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Fulltext Feature Selection for High-Dimensional and Imbalanced Biomedical Data Based on Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm

Abdulrauf Sharifai G, Zainol Z

Genes (Basel), 2020 06 27;11(7).
PMID: 32605144 DOI: 10.3390/genes11070717

The training machine learning algorithm from an imbalanced data set is an inherently challenging task. It becomes more demanding with limited samples but with a massive number of features (high dimensionality). The high dimensional and imbalanced data set has posed severe challenges in many real-world applications, such as biomedical data sets. Numerous researchers investigated either imbalanced class or high dimensional data sets and came up with various methods. Nonetheless, few approaches reported in the literature have addressed the intersection of the high dimensional and imbalanced class problem due to their complicated interactions. Lately, feature selection has become a well-known technique that has been used to overcome this problem by selecting discriminative features that represent minority and majority class. This paper proposes a new method called Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm (rCBR-BGOA); rCBR-BGOA has employed an ensemble of multi-filters coupled with the Correlation-Based Redundancy method to select optimal feature subsets. A binary Grasshopper optimisation algorithm (BGOA) is used to construct the feature selection process as an optimisation problem to select the best (near-optimal) combination of features from the majority and minority class. The obtained results, supported by the proper statistical analysis, indicate that rCBR-BGOA can improve the classification performance for high dimensional and imbalanced datasets in terms of G-mean and the Area Under the Curve (AUC) performance metrics.

Matched MeSH terms: Data Accuracy*
Errors in accident data, its types, causes and methods of rectification-analysis of the literature

Ahmed A, Sadullah AFM, Yahya AS

Accid Anal Prev, 2019 Sep;130:3-21.
PMID: 28764851 DOI: 10.1016/j.aap.2017.07.018

Most of the decisions taken to improve road safety are based on accident data, which makes it the back bone of any country's road safety system. Errors in this data will lead to misidentification of black spots and hazardous road segments, projection of false estimates pertinent to accidents and fatality rates, and detection of wrong parameters responsible for accident occurrence, thereby making the entire road safety exercise ineffective. Its extent varies from country to country depending upon various factors. Knowing the type of error in the accident data and the factors causing it enables the application of the correct method for its rectification. Therefore there is a need for a systematic literature review that addresses the topic at a global level. This paper fulfils the above research gap by providing a synthesis of literature for the different types of errors found in the accident data of 46 countries across the six regions of the world. The errors are classified and discussed with respect to each type and analysed with respect to income level; assessment with regard to the magnitude for each type is provided; followed by the different causes that result in their occurrence, and the various methods used to address each type of error. Among high-income countries the extent of error in reporting slight, severe, non-fatal and fatal injury accidents varied between 39-82%, 16-52%, 12-84%, and 0-31% respectively. For middle-income countries the error for the same categories varied between 93-98%, 32.5-96%, 34-99% and 0.5-89.5% respectively. The only four studies available for low-income countries showed that the error in reporting non-fatal and fatal accidents varied between 69-80% and 0-61% respectively. The logistic relation of error in accident data reporting, dichotomised at 50%, indicated that as the income level of a country increases the probability of having less error in accident data also increases. Average error in recording information related to the variables in the categories of location, victim's information, vehicle's information, and environment was 27%, 37%, 16% and 19% respectively. Among the causes identified for errors in accident data reporting, Policing System was found to be the most important. Overall 26 causes of errors in accident data were discussed out of which 12 were related to reporting and 14 were related to recording. "Capture-Recapture" was the most widely used method among the 11 different methods: that can be used for the rectification of under-reporting. There were 12 studies pertinent to the rectification of accident location and almost all of them utilised a Geographical Information System (GIS) platform coupled with a matching algorithm to estimate the correct location. It is recommended that the policing system should be reformed and public awareness should be created to help reduce errors in accident data.

Matched MeSH terms: Data Accuracy*
Accurate and dynamic predictive model for better prediction in medicine and healthcare

Alanazi HO, Abdullah AH, Qureshi KN, Ismail AS

Ir J Med Sci, 2018 May;187(2):501-513.
PMID: 28756541 DOI: 10.1007/s11845-017-1655-3

INTRODUCTION: Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance.
AIMS AND OBJECTIVES: In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life.
CONCLUSION: The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

Matched MeSH terms: Data Accuracy
Promoting Ethics and Integrity in Management Academic Research: Retraction Initiative

Ayodele FO, Yao L, Haron H

Sci Eng Ethics, 2019 04;25(2):357-382.
PMID: 29441445 DOI: 10.1007/s11948-017-9941-z

In the management academic research, academic advancement, job security, and the securing of research funds at one's university are judged mainly by one's output of publications in high impact journals. With bogus resumes filled with published journal articles, universities and other allied institutions are keen to recruit or sustain the appointment of such academics. This often places undue pressure on aspiring academics and on those already recruited to engage in research misconduct which often leads to research integrity. This structured review focuses on the ethics and integrity of management research through an analysis of retracted articles published from 2005 to 2016. The study employs a structured literature review methodology whereby retracted articles published between 2005 and 2016 in the field of management science were found using Crossref and Google Scholar. The searched articles were then streamlined by selecting articles based on their relevance and content in accordance with the inclusion criteria. Based on the analysed retracted articles, the study shows evidence of ethical misconduct among researchers of management science. Such misconduct includes data falsification, the duplication of submitted articles, plagiarism, data irregularity and incomplete citation practices. Interestingly, the analysed results indicate that the field of knowledge management includes the highest number of retracted articles, with plagiarism constituting the most significant ethical issue. Furthermore, the findings of this study show that ethical misconduct is not restricted to a particular geographic location; it occurs in numerous countries. In turn, avenues of further study on research misconduct in management research are proposed.

Matched MeSH terms: Data Accuracy
The Kinetics of Enzyme Mixtures

Brown S, Muhamad N, C Pedley K, C Simcock D

Mol Biol Res Commun, 2014 Mar;3(1):21-32.
PMID: 27843974

Even purified enzyme preparations are often heterogeneous. For example, preparations of aspartate aminotransferase or cytochrome oxidase can consist of several different forms of the enzyme. For this reason we consider how different the kinetics of the reactions catalysed by a mixture of forms of an enzyme must be to provide some indication of the characteristics of the species present. Based on the standard Michaelis-Menten model, we show that if the Michaelis constants (Km) of two isoforms differ by a factor of at least 20 the steady-state kinetics can be used to characterise the mixture. However, even if heterogeneity is reflected in the kinetic data, the proportions of the different forms of the enzyme cannot be estimated from the kinetic data alone. Consequently, the heterogeneity of enzyme preparations is rarely reflected in measurements of their steady-state kinetics unless the species present have significantly different kinetic properties. This has two implications: (1) it is difficult, but not impossible, to detect molecular heterogeneity using kinetic data and (2) even when it is possible, a considerable quantity of high quality data is required.

Matched MeSH terms: Data Accuracy
Fulltext Orphan drugs and high cost medications: challenges and solutions

Chuah, S.Y., Thong, M.K.

JUMMEC, 2018;21(2):53-58.
MyJurnal

There had been increased and strong public interests in rare diseases and orphan drugs as well as the issue of
compulsory licencing for expensive medications in Malaysia in the mass-media and social media. We reviewed
the issues of orphan drugs and the challenges faced in many countries in developing appropriate health financial
modelling as well as getting accurate data on rare diseases. We also reviewed the old off-patent medications
and the developments on how policy-makers can intervene to make expensive treatment affordable and
sustainable for patients and the country.

Matched MeSH terms: Data Accuracy
Evaluation of Development Datasets for Hermetia illucens (L.) (Diptera: Stratiomyidae) for Estimating the Time of Placement of Human and Swine Remains in Texas, USA

Cuttiford L, Pimsler ML, Heo CC, Zheng L, Karunaratne I, Trissini G, et al.

J Med Entomol, 2021 07 16;58(4):1654-1662.
PMID: 33970239 DOI: 10.1093/jme/tjab081

A basic tenet of forensic entomology is development data of an insect can be used to predict the time of colonization (TOC) by insect specimens collected from remains, and this prediction is related to the time of death and/or time of placement (TOP). However, few datasets have been evaluated to determine their accuracy or precision. The black soldier fly, Hermetia illucens (L.) (Diptera: Stratiomyidae) is recognized as an insect of forensic importance. This study examined the accuracy and precision of several development datasets for the black soldier fly by estimating the TOP of five sets of human and three sets of swine remains in San Marcos and College Station, TX, respectively. Data generated from this study indicate only one of these datasets consistently (time-to-prepupae 52%; time-to-eclosion 75%) produced TOP estimations that occurred within a day of the actual TOP of the remains. It is unknown if the precolonization interval (PreCI) of this species is long, but it has been observed that the species can colonize within 6 d after death. This assumption remains untested by validation studies. Accounting for this PreCI improved accuracy for the time-to-prepupae group, but reduced accuracy in the time-to-eclosion group. The findings presented here highlight a need for detailed, forensic-based development data for the black soldier fly that can reliably and accurately be used in casework. Finally, this study outlines the need for a basic understanding of the timing of resource utilization (i.e., duration of the PreCI) for forensically relevant taxa so that reasonable corrections may be made to TOC as related to minimum postmortem interval (mPMI) estimates.

Matched MeSH terms: Data Accuracy
Label-free optical spectroscopy for characterizing binding properties of highly sensitive nanocrystalline cellulose-graphene oxide based nanocomposite towards nickel ion

Daniyal WMEMM, Fen YW, Abdullah J, Sadrolhosseini AR, Saleviter S, Omar NAS

Spectrochim Acta A Mol Biomol Spectrosc, 2019 Apr 05;212:25-31.
PMID: 30594850 DOI: 10.1016/j.saa.2018.12.031

Surface plasmon resonance (SPR) is a label-free optical spectroscopy that is widely used for biomolecular interaction analysis. In this work, SPR was used to characterize the binding properties of highly sensitive nanocrystalline cellulose-graphene oxide based nanocomposite (CTA-NCC/GO) towards nickel ion. The formation of CTA-NCC/GO nanocomposite has been confirmed by FT-IR. The SPR analysis result shows that the CTA-NCC/GO has high binding affinity towards Ni2+ from 0.01 until 0.1 ppm with binding affinity constant of 1.620 × 103 M-1. The sensitivity for the CTA-NCC/GO calculated was 1.509° ppm-1. The full width at half maximum (FWHM), data accuracy (DA), and signal-to-noise ratio (SNR) have also been determined using the obtained SPR curve. For the FWHM, the value was 2.25° at 0.01 until 0.08 ppm and decreases to 2.12° at 0.1 until 10 ppm. The DA for the SPR curves is the highest at 0.01 until 0.08 ppm and lowest at 0.1 until 10 ppm. The SNR curves mirrors the curves of SPR angle shift where the SNR increases with the Ni2+ concentrations. For the selectivity test, the CTA-NCC/GO has the abilities to differentiate Ni2+ in the mixture of metal ions.

Matched MeSH terms: Data Accuracy
Fulltext The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis

He C, Levis B, Riehm KE, Saadat N, Levis AW, Azar M, et al.

Psychother Psychosom, 2020;89(1):25-37.
PMID: 31593971 DOI: 10.1159/000502294

BACKGROUND: Screening for major depression with the Patient Health Questionnaire-9 (PHQ-9) can be done using a cutoff or the PHQ-9 diagnostic algorithm. Many primary studies publish results for only one approach, and previous meta-analyses of the algorithm approach included only a subset of primary studies that collected data and could have published results.
OBJECTIVE: To use an individual participant data meta-analysis to evaluate the accuracy of two PHQ-9 diagnostic algorithms for detecting major depression and compare accuracy between the algorithms and the standard PHQ-9 cutoff score of ≥10.
METHODS: Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, Web of Science (January 1, 2000, to February 7, 2015). Eligible studies that classified current major depression status using a validated diagnostic interview.
RESULTS: Data were included for 54 of 72 identified eligible studies (n participants = 16,688, n cases = 2,091). Among studies that used a semi-structured interview, pooled sensitivity and specificity (95% confidence interval) were 0.57 (0.49, 0.64) and 0.95 (0.94, 0.97) for the original algorithm and 0.61 (0.54, 0.68) and 0.95 (0.93, 0.96) for a modified algorithm. Algorithm sensitivity was 0.22-0.24 lower compared to fully structured interviews and 0.06-0.07 lower compared to the Mini International Neuropsychiatric Interview. Specificity was similar across reference standards. For PHQ-9 cutoff of ≥10 compared to semi-structured interviews, sensitivity and specificity (95% confidence interval) were 0.88 (0.82-0.92) and 0.86 (0.82-0.88).
CONCLUSIONS: The cutoff score approach appears to be a better option than a PHQ-9 algorithm for detecting major depression.

Matched MeSH terms: Data Accuracy*
Fulltext Medical students' knowledge and perception toward family planning services: A preliminary intervention study

Htay MNN, Latt SS, Abas AL, Chuni N, Soe HHK, Moe S

J Educ Health Promot, 2018;7:137.
PMID: 30596109 DOI: 10.4103/jehp.jehp_104_18

INTRODUCTION: Family planning and contraception is the effective strategy to reduce maternal mortality, child mortality, abortion, and unwanted pregnancies. Since the medical students are the future doctors, it is important to have proper knowledge and training on family planning services. This study aimed to explore the effect of teaching-learning process at maternal and child health (MCH) clinics on the students' knowledge, perceptions toward contraception methods, and family planning counselling.
METHODS: This quasi-experimental study was conducted in the private medical institution in Malaysia. The same questionnaire was used to administer twice, before and after the posting. Moreover, a qualitative question on the issues related to family planning and contraception utilizations in Malaysia was added to the after posting survey. The quantitative data were analyzed using IBM SPSS (version 20) and qualitative data by RQDA software.
RESULTS: A total of 146 participants were recruited in this study. Knowledge on contraception method before posting was 5.11 (standard deviation [SD] ±1.36) and after posting was 6.35 (SD ± 1.38) (P < 0.001). Thematic analysis of the students' answer revealed four salient themes, which were as follows: (1) cultural barrier, (2) misconception, (3) inadequate knowledge, and (4) improvement for the health-care services.
CONCLUSIONS: The teaching-learning process at the MCH posting has an influence on their perception and upgraded their knowledge. It also reflects the role of primary health-care clinics on medical students' clinical exposure and training on family planning services during their postings.

Matched MeSH terms: Data Accuracy
Fulltext Postgraduate students' perspective on using Twitter as a learning resource in higher education

Htay MNN, McMonnies K, Kalua T, Ferley D, Hassanein M

J Educ Health Promot, 2020;9:61.
PMID: 32489996 DOI: 10.4103/jehp.jehp_321_18

CONTEXT: In the era of technology, social networking has become a platform for the teaching-learning process. Exploring international students' perspective on using Twitter would reveal the barriers and potential for its use in higher educational activities.
AIMS: This study aimed to explore the postgraduate students' perspective on using Twitter as a learning resource.
SUBJECTS AND METHODS: This qualitative study was conducted as part of a postgraduate program at a university in the United Kingdom. A focus group discussion and five in-depth interviews were conducted after receiving the informed consent. The qualitative data were analyzed by R package for Qualitative Data Analysis software.
ANALYSIS USED: Deductive content analysis was used in this study.
RESULTS: Qualitative analysis revealed four salient themes, which were (1) background knowledge about Twitter, (2) factors influencing the usage of Twitter, (3) master's students' experiences on using Twitter for education, and (4) potential of using Twitter in the postgraduate study. The students preferred to use Twitter for sharing links and appreciated the benefit on immediate dissemination of information. Meanwhile, privacy concern, unfamiliarity, and hesitation to participate in discussion discouraged the students from using Twitter as a learning platform.
CONCLUSIONS: Using social media platforms in education could be challenging for both the learners and the educators. Our study revealed that Twitter was mainly used for social communication among postgraduate students however most could see a benefit of using Twitter for their learning if they received adequate guidance on how to use the platform. The multiple barriers to using Twitter were mainly related to unfamiliarity which should be addressed early in the learning process.

Matched MeSH terms: Data Accuracy
Fulltext Vision-Based Apple Classification for Smart Manufacturing

Ismail A, Idris MYI, Ayub MN, Por LY

Sensors (Basel), 2018 Dec 10;18(12).
PMID: 30544660 DOI: 10.3390/s18124353

Smart manufacturing enables an efficient manufacturing process by optimizing production and product transaction. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods are studied to determine the best vision-based classification technique, namely Bag of Words (BOW), Spatial Pyramid Matching (SPM) and Convolutional Neural Network (CNN). The vision-based classifiers categorize the apple as defective and non-defective that can be used for automatic inspection, sorting and further analytics. A total of 550 apple images are collected to test the classifiers. The images consist of 275 non-defective and 275 defective apples. The defective category includes various types of defect and severity. The vision-based classifiers are trained and evaluated according to the K-fold cross-validation. The performances of the classifiers from 2-fold, 3-fold, 4-fold, 5-fold and 10-fold are compared. From the evaluation, SPM with SVM classifier attained 98.15% classification accuracy for 10-fold and outperformed the others. In terms of computational time, CNN with SVM classifier is the fastest. However, minimal time difference is observed between the computational time of CNN and SPM, which were separated by only 0.05 s.

Matched MeSH terms: Data Accuracy
Fulltext A data fusion method in wireless sensor networks

Izadi D, Abawajy JH, Ghanavati S, Herawan T

Sensors (Basel), 2015;15(2):2964-79.
PMID: 25635417 DOI: 10.3390/s150202964

The success of a Wireless Sensor Network (WSN) deployment strongly depends on the quality of service (QoS) it provides regarding issues such as data accuracy, data aggregation delays and network lifetime maximisation. This is especially challenging in data fusion mechanisms, where a small fraction of low quality data in the fusion input may negatively impact the overall fusion result. In this paper, we present a fuzzy-based data fusion approach for WSN with the aim of increasing the QoS whilst reducing the energy consumption of the sensor network. The proposed approach is able to distinguish and aggregate only true values of the collected data as such, thus reducing the burden of processing the entire data at the base station (BS). It is also able to eliminate redundant data and consequently reduce energy consumption thus increasing the network lifetime. We studied the effectiveness of the proposed data fusion approach experimentally and compared it with two baseline approaches in terms of data collection, number of transferred data packets and energy consumption. The results of the experiments show that the proposed approach achieves better results than the baseline approaches.

Matched MeSH terms: Data Accuracy
Assessing primary care data quality

Lim YMF, Yusof M, Sivasampu S

Int J Health Care Qual Assur, 2018 Apr 16;31(3):203-213.
PMID: 29687760 DOI: 10.1108/IJHCQA-08-2016-0111

Purpose The purpose of this paper is to assess National Medical Care Survey data quality. Design/methodology/approach Data completeness and representativeness were computed for all observations while other data quality measures were assessed using a 10 per cent sample from the National Medical Care Survey database; i.e., 12,569 primary care records from 189 public and private practices were included in the analysis. Findings Data field completion ranged from 69 to 100 per cent. Error rates for data transfer from paper to web-based application varied between 0.5 and 6.1 per cent. Error rates arising from diagnosis and clinical process coding were higher than medication coding. Data fields that involved free text entry were more prone to errors than those involving selection from menus. The authors found that completeness, accuracy, coding reliability and representativeness were generally good, while data timeliness needs to be improved. Research limitations/implications Only data entered into a web-based application were examined. Data omissions and errors in the original questionnaires were not covered. Practical implications Results from this study provided informative and practicable approaches to improve primary health care data completeness and accuracy especially in developing nations where resources are limited. Originality/value Primary care data quality studies in developing nations are limited. Understanding errors and missing data enables researchers and health service administrators to prevent quality-related problems in primary care data.

Matched MeSH terms: Data Accuracy*
Fulltext Unravelling the impact of dental workforce training and education programmes on policy evolution: a mixed-method study protocol

Lin GSS, Goh SM, Halil MHM

Health Res Policy Syst, 2023 Sep 12;21(1):95.
PMID: 37700266 DOI: 10.1186/s12961-023-01048-9

BACKGROUND: The dental workforce plays a crucial role in delivering quality oral healthcare services, requiring continuous training and education to meet evolving professional demands. Understanding the impact of dental workforce training and education programmes on policy evolution is essential for refining existing policies, implementing evidence-based reforms and ensuring the growth of the dental profession. Therefore, this study protocol aims to assess the influence of dental workforce training and education programmes on policy evolution in Malaysia.
METHODS: A mixed-method research design will be employed, combining quantitative surveys and qualitative interviews. Stakeholder theory and policy change models will form the theoretical framework of the study. Participants from various stakeholder groups will be recruited using purposive sampling. Data collection will involve surveys and one-on-one semi-structured interviews. Descriptive statistics, inferential analysis and thematic analysis will be used to analyse the data. Integration of quantitative and qualitative data will be used to provide a comprehensive understanding of the data.
DISCUSSION: This study will shed light on factors influencing policy decisions related to dental education and workforce development in Malaysia. The findings will inform evidence-based decision-making, guide the enhancement of dental education programmes and improve the quality of oral healthcare services. Challenges related to participant recruitment and data collection should be considered, and the study's unique contribution to the existing body of knowledge in the Malaysian context will be discussed.

Matched MeSH terms: Data Accuracy*
Real-world data for health technology assessment for reimbursement decisions in Asia: current landscape and a way forward

Lou J, Kc S, Toh KY, Dabak S, Adler A, Ahn J, et al.

Int J Technol Assess Health Care, 2020 Oct;36(5):474-480.
PMID: 32928330 DOI: 10.1017/S0266462320000628

There is growing interest globally in using real-world data (RWD) and real-world evidence (RWE) for health technology assessment (HTA). Optimal collection, analysis, and use of RWD/RWE to inform HTA requires a conceptual framework to standardize processes and ensure consistency. However, such framework is currently lacking in Asia, a region that is likely to benefit from RWD/RWE for at least two reasons. First, there is often limited Asian representation in clinical trials unless specifically conducted in Asian populations, and RWD may help to fill the evidence gap. Second, in a few Asian health systems, reimbursement decisions are not made at market entry; thus, allowing RWD/RWE to be collected to give more certainty about the effectiveness of technologies in the local setting and inform their appropriate use. Furthermore, an alignment of RWD/RWE policies across Asia would equip decision makers with context-relevant evidence, and improve timely patient access to new technologies. Using data collected from eleven health systems in Asia, this paper provides a review of the current landscape of RWD/RWE in Asia to inform HTA and explores a way forward to align policies within the region. This paper concludes with a proposal to establish an international collaboration among academics and HTA agencies in the region: the REAL World Data In ASia for HEalth Technology Assessment in Reimbursement (REALISE) working group, which seeks to develop a non-binding guidance document on the use of RWD/RWE to inform HTA for decision making in Asia.

Matched MeSH terms: Data Accuracy
Fulltext Life After Death in a Chinese Community

MOHD HAFIZOL AMIN BIN RAMLI, NUR AMELIA BINTI MAZLAN, SITI AIDA BINTI AZMI, MUHD SARJI BIN AWANG BULAT, PARVEEN KAUR

Trends in Undergraduate Research, 2018;1(1):0-0.
MyJurnal

According to San Fillipo (2006), death is not the end of one’s existence, but rather than a transition from one life to another. However, it is different based on how the society and individuals see the concept of death itself and how they understand about it. Thus, this article aims to explore the understanding of the relationship between culture and religion that become their identity in terms of death and life after. Qualitative approach is adopted for this study. Indeed, interview and empirical observation were used to obtain quality data.

Matched MeSH terms: Data Accuracy
Fulltext Modeling the Physical Properties of Gamma Alumina Catalyst Carrier Based on an Artificial Neural Network

Majdi HS, Saud AN, Saud SN

Materials (Basel), 2019 May 29;12(11).
PMID: 31146451 DOI: 10.3390/ma12111752

Porous γ-alumina is widely used as a catalyst carrier due to its chemical properties. These properties are strongly correlated with the physical properties of the material, such as porosity, density, shrinkage, and surface area. This study presents a technique that is less time consuming than other techniques to predict the values of the above-mentioned physical properties of porous γ-alumina via an artificial neural network (ANN) numerical model. The experimental data that was implemented was determined based on 30 samples that varied in terms of sintering temperature, yeast concentration, and socking time. Of the 30 experimental samples, 25 samples were used for training purposes, while the other five samples were used for the execution of the experimental procedure. The results showed that the prediction and experimental data were in good agreement, and it was concluded that the proposed model is proficient at providing high accuracy estimation data derived from any complex analytical equation.

Matched MeSH terms: Data Accuracy
Fulltext Development of electronic medical records for clinical and research purposes: the breast cancer module using an implementation framework in a middle income country- Malaysia

Mohd Nor NA, Taib NA, Saad M, Zaini HS, Ahmad Z, Ahmad Y, et al.

BMC Bioinformatics, 2019 Feb 04;19(Suppl 13):402.
PMID: 30717675 DOI: 10.1186/s12859-018-2406-9

BACKGROUND: Advances in medical domain has led to an increase of clinical data production which offers enhancement opportunities for clinical research sector. In this paper, we propose to expand the scope of Electronic Medical Records in the University Malaya Medical Center (UMMC) using different techniques in establishing interoperability functions between multiple clinical departments involving diagnosis, screening and treatment of breast cancer and building automatic systems for clinical audits as well as for potential data mining to enhance clinical breast cancer research in the future.
RESULTS: Quality Implementation Framework (QIF) was adopted to develop the breast cancer module as part of the in-house EMR system used at UMMC, called i-Pesakit©. The completion of the i-Pesakit© Breast Cancer Module requires management of clinical data electronically, integration of clinical data from multiple internal clinical departments towards setting up of a research focused patient data governance model. The 14 QIF steps were performed in four main phases involved in this study which are (i) initial considerations regarding host setting, (ii) creating structure for implementation, (iii) ongoing structure once implementation begins, and (iv) improving future applications. The architectural framework of the module incorporates both clinical and research needs that comply to the Personal Data Protection Act.
CONCLUSION: The completion of the UMMC i-Pesakit© Breast Cancer Module required populating EMR including management of clinical data access, establishing information technology and research focused governance model and integrating clinical data from multiple internal clinical departments. This multidisciplinary collaboration has enhanced the quality of data capture in clinical service, benefited hospital data monitoring, quality assurance, audit reporting and research data management, as well as a framework for implementing a responsive EMR for a clinical and research organization in a typical middle-income country setting. Future applications include establishing integration with external organization such as the National Registration Department for mortality data, reporting of institutional data for national cancer registry as well as data mining for clinical research. We believe that integration of multiple clinical visit data sources provides a more comprehensive, accurate and real-time update of clinical data to be used for epidemiological studies and audits.

Matched MeSH terms: Data Accuracy
Fulltext Out of hospital cardiac arrest in Kuala Lumpur: incidence; adherence to protocol; and issues: a mixed method study

Mohd Said Nurumal, Sarah Sheikh Abdul Karim

Malaysian Journal of Public Health Medicine, 2015;15(3):0-0.
MyJurnal

Information regarding out of hospital cardiac arrest incidence including outcomes in Malaysia is limited and fragmented. This study aims to identify the incidence and adherence to protocol of out of hospital cardiac arrest and also to explore the issues faced by pre-hospital personnel in regards to the management of cardiac arrest victim in Kuala Lumpur, Malaysia. A mixed method approach combining qualitative and quantitative study design was used. Two hundred eighty five (285) pre-hospital care data sheet for out of hospital cardiac arrest during the year of 2011 were examined by using checklists to identify the incidence and adherence to protocol. Nine semi-structured interviews and two focus group discussions were performed. Based on the overall incidence for out of hospital cardiac arrest cases which occurred in 2011 (n=285), the survival rate was 16.8%. On the adherence to protocol, only 89 (41.8%) of the cases adhered to the given protocol and 124 did not adhere to such protocol. All the relevant qualitative data were merged into few categories relating to issues that could affect the management of out of hospital cardiac arrest performed by pre-hospital care team. The essential elements in the handling of out of hospital cardiac arrest by pre-hospital care teamwasto ensure increased survival rates and excellent outcomes. Measures are needed to strengthen the quick activation of the pre-hospital care service, prompt bystander cardiopulmonary resuscitation, early defibrillation and timely advanced cardiac life support, and also to address all other issues highlighted in the qualitative results of this study.

Matched MeSH terms: Data Accuracy

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