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Fulltext National Cancer Patient Registry--a patient registry/clinical database to evaluate the health outcomes of patients undergoing treatment for cancers in Malaysia

Lim GC, Azura D

Med J Malaysia, 2008 Sep;63 Suppl C:55-6.
PMID: 19230247

Cancer burden in Malaysia is increasing. Although there have been improvements in cancer treatment, these new therapies may potentially cause an exponential increase in the cost of cancer treatment. Therefore, justification for the use of these treatments is mandated. Availability of local data will enable us to evaluate and compare the outcome of our patients. This will help to support our clinical decision making and local policy, improve access to treatment and improve the provision and delivery of oncology services in Malaysia. The National Cancer Patient Registry was proposed as a database for cancer patients who seek treatment in Malaysia. It will be a valuable tool to provide timely and robust data on the actual setting in oncology practice, safety and cost effectiveness of treatment and most importantly the outcome of these patients.

Matched MeSH terms: Databases, Factual/statistics & numerical data
Fulltext An Efficient Optimization Method for Solving Unsupervised Data Classification Problems

Shabanzadeh P, Yusof R

Comput Math Methods Med, 2015;2015:802754.
PMID: 26336509 DOI: 10.1155/2015/802754

Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.

Matched MeSH terms: Databases, Factual/statistics & numerical data
Correlation between subjective and objective assessment of magnetic resonance (MR) images

Chow LS, Rajagopal H, Paramesran R, Alzheimer's Disease Neuroimaging Initiative

Magn Reson Imaging, 2016 07;34(6):820-831.
PMID: 26969762 DOI: 10.1016/j.mri.2016.03.006

Medical Image Quality Assessment (IQA) plays an important role in assisting and evaluating the development of any new hardware, imaging sequences, pre-processing or post-processing algorithms. We have performed a quantitative analysis of the correlation between subjective and objective Full Reference - IQA (FR-IQA) on Magnetic Resonance (MR) images of the human brain, spine, knee and abdomen. We have created a MR image database that consists of 25 original reference images and 750 distorted images. The reference images were distorted with six types of distortions: Rician Noise, Gaussian White Noise, Gaussian Blur, DCT compression, JPEG compression and JPEG2000 compression, at various levels of distortion. Twenty eight subjects were chosen to evaluate the images resulting in a total of 21,700 human evaluations. The raw scores were then converted to Difference Mean Opinion Score (DMOS). Thirteen objective FR-IQA metrics were used to determine the validity of the subjective DMOS. The results indicate a high correlation between the subjective and objective assessment of the MR images. The Noise Quality Measurement (NQM) has the highest correlation with DMOS, where the mean Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are 0.936 and 0.938 respectively. The Universal Quality Index (UQI) has the lowest correlation with DMOS, where the mean PLCC and SROCC are 0.807 and 0.815 respectively. Student's T-test was used to find the difference in performance of FR-IQA across different types of distortion. The superior IQAs tested statistically are UQI for Rician noise images, Visual Information Fidelity (VIF) for Gaussian blur images, NQM for both DCT and JPEG compressed images, Peak Signal-to-Noise Ratio (PSNR) for JPEG2000 compressed images.

Matched MeSH terms: Databases, Factual/statistics & numerical data
Influence of light exposure during early life on the age of onset of bipolar disorder

Bauer M, Glenn T, Alda M, Andreassen OA, Angelopoulos E, Ardau R, et al.

J Psychiatr Res, 2015 May;64:1-8.
PMID: 25862378 DOI: 10.1016/j.jpsychires.2015.03.013

Environmental conditions early in life may imprint the circadian system and influence response to environmental signals later in life. We previously determined that a large springtime increase in solar insolation at the onset location was associated with a younger age of onset of bipolar disorder, especially with a family history of mood disorders. This study investigated whether the hours of daylight at the birth location affected this association.

Matched MeSH terms: Databases, Factual/statistics & numerical data
A new machine learning technique for an accurate diagnosis of coronary artery disease

Abdar M, Książek W, Acharya UR, Tan RS, Makarenkov V, Pławiak P

Comput Methods Programs Biomed, 2019 Oct;179:104992.
PMID: 31443858 DOI: 10.1016/j.cmpb.2019.104992

BACKGROUND AND OBJECTIVE: Coronary artery disease (CAD) is one of the commonest diseases around the world. An early and accurate diagnosis of CAD allows a timely administration of appropriate treatment and helps to reduce the mortality. Herein, we describe an innovative machine learning methodology that enables an accurate detection of CAD and apply it to data collected from Iranian patients.
METHODS: We first tested ten traditional machine learning algorithms, and then the three-best performing algorithms (three types of SVM) were used in the rest of the study. To improve the performance of these algorithms, a data preprocessing with normalization was carried out. Moreover, a genetic algorithm and particle swarm optimization, coupled with stratified 10-fold cross-validation, were used twice: for optimization of classifier parameters and for parallel selection of features.
RESULTS: The presented approach enhanced the performance of all traditional machine learning algorithms used in this study. We also introduced a new optimization technique called N2Genetic optimizer (a new genetic training). Our experiments demonstrated that N2Genetic-nuSVM provided the accuracy of 93.08% and F1-score of 91.51% when predicting CAD outcomes among the patients included in a well-known Z-Alizadeh Sani dataset. These results are competitive and comparable to the best results in the field.
CONCLUSIONS: We showed that machine-learning techniques optimized by the proposed approach, can lead to highly accurate models intended for both clinical and research use.

Matched MeSH terms: Databases, Factual/statistics & numerical data
Atrial fibrillation classification and association between the natural frequency and the autonomic nervous system

Abdul-Kadir NA, Mat Safri N, Othman MA

Int J Cardiol, 2016 Nov 01;222:504-8.
PMID: 27505342 DOI: 10.1016/j.ijcard.2016.07.196

BACKGROUND: The feasibility study of the natural frequency (ω) obtained from a second-order dynamic system applied to an ECG signal was discovered recently. The heart rate for different ECG signals generates different ω values. The heart rate variability (HRV) and autonomic nervous system (ANS) have an association to represent cardiovascular variations for each individual. This study further analyzed the ω for different ECG signals with HRV for atrial fibrillation classification.
METHODS: This study used the MIT-BIH Normal Sinus Rhythm (nsrdb) and MIT-BIH Atrial Fibrillation (afdb) databases for healthy human (NSR) and atrial fibrillation patient (N and AF) ECG signals, respectively. The extraction of features was based on the dynamic system concept to determine the ω of the ECG signals. There were 35,031 samples used for classification.
RESULTS: There were significant differences between the N & NSR, N & AF, and NSR & AF groups as determined by the statistical t-test (p<0.0001). There was a linear separation at 0.4s(-1) for ω of both databases upon using the thresholding method. The feature ω for afdb and nsrdb falls within the high frequency (HF) and above the HF band, respectively. The feature classification between the nsrdb and afdb ECG signals was 96.53% accurate.
CONCLUSIONS: This study found that features of the ω of atrial fibrillation patients and healthy humans were associated with the frequency analysis of the ANS during parasympathetic activity. The feature ω is significant for different databases, and the classification between afdb and nsrdb was determined.

Matched MeSH terms: Databases, Factual/classification*
Response to Linguistic and psychometric validation of the Malaysian version of Diabetes Quality of Life-Brief Clinical Inventory (DQoL-BCI): Methodological issues to avoid misinterpretation

Neoh CF, Samah S, Wong YY, Hassali MA, Shafie AA, Lim SM, et al.

Res Social Adm Pharm, 2017 02 12;13(4):884.
PMID: 28259586 DOI: 10.1016/j.sapharm.2017.02.004
Matched MeSH terms: Databases, Factual/standards
A deep convolutional neural network model to classify heartbeats

Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, et al.

Comput Biol Med, 2017 10 01;89:389-396.
PMID: 28869899 DOI: 10.1016/j.compbiomed.2017.08.022

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats.

Matched MeSH terms: Databases, Factual*
Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework

Than JCM, Saba L, Noor NM, Rijal OM, Kassim RM, Yunus A, et al.

Comput Biol Med, 2017 10 01;89:197-211.
PMID: 28825994 DOI: 10.1016/j.compbiomed.2017.08.014

Lung disease risk stratification is important for both diagnosis and treatment planning, particularly in biopsies and radiation therapy. Manual lung disease risk stratification is challenging because of: (a) large lung data sizes, (b) inter- and intra-observer variability of the lung delineation and (c) lack of feature amalgamation during machine learning paradigm. This paper presents a two stage CADx cascaded system consisting of: (a) semi-automated lung delineation subsystem (LDS) for lung region extraction in CT slices followed by (b) morphology-based lung tissue characterization, thereby addressing the above shortcomings. LDS primarily uses entropy-based region extraction while ML-based lung characterization is mainly based on an amalgamation of directional transforms such as Riesz and Gabor along with texture-based features comprising of 100 greyscale features using the K-fold cross-validation protocol (K = 2, 3, 5 and 10). The lung database consisted of 96 patients: 15 normal and 81 diseased. We use five high resolution Computed Tomography (HRCT) levels representing different anatomy landmarks where disease is commonly seen. We demonstrate the amalgamated ML stratification accuracy of 99.53%, an increase of 2% against the conventional non-amalgamation ML system that uses alone Riesz-based feature embedded with feature selection based on feature strength. The robustness of the system was determined based on the reliability and stability that showed a reliability index of 0.99 and the deviation in risk stratification accuracies less than 5%. Our CADx system shows 10% better performance when compared against the mean of five other prominent studies available in the current literature covering over one decade.

Matched MeSH terms: Databases, Factual*
Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images

Raghavendra U, Gudigar A, Maithri M, Gertych A, Meiburger KM, Yeong CH, et al.

Comput Biol Med, 2018 04 01;95:55-62.
PMID: 29455080 DOI: 10.1016/j.compbiomed.2018.02.002

Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the location of thyroid nodules. However, visual assessment of nodules is difficult and often affected by inter- and intra-observer variabilities. Thus, a computer-aided diagnosis (CAD) system can be helpful to cross-verify the severity of nodules. This paper proposes a new CAD system to characterize thyroid nodules using optimized multi-level elongated quinary patterns. In this study, higher order spectral (HOS) entropy features extracted from these patterns appropriately distinguished benign and malignant nodules under particle swarm optimization (PSO) and support vector machine (SVM) frameworks. Our CAD algorithm achieved a maximum accuracy of 97.71% and 97.01% in private and public datasets respectively. The evaluation of this CAD system on both private and public datasets confirmed its effectiveness as a secondary tool in assisting radiological findings.

Matched MeSH terms: Databases, Factual*
Fulltext Determining the endocrine disruption potential of industrial chemicals using an integrative approach: Public databases, in vitro exposure, and modeling receptor interactions

Alofe O, Kisanga E, Inayat-Hussain SH, Fukumura M, Garcia-Milian R, Perera L, et al.

Environ Int, 2019 10;131:104969.
PMID: 31310931 DOI: 10.1016/j.envint.2019.104969

Environmental and occupational exposure to industrial chemicals has been linked to toxic and carcinogenic effects in animal models and human studies. However, current toxicology testing does not thoroughly explore the endocrine disrupting effects of industrial chemicals, which may have low dose effects not predicted when determining the limit of toxicity. The objective of this study was to evaluate the endocrine disrupting potential of a broad range of chemicals used in the petrochemical sector. Therefore, 139 chemicals were classified for reproductive toxicity based on the United Nations Globally Harmonized System for hazard classification. These chemicals were evaluated in PubMed for reported endocrine disrupting activity, and their endocrine disrupting potential was estimated by identifying chemicals with active nuclear receptor endpoints publicly available databases. Evaluation of ToxCast data suggested that these chemicals preferentially alter the activity of the estrogen receptor (ER). Four chemicals were prioritized for in vitro testing using the ER-positive, immortalized human uterine Ishikawa cell line and a range of concentrations below the reported limit of toxicity in humans. We found that 2,6-di-tert-butyl-p-cresol (BHT) and diethanolamine (DEA) repressed the basal expression of estrogen-responsive genes PGR, NPPC, and GREB1 in Ishikawa cells, while tetrachloroethylene (PCE) and 2,2'-methyliminodiethanol (MDEA) induced the expression of these genes. Furthermore, low-dose combinations of PCE and MDEA produced additive effects. All four chemicals interfered with estradiol-mediated induction of PGR, NPPC, and GREB1. Molecular docking demonstrated that these chemicals could bind to the ligand binding site of ERα, suggesting the potential for direct stimulatory or inhibitory effects. We found that these chemicals altered rates of proliferation and regulated the expression of cell proliferation associated genes. These findings demonstrate previously unappreciated endocrine disrupting effects and underscore the importance of testing the endocrine disrupting potential of chemicals in the future to better understand their potential to impact public health.

Matched MeSH terms: Databases, Factual*
Fulltext Preliminary results from the pilot study on the National Cancer Patient Registry--Cervical Cancer

Devi Beena CR, Tang TS, Gerard LC

Med J Malaysia, 2008 Sep;63 Suppl C:63-5.
PMID: 19230250

Carcinoma of the cervix is the most common malignancy in many developing countries. The purpose of this pilot study on cervical cancer patients treated at selected sites in Malaysia is to examine the achievability of collecting information on patients. The data was collected from the medical records of the patients using case report form. The results reveal that more than 90% of the forms had completed data from all sites. The pilot study has demonstrated that it is feasible to register and collect information on cervical cancer patients using the case report forms. Treatment outcome obtained from this data will form the baseline to establish existing clinical practice and will be useful for treating physicians to monitor the treatment outcome and the late complications and with longer followup to measure the disease free and overall survival. In addition, it is an useful tool as the national indicator.

Matched MeSH terms: Databases, Factual/standards; Databases, Factual/statistics & numerical data*
Fulltext Non-face-to-face physical activity interventions in older adults: a systematic review

Müller AM, Khoo S

Int J Behav Nutr Phys Act, 2014;11(1):35.
PMID: 24612748 DOI: 10.1186/1479-5868-11-35

Physical activity is effective in preventing chronic diseases, increasing quality of life and promoting general health in older adults, but most older adults are not sufficiently active to gain those benefits. A novel and economically viable way to promote physical activity in older adults is through non-face-to-face interventions. These are conducted with reduced or no in-person interaction between intervention provider and program participants. The aim of this review was to summarize the scientific literature on non-face-to-face physical activity interventions targeting healthy, community dwelling older adults (≥ 50 years). A systematic search in six databases was conducted by combining multiple key words of the three main search categories "physical activity", "media" and "older adults". The search was restricted to English language articles published between 1st January 2000 and 31st May 2013. Reference lists of relevant articles were screened for additional publications. Seventeen articles describing sixteen non-face-to-face physical activity interventions were included in the review. All studies were conducted in developed countries, and eleven were randomized controlled trials. Sample size ranged from 31 to 2503 participants, and 13 studies included 60% or more women. Interventions were most frequently delivered via print materials and phone (n=11), compared to internet (n=3) and other media (n=2). Every intervention was theoretically framed with the Social Cognitive Theory (n=10) and the Transtheoretical Model of Behavior Change (n=6) applied mostly. Individual tailoring was reported in 15 studies. Physical activity levels were self-assessed in all studies. Fourteen studies reported significant increase in physical activity. Eight out of nine studies conducted post-intervention follow-up analysis found that physical activity was maintained over a longer time. In the six studies where intervention dose was assessed the results varied considerably. One study reported that 98% of the sample read the respective intervention newsletters, whereas another study found that only 4% of its participants visited the intervention website more than once. From this review, non-face-to-face physical activity interventions effectively promote physical activity in older adults. Future research should target diverse older adult populations in multiple regions while also exploring the potential of emerging technologies.

Matched MeSH terms: Databases, Factual
Fulltext Severity-based adaptation with limited data for ASR to aid dysarthric speakers

Mustafa MB, Salim SS, Mohamed N, Al-Qatab B, Siong CE

PLoS One, 2014;9(1):e86285.
PMID: 24466004 DOI: 10.1371/journal.pone.0086285

Automatic speech recognition (ASR) is currently used in many assistive technologies, such as helping individuals with speech impairment in their communication ability. One challenge in ASR for speech-impaired individuals is the difficulty in obtaining a good speech database of impaired speakers for building an effective speech acoustic model. Because there are very few existing databases of impaired speech, which are also limited in size, the obvious solution to build a speech acoustic model of impaired speech is by employing adaptation techniques. However, issues that have not been addressed in existing studies in the area of adaptation for speech impairment are as follows: (1) identifying the most effective adaptation technique for impaired speech; and (2) the use of suitable source models to build an effective impaired-speech acoustic model. This research investigates the above-mentioned two issues on dysarthria, a type of speech impairment affecting millions of people. We applied both unimpaired and impaired speech as the source model with well-known adaptation techniques like the maximum likelihood linear regression (MLLR) and the constrained-MLLR(C-MLLR). The recognition accuracy of each impaired speech acoustic model is measured in terms of word error rate (WER), with further assessments, including phoneme insertion, substitution and deletion rates. Unimpaired speech when combined with limited high-quality speech-impaired data improves performance of ASR systems in recognising severely impaired dysarthric speech. The C-MLLR adaptation technique was also found to be better than MLLR in recognising mildly and moderately impaired speech based on the statistical analysis of the WER. It was found that phoneme substitution was the biggest contributing factor in WER in dysarthric speech for all levels of severity. The results show that the speech acoustic models derived from suitable adaptation techniques improve the performance of ASR systems in recognising impaired speech with limited adaptation data.

Matched MeSH terms: Databases, Factual
Fulltext Challenges of the next decade for the Asia Pacific region: 2010 International Conference in Bioinformatics (InCoB 2010)

Ranganathan S, Schönbach C, Nakai K, Tan TW

BMC Genomics, 2010;11 Suppl 4:S1.
PMID: 21143792 DOI: 10.1186/1471-2164-11-S4-S1

The 2010 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation formed in 1998, was organized as the 9th International Conference on Bioinformatics (InCoB), Sept. 26-28, 2010 in Tokyo, Japan. Initially, APBioNet created InCoB as forum to foster bioinformatics in the Asia Pacific region. Given the growing importance of interdisciplinary research, InCoB2010 included topics targeting scientists in the fields of genomic medicine, immunology and chemoinformatics, supporting translational research. Peer-reviewed manuscripts that were accepted for publication in this supplement, represent key areas of research interests that have emerged in our region. We also highlight some of the current challenges bioinformatics is facing in the Asia Pacific region and conclude our report with the announcement of APBioNet's 100 BioDatabases (BioDB100) initiative. BioDB100 will comply with the database criteria set out earlier in our proposal for Minimum Information about a Bioinformatics and Investigation (MIABi), setting the standards for biocuration and bioinformatics research, on which we will report at the next InCoB, Nov. 27 - Dec. 2, 2011 at Kuala Lumpur, Malaysia.

Matched MeSH terms: Databases, Factual
Risk management from an Asian/Pacific Rim regulatory perspective

McEwen J

Drug Saf, 2004;27(8):491-7.
PMID: 15154822

This article reviews the state of adverse drug reaction monitoring in five Asian/Pacific Rim countries (Australia, Japan, Malaysia, New Zealand and Singapore). Each country has an active pharmacovigilance programme managed by a national regulatory agency. Current methods for assessing risks and current methods used for risk management and communication are compared with the 'tools' used by the US FDA. Major positive attributes of the programmes in all five countries include active involvement of independent expert clinical advisory committees in identifying and evaluating risks through the assessment of reports of serious and unusual reactions, and regular communications about risks from the national agencies to doctors and pharmacists by means of pharmacovigilance bulletins. Most components of the risk-management toolbox are currently used, in some instances without legislated support. Variations in the way risk-management tools are implemented within individual national health systems are illustrated.

Matched MeSH terms: Databases, Factual
Allopurinol-induced severe cutaneous adverse drug reactions: Risk minimization measures in Malaysia

Panickar R, Wo WK, Ali NM, Tang MM, Ramanathan GRL, Kamarulzaman A, et al.

Pharmacoepidemiol Drug Saf, 2020 10;29(10):1254-1262.
PMID: 33084196 DOI: 10.1002/pds.5033

PURPOSE: To describe risk minimization measures (RMMs) implemented in Malaysia for allopurinol-induced severe cutaneous adverse drug reactions (SCARs) and examine their impact using real-world data on allopurinol usage and adverse drug reaction (ADR) reports associated with allopurinol.
METHODS: Data on allopurinol ADR reports (2000-2018) were extracted from the Malaysian ADR database. We identified RMMs implemented between 2000 and 2018 from the minutes of relevant meetings and the national pharmacovigilance newsletter. We obtained allopurinol utilization data (2004-2018) from the Pharmaceutical Services Programme. To determine the impact of RMMs on ADR reporting, we considered ADR reports received within 1 year of RMM implementation. We used the Pearson χ2 test to examine the relation between the implementation of RMMs and allopurinol ADR reports.
RESULTS: The 16 RMMs for allopurinol-related SCARs implemented in Malaysia involved nine risk communications, four prescriber or patient educational material, and three health system innovations. Allopurinol utilization decreased by 21.5% from 2004 to 2018. ADR reporting rates for all drugs (n = 144 507) and allopurinol (n = 1747) increased. ADR reports involving off-label use decreased by 6% from 2011. SCARs cases remained between 20% and 50%. RMMs implemented showed statistically significant reduction in ADR reports involving off-label use for August 2014 [χ2(1, N = 258) = 5.32, P = .021] and October 2016 [χ2(1, N = 349) = 3.85, P = .0499].
CONCLUSIONS: RMMs to promote the appropriate use of allopurinol and prescriber education have a positive impact. We need further measures to reduce the incidence and severity of allopurinol-induced SCARs, such as patient education and more research into pharmacogenetic screening.

Matched MeSH terms: Databases, Factual
Flickering of cardiac state before the onset and termination of atrial fibrillation

Lan BL, Liew YW, Toda M, Kamsani SH

Chaos, 2020 May;30(5):053137.
PMID: 32491883 DOI: 10.1063/1.5130524

Complex dynamical systems can shift abruptly from a stable state to an alternative stable state at a tipping point. Before the critical transition, the system either slows down in its recovery rate or flickers between the basins of attraction of the alternative stable states. Whether the heart critically slows down or flickers before it transitions into and out of paroxysmal atrial fibrillation (PAF) is still an open question. To address this issue, we propose a novel definition of cardiac states based on beat-to-beat (RR) interval fluctuations derived from electrocardiogram data. Our results show the cardiac state flickers before PAF onset and termination. Prior to onset, flickering is due to a "tug-of-war" between the sinus node (the natural pacemaker) and atrial ectopic focus/foci (abnormal pacemakers), or the pacing by the latter interspersed among the pacing by the former. It may also be due to an abnormal autonomic modulation of the sinus node. This abnormal modulation may be the sole cause of flickering prior to termination since atrial ectopic beats are absent. Flickering of the cardiac state could potentially be used as part of an early warning or screening system for PAF and guide the development of new methods to prevent or terminate PAF. The method we have developed to define system states and use them to detect flickering can be adapted to study critical transition in other complex systems.

Matched MeSH terms: Databases, Factual
Fulltext Prevalence of occupational diseases among small and medium industry workers in Malaysia: a systematic review

Muhammad Zubir Yusof, Nik Ahmad Kamal Nik Mahmod, Nor Azlina A. Rahman, Ailin Razali, Niza Samsuddin, Nik Mohamed Nizan Nik Mohamed, et al.

Journal of Clinical and Health Sciences, 2019;4(2):4-30.
MyJurnal

Occupational diseases are one of the major health problems related to workplace hazards.
However, the epidemiological data for this problem is scarce especially among Small and
Medium Industry (SMI) workers. These workers are vulnerable to occupational health problem
due to lack of knowledge and implementation of health and safety in the workplace. In Malaysia,
most of the SMI workers have limited coverage for basic occupational health services which
may worsen their health. Thus, this article aims to provide a review on the burden of
occupational health problems among them. The electronic and library searches were used to
extract the information from both published and unpublished articles that were not limited to any
year of publication until 2017. One hundred and ninety-six published articles and 198
unpublished articles were retrieved from the database. Only 19 published articles and 25
unpublished articles met the eligibility criteria. Prevalence data of occupational
diseases/poisoning, including overall and body specific (musculoskeletal disorders) was
extracted in raw data from the eligible studies. Prevalent statistics on occupational
musculoskeletal diseases (1.3% - 97.6%), noise-induced hearing loss (29.4% - 73.3%),
occupational skin diseases (10.5% - 84.3%), respiratory (1.9% - 92.2%) and occupational
poisoning (14.9% - 17.7%) among the working population is different within published papers
compared to unpublished ones. In Malaysia, there are no specific statistic that give a true picture
of the burden of occupational diseases in the SMI. However, this review concludes that
musculoskeletal diseases are significant occupational problems among SMI workers.

Matched MeSH terms: Databases, Factual
Fulltext An Automated Patient Self-Monitoring System to Reduce Health Care System Burden During the COVID-19 Pandemic in Malaysia: Development and Implementation Study

Lim HM, Teo CH, Ng CJ, Chiew TK, Ng WL, Abdullah A, et al.

JMIR Med Inform, 2021 Feb 26;9(2):e23427.
PMID: 33600345 DOI: 10.2196/23427

BACKGROUND: During the COVID-19 pandemic, there was an urgent need to develop an automated COVID-19 symptom monitoring system to reduce the burden on the health care system and to provide better self-monitoring at home.
OBJECTIVE: This paper aimed to describe the development process of the COVID-19 Symptom Monitoring System (CoSMoS), which consists of a self-monitoring, algorithm-based Telegram bot and a teleconsultation system. We describe all the essential steps from the clinical perspective and our technical approach in designing, developing, and integrating the system into clinical practice during the COVID-19 pandemic as well as lessons learned from this development process.
METHODS: CoSMoS was developed in three phases: (1) requirement formation to identify clinical problems and to draft the clinical algorithm, (2) development testing iteration using the agile software development method, and (3) integration into clinical practice to design an effective clinical workflow using repeated simulations and role-playing.
RESULTS: We completed the development of CoSMoS in 19 days. In Phase 1 (ie, requirement formation), we identified three main functions: a daily automated reminder system for patients to self-check their symptoms, a safe patient risk assessment to guide patients in clinical decision making, and an active telemonitoring system with real-time phone consultations. The system architecture of CoSMoS involved five components: Telegram instant messaging, a clinician dashboard, system administration (ie, back end), a database, and development and operations infrastructure. The integration of CoSMoS into clinical practice involved the consideration of COVID-19 infectivity and patient safety.
CONCLUSIONS: This study demonstrated that developing a COVID-19 symptom monitoring system within a short time during a pandemic is feasible using the agile development method. Time factors and communication between the technical and clinical teams were the main challenges in the development process. The development process and lessons learned from this study can guide the future development of digital monitoring systems during the next pandemic, especially in developing countries.

Matched MeSH terms: Databases, Factual

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