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  1. Ahmadi H, Nilashi M, Ibrahim O, Raisian K
    Curr Health Sci J, 2016 03 29;42(1):82-93.
    PMID: 30568817 DOI: 10.12865/CHSJ.42.01.12
    As Electronic Medical Records (EMRs) have a great possibility for rising physician's performance in their daily work which improves quality, safety and efficiency in healthcare, they are implemented throughout the world (Boonstra and Broekhuis, 2010). In physician practices the rate of EMRs adoption has been slow and restricted (around 25%) according to Endsley, Baker, Kershner, and Curtin (2005) in spite of the cost savings through lower administrative costs and medical errors related with EMRs systems. The core objective of this research is to identify, categorize, and analyse meso-level factors introduced by Lau et al, 2012, perceived by physicians to the adoption of EMRs in order to give more knowledge in primary care setting. Finding was extracted through questionnaire which distributed to 350 physicians in primary cares in Malaysia to assess their perception towards EMRs adoption. The findings showed that Physicians had positive perception towards some features related to technology adoption success and emphasized EMRs had helpful impact in their office. The fuzzy TOPSIS physician EMRs adoption model in meso-level developed and its factors and sub-factors discussed in this study which provide making sense of EMRs adoption. The related factors based on meso-level perspective prioritized and ranked by using the fuzzy TOPSIS. The purpose of ranking using these approaches is to inspect which factors are more imperative in EMRs adoption among primary care physicians. The result of performing fuzzy TOPSIS is as a novelty method to identify the critical factors which assist healthcare organizations to inspire their users in accepting of new technology.
  2. Yassin AA, Mohamed IO, Ibrahim MN, Yusoff MS
    Appl Biochem Biotechnol, 2003 Jul;110(1):45-52.
    PMID: 12909731
    Immobilized PS-C 'Amano' II lipase was used to catalyze the interesterification of palm olein (POo) with 30, 50, and 70% stearic acid in n-hexane at 60 degrees C. The catalytic performance of the immobilized lipase was evaluated by determining the composition change of fatty acyl groups and triacylglycerol (TAG) by gas liquid chromatography and high-performance liquid chromatography, respectively. The interesterification process resulted in the formation of new TAGs, mainly tripalmitin and dipalmitostearin, both of which were absent in the original oil. These changes in TAG composition resulted in an increase in slip melting point, from the original 25.5 degrees C to 36.3, 37.0, and 40.0 degrees C in the modified POo with 30, 50, and 70% stearic acid, respectively. All the reactions attained steady state in about 6 h. This type of work will find great applications in food industries, such as confectionery.
  3. Ahmadi H, Nilashi M, Ibrahim O
    Int J Med Inform, 2015 Mar;84(3):166-88.
    PMID: 25612792 DOI: 10.1016/j.ijmedinf.2014.12.004
    This study mainly integrates the mature Technology-Organization-Environment (TOE) framework and recently developed Human-Organization-Technology (HOT) fit model to identify factors that affect the hospital decision in adopting Hospital Information System (HIS).
  4. Nilashi M, Ibrahim O, Ahani A
    Sci Rep, 2016 Sep 30;6:34181.
    PMID: 27686748 DOI: 10.1038/srep34181
    Parkinson's disease (PD) is a member of a larger group of neuromotor diseases marked by the progressive death of dopamineproducing cells in the brain. Providing computational tools for Parkinson disease using a set of data that contains medical information is very desirable for alleviating the symptoms that can help the amount of people who want to discover the risk of disease at an early stage. This paper proposes a new hybrid intelligent system for the prediction of PD progression using noise removal, clustering and prediction methods. Principal Component Analysis (PCA) and Expectation Maximization (EM) are respectively employed to address the multi-collinearity problems in the experimental datasets and clustering the data. We then apply Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for prediction of PD progression. Experimental results on public Parkinson's datasets show that the proposed method remarkably improves the accuracy of prediction of PD progression. The hybrid intelligent system can assist medical practitioners in the healthcare practice for early detection of Parkinson disease.
  5. Oyehan TA, Alade IO, Bagudu A, Sulaiman KO, Olatunji SO, Saleh TA
    Comput Biol Med, 2018 07 01;98:85-92.
    PMID: 29777986 DOI: 10.1016/j.compbiomed.2018.04.024
    The optical properties of blood play crucial roles in medical diagnostics and treatment, and in the design of new medical devices. Haemoglobin is a vital constituent of the blood whose optical properties affect all of the optical properties of human blood. The refractive index of haemoglobin has been reported to strongly depend on its concentration which is a function of the physiology of biological cells. This makes the refractive index of haemoglobin an essential non-invasive bio-marker of diseases. Unfortunately, the complexity of blood tissue makes it challenging to experimentally measure the refractive index of haemoglobin. While a few studies have reported on the refractive index of haemoglobin, there is no solid consensus with the data obtained due to different measuring instruments and the conditions of the experiments. Moreover, obtaining the refractive index via an experimental approach is quite laborious. In this work, an accurate, fast and relatively convenient strategy to estimate the refractive index of haemoglobin is reported. Thus, the GA-SVR model is presented for the prediction of the refractive index of haemoglobin using wavelength, temperature, and the concentration of haemoglobin as descriptors. The model developed is characterised by an excellent accuracy and very low error estimates. The correlation coefficients obtained in these studies are 99.94% and 99.91% for the training and testing results, respectively. In addition, the result shows an almost perfect match with the experimental data and also demonstrates significant improvement over a recent mathematical model available in the literature. The GA-SVR model predictions also give insights into the influence of concentration, wavelength, and temperature on the RI measurement values. The model outcome can be used not only to accurately estimate the refractive index of haemoglobin but also could provide a reliable common ground to benchmark the experimental refractive index results.
  6. Ibrahim O, Maskon O, Darinah N, Raymond AA, Rahman MM
    Pak J Med Sci, 2013 Nov;29(6):1319-22.
    PMID: 24550945
    OBJECTIVES: To determine the prevalence of aspirin resistance and associated risk factors based on biochemical parameters using whole blood multiple electrode aggregometry.
    METHODS: The study was conducted at the outpatients cardiology clinic of the Universiti Kebangsaan Malaysia Medical Centre (UKMMC) from August 2011 to February 2012. Subjects on aspirin therapy were divided into two groups; first-ever coronary event and recurrent coronary event. Aspirin resistance was measured by a Multiplate(®) platelet analyser.
    RESULTS: A total of 74 patients (63 male, 11 female), with a mean age of 57.93 ± 74.1years were enrolled in the study. The patients were divided into two groups -first-ever coronary event group (n=52) and recurrent coronary event group (n=22). Aspirin resistance was observed in 12 out of 74 (16%) of the study patients, which consisted of 11 patients from the first-ever coronary event group and one patient from the recurrent coronary event group. There were significant correlations between aspirin resistance and age (r = -0.627; p = 0.029), total cholesterol (r = 0.608; p = 0.036) and LDL (r = 0.694; p = 0.012). LDL was the main predictor for area under the curve (AUC) for aspirin resistance. However, there was no association between aspirin resistance and cardiovascular events in both groups in this study.
    CONCLUSIONS: Aspirin resistance was observed in 16% of the study population. LDL was the major predictor of aspirin resistance. No association was found in the study between aspirin resistance with recurrent coronary events.
    KEYWORDS: Aspirin resistance; Multiplate® platelet analyser; aspirin responsiveness; first-ever coronary event; recurrent coronary event
  7. Nilashi M, Ahmadi H, Shahmoradi L, Ibrahim O, Akbari E
    J Infect Public Health, 2018 10 04;12(1):13-20.
    PMID: 30293875 DOI: 10.1016/j.jiph.2018.09.009
    BACKGROUND: Hepatitis is an inflammation of the liver, most commonly caused by a viral infection. Supervised data mining techniques have been successful in hepatitis disease diagnosis through a set of datasets. Many methods have been developed by the aids of data mining techniques for hepatitis disease diagnosis. The majority of these methods are developed by single learning techniques. In addition, these methods do not support the ensemble learning of the data. Combining the outputs of several predictors can result in improved accuracy in classification problems. This study aims to propose an accurate method for the hepatitis disease diagnosis by taking the advantages of ensemble learning.

    METHODS: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies.

    RESULTS: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine.

    CONCLUSIONS: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare.

  8. Nilashi M, Bin Ibrahim O, Mardani A, Ahani A, Jusoh A
    Health Informatics J, 2018 12;24(4):379-393.
    PMID: 30376769 DOI: 10.1177/1460458216675500
    As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.
  9. Ibrahim O, Oteh M, A Syukur A, Che Hassan HH, S Fadilah W, Rahman MM
    Pak J Med Sci, 2013 Jan;29(1):97-102.
    PMID: 24353516 DOI: 10.12669/pjms.291.2820
    OBJECTIVES: To evaluate aspirin and clopidogrel resistance/non-responders in patients with acute coronary syndrome (ACS) by using adenosine diposphate and aspirin tests.
    METHODOLOGY: In the study patients with ACS loaded with 300 mg of clopidogrel and 300 mg aspirin and patients on stable daily dose of 75 mg of clopidogrel (more than 3 days) underwent PCI. Response to clopidogrel and Aspirin was assessed by Adenosine Diphosphate (ADP) Test (20 µmol/L) and Aspirin Test (Acetyl Acid) (ASP) 20 µmol/L, respectively, using the Multiplate Platelet Function Analyzer (Dynabyte Medical, Munich, Germany).
    RESULTS: Sixty four patients were included in this study out of which 57 were with ACS and 7 scheduled for percutaneous coronary intervention (PCI) electively. The proportion of Aspirin good responders and adequate responders were 76.56% and 18.75%, respectively while adequate response and good response to Clopidogrel accounted for 29.7 and 48.4%, respectively Hyperlipidaemia was only co-morbidity associated with higher AUC ADP value (p: 0.046). Hypertriglyceridaemia and serum calcium were weakly correlated with higher AUC ADP serum calcium r=0.08, triglyceride r=0.12. Patients admitted for scheduled PCI and on stable dose of 75mg clopidogrel exhibited lower AUC ADP value as compared to those admitted with acute coronary syndrome given loading dose of 300mg of Clopidogrel. Post loading dose measurement of anti-platelet therapy among ACS patients using the Multiplate Platelet Function Analyzer showed comparable results with other methods.
    Conclusions : As determined by Multiplate Platelet Function Analyzer, Aspirin resistance/non-responders in this study in acute coronary syndrome patients accounted for 4.69% while Non-responders in Clopidogrel was 21.9%.
    KEYWORDS: Acute coronary syndromes; Anti-platelet therapy; Aspirin; Clopidogrel; Hyperlipidaemia
  10. Ibrahim O, Oteh M, Anwar IR, Che Hassan HH, Choor CK, Hamzaini AH, et al.
    Clin Ter, 2013;164(5):391-5.
    PMID: 24217823 DOI: 10.7417/CT.2013.1601
    Coronary heart disease is a major health problem in Malaysia with high morbidity and mortality. Common primary screening tool of cardiovascular risk stratification is exercise treadmill test (ETT). This communication is to determine the performance of coronary artery calcium score a new method to stratify the presence of obstructive coronary artery disease (CAD) in comparison to traditional ETT in patients having coronary artery diseases.
  11. Pahl C, Zare M, Nilashi M, de Faria Borges MA, Weingaertner D, Detschew V, et al.
    J Biomed Inform, 2015 Jun;55:174-87.
    PMID: 25900270 DOI: 10.1016/j.jbi.2015.04.004
    This work investigates, whether openEHR with its reference model, archetypes and templates is suitable for the digital representation of demographic as well as clinical data. Moreover, it elaborates openEHR as a tool for modelling Hospital Information Systems on a regional level based on a national logical infrastructure. OpenEHR is a dual model approach developed for the modelling of Hospital Information Systems enabling semantic interoperability. A holistic solution to this represents the use of dual model based Electronic Healthcare Record systems. Modelling data in the field of obstetrics is a challenge, since different regions demand locally specific information for the process of treatment. Smaller health units in developing countries like Brazil or Malaysia, which until recently handled automatable processes like the storage of sensitive patient data in paper form, start organizational reconstruction processes. This archetype proof-of-concept investigation has tried out some elements of the openEHR methodology in cooperation with a health unit in Colombo, Brazil. Two legal forms provided by the Brazilian Ministry of Health have been analyzed and classified into demographic and clinical data. LinkEHR-Ed editor was used to read, edit and create archetypes. Results show that 33 clinical and demographic concepts, which are necessary to cover data demanded by the Unified National Health System, were identified. Out of the concepts 61% were reused and 39% modified to cover domain requirements. The detailed process of reuse, modification and creation of archetypes is shown. We conclude that, although a major part of demographic and clinical patient data were already represented by existing archetypes, a significant part required major modifications. In this study openEHR proved to be a highly suitable tool in the modelling of complex health data. In combination with LinkEHR-Ed software it offers user-friendly and highly applicable tools, although the complexity built by the vast specifications requires expert networks to define generally excepted clinical models. Finally, this project has pointed out main benefits enclosing high coverage of obstetrics data on the Clinical Knowledge Manager, simple modelling, and wide network and support using openEHR. Moreover, barriers described are enclosing the allocation of clinical content to respective archetypes, as well as stagnant adaption of changes on the Clinical Knowledge Manager leading to redundant efforts in data contribution that need to be addressed in future works.
  12. Abumalloh RA, Asadi S, Nilashi M, Minaei-Bidgoli B, Nayer FK, Samad S, et al.
    Technol Soc, 2021 Nov;67:101728.
    PMID: 34538984 DOI: 10.1016/j.techsoc.2021.101728
    To avoid the spread of the COVID-19 crisis, many countries worldwide have temporarily shut down their academic organizations. National and international closures affect over 91% of the education community of the world. E-learning is the only effective manner for educational institutions to coordinate the learning process during the global lockdown and quarantine period. Many educational institutions have instructed their students through remote learning technologies to face the effect of local closures and promote the continuity of the education process. This study examines the expected benefits of e-learning during the COVID-19 pandemic by providing a new model to investigate this issue using a survey collected from the students at Imam Abdulrahman Bin Faisal University. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed on 179 useable responses. This study applied Push-Pull-Mooring theory and examined how push, pull, and mooring variables impact learners to switch to virtual and remote educational laboratories. The Protection Motivation theory was employed to explain how the potential health risk and environmental threat can influence the expected benefits from e-learning services. The findings revealed that the push factor (environmental threat) is significantly related to perceived benefits. The pull factors (e-learning motivation, perceived information sharing, and social distancing) significantly impact learners' benefits. The mooring factor, namely perceived security, significantly impacts learners' benefits.
  13. Al Bayaty FH, Mahmod SA, Jamil Al-Obaidi MM, Emad Ibrahim O, Dahir A, Adam FA, et al.
    J Periodontal Res, 2023 Feb;58(1):22-28.
    PMID: 36321414 DOI: 10.1111/jre.13064
    BACKGROUND: There is scarce information about the relationship between periodontal disease and osteoarthritis. This study investigated the effect of surgically induced osteoarthritis on alveolar bone loss in experimental periodontitis in rats.

    METHODS: 12 rats were divided into test and control groups. On day 1, the animals were anaesthetized, and silk ligatures were ligated around 6 maxillary posterior teeth in each animal from both groups. Surgical induction of osteoarthritis was performed on the left knees in the test group. No knee surgeries were performed in the control group. The ligatures were kept in place for 30 days, at which time the animals were euthanatized, and the maxillae and knee joints were harvested and processed for histological analysis. The alveolar bone loss was assessed using a zoom stereomicroscope.

    RESULTS: The knee joint histologic sections of the control group showed normal joint features, whereas in the test group there were substantial changes typical of osteoarthritis, including wide joint spaces, prominent monocytic infiltration of the synovium, invasion of periarticular bone, and decreased chondrocyte density. Comparison of the bone height between the groups showed a significantly higher bone loss in the test than in the control group The marginal mean bone height, adjusted for covariates and the intraclass correlation between sites, was 1.19 and 0.78 mm in the test and control groups, respectively (p 

  14. Global Retinoblastoma Study Group, Fabian ID, Abdallah E, Abdullahi SU, Abdulqader RA, Adamou Boubacar S, et al.
    JAMA Oncol, 2020 May 01;6(5):685-695.
    PMID: 32105305 DOI: 10.1001/jamaoncol.2019.6716
    IMPORTANCE: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale.

    OBJECTIVES: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis.

    DESIGN, SETTING, AND PARTICIPANTS: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017.

    MAIN OUTCOMES AND MEASURES: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis.

    RESULTS: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]).

    CONCLUSIONS AND RELEVANCE: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs.

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