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Fulltext Bibliography of clinical research in Malaysia: methods and brief results

Teng CL, Zuhanariah MN, Ng CS, Goh CC

Med J Malaysia, 2014 Aug;69 Suppl A:4-7.
PMID: 25417946

This article describes the methodology of this bibliography. A search was conducted on the following: (1) bibliographic databases (PubMed, Scopus, and other databases) using search terms that maximize the retrieval of Malaysian publications; (2) Individual journal search of Malaysian healthrelated journals; (3) A targeted search of Google and Google Scholar; (4) Searching of Malaysian institutional repositories; (5) Searching of Ministry of Health and Clinical Research Centre website. The publication years were limited to 2000- 2013. The citations were imported or manually entered into bibliographic software Refworks. After removing duplicates, and correcting data entry errors, PubMed's Medical Subject Headings (MeSH terms) were added. Clinical research is coded using the definition "patient-oriented-research or research conducted with human subjects (or on material of human origin) for which the investigator directly interacts with the human subjects at some point during the study." A bibliography of citations [n=2056] that fit the criteria of clinical research in Malaysia in selected topics within five domains was generated: Cancers [589], Cardiovascular diseases [432], Infections [795], Injuries [142], and Mental Health [582]. This is done by retrieving citations with the appropriate MESH terms, as follow: For cancers (Breast Neoplasms; Colorectal Neoplasms; Uterine Cervical Neoplasms), for cardiovascular diseases (Coronary Disease; Hypertension; Stroke), for infections (Dengue; Enterovirus Infections, HIV Infections; Malaria; Nipah Virus; Tuberculosis), for injuries (Accidents, Occupational; Accidents, Traffic; Child Abuse; Occupational Injuries), for mental health (Depression; Depressive Disorder; Depressive Disorder, Major; Drug Users; Psychotic Disorders; Suicide; Suicide, Attempted; Suicidal Ideation; Substance- Related Disorders).
Achieving triple treatment goals in multi-ethnic Asian patients with type 2 diabetes mellitus in primary care

Goh CC, Koh KH, Goh S, Koh Y, Tan NC

Malays Fam Physician, 2018;13(2):10-18.
PMID: 30302178

Introduction: Achieving optimal glycated hemoglobin (HbA1c), blood pressure (BP), and LDL-Cholesterol (LDL-C) in patients mitigates macro- and micro-vascular complications, which is the key treatment goal in managing type 2 diabetes mellitus (T2DM). This study aimed to determine the proportion of patients in an urban community with T2DM and the above modifiable conditions attaining triple vascular treatment goals based on current practice guidelines.
Methods: A questionnaire was distributed to adult Asian patients with dyslipidemia at two primary care clinics (polyclinics) in northeastern Singapore. The demographic and clinical data for this sub-population with both T2DM and dyslipidemia were collated with laboratory and treatment information retrieved from their electronic health records. The combined data was then analyzed to determine the proportion of patients who attained triple treatment goals, and logistic regression analysis was used to identify factors associated with this outcome.
Results: 665 eligible patients [60.5% female, 30.5% Chinese, 35% Malays, and 34.4% Indians] with a mean age of 60.6 years were recruited. Of these patients, 71% achieved LDL-C ≤2.6 mmol/L, 70.4% had BP
Fulltext Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm

Goh CC, Kamarudin LM, Zakaria A, Nishizaki H, Ramli N, Mao X, et al.

Sensors (Basel), 2021 Jul 21;21(15).
PMID: 34372192 DOI: 10.3390/s21154956

This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers' drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.

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