Displaying publications 41 - 60 of 852 in total

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  1. Aziz Shafie
    Sains Malaysiana, 2011;40:1179-1186.
    In Malaysia, the incidence of Dengue Fever (DF) and Dengue Hemorrhagic Fever (DHF) have risen dramatically in the last twenty years. With the use of Geographical Information System an explanation for the spread and control of these diseases can be obtained. This study aims to develop a spatial modeling that can predict the risks for DF and DHF based on environmental factors such as physical surroundings, land use, rainfall, temperature and GIS application using logistic regression. A total of 16 variables were used in the process of spatial modeling development. At the significant level of 0.05, the results of logistic regression showed that only 10 out of 16 significant variables in the modeling process. The accuracy of the resulting model is 70.3%. A crucial feature of this study is a risk area map for incidence of DF and DHF in the study area. This study also highlights the application of spatial analysis in planning and implementing the process for the prevention and control activities of DF and DHF in Malaysia.
    Matched MeSH terms: Logistic Models
  2. Mohammed M, Omar N
    PLoS One, 2020;15(3):e0230442.
    PMID: 32191738 DOI: 10.1371/journal.pone.0230442
    The assessment of examination questions is crucial in educational institutes since examination is one of the most common methods to evaluate students' achievement in specific course. Therefore, there is a crucial need to construct a balanced and high-quality exam, which satisfies different cognitive levels. Thus, many lecturers rely on Bloom's taxonomy cognitive domain, which is a popular framework developed for the purpose of assessing students' intellectual abilities and skills. Several works have been proposed to automatically handle the classification of questions in accordance with Bloom's taxonomy. Most of these works classify questions according to specific domain. As a result, there is a lack of technique of classifying questions that belong to the multi-domain areas. The aim of this paper is to present a classification model to classify exam questions based on Bloom's taxonomy that belong to several areas. This study proposes a method for classifying questions automatically, by extracting two features, TFPOS-IDF and word2vec. The purpose of the first feature was to calculate the term frequency-inverse document frequency based on part of speech, in order to assign a suitable weight for essential words in the question. The second feature, pre-trained word2vec, was used to boost the classification process. Then, the combination of these features was fed into three different classifiers; K-Nearest Neighbour, Logistic Regression, and Support Vector Machine, in order to classify the questions. The experiments used two datasets. The first dataset contained 141 questions, while the other dataset contained 600 questions. The classification result for the first dataset achieved an average of 71.1%, 82.3% and 83.7% weighted F1-measure respectively. The classification result for the second dataset achieved an average of 85.4%, 89.4% and 89.7% weighted F1-measure respectively. The finding from this study showed that the proposed method is significant in classifying questions from multiple domains based on Bloom's taxonomy.
    Matched MeSH terms: Logistic Models
  3. Yung DTC, Jani R, Azizi R, Ramli MN, Haidi Y, Zainudin AN, et al.
    Data Brief, 2020 Apr;29:105082.
    PMID: 31993462 DOI: 10.1016/j.dib.2019.105082
    In this data article we present the determinations of the diet preference and growth of a pair of the giant panda, Ailuropoda melanoleuca (David, 1869) from Zoo Negara Malaysia. Once considered as endangered, the captive giant pandas were given with nine species of local bamboo in separate indoor enclosures. We recorded data between May 25, 2014 and December 31, 2016 and analysed it based on food preference, the pattern toward food consumption and body weights using SPSS v25.0 (IBM, USA). Data on the bamboo preference, daily average bamboo provided and consumed, and factors predicting of body weight per individual are reported in this article. The data highlight correlation between panda growth (kg) to the part of bamboo consumed (kg) and exhibit the pattern of preferred part of food (i.e.: either the leaf, culm or shoots of bamboo variety) for panda consumptions. The food consumption toward the body weight was modelled using logistic regression analysis to help determine the pattern of food consumption and body weight of giant panda in the future and based on regression model 1, only consumed variable is significance to the model.
    Matched MeSH terms: Logistic Models
  4. Philip N, Lung Than LT, Shah AM, Yuhana MY, Sekawi Z, Neela VK
    BMC Infect Dis, 2021 Oct 19;21(1):1081.
    PMID: 34666707 DOI: 10.1186/s12879-021-06766-5
    BACKGROUND: Leptospirosis is a re-emerging disease with vast clinical presentations, that ranges from subclinical or mild to severe and fatal outcomes. Leptospirosis can be managed well if diagnosed earlier, however, similar clinical presentations by several other febrile illnesses or co-infections, and laboratory diagnostic challenges due to the biphasic nature of the illness, often result in mis- or underdiagnosis, thereby lead to severe illness. Identification of clinical predictors for the severe form of the disease plays a crucial role in reducing disease complication and mortality. Therefore, we aimed to determine the clinical predictors associated with severe illness among leptospirosis patients from Central Malaysia through a prospective multicenter observational study.

    METHODS: A prospective multicenter observational study was performed on patients admitted for clinically suspected leptospirosis. Three hospitals namely Hospital Serdang, Hospital Tengku Ampuan Rahimah and Hospital Teluk Intan were included in the study. Among a total of 165 clinically suspected leptospirosis patients, 83 confirmed cases were investigated for clinical predictors for severe illness. Qualitative variables were performed using χ2 and the relationship between mild and severe cases was evaluated using logistic regression. Multivariable logistic regression was used to predict the independent variable for severity.

    RESULTS: Among the 83 patients, 50 showed mild disease and 33 developed severe illness. The mean age of the patients was 41.92 ± 17.99 and most were males (n = 54, 65.06%). We identified mechanical ventilation, acute kidney injury, septic shock, creatinine level of > 1.13 mg/dL, urea > 7 mmol/L, alanine aminotransferase > 50 IU, aspartate aminotransferase > 50 IU, and platelet  50 IU and platelet 

    Matched MeSH terms: Logistic Models
  5. Nettemu SK, Nettem S, Singh VP, William SS, Gunasekaran SS, Krisnan M, et al.
    Int J Implant Dent, 2021 06 10;7(1):77.
    PMID: 34109477 DOI: 10.1186/s40729-021-00315-0
    AIM: This study was to evaluate the association between peri-implant bleeding on probing in peri-implant diseases and its association with multilevel factors (site specific factors, implant factors, and patient level factors).

    METHODOLOGY: A cross-sectional study involved consented adult patients with ≥ 1 dental implant. Two calibrated operators examined the patients. BoP was outcome variable and peri-implant gingival biotype was principal predictor variable. The effects of site, implant, and patient level factors on BoP were assessed using a multilevel logistic regression model.

    RESULTS: Eighty patients for a total of 119 implants and 714 sites were included in the study. Bleeding on probing was observed in 42 implants (35.29%) with a significant higher risk observed in presence of gingival recession, thin peri-implant gingival biotype, duration of implant placement, smokers, and male patients.

    CONCLUSION: Peri-implant bleeding on probing was associated with site specific, implant, and patient level factors.

    Matched MeSH terms: Logistic Models
  6. Sadagatullah AN, Nazeeb MN, Ibrahim S
    Malays Orthop J, 2017 Nov;11(3):31-35.
    PMID: 29326763 MyJurnal DOI: 10.5704/MOJ.1711.013
    Introduction: Osteosynthesis of the femur using an interlocking nail is the gold standard for treating diaphyseal fractures of the femur. There are two established entry points for the antegrade interlocking nails which is the piriformis fossa or the greater trochanter. It has been reported that varus malalignment was frequently seen in proximal femur fracture which were treated with interlocking nail utilizing the greater trochanter entry point. The study was done to find out if the problem was of significance. Materials and Methods: This was a retrospective study which included 179 patients with femur fractures which were treated from January 2013 till September 2015 in one Hospital. They were treated with interlocking nail either by utilizing the piriformis fossa (PF) or the greater trochanter (GT) entry points. Post-operative radiographs of the femur were used to measure the varus deformity. Results: Out of 179 patients, there were 5 patients who were reported to have unacceptable varus malalignment (2.79%). These 5 patients were out of the 88 (5.68%) patients utilizing the greater trochanter as the entry point. The same 5 patients were out 90 patients that were diagnosed with proximal femur shaft fractures (5.55%). Analysis with logistic regression was statistically not significant. Conclusion: There was higher rate of varus malalignment seen in proximal femur shaft fractures treated with interlocking nails utilizing the greater trochanter entry point. The incidence of varus malalignment was not significant statistically.
    Matched MeSH terms: Logistic Models
  7. Jani P, Mishra U, Buchmayer J, Maheshwari R, D'Çruz D, Walker K, et al.
    World J Pediatr, 2023 Feb;19(2):139-157.
    PMID: 36372868 DOI: 10.1007/s12519-022-00625-2
    BACKGROUND: Globally, are skincare practices and skin injuries in extremely preterm infants comparable? This study describes skin injuries, variation in skincare practices and investigates any association between them.

    METHODS: A web-based survey was conducted between February 2019 and August 2021. Quantifying skin injuries and describing skincare practices in extremely preterm infants were the main outcomes. The association between skin injuries and skincare practices was established using binary multivariable logistic regression adjusted for regions.

    RESULTS: Responses from 848 neonatal intensive care units, representing all geographic regions and income status groups were received. Diaper dermatitis (331/840, 39%) and medical adhesive-related skin injuries (319/838, 38%) were the most common injuries. Following a local skincare guideline reduced skin injuries [medical adhesive-related injuries: adjusted odds ratios (aOR) = 0.63, 95% confidence interval (CI) = 0.45-0.88; perineal injuries: aOR = 0.66, 95% CI = 0.45-0.96; local skin infections: OR = 0.41, 95% CI = 0.26-0.65; chemical burns: OR = 0.46, 95% CI = 0.26-0.83; thermal burns: OR = 0.51, 95% CI = 0.27-0.96]. Performing skin assessments at least every four hours reduced skin injuries (abrasion: aOR = 0.48, 95% CI = 0.33-0.67; pressure: aOR = 0.51, 95% CI = 0.34-0.78; diaper dermatitis: aOR = 0.71, 95% CI = 0.51-0.99; perineal: aOR = 0.52, 95% CI = 0.36-0.75). Regional and resource settings-based variations in skin injuries and skincare practices were observed.

    CONCLUSIONS: Skin injuries were common in extremely preterm infants. Consistency in practice and improved surveillance appears to reduce the occurrence of these injuries. Better evidence regarding optimal practices is needed to reduce skin injuries and minimize practice variations.

    Matched MeSH terms: Logistic Models
  8. Ibrahim N, Foo LK, Chua SL
    PMID: 36833984 DOI: 10.3390/ijerph20043289
    Osteoporosis is a serious bone disease that affects many people worldwide. Various drugs have been used to treat osteoporosis. However, these drugs may cause severe adverse events in patients. Adverse drug events are harmful reactions caused by drug usage and remain one of the leading causes of death in many countries. Predicting serious adverse drug reactions in the early stages can help save patients' lives and reduce healthcare costs. Classification methods are commonly used to predict the severity of adverse events. These methods usually assume independence among attributes, which may not be practical in real-world applications. In this paper, a new attribute weighted logistic regression is proposed to predict the severity of adverse drug events. Our method relaxes the assumption of independence among the attributes. An evaluation was performed on osteoporosis data obtained from the United States Food and Drug Administration databases. The results showed that our method achieved a higher recognition performance and outperformed baseline methods in predicting the severity of adverse drug events.
    Matched MeSH terms: Logistic Models
  9. Musa MF, Hassan SA, Mashros N
    PLoS One, 2020;15(7):e0235564.
    PMID: 32628689 DOI: 10.1371/journal.pone.0235564
    The fatal accidents on the roads remain a global concern. Daily, approximately 18 traffic accidents occur in the Peninsular Malaysia that cause on an average one death in every hour, a situation that needs preventive measures. The development of the effective strategies to reduce such fatal accidents requires the identification of various risk factors including the road condition. We identified such accident severity issues using the public work and police department databases that consisted of 1067 cases of various severity levels occurred on the Malaysian federal roads during 2008 to 2015. These records were used to develop ordered logistic regression model for the accident severity and nine variables were analyzed. The results revealed that the presence of poor horizontal alignment affected the model outcomes. The likelihood of the more serious accident severity due to the poor horizontal alignment was correspondingly about 0.4 times less compared to the absence of such factors. It is established that the present findings may assist the local authorities to take proactive actions to prevent serious road accidents on the road segments possessing the standard horizontal alignment.
    Matched MeSH terms: Logistic Models
  10. Song J, Shin SD, Jamaluddin SF, Chiang WC, Tanaka H, Song KJ, et al.
    J Neurotrauma, 2023 Jul;40(13-14):1376-1387.
    PMID: 36656672 DOI: 10.1089/neu.2022.0280
    Abstract Traumatic brain injury (TBI) is a significant healthcare concern in several countries, accounting for a major burden of morbidity, mortality, disability, and socioeconomic losses. Although conventional prognostic models for patients with TBI have been validated, their performance has been limited. Therefore, we aimed to construct machine learning (ML) models to predict the clinical outcomes in adult patients with isolated TBI in Asian countries. The Pan-Asian Trauma Outcome Study registry was used in this study, and the data were prospectively collected from January 1, 2015, to December 31, 2020. Among a total of 6540 patients (≥ 15 years) with isolated moderate and severe TBI, 3276 (50.1%) patients were randomly included with stratification by outcomes and subgrouping variables for model evaluation, and 3264 (49.9%) patients were included for model training and validation. Logistic regression was considered as a baseline, and ML models were constructed and evaluated using the area under the precision-recall curve (AUPRC) as the primary outcome metric, area under the receiver operating characteristic curve (AUROC), and precision at fixed levels of recall. The contribution of the variables to the model prediction was measured using the SHapley Additive exPlanations (SHAP) method. The ML models outperformed logistic regression in predicting the in-hospital mortality. Among the tested models, the gradient-boosted decision tree showed the best performance (AUPRC, 0.746 [0.700-0.789]; AUROC, 0.940 [0.929-0.952]). The most powerful contributors to model prediction were the Glasgow Coma Scale, O2 saturation, transfusion, systolic and diastolic blood pressure, body temperature, and age. Our study suggests that ML techniques might perform better than conventional multi-variate models in predicting the outcomes among adult patients with isolated moderate and severe TBI.
    Matched MeSH terms: Logistic Models
  11. Law ZK, Appleton JP, Scutt P, Roberts I, Al-Shahi Salman R, England TJ, et al.
    Stroke, 2022 Apr;53(4):1141-1148.
    PMID: 34847710 DOI: 10.1161/STROKEAHA.121.035191
    BACKGROUND: Seeking consent rapidly in acute stroke trials is crucial as interventions are time sensitive. We explored the association between consent pathways and time to enrollment in the TICH-2 (Tranexamic Acid in Intracerebral Haemorrhage-2) randomized controlled trial.

    METHODS: Consent was provided by patients or by a relative or an independent doctor in incapacitated patients, using a 1-stage (full written consent) or 2-stage (initial brief consent followed by full written consent post-randomization) approach. The computed tomography-to-randomization time according to consent pathways was compared using the Kruskal-Wallis test. Multivariable logistic regression was performed to identify variables associated with onset-to-randomization time of ≤3 hours.

    RESULTS: Of 2325 patients, 817 (35%) gave self-consent using 1-stage (557; 68%) or 2-stage consent (260; 32%). For 1507 (65%), consent was provided by a relative (1 stage, 996 [66%]; 2 stage, 323 [21%]) or a doctor (all 2-stage, 188 [12%]). One patient did not record prerandomization consent, with written consent obtained subsequently. The median (interquartile range) computed tomography-to-randomization time was 55 (38-93) minutes for doctor consent, 55 (37-95) minutes for 2-stage patient, 69 (43-110) minutes for 2-stage relative, 75 (48-124) minutes for 1-stage patient, and 90 (56-155) minutes for 1-stage relative consents (P<0.001). Two-stage consent was associated with onset-to-randomization time of ≤3 hours compared with 1-stage consent (adjusted odds ratio, 1.9 [95% CI, 1.5-2.4]). Doctor consent increased the odds (adjusted odds ratio, 2.3 [1.5-3.5]) while relative consent reduced the odds of randomization ≤3 hours (adjusted odds ratio, 0.10 [0.03-0.34]) compared with patient consent. Only 2 of 771 patients (0.3%) in the 2-stage pathways withdrew consent when full consent was sought later. Two-stage consent process did not result in higher withdrawal rates or loss to follow-up.

    CONCLUSIONS: The use of initial brief consent was associated with shorter times to enrollment, while maintaining good participant retention. Seeking written consent from relatives was associated with significant delays.

    REGISTRATION: URL: https://www.isrctn.com; Unique identifier: ISRCTN93732214.

    Matched MeSH terms: Logistic Models
  12. Thanigasalam T, Sahoo S, Kyaw Soe HH
    Malays J Med Sci, 2014 Jul;21(4):51-3.
    PMID: 25977622
    This study was done to correlate the occurrence of posterior capsule rupture among patients with pseudoexfoliation during phacoemulsification. This was a retrospective audit of patients who underwent phacoemulsification type cataract surgery from January 2011 to December 2012 in a tertiary hospital in Malaysia. Data was obtained from the National Eye Database (NED) of Malaysia. The data was analysed using SPSS version 21.0. By using logistic regression analysis, it was found that there was no significant increase in the occurrence of posterior capsule rupture among patients with pseudoexfoliation. Hence, we concluded that there was no correlation between the occurrence of posterior capsule rupture and the presence of pesudoexfoliation among patients who underwent phacoemulsification.
    Matched MeSH terms: Logistic Models
  13. Lim OW, Yong CC
    Malays J Med Sci, 2019 Sep;26(5):98-112.
    PMID: 31728122 MyJurnal DOI: 10.21315/mjms2019.26.5.9
    Background: The prevalence of known hypertension has resulted from the progression of undiagnosed hypertension. This study is targeted to examine and compare the risk factors based on the estimated odds ratios of modifiable and non-modifiable risk factors on different outcome levels of hypertension.
    Methods: A nationwide representative secondary data from the Fourth National Health of Morbidity Survey (NHMS IV) which consists of 24,632 non-institutionalised Malaysian population conducted by the Ministry of Health in 2011 has been used. Odds ratio (OR) with 95% confidence interval has been estimated using multinomial logistic regression.
    Results: Obese and overweight respondents exhibit increased likelihood of having undiagnosed and known hypertension. Physically inactive, ex-smokers and unclassified drinkers are found having higher likelihood to have known hypertension. However, current drinkers are found to have higher likelihood of having undiagnosed hypertension. Elderly, retirees, home makers and lower educated respondents are shown higher odds to have undiagnosed hypertension. Likewise, the likelihood of having known hypertension has been found to increase among the elderly and other Bumiputra.
    Conclusion: Through this research, significant predictors which consist of obese and overweight respondents, current drinkers, older respondents (above 65 years old) and primary educated respondents are having higher likelihood to have undiagnosed hypertension.
    Study name: National Health and Morbidity Survey (NHMS-2011)
    Matched MeSH terms: Logistic Models
  14. Loo CK, Rajeswari M, Rao MV
    IEEE Trans Neural Netw, 2004 Nov;15(6):1378-95.
    PMID: 15565767
    This paper presents two novel approaches to determine optimum growing multi-experts network (GMN) structure. The first method called direct method deals with expertise domain and levels in connection with local experts. The growing neural gas (GNG) algorithm is used to cluster the local experts. The concept of error distribution is used to apportion error among the local experts. After reaching the specified size of the network, redundant experts removal algorithm is invoked to prune the size of the network based on the ranking of the experts. However, GMN is not ergonomic due to too many network control parameters. Therefore, a self-regulating GMN (SGMN) algorithm is proposed. SGMN adopts self-adaptive learning rates for gradient-descent learning rules. In addition, SGMN adopts a more rigorous clustering method called fully self-organized simplified adaptive resonance theory in a modified form. Experimental results show SGMN obtains comparative or even better performance than GMN in four benchmark examples, with reduced sensitivity to learning parameters setting. Moreover, both GMN and SGMN outperform the other neural networks and statistical models. The efficacy of SGMN is further justified in three industrial applications and a control problem. It provides consistent results besides holding out a profound potential and promise for building a novel type of nonlinear model consisting of several local linear models.
    Matched MeSH terms: Logistic Models*
  15. Danial M, Hassali MA, Meng OL, Kin YC, Khan AH
    BMC Pharmacol Toxicol, 2019 07 08;20(1):41.
    PMID: 31287030 DOI: 10.1186/s40360-019-0318-6
    BACKGROUND: Chronic kidney disease (CKD) is a significant health burden that increases the risk of adverse events. Currently, there is no validated models to predict risk of mortality among CKD patients experienced adverse drug reactions (ADRs) during hospitalization. This study aimed to develop a mortality risk prediction model among hospitalized CKD patients whom experienced ADRs.

    METHODS: Patients data with CKD stages 3-5 admitted at various wards were included in the model development. The data collected included demographic characteristics, comorbid conditions, laboratory tests and types of medicines taken. Sequential series of logistic regression models using mortality as the dependent variable were developed. Bootstrapping method was used to evaluate the model's internal validation. Variables odd ratio (OR) of the best model were used to calculate the predictive capacity of the risk scores using the area under the curve (AUC).

    RESULTS: The best prediction model included comorbidities heart disease, dyslipidaemia and electrolyte imbalance; psychotic agents; creatinine kinase; number of total medication use; and conservative management (Hosmer and Lemeshow test =0.643). Model performance was relatively modest (R square = 0.399) and AUC which determines the risk score's ability to predict mortality associated with ADRs was 0.789 (95% CI, 0.700-0.878). Creatinine kinase, followed by psychotic agents and electrolyte disorder, was most strongly associated with mortality after ADRs during hospitalization. This model correctly predicts 71.4% of all mortality pertaining to ADRs (sensitivity) and with specificity of 77.3%.

    CONCLUSION: Mortality prediction model among hospitalized stages 3 to 5 CKD patients experienced ADR was developed in this study. This prediction model adds new knowledge to the healthcare system despite its modest performance coupled with its high sensitivity and specificity. This tool is clinically useful and effective in identifying potential CKD patients at high risk of ADR-related mortality during hospitalization using routinely performed clinical data.

    Matched MeSH terms: Logistic Models*
  16. Chemoh W, Sawangjaroen N, Siripaitoon P, Andiappan H, Hortiwakul T, Sermwittayawong N, et al.
    Front Microbiol, 2015;6:1304.
    PMID: 26635769 DOI: 10.3389/fmicb.2015.01304
    Toxoplasmosis is one of the most common opportunistic parasitic diseases in patients living with HIV/AIDS. This study aimed to determine the seroprevalence of Toxoplasma infection in HIV-infected patients and to identify associated risk factors in Toxoplasma seropositive patients. This study was conducted at a regional public hospital in Hat Yai, southern Thailand during October 2009 to June 2010. Blood samples were collected from 300 HIV-infected patients. Each subject also answered a socio-demographic and risk factors associated with Toxoplasma infection. The prevalence of anti-Toxoplasma IgG antibodies in HIV-infected patients was 109 (36.3%), of which 83 (76.2%) had past infection and 26 (23.9%) had recently acquired Toxoplasma infection as indicated by their IgG avidity. Multivariate analysis using logistic regression showed that gender difference (adjusted OR = 1.69, 95% CI = 1.05-2.72) was the only factor associated with Toxoplasma infection. From the results obtained, these HIV-infected patients could be at high risk of developing clinical evidence of severe toxoplasmosis. Therefore, it is necessary to introduce primary behavioral practices to prevent Toxoplasma infection among HIV-infected patients.
    Matched MeSH terms: Logistic Models
  17. Sien YP, Sahril N, Abdul Mutalip MH, Zaki NA, Abdul Ghaffar S
    Asia Pac J Public Health, 2014 Sep;26(5 Suppl):36S-43S.
    PMID: 25070694 DOI: 10.1177/1010539514543681
    Dietary supplements use is relatively widespread in some countries but knowledge of supplements consumption in Malaysia is limited, more so among adolescents. This study aimed to investigate the determinants of dietary supplements use among Malaysian adolescents using multiple logistic regressions analysis. Data from the Malaysia School-based Nutrition Survey 2012 based on a 2-stage stratified sampling was used. The prevalence of vitamin/mineral supplements and food supplements intake among adolescents was 54.1% and 40.2%, respectively. Usage was significantly higher among younger adolescents and among boys. Dietary supplements were also taken mostly by those who thought they were underweight. The most common vitamin/mineral supplements and food supplements consumed were vitamin C and bee products. The main reason for taking supplements was parents' instruction. These findings can be useful for developing health communications on supplement use targeted to adolescents and their parents.
    Matched MeSH terms: Logistic Models
  18. Mohebbi K, Ibrahim S, Zamani M, Khezrian M
    PLoS One, 2014;9(8):e104735.
    PMID: 25157872 DOI: 10.1371/journal.pone.0104735
    In this paper, a Semantic Web service matchmaker called UltiMatch-NL is presented. UltiMatch-NL applies two filters namely Signature-based and Description-based on different abstraction levels of a service profile to achieve more accurate results. More specifically, the proposed filters rely on semantic knowledge to extract the similarity between a given pair of service descriptions. Thus it is a further step towards fully automated Web service discovery via making this process more semantic-aware. In addition, a new technique is proposed to weight and combine the results of different filters of UltiMatch-NL, automatically. Moreover, an innovative approach is introduced to predict the relevance of requests and Web services and eliminate the need for setting a threshold value of similarity. In order to evaluate UltiMatch-NL, the repository of OWLS-TC is used. The performance evaluation based on standard measures from the information retrieval field shows that semantic matching of OWL-S services can be significantly improved by incorporating designed matching filters.
    Matched MeSH terms: Logistic Models
  19. Amin NA, Nordin R, Fatt QK, Noah RM, Oxley J
    PMID: 25852937 DOI: 10.1186/s40557-014-0023-2
    OBJECTIVE: This study examined the relationships between psychosocial work factors and risk of WRMSDs among public hospital nurses in the Klang Valley, Malaysia.

    METHODS: We conducted a cross-sectional study among 660 public hospital nurses. A self-administered questionnaire was used to collect data on the occurrence of WRMSDs according to body regions, socio-demographic profiles, occupational information and psychosocial risk factors. 468 questionnaires were returned (response rate of 71%), and 376 questionnaires qualified for subsequent analysis. Univariate analyses were applied to test for mean and categorical differences across the WRMSDs; multiple logistic regression was applied to predict WRMSDs based on the Job Strain Model's psychosocial risk factors.

    RESULTS: Over two thirds of the sample of nurses experienced discomfort or pain in at least one site of the musculoskeletal system within the last year. The neck was the most prevalent site (48.94%), followed by the feet (47.20%), the upper back (40.69%) and the lower back (35.28%). More than 50% of the nurses complained of having discomfort in region one (neck, shoulders and upperback) and region four (hips, knees, ankles, and feet). The results also revealed that psychological job demands, job strain and iso-strain ratio demonstrated statistically significant mean differences (p

    Matched MeSH terms: Logistic Models
  20. Manaf MR, Tahir MM, Sidi H, Midin M, Nik Jaafar NR, Das S, et al.
    Compr Psychiatry, 2014 Jan;55 Suppl 1:S82-8.
    PMID: 23587530 DOI: 10.1016/j.comppsych.2013.03.008
    This study aimed to examine the prevalence of pre-marital sex and its predicting factors among youth trainees undergoing a national skill training programme in the state of Malaysia.
    Matched MeSH terms: Logistic Models
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