Int. J. Comput. Model. Algorithms Medicine最新文献

{"title":"Medical Outcome Prediction for Intensive Care Unit Patients","authors":"Simone A. Ludwig, Stefanie Roos, M. Frize, N. Yu","doi":"10.4018/jcmam.2010100102","DOIUrl":"https://doi.org/10.4018/jcmam.2010100102","url":null,"abstract":"The rate of people dying from medical errors in hospitals each year is very high. Errors that frequently occur during the course of providing health care are adverse drug events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken patient identities. Medical decision support systems play an increasingly important role in medical practice. By assisting physicians in making clinical decisions, medical decision support systems improve the quality of medical care. Two approaches have been investigated for the prediction of medical outcomes: “hours of ventilation” and the “mortality rate” in the adult intensive care unit. The first approach is based on neural networks with the weight-elimination algorithm, and the second is based on genetic programming. Both approaches are compared to commonly used machine learning algorithms. Results show that both algorithms developed score well for the outcomes selected. DOI: 10.4018/978-1-61350-456-7.ch4.19","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126658260","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":"Upper GI Bleed, Etiology, Role of Endoscopy in Rural Population of Punjab","authors":"R. S. Malhotra, K. S. Ded, Arun Gupta, D. Bansal, Harneet Singh","doi":"10.4018/jcmam.2010070103","DOIUrl":"https://doi.org/10.4018/jcmam.2010070103","url":null,"abstract":"Haematemesis and malena are the two most important symptoms of upper gastrointestinal bleeding . The most common cause of upper gastrointestinal bleeding is due to a peptic ulcer. In this paper, the authors research the cause of bleeding. Contrary to previous studies, results favor esophageal varices, e.g., alcoholism or cirrhosis liver post necrotic, as the most common cause of bleeding rather than a peptic ulcer. The authors’ study is based on an observational retrospective protocol with records of 50 consecutive patients with GI bleeding, attending the emergency room from February 2007 until September 2009. Results show that the treatment of UGI bleeding has made important progress since the introduction of emergency endoscopy and endoscopic techniques for haemostasis. The application of specific protocols significantly decreases rebleeding and the need for surgery, whereas mortality is still high. The data highlight the decreasing trend of peptic ulcer as the sole cause of bleeding, as shown in previous literature, ascertaining that varices are now the most common variable. which can indicate simple, benign, complex or malignant disorders and result in disaster if proper steps are not taken to identify the source of bleeding and treat it. Bleeding proximal to ligament of Treitz, i.e., from esophagus, stomach and duodenum is called upper gastrointestinal DOI: 10.4018/jcmam.2010070103 56 International Journal of Computational Models and Algorithms in Medicine, 1(3), 55-68, July-September 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. bleeding while bleeding from jejunum, ileum, colon, rectum are grouped under lower gastrointestinal bleeding.Various causes of upper GI bleed being esophageal, gastric, duodenal ulcers (40%), followed by erosions (20%), varices (10%), Mallory Weiss tear, tumors, vascular lesions and others constituting the rest. Haematemesis and malena are the two important symptoms of upper gastrointestinal bleeding. Endoscopy remains the gold standard in the diagnosis and management of acute upper gastrointestinal bleeding. (Russell, 2004) Major advantage of endoscopy is that it gives direct visualization, and ability to perform therapeutic interventions. For most upper gastrointestinal lesions the sensitivity (about 90%) and specificity (about 100%) of endoscopy are far higher than those of barium radiography (about 50 and 90% respectively). Endoscopic therapy controls bleeding in greater than 90% of patients and reduces rebleeding (up to 50%), thus decreasing morbidity and improving survival . Endoscopic sclerotherapy/banding has been the most successful and safest procedure in the management of first bleed of oesophageal varices. It can stop bleeding in 80-90% of patients. (D’Amico, 1995) With the advent of newer modalities of endoscopic treatment and latest facilities, this life threatening sequence can be arrested. So looking at ","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125497760","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":"Evaluation of Clustering Patterns using Singular Value Decomposition (SVD): A Case Study of Metabolic Syndrome","authors":"J. Namayanja","doi":"10.4018/jcmam.2010070104","DOIUrl":"https://doi.org/10.4018/jcmam.2010070104","url":null,"abstract":"Computational techniques, such as Simple K, have been used for exploratory analysis in applications ranging from data mining research, machine learning, and computational biology. The medical domain has benefitted from these applications, and in this regard, the authors analyze patterns in individuals of selected age groups linked with the possibility of Metabolic Syndrome (MetS), a disorder affecting approximately 45% of the elderly. The study identifies groups of individuals behaving in two defined categories, that is, those diagnosed with MetS (MetS Positive) and those who are not (MetS Negative), comparing the pattern definition. The paper compares the cluster formation in patterns when using a data reduction technique referred to as Singular Value Decomposition (SVD) versus eliminating its application in clustering. Data reduction techniques like SVD have proved to be very useful in projecting only what is considered to be key relations in the data by suppressing the less important ones. With the existence of high dimensionality, the importance of SVD can be highly effective. By applying two internal measures to validate the cluster quality, findings in this study prove interesting in context to both approaches. MetS (MetS Positive) and is furthered with a comparison of those do not fit into the diagnosis (MetS Negative). In order to understand the patterns of the clusters formed, there are two techniques applied – 1) Simple K – Means clustering and 2) Singular Value Decomposition (SVD) as a pre-processing task performed prior to the former. Because the process of collecting medical data can be quite tasking on the part of health professionals due to the limitless number of features that surround its domain, it presents even more of a problem for those, such DOI: 10.4018/jcmam.2010070104 70 International Journal of Computational Models and Algorithms in Medicine, 1(3), 69-80, July-September 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. as researchers and analysts, who try to expand the use of this type of data. Such datasets are highly dimensional and thus contain just about too many attributes for a single record. This often makes it difficult for knowledge workers to not only make a viable selection but to also represent this information in a manner that supports both visualization and clarity. Therefore (Thomasian, Castelli, & Li, 1998) commend that computational research presents data reduction techniques such as SVD and Principle Component Analysis (PCA) that can be applied to manage this curse of dimensionality. In the first approach (also referred to as NonSVD) of this study, a Simple K-means clustering algorithm is used as an exploratory analysis technique to group individuals of selected age groups with similar characteristics into k clusters where k refers to the ideal number of clusters (Bunn & Ostrovsky, 2007) with an aim of maximizi","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":" 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113951770","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":"Privacy Preserving Clustering for Distributed Homogeneous Gene Expression Data Sets","authors":"X. Li","doi":"10.4018/jcmam.2010070102","DOIUrl":"https://doi.org/10.4018/jcmam.2010070102","url":null,"abstract":"In this paper, the authors present a new approach to perform principal component analysis (PCA)-based gene clustering on genomic data distributed in multiple sites (horizontal partitions) with privacy protection. This approach allows data providers to collaborate together to identify gene profiles from a global viewpoint, and at the same time, protect the sensitive genomic data from possible privacy leaks. The authors developed a framework for privacy preserving PCA-based gene clustering, which includes two types of participants such as data providers and a trusted central site. Within this mechanism, distributed horizontal partitions of genomic data can be globally clustered with privacy preservation. Compared to results from centralized scenarios, the result generated from distributed partitions achieves 100% accuracy by using this approach. An experiment on a real genomic data set is conducted, and result shows that the proposed framework produces exactly the same cluster formation as that from the centralized data set. multiplex technology used in molecular biology. It consists of an arrayed series of thousands of microscopic spots of DNA oligonucleotides, called features, each containing picomoles (10-12 moles) of a specific DNA sequence, known as probes (or reporters). This can be a short section of a gene or other DNA element that is used to hybridize a cDNA or cRNA sample (called target) under high-stringency conditions. Probe-target hybridization is usually detected and quantified by detection of fluorophore-, silver-, or chemiluminescence-labeled targets to determine relative abundance of nucleic acid sequences in the target. Since an array can contain tens of thousands of probes, a microarray DOI: 10.4018/jcmam.2010070102 32 International Journal of Computational Models and Algorithms in Medicine, 1(3), 31-54, July-September 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. experiment can accomplish many genetic tests in parallel. Therefore arrays have dramatically accelerated many types of investigation. The microarray data processing pipeline (Hackl, Sanchez Cabo et al., 2004) includes a variety of statistical steps: pre-processing (including background correction, normalization, and summarization), differential analysis which contains raw p-value computation and false discovery rate (FDR) correction, and gene clustering / profiling analysis. Figure 1 shows that microarray experiment process in the lab and Figure 2 illustrates its gene clustering result. However, these exciting advances do come with an inevitable issue, that is, the richer and richer human genomic data contains privacy sensitive information, such as, genetic markers, diseases, etc., which may further lead to an individual’s race, family, or even identity. Unfortunately, because genomic data does not directly carry individual identity information and it used to be believed th","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117169419","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":"An Effective Streams Clustering Method for Biomedical Signals","authors":"Pimwadee Chaovalit","doi":"10.4018/jcmam.2010070101","DOIUrl":"https://doi.org/10.4018/jcmam.2010070101","url":null,"abstract":"In the healthcare industry, the ability to monitor patients via biomedical signals assists healthcare professionals in detecting early signs of conditions such as blocked arteries and abnormal heart rhythms. Using data clustering, it is possible to interpret these signals to look for patterns that may indicate emerging or developing conditions. This can be accomplished by basing monitoring systems on a fast clustering algorithm that processes fast-paced streams of raw data effectively. This paper presents a clustering method, POD-Clus, which can be useful in computer-aided diagnosis. The proposed method clusters data streams in linear time and outperforms a competing algorithm in capturing changes of clusters in data streams. be recorded for days (Sun & Sclabassi, 1999). These streams of data collected from sensors and other equipments can be referred to in general as “data streams”. An automatic monitoring device such as a digital ECG is used in monitoring heart-related conditions. An ECG can have up to 10 wires to collect a heart’s electrical signal, providing up to 12 or even 15 “angles” of how a heart functions (Cowley, 2006) . The heart’s electrical activities are monitored via the electrical signals from various designated places on the body. Wires are often placed on a patient’s limbs and chest, capturing signals of tissue muscle contractions which relate to the heart’s pumping motion. As these heart’s electrical data are continuously gathered at the rate of 100 – 1,000 samples per second (Bragge et al., 2004), a 5-minute recording of ECG can produce up to 10 time series (from 10 wires), each with 300,000 samples, DOI: 10.4018/jcmam.2010070101 2 International Journal of Computational Models and Algorithms in Medicine, 1(3), 1-30, July-September 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. leading to 3,000,000 data points. This is an enormous amount of data to handle. Various conditions can be revealed from the analysis of abnormal heartbeats. For example, the height of waves can be used to diagnose tissue death caused by blocked blood supply. Retrieving and analyzing these large amounts of data in a timely manner for quick diagnosis are challenging, as constantly arriving data may cause the data to become too large to either transmit over a network (Sun & Sclabassi, 1999) or fit in the main memory of the device. Hence, processing on data streams has to happen online and incrementally while data streams arrive, instead of offline and in batches. There exists a need for effective data streams mining techniques that can handle such data streams efficiently. This paper presents a study on data streams clustering, which can be incorporated into computer-aided analysis and used by physicians to cluster biomedical signals for diagnosis. By grouping biomedical signals into homogeneous clusters, we learn about data characteristics which may indicate e","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128488911","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":"Computer Aided Detection and Recognition of Lesions in Ultrasound Breast Images","authors":"Moi Hoon Yap, E. Edirisinghe, H. Bez","doi":"10.4018/jcmam.2010040104","DOIUrl":"https://doi.org/10.4018/jcmam.2010040104","url":null,"abstract":"The authors extend their previous work on Ultrasound (US) image lesion detection and segmentation, to classification, proposing a complete end-to-end solution for automatic Ultrasound Computer Aided Detection (US CAD). Carried out is a comprehensive analysis to determine the best classifier-feature set combination that works optimally in US imaging. In particular the use of nineteen features categorised into three groups (shape, texture and edge), ten classifiers and 22 feature selection approaches are used in the analysis. From the overall performance, the classifier RBFNetworks defined by the WEKA pattern recognition tool set, with a feature set comprising of the area to perimeter ratio, solidity, elongation, roundness, standard deviation, two Fourier related and a fractal related texture measures out-performed other combinations of feature-classifiers, with an achievement of predicted Az value of 0.948. Next analyzed is the use of a number of different metrics in performance analysis and provide an insight to future improvements and extension. DOI: 10.4018/jcmam.2010040104 54 International Journal of Computational Models and Algorithms in Medicine, 1(2), 53-81, April-June 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. (CAD) systems with high sensitivity, specificity and consistency. Despite these efforts CAD of ultrasound images still remains an area with many open research problems that needs solutions. In this article we identify a key open research problem in ultrasound imaging which is thoroughly investigated in order to develop a new method of ultrasound image processing for extracting relevant tissue structure information that will help differentiate between normal and malignant tissues. The ultimate goal is to provide fast and reliable tools for the early detection of malignant tissues in ultrasound images. The current practical use of a typical US CAD system can be illustrated as in figure 1. The input of a CAD system consists of a rectangular region of interests, manually selected by a radiologist. The output provides a statistical analysis that can aid the radiologist in the final decision making, i.e., the malignancy and/or type of cancer. The above approach (Figure 1) to CAD of breast ultrasound images has a limitation in that no aid is provided to the radiologist who selects the Region of Interest (ROI) of the lesions. Modern computer vision approaches can be used for fully automatic initial lesion detection, which can then be used as an aid to the decision making process of the radiologist, thus improving the accuracy of their performance. Further at this initial stage the need of a radiologist can be completely eliminated by allowing for a higher degree of false positives which can later be removed by further CAD or the presence of a radiologist at the final decision making stage. Thus in this article we aim to use our previous work ","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126419116","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":"Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme to Securely Manage Personal Health Records","authors":"L. Ibraimi, Qiang Tang, P. Hartel, W. Jonker","doi":"10.4018/jcmam.2010040101","DOIUrl":"https://doi.org/10.4018/jcmam.2010040101","url":null,"abstract":"Commercial Web-based Personal-Health Record (PHR) systems can help patients to share their personal health records (PHRs) anytime from anywhere. PHRs are very sensitive data and an inappropriate disclosure may cause serious problems to an individual. Therefore commercial Web-based PHR systems have to ensure that the patient health data is secured using state-of-the-art mechanisms. In current commercial PHR systems, even though patients have the power to define the access control policy on who can access their data, patients have to trust entirely the access-control manager of the commercial PHR system to properly enforce these policies. Therefore patients hesitate to upload their health data to these systems as the data is processed unencrypted on untrusted platforms. Recent proposals on enforcing access control policies exploit the use of encryption techniques to enforce access control policies. In such systems, information is stored in an encrypted form by the third party and there is no need for an access control manager. This implies that data remains confidential even if the database maintained by the third party is compromised. In this paper we propose a new encryption technique called a type-and-identity-based proxy re-encryption scheme which is suitable to be used in the healthcare setting. The proposed scheme allows users (patients) to securely store their PHRs on commercial Web-based PHRs, and securely share their PHRs with other users (doctors).","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128688846","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":"Regulatory Compliance and the Correlation to Privacy Protection in Healthcare","authors":"Tyrone Grandison, Rafae Bhatti","doi":"10.4018/jcmam.2010040103","DOIUrl":"https://doi.org/10.4018/jcmam.2010040103","url":null,"abstract":"Recent government-led efforts and industry-sponsored privacy initiatives in the healthcare sector have received heightened publicity. The current set of privacy laws and regulations mandate that all parties involved in the delivery of care specify and publish privacy policies regarding the use and disclosure of personal health information. Our study of actual privacy policies in the healthcare industry indicates that the vague representations in published privacy policies are not strongly correlated with adequate privacy protection for the patient. This phenomenon is not due to a lack of available technology to enforce privacy policies, but rather to the will of the healthcare entities to enforce strong privacy protections and their interpretation of minimum compliance obligations. Using available information systems and data mining techniques, we describe an infrastructure for privacy protection based on the idea of policy refinement to allow the transition from the current state of perceived to be privacy-preserving systems to actually privacy-preserving systems.","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134077568","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":"Similarity Searching of Medical Image Data in Distributed Systems: Facilitating Telemedicine Applications","authors":"A. Charisi, Panagiotis Korvesis, V. Megalooikonomou","doi":"10.4018/jcmam.2011010104","DOIUrl":"https://doi.org/10.4018/jcmam.2011010104","url":null,"abstract":"","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124006090","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":"Understanding Human-Computer Interactions in Intensive Care Unit Clinical Communication","authors":"Saif S. Khairat, C. Craven, Y. Gong","doi":"10.4018/jcmam.2012010102","DOIUrl":"https://doi.org/10.4018/jcmam.2012010102","url":null,"abstract":"Clinical communication failures are considered the leading cause of medical errors (Bates et al., 1997). The complexity of the clinical culture and the significant variance in training and education levels form a challenge to enhancing communication within the clinical team. In order to improve communication, a comprehensive understanding of the overall communication process in health care is required. In an attempt to further understand clinical communication, the authors conducted a thorough methodology literature review to identify strengths and limitations of previous approaches. Their research proposes a new data collection method to study the clinical communication activities among Intensive Care Unit (ICU) clinical teams with a primary focus on the attending physician. In this paper, the authors present the first ICU communication instrument, they introduce the use of database management system to aid in discovering patterns and associations within our ICU communications data repository, and they present the authors’ Human-Computer Interaction observational study results. The authors have identified and analyzed key Human-Interaction behaviors and tools in the ICU in addition to refining the clinical communication model they previously proposed (Khairat & Gong, 2010b). their goal is to build an exhaustive knowledge representation of the clinical communication process through utilizing an ontological approach. DOI: 10.4018/jcmam.2012010102 International Journal of Computational Models and Algorithms in Medicine, 3(1), 14-31, January-March 2012 15 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. ficient communication is a significant factor in the occurrence of medical errors. By deduction, the quality of clinical communication is an essential factor towards error-free practices and safer patients. This paper aims to introduce a novel approach to further understand clinical communication by: (1) studying previously used methods, (2) developing a hybrid research instrument, and (3) utilizing informatics to collect, organize and analyze our clinical communication observations data repository. In this hypothesis-driven research, we hypothesize that there is a relationship between the communication skills of a lead physician and the level of understanding among the clinical team, through analyzing Human-Human and HumanComputer interactions. This research refers to patient safety as the concept of patients receiving care services free from accidental injuries. The term medical errors are defined as preventable adverse events that can result in near misses, injuries, or death. Furthermore, we define clinical communication as the exchange of ideas, messages or knowledge between two or more entities through verbal, non-verbal, written, and visual forms where entities represent clinicians or technological components. Because health care includes communication th","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128087710","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}