral hospitals managed severe COVID-19 patients, while lower-level health facilities screened, isolated, and managed mild cases. We assessed 17 of 20 referral hospitals in Uganda and 71 of 3,107 lower-level health facilities, selected using multistage sampling. We interviewed health facility heads in person about case management, coordination and communication and reporting, and preparation for the surge of COVID-19 during first and the start of the second waves of COVID-19, inspected COVID-19 treatment units (CTUs) and other service delivery points. We used an observational checklist to evaluate capacity in infection prevention, medicines, personal protective equipment (PPE), and CTU surge capacity. We used the "ReadyScore" criteria to classify readiness levels as > 80% ('ready'), 40-80% ('work to do'), and < 40% ('not ready') and tailored the assessments to the health facility level. Scores for the lower-level health facilities were weighted to approximate representativeness for their health facility type in Uganda. Results: The median (interquartile range (IQR)) readiness scores were: 39% (IQR: 30, 51%) for all health facilities, 63% (IQR: 56, 75%) for referral hospitals, and 32% (IQR: 24, 37%) for lower-level facilities. Of 17 referral facilities, two (12%) were 'ready' and 15 (88%) were in the "work to do" category. Fourteen (82%) had an inadequate supply of medicines, 12 (71%) lacked adequate supply of oxygen, and 11 (65%) lacked space to expand their CTU. Fifty-five (77%) lower-level health facilities were "not ready," and 16 (23%) were in the "work to do" category. Seventy (99%) lower-level health facilities lacked medicines, 65 (92%) lacked PPE, and 53 (73%) lacked an emergency plan for COVID-19. Conclusion: Few health facilities were ready to manage the second wave of COVID-19 in Uganda during June 2021. Significant gaps existed for essential medicines, PPE, oxygen, and space to expand CTUs. The Uganda Ministry of Health utilized our findings to set up additional COVID-19 wards in hospitals and deliver medicines and PPE to referral hospitals. Adequate readiness for future waves of COVID-19 requires additional support and action in Uganda.
MeSH term(s)	Humans ; Uganda/epidemiology ; COVID-19 Drug Treatment ; Pandemics ; COVID-19/epidemiology ; COVID-19/therapy ; Health Facilities
Language	English
Publishing date	2023-05-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2050434-2
ISSN	1472-6963 ; 1472-6963
ISSN (online)	1472-6963
ISSN	1472-6963
DOI	10.1186/s12913-023-09380-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Comparison of effective factors on sleeping the nurses and hospitalized patients’ viewpoints

Zakerimoghadam M / Shaban M / Kazemnejad A / Ghadyani L

Hayat Journal of Faculty of Nursing & Midwifery , Vol 12, Iss 2, Pp 5-

2006 Volume 12

Abstract: ... working in CCU wards and 50 patients who were hospitalized in CCU wards that were selected by interviewing ...

Abstract	Background & Aim: One of the responsibilities of nurses is to identify of effective factors on sleeping, because identification of these factors prevents from occurrence of sleep disorders, improves sleeping, decreases duration of hospitalization, and reduces use of hypnotic drugs. Methods & Materials: This research is a comparative descriptive study. The population under research was included 50 nurses who were working in CCU wards and 50 patients who were hospitalized in CCU wards that were selected by interviewing and information gathering tools was a questionnaires which consisted of tow parts and for each group one questionnaire was used. The first part was included demographic specification. Second part is consisted of 56 questions (four rating) related to effective factors on patient's sleeping in the domains such as environmental factors, personal (physical and mental) factors, pre-sleeping habits and an extra question (to explain other factors with the except of factors that mentioned in sleeping). Gathered data is processed by SPSS software, 12'Th version, and for achieving to research goals, descriptive and perceptive statistical methods (such as t-test, ANOVA test, and Pearson coefficient of correlation) were used. Then descriptive statistic was used in data analysis and statistical t-tests were used to compare of these two groups opinions. Results: The results of this research showed that environmental factors such as turned on light, pain, anxiety due to loss of job, fears of outcome of disease, connection to monitoring systems are the important effective factors on sleeping according to the nurses points of view however patients believe that phone ring, pain, anxiety from loss of job, fears of outcomes of disease, connection to monitoring systems are important. Conclusion: According to the research results, the most important effective factors on sleeping are "turned on light", "phone ring" "pain", "anxiety from loss of job", "fears of outcome of illness", "connection to monitoring systems". The foundation of this schedule is based on identification of effective factors on sleeping according to viewpoint of patients and then eliminating the disturbing factors.
Keywords	Sleep ; viewpoint ; Coronary care unit ; Nursing ; RT1-120 ; Medicine ; R ; DOAJ:Nursing ; DOAJ:Health Sciences
Subject code	610
Language	Persian
Publishing date	2006-11-01T00:00:00Z
Publisher	Tehran University of Medical Sciences
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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