Displaying publications 101 - 120 of 313 in total

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  1. Fulltext Remote Arrhythmia Detection for Eldercare in Malaysia
    Chew KT, Raman V, Then PHH
    Sensors (Basel), 2021 Dec 08;21(24).
    PMID: 34960291 DOI: 10.3390/s21248197
    Cardiovascular disease continues to be one of the most prevalent medical conditions in modern society, especially among elderly citizens. As the leading cause of deaths worldwide, further improvements to the early detection and prevention of these cardiovascular diseases is of the utmost importance for reducing the death toll. In particular, the remote and continuous monitoring of vital signs such as electrocardiograms are critical for improving the detection rates and speed of abnormalities while improving accessibility for elderly individuals. In this paper, we consider the design and deployment characteristics of a remote patient monitoring system for arrhythmia detection in elderly individuals. Thus, we developed a scalable system architecture to support remote streaming of ECG signals at near real-time. Additionally, a two-phase classification scheme is proposed to improve the performance of existing ECG classification algorithms. A prototype of the system was deployed at the Sarawak General Hospital, remotely collecting data from 27 unique patients. Evaluations indicate that the two-phase classification scheme improves algorithm performance when applied to the MIT-BIH Arrhythmia Database and the remotely collected single-lead ECG recordings.
    Matched MeSH terms: Databases, Factual
  2. Fulltext Machine learning in internet financial risk management: A systematic literature review
    Tian X, Tian Z, Khatib SFA, Wang Y
    PLoS One, 2024;19(4):e0300195.
    PMID: 38625972 DOI: 10.1371/journal.pone.0300195
    Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks.
    Matched MeSH terms: Databases, Factual
  3. Fulltext Malaysian Psoriasis Registry--preliminary report of a pilot study using a newly revised registry form
    Chang CC, Gangaram HB, Hussein SH
    Med J Malaysia, 2008 Sep;63 Suppl C:68-71.
    PMID: 19227676
    The Malaysian Psoriasis Registry, established in 1998, is the first skin disease clinical registry in Malaysia. It aims to provide useful data on various aspects of psoriasis. Following an extensive revision of the registry form in 2007, a total of 509 psoriasis patients from 10 government dermatologic centres were reviewed in a three month pilot study. The onset of psoriasis was during the second to fourth decade of life in the majority of patients. There was no sexual and ethnic predilection. A positive family history was present in 21.2%, and more common in patients with younger disease onset. The main aggravating factors of psoriasis were stress, sunlight and infection. Plaque psoriasis was the commonest clinical type (80.9%). Joint disease was present in 17.3% of patients, among which mono-/oligoarticular type being the commonest. Nail changes occurred in 68%. More psoriasis patients were overweight and obese compared to the normal population. The mean Dermatologic Life Quality Index (DLQI) score was 8.08 +/- 6.29, and changes during subsequent follow-up may reflect therapeutic effectiveness. This study enabled evaluation of the revised registry form and helped in identifying shortcomings in the implementation of the registry.
    Matched MeSH terms: Databases, Factual/standards; Databases, Factual/statistics & numerical data
  4. Fulltext National Cancer Patient Registry--Haematology Malignancy (NCPR-HM)
    Chang KM, Ong TC
    Med J Malaysia, 2008 Sep;63 Suppl C:66-7.
    PMID: 19227675
    Treatment option of Haematological malignancies has expanded over the last decade. The outcome of treatment is expected to be better compare to previously. However, study of treatment outcome for haematological malignancies has not been carried out in Malaysia. The goal of this study is to measure the treatment outcome in patients with haematological malignancy.
    Matched MeSH terms: Databases, Factual/standards; Databases, Factual/statistics & numerical data
  5. Fulltext National Orthopedic Registry in Malaysia--National Orthopedic Hip Fracture Database (NOHFD)
    Muhammad Anwar Hau A
    Med J Malaysia, 2008 Sep;63 Suppl C:74.
    PMID: 19227678
    Matched MeSH terms: Databases, Factual/standards; Databases, Factual/statistics & numerical data
  6. Fulltext National Cancer Patient Registry--Breast Cancer (NCPR-Breast Cancer)
    Nor Aina E
    Med J Malaysia, 2008 Sep;63 Suppl C:72-3.
    PMID: 19227677
    Breast cancer is the most common cancer in most part of the world and it is the most common cancer among Malaysian women. In order to estimate the overall survival and prognosis, it was decided that a National Cancer Patient Registry-Breast cancer be set up. It would be a tracking system form for breast cancer patients in Malaysia to help treatment outcomes. There would be useful for evaluating clinical management.
    Matched MeSH terms: Databases, Factual/standards; Databases, Factual/statistics & numerical data
  7. Fulltext Nasopharyngeal Carcinoma Database
    Pua KC, Khoo AS, Yap YY, Subramaniam SK, Ong CA, Gopala Krishnan G, et al.
    Med J Malaysia, 2008 Sep;63 Suppl C:59-62.
    PMID: 19230249
    Nasopharyngeal carcinoma (NPC) is a cancer which is common in Asia. We report the establishment and early results of a multi-institutional prospective study of nasopharyngeal carcinoma, which seeks to systematically collect data as well as blood and tumour tissue samples from patients diagnosed with nasopharyngeal cancer at six centres in Malaysia. A total of 484 confirmed NPC cases were reported from the six participating centres between 1st July 2007 and 29th February 2008. Of these, 225 were newly diagnosed cases, 53 were recurrent cases and 206 were in remission at the time of reporting. Amongst the newly diagnosed cases, the most common presenting symptom was the presence of neck lumps (42%). Ophthalmo-neurologic symptoms were the presenting symptoms of 11% of the new cases. The majority of cases (75%) presented at stage III/IV.
    Matched MeSH terms: Databases, Factual/standards; Databases, Factual/statistics & numerical data
  8. Fulltext New registry: National Cancer Patient Registry--Colorectal Cancer
    Wendy L, Radzi M
    Med J Malaysia, 2008 Sep;63 Suppl C:57-8.
    PMID: 19230248
    Colorectal cancer is emerging as one of the commonest cancers in Malaysia. Data on colorectal cancer from the National Cancer Registry is very limited. Comprehensive information on all aspects of colorectal cancer, including demographic details, pathology and treatment outcome are needed as the management of colorectal cancer has evolved rapidly over the years involving several disciplines including gastroenterology, surgery, radiology, pathology and oncology. This registry will be an important source of information that can help the development of guidelines to improve colorectal cancer care relevant to this country. The database will initially recruit all colorectal cancer cases from eight hospitals. The data will be stored on a customized web-based case report form. The database has begun collecting data from 1 October 2007 and will report on its first year findings at the end of 2008.
    Matched MeSH terms: Databases, Factual/standards; Databases, Factual/statistics & numerical data
  9. The Will Rogers phenomenon in the staging of breast cancer - does it matter?
    Tan GH, Bhoo-Pathy N, Taib NA, See MH, Jamaris S, Yip CH
    Cancer Epidemiol, 2015 Feb;39(1):115-7.
    PMID: 25475062 DOI: 10.1016/j.canep.2014.11.005
    Changes in the American Joint Commission on Cancer staging for breast cancer occurred when the 5th Edition was updated to the 6th Edition.
    Matched MeSH terms: Databases, Factual
  10. Optimal selection of mother wavelet for accurate infant cry classification
    Saraswathy J, Hariharan M, Nadarajaw T, Khairunizam W, Yaacob S
    Australas Phys Eng Sci Med, 2014 Jun;37(2):439-56.
    PMID: 24691930 DOI: 10.1007/s13246-014-0264-y
    Wavelet theory is emerging as one of the prevalent tool in signal and image processing applications. However, the most suitable mother wavelet for these applications is still a relative question mark amongst researchers. Selection of best mother wavelet through parameterization leads to better findings for the analysis in comparison to random selection. The objective of this article is to compare the performance of the existing members of mother wavelets and to select the most suitable mother wavelet for accurate infant cry classification. Optimal wavelet is found using three different criteria namely the degree of similarity of mother wavelets, regularity of mother wavelets and accuracy of correct recognition during classification processes. Recorded normal and pathological infant cry signals are decomposed into five levels using wavelet packet transform. Energy and entropy features are extracted at different sub bands of cry signals and their effectiveness are tested with four supervised neural network architectures. Findings of this study expound that, the Finite impulse response based approximation of Meyer is the best wavelet candidate for accurate infant cry classification analysis.
    Matched MeSH terms: Databases, Factual
  11. Identifying contributing factors to fatal and serious injury motorcycle collisions involving children in Malaysia
    Oxley J, Ravi MD, Yuen J, Hoareau E, Hashim HH
    Ann Adv Automot Med, 2014 1 11;57:329-36.
    PMID: 24406968
    In Malaysia, motorcycle crashes constitute approximately 60 percent of all road trauma, and a substantial proportion involve children 16 years and younger. There are, however, many gaps in our knowledge on contributing factors to crashes and injury patterns amongst children killed and seriously injured in motorcycle crashes. The aim of this study was to examine fatal and serious injury motorcycle-related collisions to identify contributing factors and injury patterns amongst child motorcyclists. All identified motorcyclist fatal crashes between 2007 and 2011 (inclusive) were extracted from the national Police-reported crash database (M-ROADS) and a range of variables were selected for examination. A total of 17,677 crashes were extracted where a rider or pillion was killed and of these crashes 2,038 involved children, equating to 12 percent. Examination of crashes involving children revealed that some crashes involved more than two children on the motorcycle, therefore, overall children constituted 9.5% of fatal and 18.4% of serious injury collisions. A high proportion of child fatal or serious injury collisions involved the child as the rider (62%), and this was most common for children aged between 10 and 16 years. The majority of collisions occurred on rural roads, in speed limit zones of 50-70km/h, and approximately one-third occurred at an intersection. Collisions involving another motorcycle or a passenger vehicle contributed to 41% and 53% of the total fatalities and severe injuries, respectively. A high proportion (43.9%) of the children (25.5% riders and 18.8% pillion) sustained head injuries with 37.7% being in the 10-16 age group. Furthermore, 52.4% of the children sustaining head injuries did not wear a helmet. The implications of these findings for countermeasures within a Safe System framework, particularly interventions aimed at reducing the rate of unlicensed riding and helmet wearing, and infrastructure countermeasures are discussed.
    Matched MeSH terms: Databases, Factual
  12. Fulltext InCoB celebrates its tenth anniversary as first joint conference with ISCB-Asia
    Schönbach C, Tan TW, Kelso J, Rost B, Nathan S, Ranganathan S
    BMC Genomics, 2011 Nov 30;12 Suppl 3:S1.
    PMID: 22369160 DOI: 10.1186/1471-2164-12-S3-S1
    In 2009 the International Society for Computational Biology (ISCB) started to roll out regional bioinformatics conferences in Africa, Latin America and Asia. The open and competitive bid for the first meeting in Asia (ISCB-Asia) was awarded to Asia-Pacific Bioinformatics Network (APBioNet) which has been running the International Conference on Bioinformatics (InCoB) in the Asia-Pacific region since 2002. InCoB/ISCB-Asia 2011 is held from November 30 to December 2, 2011 in Kuala Lumpur, Malaysia. Of 104 manuscripts submitted to BMC Genomics and BMC Bioinformatics conference supplements, 49 (47.1%) were accepted. The strong showing of Asia among submissions (82.7%) and acceptances (81.6%) signals the success of this tenth InCoB anniversary meeting, and bodes well for the future of ISCB-Asia.
    Matched MeSH terms: Databases, Factual
  13. Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network
    Hariharan M, Sindhu R, Yaacob S
    Comput Methods Programs Biomed, 2012 Nov;108(2):559-69.
    PMID: 21824676 DOI: 10.1016/j.cmpb.2011.07.010
    Crying is the most noticeable behavior of infancy. Infant cry signals can be used to identify physical or psychological status of an infant. Recently, acoustic analysis of infant cry signal has shown promising results and it has been proven to be an excellent tool to investigate the pathological status of an infant. This paper proposes short-time Fourier transform (STFT) based time-frequency analysis of infant cry signals. Few statistical features are derived from the time-frequency plot of infant cry signals and used as features to quantify infant cry signals. General Regression Neural Network (GRNN) is employed as a classifier for discriminating infant cry signals. Two classes of infant cry signals are considered such as normal cry signals and pathological cry signals from deaf infants. To prove the reliability of the proposed features, two neural network models such as Multilayer Perceptron (MLP) and Time-Delay Neural Network (TDNN) trained by scaled conjugate gradient algorithm are also used as classifiers. The experimental results show that the GRNN classifier gives very promising classification accuracy compared to MLP and TDNN and the proposed method can effectively classify normal and pathological infant cries.
    Matched MeSH terms: Databases, Factual
  14. New fragment weighting scheme for the Bayesian inference network in ligand-based virtual screening
    Abdo A, Salim N
    J Chem Inf Model, 2011 Jan 24;51(1):25-32.
    PMID: 21155550 DOI: 10.1021/ci100232h
    Many of the conventional similarity methods assume that molecular fragments that do not relate to biological activity carry the same weight as the important ones. One possible approach to this problem is to use the Bayesian inference network (BIN), which models molecules and reference structures as probabilistic inference networks. The relationships between molecules and reference structures in the Bayesian network are encoded using a set of conditional probability distributions, which can be estimated by the fragment weighting function, a function of the frequencies of the fragments in the molecule or the reference structure as well as throughout the collection. The weighting function combines one or more fragment weighting schemes. In this paper, we have investigated five different weighting functions and present a new fragment weighting scheme. Later on, these functions were modified to combine the new weighting scheme. Simulated virtual screening experiments with the MDL Drug Data Report (23) and maximum unbiased validation data sets show that the use of new weighting scheme can provide significantly more effective screening when compared with the use of current weighting schemes.
    Matched MeSH terms: Databases, Factual
  15. What is of interest in Endodontology? A bibliometric review of research published in the International Endodontic Journal and the Journal of Endodontics from 1980 to 2019
    Ordinola-Zapata R, Peters OA, Nagendrababu V, Azevedo B, Dummer PMH, Neelakantan P
    Int Endod J, 2020 Jan;53(1):36-52.
    PMID: 31454086 DOI: 10.1111/iej.13210
    AIM: To report the most common terminology used in titles of scientific papers published in the International Endodontic Journal (IEJ) and Journal of Endodontics (JOE) between 1980 and 2019 and to identify the most-cited papers in these journals.

    METHODOLOGY: The Web of Science database was searched to retrieve all the manuscripts published in the IEJ and JOE between 1980 and 2019. The articles were analysed using the VOS viewer software and the terms within the titles extracted. The top-10 terms were categorized according to the number of occurrences and the decade of publication. Maps were created using the text data for each decade of publication. Classic papers were identified when the number of citations was >400. During the same period of time, highly cited studies were identified including the authors, institutions and countries associated with these papers.

    RESULTS: Terms such as canal, molar and periapical lesion were the most commonly used in titles between 1980 and 1999. The terms instruments, expression, case report and cell were the most often terms used between 2000 and 2019. During the last 10 years, an increase in the number of reviews and papers on cone beam computed tomography occurred. The organizations with the largest number of citations in each decade were University of São Paulo, University College London, Loma Linda University and United States Army. The country with the largest number of citations and greatest number of top 10 and top 100 manuscripts was the United States. A paper had to be associated with more than 167 citations to be included in the top-100 most-cited list; at least 14 papers met the criteria to be categorized as a citation classic (>400 citations).

    CONCLUSION: While many diverse areas of endodontics have been explored in the last 40 years within the IEJ and JOE, only a relatively few topics are highly cited and can be considered as classics.

    Matched MeSH terms: Databases, Factual
  16. Fulltext Prescription drug monitoring programs in the US: A systematic literature review on its strength and weakness
    Alogaili F, Abdul Ghani N, Ahmad Kharman Shah N
    J Infect Public Health, 2020 Oct;13(10):1456-1461.
    PMID: 32694082 DOI: 10.1016/j.jiph.2020.06.035
    Prescription Drug Monitoring Program (PDMP) is an electronic database that tracks the prescriptions of controlled drugs with its aims to combat the incidence of drug abuse. Although the establishment of PDMP in the US was since 2003, evidence of the impact of PDMP's strength and weakness towards its implementation is still scarce. A systematic literature review according to Preferred Reporting Items for Systematic Review (PRISMA) standard was conducted to investigate the influence of PDMP's strength in combating the incidence of drug abuse and also to review the weaknesses of PDMP that prohibit its implementation. Results from this study reveal that the implementation of PDMP has mitigated the issue of drug abuse and has increased work efficiency among healthcare practitioners. However, the implementation rate of this system is low due to its weaknesses such as limited internet access and limited access to the PDMP system. Therefore, efforts to overcome the weaknesses of PDMP need to be instituted to ensure the healthcare system could fully optimize PDMP's benefits.
    Matched MeSH terms: Databases, Factual
  17. Fulltext The appropriateness of clinical decision support systems alerts in supporting clinical workflows: A systematic review
    Olakotan OO, Mohd Yusof M
    Health Informatics J, 2021 4 16;27(2):14604582211007536.
    PMID: 33853395 DOI: 10.1177/14604582211007536
    A CDSS generates a high number of inappropriate alerts that interrupt the clinical workflow. As a result, clinicians silence, disable, or ignore alerts, thereby undermining patient safety. Therefore, the effectiveness and appropriateness of CDSS alerts need to be evaluated. A systematic review was carried out to identify the factors that affect CDSS alert appropriateness in supporting clinical workflow. Seven electronic databases (PubMed, Scopus, ACM, Science Direct, IEEE, Ovid Medline, and Ebscohost) were searched for English language articles published between 1997 and 2018. Seventy six papers met the inclusion criteria, of which 26, 24, 15, and 11 papers are retrospective cohort, qualitative, quantitative, and mixed-method studies, respectively. The review highlights various factors influencing the appropriateness and efficiencies of CDSS alerts. These factors are categorized into technology, human, organization, and process aspects using a combination of approaches, including socio-technical framework, five rights of CDSS, and Lean. Most CDSS alerts were not properly designed based on human factor methods and principles, explaining high alert overrides in clinical practices. The identified factors and recommendations from the review may offer valuable insights into how CDSS alerts can be designed appropriately to support clinical workflow.
    Matched MeSH terms: Databases, Factual
  18. Fulltext Efficacy of Edible Bird's Nest on Cognitive Functions in Experimental Animal Models: A Systematic Review
    Ismail M, Alsalahi A, Aljaberi MA, Ibrahim RM, Bakar FA, Ideris A
    Nutrients, 2021 Mar 23;13(3).
    PMID: 33806762 DOI: 10.3390/nu13031028
    Edible bird's nest (EBN) is constructed from saliva of swiftlets birds and consumed largely by Southeast and East Asians for its nutritional value and anti-aging properties. Although the neuroprotection of EBN in animals has been reported, there has not been yet systemically summarized. Thus, this review systemically outlined the evidence of the neuroprotective activity of EBN in modulating the cognitive functions of either healthy or with induced-cognitive dysfunction animals as compared to placebos. The related records from 2010 to 2020 were retrieved from PubMed, Scopus, Web of Science and ScienceDirect using pre-specified keywords. The relevant records to the effect of EBN on cognition were selected according to the eligibility criteria and these studies underwent appraisal for the risk of bias. EBN improved the cognitive functions of induced-cognitive dysfunction and enhanced the cognitive performance of healthy animals as well as attenuated the neuroinflammations and neuro-oxidative stress in the hippocampus of these animals. Malaysian EBN could improve the cognitive functions of experimental animals as a treatment in induced cognitive dysfunction, a nutritional cognitive-enhancing agent in offspring and a prophylactic conservative effect on cognition against exposure to subsequent noxious cerebral accidents in a dose-depended manner through attenuating neuroinflammation and neuro-oxidative stress. This systemic review did not proceed meta-analysis.
    Matched MeSH terms: Databases, Factual
  19. Fulltext Automatic Polyp Segmentation in Colonoscopy Images Using a Modified Deep Convolutional Encoder-Decoder Architecture
    Eu CY, Tang TB, Lin CH, Lee LH, Lu CK
    Sensors (Basel), 2021 Aug 20;21(16).
    PMID: 34451072 DOI: 10.3390/s21165630
    Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the preferred tool of choice for the diagnosis of polyps and to avert colorectal cancer. Colon screening is time-consuming and highly operator dependent. In view of this, a computer-aided diagnosis (CAD) method needs to be developed for the automatic segmentation of polyps in colonoscopy images. This paper proposes a modified SegNet Visual Geometry Group-19 (VGG-19), a form of convolutional neural network, as a CAD method for polyp segmentation. The modifications include skip connections, 5 × 5 convolutional filters, and the concatenation of four dilated convolutions applied in parallel form. The CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB databases were used to evaluate the model, and it was found that our proposed polyp segmentation model achieved an accuracy, sensitivity, specificity, precision, mean intersection over union, and dice coefficient of 96.06%, 94.55%, 97.56%, 97.48%, 92.3%, and 95.99%, respectively. These results indicate that our model performs as well as or better than previous schemes in the literature. We believe that this study will offer benefits in terms of the future development of CAD tools for polyp segmentation for colorectal cancer diagnosis and management. In the future, we intend to embed our proposed network into a medical capsule robot for practical usage and try it in a hospital setting with clinicians.
    Matched MeSH terms: Databases, Factual
  20. Fulltext Is School Gardening Combined with Physical Activity Intervention Effective for Improving Childhood Obesity? A Systematic Review and Meta-Analysis
    Qi Y, Hamzah SH, Gu E, Wang H, Xi Y, Sun M, et al.
    Nutrients, 2021 Jul 28;13(8).
    PMID: 34444765 DOI: 10.3390/nu13082605
    School gardening activities (SGA) combined with physical activities (PA) may improve childhood dietary intake and prevent overweight and obesity. This study aims to evaluate the effect of SGA combined with PA on children's dietary intake and anthropometric outcomes. We searched studies containing randomized controlled trials up to January 2021 in Web of Science, PubMed, Cochrane Library, and the EBSCO database on this topic for children aged 7 to 12 years. Fourteen studies met the requirements for meta-analysis (n = 9187). We found that SGA has no obvious effect on improving children's BMI (WMD = -0.49; p = 0.085; I2 = 86.3%), BMI z-score (WMD = -0.12; p = 0.235; I2 = 63.0%), and WC (WMD = -0.98; p = 0.05; I2 = 72.9%). SGA can effectively improve children's FVs (WMD = 0.59, p = 0.003, I2 = 95.3%). SGA combined with PA can significantly increase children's FVs but cannot greatly improve weight status. Although more studies on this topic are needed to prove the effectiveness of this method, the results of our review show that both SGA and SGA combined with PA has a modest but positive impact of reducing BMI and WC outcomes but can significantly increase children's FVs.
    Matched MeSH terms: Databases, Factual
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