The open medical informatics journal最新文献

{"title":"Data mining techniques in medical informatics.","authors":"U Rajendra Acharya, Wenwei Yu","doi":"10.2174/1874431101004020021","DOIUrl":"https://doi.org/10.2174/1874431101004020021","url":null,"abstract":"The advent of high-performance computing has benefited various disciplines in finding practical solutions to their problems, and our health care is no exception to this. Signal processing, image processing, and data mining tools have been developed for effective analysis of medical information, in order to help clinicians in making better diagnosis for treatment purposes. \u0000 \u0000Data mining has become a fundamental methodology for computing applications in medical informatics. Progress in data mining applications and its implications are manifested in the areas of information management in healthcare organizations, health informatics, epidemiology, patient care and monitoring systems, assistive technology, large-scale image analysis to information extraction and automatic identification of unknown classes. Various algorithms associated with data mining have significantly helped to understand medical data more clearly, by distinguishing pathological data from normal data, for supporting decision-making as well as visualization and identification of hidden complex relationships between diagnostic features of different patient groups. There are nine papers in this Special issue, covering different areas in medical informatics. \u0000 \u0000Paper 1 proposes a metabonomic study applied to medical diagnosis. Metabolomics and metabonomics belong to the “-omics” sciences. Particularly, metabonomic correlates the metabolic fingerprint to characteristics of specific patient categories. Usually, metabonomic studies are conducted by in-vitro spectroscopy. The aim of this study was to apply data-mining metabonomic techniques to the clinical diagnosis of genetic mutations in migraine sufferers. This is one of the first applications of advanced data-mining techniques to a mixed database consisting of hematochemical, instrumental, and genetic variables. \u0000 \u0000There has been an effort to use motion-related surface vibration, to detect independent finger motions is in practice. Accelerometers have been used in a finger tapping experiment to collect the finger motion related mechanical vibration patterns. The extracted time-domain and frequency-domain features were fed to back-propagation neural networks, to classify different finger motions. The insights provided in paper 2 will be helpful for prosthetic hand control. \u0000 \u0000Microscopic imaging is ubiquitous in several medical informatics disciplines, including but not limited to cancer informatics, neuro-informatics, and other emerging health informatics disciplines. The decision support applications frequently require the sensitive and specific detection of pathological changes in cells, which further require the accurate measurement of their geometric parameters. In paper 3, Du et al. have suggested that due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. They have evaluated the performance of multiple unsupervis","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"4 ","pages":"21-2"},"PeriodicalIF":0.0,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2174/1874431101004020021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29176045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Datamining Approach for Automation of Diagnosis of Breast Cancer in Immunohistochemically Stained Tissue Microarray Images~!2009-10-04~!2009-11-14~!2010-05-28~!","authors":"K. Prasad","doi":"10.2174/1874431101004020086","DOIUrl":"https://doi.org/10.2174/1874431101004020086","url":null,"abstract":"","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"54 1","pages":"86-93"},"PeriodicalIF":0.0,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80103669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association Rule Based Similarity Measures for the Clustering of Gene Expression Data~!2009-10-10~!2009-11-05~!2010-05-28~!","authors":"P. Sethi","doi":"10.2174/1874431101004020063","DOIUrl":"https://doi.org/10.2174/1874431101004020063","url":null,"abstract":"","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"53 1","pages":"63-73"},"PeriodicalIF":0.0,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83093719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic modelling of heart rate response under different exercise intensity.","authors":"Steven W Su,&nbsp;Weidong Chen,&nbsp;Dongdong Liu,&nbsp;Yi Fang,&nbsp;Weijun Kuang,&nbsp;Xiaoxiang Yu,&nbsp;Tian Guo,&nbsp;Branko G Celler,&nbsp;Hung T Nguyen","doi":"10.2174/1874431101004020081","DOIUrl":"https://doi.org/10.2174/1874431101004020081","url":null,"abstract":"<p><p>Heart rate is one of the major indications of human cardiovascular response to exercises. This study investigates human heart rate response dynamics to moderate exercise. A healthy male subject has been asked to walk on a motorised treadmill under a predefined exercise protocol. ECG, body movements, and oxygen saturation (SpO2) have been reliably monitored and recorded by using non-invasive portable sensors. To reduce heart rate variation caused by the influence of various internal or external factors, the designed step response protocol has been repeated three times. Experimental results show that both steady state gain and time constant of heart rate response are not invariant when walking speed is faster than 3 miles/hour, and time constant of offset exercise is noticeably longer than that of onset exercise.</p>","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"4 ","pages":"81-5"},"PeriodicalIF":0.0,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2174/1874431101004020081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29176048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of esophagitis based on face recognition techniques.","authors":"Santosh S Saraf,&nbsp;Gururaj R Udupi,&nbsp;Santosh D Hajare","doi":"10.2174/1874431101004020058","DOIUrl":"https://doi.org/10.2174/1874431101004020058","url":null,"abstract":"<p><p>Face recognition technology has evolved over years with the Principal Component Analysis (PCA) method being the benchmark for recognition efficiency. The face recognition techniques take care of variation of illumination, pose and other features of the face in the image. We envisage an application of these face recognition techniques for classification of medical images. The motivating factor being, given a condition of an organ it is represented by some typical features. In this paper we report the use of the face recognition techniques to classify the type of Esophagitis, a condition of inflammation of the esophagus. The image of the esophagus is captured in the process of endoscopy. We test PCA, Fisher Face method and Independent Component Analysis techniques to classify the images of the esophagus. Esophagitis is classified into four categories. The results of classification for each method are reported and the results are compared.</p>","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"4 ","pages":"58-62"},"PeriodicalIF":0.0,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2174/1874431101004020058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29176046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Datamining approach for automation of diagnosis of breast cancer in immunohistochemically stained tissue microarray images.","authors":"Keerthana Prasad,&nbsp;Bernhard Zimmermann,&nbsp;Gopalakrishna Prabhu,&nbsp;Muktha Pai","doi":"10.2174/1874431101004010086","DOIUrl":"https://doi.org/10.2174/1874431101004010086","url":null,"abstract":"<p><p>Cancer of the breast is the second most common human neoplasm, accounting for approximately one quarter of all cancers in females after cervical carcinoma. Estrogen receptor (ER), Progesteron receptor and human epidermal growth factor receptor (HER-2/neu) expressions play an important role in diagnosis and prognosis of breast carcinoma. Tissue microarray (TMA) technique is a high throughput technique which provides a standardized set of images which are uniformly stained, facilitating effective automation of the evaluation of the specimen images. TMA technique is widely used to evaluate hormone expression for diagnosis of breast cancer. If one considers the time taken for each of the steps in the tissue microarray process workflow, it can be observed that the maximum amount of time is taken by the analysis step. Hence, automated analysis will significantly reduce the overall time required to complete the study. Many tools are available for automated digital acquisition of images of the spots from the microarray slide. Each of these images needs to be evaluated by a pathologist to assign a score based on the staining intensity to represent the hormone expression, to classify them into negative or positive cases. Our work aims to develop a system for automated evaluation of sets of images generated through tissue microarray technique, representing the ER expression images and HER-2/neu expression images. Our study is based on the Tissue Microarray Database portal of Stanford university at http://tma.stanford.edu/cgi-bin/cx?n=her1, which has made huge number of images available to researchers. We used 171 images corresponding to ER expression and 214 images corresponding to HER-2/neu expression of breast carcinoma. Out of the 171 images corresponding to ER expression, 104 were negative and 67 were representing positive cases. Out of the 214 images corresponding to HER-2/neu expression, 112 were negative and 102 were representing positive cases. Our method has 92.31% sensitivity and 93.18% specificity for ER expression image classification and 96.67% sensitivity and 88.24% specificity for HER-2/neu expression image classification.</p>","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":" ","pages":"86-93"},"PeriodicalIF":0.0,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2d/03/TOMINFOJ-4-86.PMC3095117.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40090161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Region quad-tree decomposition based edge detection for medical images.","authors":"Sumeet Dua,&nbsp;Naveen Kandiraju,&nbsp;Pradeep Chowriappa","doi":"10.2174/1874431101004020050","DOIUrl":"https://doi.org/10.2174/1874431101004020050","url":null,"abstract":"<p><p>Edge detection in medical images has generated significant interest in the medical informatics community, especially in recent years. With the advent of imaging technology in biomedical and clinical domains, the growth in medical digital images has exceeded our capacity to analyze and store them for efficient representation and retrieval, especially for data mining applications. Medical decision support applications frequently demand the ability to identify and locate sharp discontinuities in an image for feature extraction and interpretation of image content, which can then be exploited for decision support analysis. However, due to the inherent high dimensional nature of the image content and the presence of ill-defined edges, edge detection using classical procedures is difficult, if not impossible, for sensitive and specific medical informatics-based discovery. In this paper, we propose a new edge detection technique based on the regional recursive hierarchical decomposition using quadtree and post-filtration of edges using a finite difference operator. We show that in medical images of common origin, focal and/or penumbral blurred edges can be characterized by an estimable intensity gradient. This gradient can further be used for dismissing false alarms. A detailed validation and comparison with related works on diabetic retinopathy images and CT scan images show that the proposed approach is efficient and accurate.</p>","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"4 ","pages":"50-7"},"PeriodicalIF":0.0,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2174/1874431101004020050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29176047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anamneses-Based Internet Information Supply: Can a Combination of an Expert System and Meta-Search Engine Help Consumers find the Health Information they Require?","authors":"Wilfried Honekamp, Herwig Ostermann","doi":"10.2174/1874431101004010012","DOIUrl":"10.2174/1874431101004010012","url":null,"abstract":"<p><p>An increasing number of people search for health information online. During the last 10 years various researchers have determined the requirements for an ideal consumer health information system. The aim of this study was to figure out, whether medical laymen can find a more accurate diagnosis for a given anamnesis via the developed prototype health information system than via ordinary internet search.In a randomized controlled trial, the prototype information system was evaluated by the assessment of two sample cases. Participants had to determine the diagnosis of a patient with a headache via information found searching the web. A patient's history sheet and a computer with internet access were provided to the participants and they were guided through the study by an especially designed study website. The intervention group used the prototype information system; the control group used common search engines and portals. The numbers of correct diagnoses in each group were compared.A total of 140 (60/80) participants took part in two study sections. In the first case, which determined a common diagnosis, both groups did equally well. In the second section, which determined a less common and more complex case, the intervention group did significantly better (P=0.031) due to the tailored information supply.Using medical expert systems in combination with a portal searching meta-search engine represents a feasible strategy to provide reliable patient-tailored information and can ultimately contribute to patient safety with respect to information found via the internet.</p>","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"4 ","pages":"12-20"},"PeriodicalIF":0.0,"publicationDate":"2010-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/98/a5/TOMINFOJ-4-12.PMC2874219.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29016770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nip, tuck and click: medical tourism and the emergence of web-based health information.","authors":"Neil Lunt, Mariann Hardey, Russell Mannion","doi":"10.2174/1874431101004010001","DOIUrl":"10.2174/1874431101004010001","url":null,"abstract":"<p><p>An emerging trend is what has become commonly known as 'Medical Tourism' where patients travel to overseas destinations for specialised surgical treatments and other forms of medical care. With the rise of more affordable cross-border travel and rapid technological developments these movements are becoming more commonplace. A key driver is the platform provided by the internet for gaining access to healthcare information and advertising. There has been relatively little attention given to the role and impact of web-based information to inform Medical Tourism decisions.This article provides a brief overview of the most recent development in Medical Tourism and examines how this is linked to the emergence of specialized internet web sites. It produces a summary of the functionality of medical tourist sites, and situates Medical Tourism informatics within the broader literatures relating to information search, information quality and decision-making.This paper is both a call to strengthen the empirical evidence in this area, and also to advocate integrating Medical Tourism research within a broader conceptual framework.</p>","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":"4 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2010-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/0f/a2/TOMINFOJ-4-1.PMC2874214.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29029036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association rule based similarity measures for the clustering of gene expression data.","authors":"Prerna Sethi,&nbsp;Sathya Alagiriswamy","doi":"10.2174/1874431101004010063","DOIUrl":"https://doi.org/10.2174/1874431101004010063","url":null,"abstract":"<p><p>In life threatening diseases, such as cancer, where the effective diagnosis includes annotation, early detection, distinction, and prediction, data mining and statistical approaches offer the promise for precise, accurate, and functionally robust analysis of gene expression data. The computational extraction of derived patterns from microarray gene expression is a non-trivial task that involves sophisticated algorithm design and analysis for specific domain discovery. In this paper, we have proposed a formal approach for feature extraction by first applying feature selection heuristics based on the statistical impurity measures, the Gini Index, Max Minority, and the Twoing Rule and obtaining the top 100-400 genes. We then analyze the associative dependencies between the genes and assign weights to the genes based on their degree of participation in the rules. Consequently, we present a weighted Jaccard and vector cosine similarity measure to compute the similarity between the discovered rules. Finally, we group the rules by applying hierarchical clustering. To demonstrate the usability and efficiency of the concept of our technique, we applied it to three publicly available, multiclass cancer gene expression datasets and performed a biomedical literature search to support the effectiveness of our results.</p>","PeriodicalId":88331,"journal":{"name":"The open medical informatics journal","volume":" ","pages":"63-73"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2174/1874431101004010063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40101162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}