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  1. Article ; Online: Prediction of Mental Health Support of Employee Perceiving by Using Machine Learning Methods.

    Jamalirad, Hossein / Jajroudi, Mahdie

    Studies in health technology and informatics

    2023  Volume 302, Page(s) 903–904

    Abstract: Employees' mental health addresses concerns in the technology industry phenomenon. Machine Learning (ML) approaches show promise in predicting mental health problems and identifying related factors. This study used three machine learning models on OSMI ... ...

    Abstract Employees' mental health addresses concerns in the technology industry phenomenon. Machine Learning (ML) approaches show promise in predicting mental health problems and identifying related factors. This study used three machine learning models on OSMI 2019 dataset: MLP, SVM, and Decision Tree. Five features are extracted by permutation ML's method on the dataset. The results indicate that the models have been reasonably accurate. Moreover, they could effectively support predicting employee mental health comprehension in the technology industry.
    MeSH term(s) Humans ; Mental Health ; Machine Learning
    Language English
    Publishing date 2023-05-19
    Publishing country Netherlands
    Document type Journal Article
    ISSN 1879-8365
    ISSN (online) 1879-8365
    DOI 10.3233/SHTI230302
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Telemedicine solutions for clinical care delivery during COVID-19 pandemic: A scoping review.

    Ganjali, Raheleh / Jajroudi, Mahdie / Kheirdoust, Azam / Darroudi, Ali / Alnattah, Ashraf

    Frontiers in public health

    2022  Volume 10, Page(s) 937207

    Abstract: Background: The unexpected emergence of coronavirus disease 2019 (COVID-19) has changed mindsets about the healthcare system and medical practice in many fields, forcing physicians to reconsider their approaches to healthcare provision. It is necessary ... ...

    Abstract Background: The unexpected emergence of coronavirus disease 2019 (COVID-19) has changed mindsets about the healthcare system and medical practice in many fields, forcing physicians to reconsider their approaches to healthcare provision. It is necessary to add new, unique, and efficient solutions to traditional methods to overcome this critical challenge. In this regard, telemedicine offers a solution to this problem. Remote medical activities could diminish unnecessary visits and provide prompt medical services in a timely manner.
    Objective: This scoping review aimed to provide a map of the existing evidence on the use of telemedicine during the COVID-19 pandemic by focusing on delineation functions and technologies, analyzing settings, and identifying related outcomes.
    Methods: This review was conducted following the Arksey and O'Malley framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist. PubMed and Scopus databases were systematically searched based on specific eligibility criteria. The English publications included in this study focused on telemedicine systems implemented during the COVID-19 pandemic to provide clinical care services. Two independent reviewers screened the articles based on predefined inclusion and exclusion criteria. The relevant features of telemedicine systems were summarized and presented into the following four domains and their subcategories, including functionality, technology, context, and outcomes.
    Results: Out of a total of 1,602 retrieved papers, 66 studies met the inclusion criteria. The most common function implemented was counseling, and telemedicine was used for diagnosis in seven studies. In addition, in 12 studies, tele-monitoring of patients was performed by phone, designed platforms, social media, Bluetooth, and video calls. Telemedicine systems were predominantly implemented synchronously (50 studies). Moreover, 10 studies used both synchronous and asynchronous technologies. Although most studies were performed in outpatient clinics or centers, three studies implemented a system for hospitalized patients, and four studies applied telemedicine for emergency care. Telemedicine was effective in improving 87.5% of health resource utilization outcomes, 85% of patient outcomes, and 100% of provider outcomes.
    Conclusion: The benefits of using telemedicine in medical care delivery systems in pandemic conditions have been well-documented, especially for outpatient care. It could potentially improve patient, provider, and healthcare outcomes. This review suggests that telemedicine could support outpatient and emergency care in pandemic situations. However, further studies using interventional methods are required to increase the generalizability of the findings.
    MeSH term(s) Ambulatory Care Facilities ; COVID-19/epidemiology ; Humans ; Pandemics ; Telemedicine/methods
    Language English
    Publishing date 2022-07-22
    Publishing country Switzerland
    Document type Journal Article ; Systematic Review
    ZDB-ID 2711781-9
    ISSN 2296-2565 ; 2296-2565
    ISSN (online) 2296-2565
    ISSN 2296-2565
    DOI 10.3389/fpubh.2022.937207
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Prediction of medical sciences students' performance on high-stakes examinations using machine learning models: a protocol for a systematic review.

    Mastour, Haniye / Dehghani, Toktam / Jajroudi, Mahdie / Moradi, Ehsan / Zarei, Mitra / Eslami, Saeid

    BMJ open

    2023  Volume 13, Issue 5, Page(s) e064956

    Abstract: Introduction: Predicting medical science students' performance on high-stakes examinations has received considerable attention. Machine learning (ML) models are well-known approaches to enhance the accuracy of determining the students' performance. ... ...

    Abstract Introduction: Predicting medical science students' performance on high-stakes examinations has received considerable attention. Machine learning (ML) models are well-known approaches to enhance the accuracy of determining the students' performance. Accordingly, we aim to provide a comprehensive framework and systematic review protocol for applying ML in predicting medical science students' performance on high-stakes examinations. Improving the current understanding of the input and output features, preprocessing methods, setting of ML models and required evaluation metrics seems essential.
    Methods and analysis: A systematic review will be conducted by searching the electronic bibliographic databases of MEDLINE/PubMed, EMBASE, SCOPUS and Web of Science. The search will be limited to studies published from January 2013 to June 2023. Studies explicitly predicting student performance in high-stakes examinations and referencing their learning outcomes and use of ML models will be included. Two team members will first screen literature meeting the inclusion criteria at the title, abstract and full-text levels. Second, the Best Evidence Medical Education quality framework rates the included literature. Later, two team members will extract data, including the studies' general data and the ML approach's details. Finally, the information consensus will be reached and submitted for analysis. The synthesised evidence from this review provides helpful information for medical education policy-makers, stakeholders and other researchers in adopting the ML models to evaluate medical science students' performance in high-stakes exams.
    Ethics and dissemination: This systematic review protocol summarises findings of existing publications rather than primary data and does not require an ethics review. The results will be disseminated in publications of peer-reviewed journals.
    MeSH term(s) Humans ; Students, Medical ; Education, Medical/methods ; Medicine ; Publications ; Systematic Reviews as Topic
    Language English
    Publishing date 2023-05-04
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2599832-8
    ISSN 2044-6055 ; 2044-6055
    ISSN (online) 2044-6055
    ISSN 2044-6055
    DOI 10.1136/bmjopen-2022-064956
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: MRI-based machine learning for determining quantitative and qualitative characteristics affecting the survival of glioblastoma multiforme.

    Jajroudi, Mahdie / Enferadi, Milad / Homayoun, Amir Azar / Reiazi, Reza

    Magnetic resonance imaging

    2021  Volume 85, Page(s) 222–227

    Abstract: Purpose: Our current study aims to consider the image biomarkers extracted from the MRI images for exploring their effects on glioblastoma multiforme (GBM) patients' survival. Determining its biomarker helps better manage the disease and evaluate ... ...

    Abstract Purpose: Our current study aims to consider the image biomarkers extracted from the MRI images for exploring their effects on glioblastoma multiforme (GBM) patients' survival. Determining its biomarker helps better manage the disease and evaluate treatments. It has been proven that imaging features could be used as a biomarker. The purpose of this study is to investigate the features in MRI and clinical features as the biomarker association of survival of GBM.
    Methods: 55 patients were considered with five clinical features, 10 qualities pre-operative MRI image features, and six quantitative features obtained using BraTumIA software. It was run ANN, C5, Bayesian, and Cox models in two phases for determining important variables. In the first phase, we selected the quality features that occur at least in three models and quantitative in two models. In the second phase, models were run with the extracted features, and then the probability value of variables in each model was calculated.
    Results: The mean of accuracy, sensitivity, specificity, and area under curve (AUC) after running four machine learning techniques were 80.47, 82.54, 79.78, and 0.85, respectively. In the second step, the mean of accuracy, sensitivity, specificity, and AUC were 79.55, 78.71, 79.83, and 0.87, respectively.
    Conclusion: We found the largest size of the width, the largest size of length, radiotherapy, volume of enhancement, volume of nCET, satellites, enhancing margin, and age feature are important features.
    MeSH term(s) Bayes Theorem ; Brain Neoplasms/diagnostic imaging ; Glioblastoma/diagnostic imaging ; Humans ; Machine Learning ; Magnetic Resonance Imaging/methods ; Retrospective Studies
    Language English
    Publishing date 2021-10-20
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 604885-7
    ISSN 1873-5894 ; 0730-725X
    ISSN (online) 1873-5894
    ISSN 0730-725X
    DOI 10.1016/j.mri.2021.10.023
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1817: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
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  5. Article: The Benefits of Decision Tree to Predict Survival in Patients with Glioblastoma Multiforme with the Use of Clinical and Imaging Features.

    Nematollahi, Mohtaram / Jajroudi, Mahdie / Arbabi, Farshid / Azarhomayoun, Amir / Azimifar, Zohreh

    Asian journal of neurosurgery

    2018  Volume 13, Issue 3, Page(s) 697–702

    Abstract: Background: Machine learning is a type of artificial intelligence which aims to improve machine with the ability of extracting knowledge from the environment. Glioblastoma multiforme (GBM) is one of the most common and aggressive primary malignant brain ...

    Abstract Background: Machine learning is a type of artificial intelligence which aims to improve machine with the ability of extracting knowledge from the environment. Glioblastoma multiforme (GBM) is one of the most common and aggressive primary malignant brain tumors in adults. Due to a low rate of survival in patients with these tumors, machine learning can help physicians for better decision-making. The aim of this paper is to develop a machine learning model for predicting the survival rate of patients with GBM based on clinical features and magnetic resonance imaging (MRI).
    Materials and methods: The present investigation is an observational study conducted to predict the survival rate in patients with GBM in 12 months. Fifty-five patients who were registered in five Iranian Hospitals (Tehran) during 2012-2014 were selected in this study.
    Results: This study used Cox and C5.0 decision tree models based on clinical features and combined them with MRI. Accuracy, sensitivity, and specification parameters used to evaluate the models. The result of Cox and C5.0 for clinical feature was <32.73%, 22.5%, 45.83%>, <72.73%, 67.74%, 79.19%>, respectively; also, the result of Cox and C5.0 for both features was <60%, 48.58%, 75%>, <90.91%, 96.77%, 88.33%>, respectively.
    Conclusion: Using C5.0 decision tree model in both survival models including clinical features, both the imaging features and the clinical features as the covariates, shows additional predictive values and better results. The tumor width and Karnofsky performance status scores were determined as the most important parameters in the survival prediction of these types of patients.
    Language English
    Publishing date 2018-09-28
    Publishing country India
    Document type Journal Article
    ZDB-ID 2621446-5
    ISSN 2248-9614 ; 1793-5482
    ISSN (online) 2248-9614
    ISSN 1793-5482
    DOI 10.4103/ajns.AJNS_336_16
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: The benefits of decision tree to predict survival in patients with glioblastoma multiforme with the use of clinical and imaging features

    Nematollahi, Mohtaram / Jajroudi, Mahdie / Arbabi, Farshid / Azarhomayoun, Amir / Azimifar, Zohreh

    Asian Journal of Neurosurgery

    2018  Volume 13, Issue 03, Page(s) 697–702

    Abstract: Background: Machine learning is a type of artificial intelligence which aims to improve machine with the ability of extracting knowledge from the environment. Glioblastoma multiforme (GBM) is one of the most common and aggressive primary malignant brain ... ...

    Abstract Background: Machine learning is a type of artificial intelligence which aims to improve machine with the ability of extracting knowledge from the environment. Glioblastoma multiforme (GBM) is one of the most common and aggressive primary malignant brain tumors in adults. Due to a low rate of survival in patients with these tumors, machine learning can help physicians for better decision-making. The aim of this paper is to develop a machine learning model for predicting the survival rate of patients with GBM based on clinical features and magnetic resonance imaging (MRI). Materials and Methods: The present investigation is an observational study conducted to predict the survival rate in patients with GBM in 12 months. Fifty-five patients who were registered in five Iranian Hospitals (Tehran) during 2012–2014 were selected in this study. Results: This study used Cox and C5.0 decision tree models based on clinical features and combined them with MRI. Accuracy, sensitivity, and specification parameters used to evaluate the models. The result of Cox and C5.0 for clinical feature was <32.73%, 22.5%, 45.83%>, <72.73%, 67.74%, 79.19%>, respectively; also, the result of Cox and C5.0 for both features was <60%, 48.58%, 75%>, <90.91%, 96.77%, 88.33%>, respectively. Conclusion: Using C5.0 decision tree model in both survival models including clinical features, both the imaging features and the clinical features as the covariates, shows additional predictive values and better results. The tumor width and Karnofsky performance status scores were determined as the most important parameters in the survival prediction of these types of patients.
    Keywords C5.0 decision tree ; Cox ; glioblastoma multiforme ; survival rate
    Language English
    Publishing date 2018-09-01
    Publisher Thieme Medical and Scientific Publishers Pvt. Ltd.
    Publishing place Stuttgart ; New York
    Document type Article
    ZDB-ID 2621446-5
    ISSN 2248-9614 ; 1793-5482 ; 2248-9614
    ISSN (online) 2248-9614
    ISSN 1793-5482 ; 2248-9614
    DOI 10.4103/ajns.AJNS_336_16
    Database Thieme publisher's database

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  7. Article ; Online: Color and power Doppler US for diagnosing carpal tunnel syndrome and determining its severity: a quantitative image processing method.

    Ghasemi-Esfe, Ahmad Reza / Khalilzadeh, Omid / Vaziri-Bozorg, Seyed Mehran / Jajroudi, Mahdie / Shakiba, Madjid / Mazloumi, Mehdi / Rahmani, Maryam

    Radiology

    2011  Volume 261, Issue 2, Page(s) 499–506

    Abstract: Purpose: To determine whether intraneural vascularity seen at color Doppler ultrasonography (US) can be used to diagnose carpal tunnel syndrome (CTS) and to evaluate an image processing method for quantifying the severity of CTS on the basis of this ... ...

    Abstract Purpose: To determine whether intraneural vascularity seen at color Doppler ultrasonography (US) can be used to diagnose carpal tunnel syndrome (CTS) and to evaluate an image processing method for quantifying the severity of CTS on the basis of this vascularity.
    Materials and methods: This study was approved by the university ethics review committee. One hundred one patients with clinical evidence of CTS and 55 healthy control subjects were enrolled. Electrodiagnostic testing (EDT) was performed in all participants, and the presence of intraneural vascularity was evaluated with color Doppler US. An image processing program was designed by using software to determine the sum of pixels in the intraneural vascular area on power Doppler US scans of the median nerve. The relationship between the number of pixels and the severity of the abnormality at EDT was determined.
    Results: The sensitivity (83%) and specificity (89%) of intraneural vascularity in the diagnosis of CTS were similar to those of EDT (81% and 84%, respectively). Intraneural vascularity was seen in 91.4% of patients with mild CTS and 100% of patients with moderate or severe CTS. In participants with positive intraneural vascularity, the sum of pixels in the intraneural vascular area was significantly higher in patients than in control subjects and paralleled the severity of the abnormality at EDT (P < .01).
    Conclusion: Color Doppler US can be used to accurately diagnose CTS. By processing the recorded power Doppler images and determining the number of pixels in the intraneural vascular area, the severity of CTS can be assessed. 2011 SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11110150/-/DC1.
    MeSH term(s) Adult ; Aged ; Carpal Tunnel Syndrome/diagnostic imaging ; Case-Control Studies ; Cross-Sectional Studies ; Electrodiagnosis ; Female ; Humans ; Image Interpretation, Computer-Assisted ; Male ; Median Nerve/blood supply ; Median Nerve/diagnostic imaging ; Middle Aged ; Sensitivity and Specificity ; Severity of Illness Index ; Ultrasonography, Doppler/methods ; Ultrasonography, Doppler, Color
    Language English
    Publishing date 2011-11
    Publishing country United States
    Document type Journal Article
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.11110150
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Ue I Zs.106: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
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