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Fulltext Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS

Zaidan AA, Zaidan BB, Al-Haiqi A, Kiah ML, Hussain M, Abdulnabi M

J Biomed Inform, 2015 Feb;53:390-404.
PMID: 25483886 DOI: 10.1016/j.jbi.2014.11.012

Evaluating and selecting software packages that meet the requirements of an organization are difficult aspects of software engineering process. Selecting the wrong open-source EMR software package can be costly and may adversely affect business processes and functioning of the organization. This study aims to evaluate and select open-source EMR software packages based on multi-criteria decision-making. A hands-on study was performed and a set of open-source EMR software packages were implemented locally on separate virtual machines to examine the systems more closely. Several measures as evaluation basis were specified, and the systems were selected based a set of metric outcomes using Integrated Analytic Hierarchy Process (AHP) and TOPSIS. The experimental results showed that GNUmed and OpenEMR software can provide better basis on ranking score records than other open-source EMR software packages.

Matched MeSH terms: Medical Informatics/methods*
Computerized segmentation and measurement of chronic wound images

Ahmad Fauzi MF, Khansa I, Catignani K, Gordillo G, Sen CK, Gurcan MN

Comput Biol Med, 2015 May;60:74-85.
PMID: 25756704 DOI: 10.1016/j.compbiomed.2015.02.015

An estimated 6.5 million patients in the United States are affected by chronic wounds, with more than US$25 billion and countless hours spent annually for all aspects of chronic wound care. There is a need for an intelligent software tool to analyze wound images, characterize wound tissue composition, measure wound size, and monitor changes in wound in between visits. Performed manually, this process is very time-consuming and subject to intra- and inter-reader variability. In this work, our objective is to develop methods to segment, measure and characterize clinically presented chronic wounds from photographic images. The first step of our method is to generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the segmentation process using either optimal thresholding or region growing. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively; while the white probability map is to detect the white label card for measurement calibration purposes. The innovative aspects of this work include defining a four-dimensional probability map specific to wound characteristics, a computationally efficient method to segment wound images utilizing the probability map, and auto-calibration of wound measurements using the content of the image. These methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians. While the mean inter-reader agreement between the readers varied between 67.4% and 84.3%, the computer achieved an average accuracy of 75.1%.

Matched MeSH terms: Medical Informatics/methods
Deep learning for healthcare applications based on physiological signals: A review

Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR

Comput Methods Programs Biomed, 2018 Jul;161:1-13.
PMID: 29852952 DOI: 10.1016/j.cmpb.2018.04.005

BACKGROUND AND OBJECTIVE: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017.
METHODS: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review.
RESULTS: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input.
CONCLUSIONS: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.

Matched MeSH terms: Medical Informatics/methods*
Fulltext Classification of forensic autopsy reports through conceptual graph-based document representation model

Mujtaba G, Shuib L, Raj RG, Rajandram R, Shaikh K, Al-Garadi MA

J Biomed Inform, 2018 06;82:88-105.
PMID: 29738820 DOI: 10.1016/j.jbi.2018.04.013

Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation, a clinical report is transformed into a format that is suitable for classification. The traditional document representation technique for text categorization is the bag-of-words (BoW) technique. In this study, the traditional BoW technique is ineffective in classifying forensic autopsy reports because it merely extracts frequent but discriminative features from clinical reports. Moreover, this technique fails to capture word inversion, as well as word-level synonymy and polysemy, when classifying autopsy reports. Hence, the BoW technique suffers from low accuracy and low robustness unless it is improved with contextual and application-specific information. To overcome the aforementioned limitations of the BoW technique, this research aims to develop an effective conceptual graph-based document representation (CGDR) technique to classify 1500 forensic autopsy reports from four (4) manners of death (MoD) and sixteen (16) causes of death (CoD). Term-based and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) based conceptual features were extracted and represented through graphs. These features were then used to train a two-level text classifier. The first level classifier was responsible for predicting MoD. In addition, the second level classifier was responsible for predicting CoD using the proposed conceptual graph-based document representation technique. To demonstrate the significance of the proposed technique, its results were compared with those of six (6) state-of-the-art document representation techniques. Lastly, this study compared the effects of one-level classification and two-level classification on the experimental results. The experimental results indicated that the CGDR technique achieved 12% to 15% improvement in accuracy compared with fully automated document representation baseline techniques. Moreover, two-level classification obtained better results compared with one-level classification. The promising results of the proposed conceptual graph-based document representation technique suggest that pathologists can adopt the proposed system as their basis for second opinion, thereby supporting them in effectively determining CoD.

Matched MeSH terms: Medical Informatics/methods*
Fulltext A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

Kamarudin ND, Ooi CY, Kawanabe T, Odaguchi H, Kobayashi F

J Healthc Eng, 2017;2017:7460168.
PMID: 29065640 DOI: 10.1155/2017/7460168

In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

Matched MeSH terms: Medical Informatics/methods*
Improved biliary detection and diagnosis through intelligent machine analysis

Logeswaran R

Comput Methods Programs Biomed, 2012 Sep;107(3):404-12.
PMID: 21194781 DOI: 10.1016/j.cmpb.2010.12.002

This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases.

Matched MeSH terms: Medical Informatics/methods
An analysis of the healthcare informatics and systems in Southeast Asia: a current perspective from seven countries

Nguyen QT, Naguib RN, Abd Ghani MK, Bali RK, Lee IM

Int J Electron Healthc, 2008;4(2):184-207.
PMID: 18676343

This paper presents an overview of the healthcare systems in Southeast Asia, with a focus on the healthcare informatics development and deployment in seven countries, namely, Singapore, Cambodia, Malaysia, Thailand, Laos, the Philippines and Vietnam. Brief geographic and demographic information is provided for each country, followed by a historical review of the national strategies for healthcare informatics development. An analysis of the state-of-the-art healthcare infrastructure is also given, along with a critical appraisal of national healthcare provisions.

Matched MeSH terms: Medical Informatics/methods
Fulltext Development of a Search Strategy for an Evidence Based Retrieval Service

Ho GJ, Liew SM, Ng CJ, Hisham Shunmugam R, Glasziou P

PLoS One, 2016;11(12):e0167170.
PMID: 27935993 DOI: 10.1371/journal.pone.0167170

BACKGROUND: Physicians are often encouraged to locate answers for their clinical queries via an evidence-based literature search approach. The methods used are often not clearly specified. Inappropriate search strategies, time constraint and contradictory information complicate evidence retrieval.
AIMS: Our study aimed to develop a search strategy to answer clinical queries among physicians in a primary care setting.
METHODS: Six clinical questions of different medical conditions seen in primary care were formulated. A series of experimental searches to answer each question was conducted on 3 commonly advocated medical databases. We compared search results from a PICO (patients, intervention, comparison, outcome) framework for questions using different combinations of PICO elements. We also compared outcomes from doing searches using text words, Medical Subject Headings (MeSH), or a combination of both. All searches were documented using screenshots and saved search strategies.
RESULTS: Answers to all 6 questions using the PICO framework were found. A higher number of systematic reviews were obtained using a 2 PICO element search compared to a 4 element search. A more optimal choice of search is a combination of both text words and MeSH terms. Despite searching using the Systematic Review filter, many non-systematic reviews or narrative reviews were found in PubMed. There was poor overlap between outcomes of searches using different databases. The duration of search and screening for the 6 questions ranged from 1 to 4 hours.
CONCLUSION: This strategy has been shown to be feasible and can provide evidence to doctors' clinical questions. It has the potential to be incorporated into an interventional study to determine the impact of an online evidence retrieval system.

Matched MeSH terms: Medical Informatics/methods