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

{"title":"Dynamic Document Clustering Using Singular Value Decomposition","authors":"Rashmi Nadubeediramesh, A. Gangopadhyay","doi":"10.4018/jcmam.2012070103","DOIUrl":"https://doi.org/10.4018/jcmam.2012070103","url":null,"abstract":"Incremental document clustering is important in many applications, but particularly so in healthcare contexts where text data is found in abundance, ranging from published research in journals to day-to-day healthcare data such as discharge summaries and nursing notes. In such dynamic environments new documents are constantly added to the set of documents that have been used in the initial cluster formation. Hence it is important to be able to incrementally update the clusters at a low computational cost as new documents are added. In this paper the authors describe a novel, low cost approach for incremental document clustering. Their method is based on conducting singular value decomposition (SVD) incrementally. They dynamically fold in new documents into the existing term-document space and dynamically assign these new documents into pre-defined clusters based on intra-cluster similarity. This saves the cost of re-computing SVD on the entire document set every time updates occur. The authors also provide a way to retrieve documents based on different window sizes with high scalability and good clustering accuracy. They have tested their proposed method experimentally with 960 medical abstracts retrieved from the PubMed medical library. The authors’ incremental method is compared with the default situation where complete re-computation of SVD is done when new documents are added to the initial set of documents. The results show minor decreases in the quality of the cluster formation but much larger gains in computational throughput. Dynamic Document Clustering Using Singular Value Decomposition","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128964796","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":"Risk Factors of Breast Cancer in Indian Context: A Systematic Review","authors":"Biswa Bandita, Dipti Mohanty, S. Pradhan, S. Rath, M. Sahu, A. Joshi","doi":"10.4018/jcmam.2012070101","DOIUrl":"https://doi.org/10.4018/jcmam.2012070101","url":null,"abstract":"The upward trend in breast cancer globally and in India has become a matter of great concern. Breast cancer is the most common malignancy among women globally. The objective of the authors’ study was to explore the various risk factors of breast cancer in among women in an Indian context. A search was performed using the search engine Pubmed during years 2005-2011 using key words risk factor and breast cancer and India. They searched criteria found 16 final analyzable articles. Results of the review showed high mortality due to late stage breast cancer diagnosis as women usually present at an advanced stage. The results showed that the predominant reason was because of lack of awareness about the risk factors of breast cancer and nonexistence of breast cancer screening programs. Financial and social reasons were other factors that resulted in delay in seeking advice for this problem resulting in its delayed diagnosis. Educational awareness might be an effective tool for modifying lifestyles and thereby reducing breast cancer risks. Risk Factors of Breast Cancer in Indian Context: A Systematic Review","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130670820","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":"Using Radio Frequency Identification (RFID) Tags to Store Medical Information Needed by First Responders: Data Format, Privacy, and Security","authors":"Chris Hart, P. Hawrylak","doi":"10.4018/jcmam.2012070102","DOIUrl":"https://doi.org/10.4018/jcmam.2012070102","url":null,"abstract":"In the event of an accident or emergency, a victim’s medical information such as blood type, prescribed drugs, and other pertinent medical history is critical to Emergency Medical Technicians (EMTs) so that the correct treatment can be provided to the victim as quickly as possible. Victims of car accidents, heart attacks, etc., are not always able to answer simple but crucial medical questions. Treatment time is critical in an emergency situation and the EMT must quickly obtain correct medical information to provide treatment until the victim is stabilized or admitted to the hospital. With an unconscious patient, the EMT must perform a number of tests to obtain these details. A Radio Frequency Identification (RFID) tag encoded with this information could provide this information quickly and correctly, while saving the time and expense of the tests to answer these questions. The ability of the RFID tag to communicate through objects can minimize the movement of the victim to obtain the necessary information. This paper presents a standardized format for encoding (storing) this information in the RFID tag for use in the United States. The use of data compression techniques are explored to maximize the amount of information able to be stored in the RFID tag. Privacy and security issues with this application are discussed and a potential solution is presented. Using Radio Frequency Identification (RFID) Tags to Store Medical Information Needed by First Responders: Data Format, Privacy, and Security","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115475837","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":"Algorithmic Analysis of Clinical and Neuropsychological Data in Localization-Related Epilepsy","authors":"Masoud Latifi-Navid, K. Elisevich, H. Soltanian-Zadeh","doi":"10.4018/ijcmam.2014010103","DOIUrl":"https://doi.org/10.4018/ijcmam.2014010103","url":null,"abstract":"The current study examines algorithmic approaches for the analysis of clinical and neuropsychological attributes in localization-related epilepsy (LRE), specifically, their impact in the selection of patients for surgical consideration. Both electrographic and imaging data are excluded here to concentrate upon the initial clinical presentation and the varied elements of the seizure history, ictal semiology, risk and seizure-precipitating factors and physical findings in addition to several features of the neuropsychological profile including various parameters of cognition and both speech and memory lateralization. The data was accrued in a database of temporal lobe epilepsy patients and accessible in the public domain (HBIDS). Six algorithms comprising feature selection, clustering and classification approaches were used. The Correlation-Based Feature Selection (CFS) and the Classifier Subset Evaluator (CSE) with the Genetic Algorithm (GA) search tool and ReliefF Attribute Evaluation approaches provided for feature selection, the Expectation Maximization (EM) Class Clustering and Incremental Conceptual Clustering (COBWEB) provided data clustering and the Multilayer Perceptron (MLP) Classifier was the classification tool at all stages of the study. The Engel Classification was used as an output of classifier for surgical success. Attributes demonstrating the highest correlation with outcome class and the least intercorrelation with each other, according to CFS, were selected. These were then ranked using ReliefF and the top rankings chosen. The best attribute combination for each cluster was found by the MLP. COBWEB provided the best results showing an association of 56% with Engel class. An algorithmic approach to the study of LRE is feasible with current findings supporting the need for correlative electrographic and imaging data and a greater archival population. Â","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126865440","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":"Managing Privacy and Effectiveness of Patient-Administered Authorization Policies","authors":"Thomas Trojer, Basel Katt, R. Breu, T. Schabetsberger, Richard Mair","doi":"10.4018/jcmam.2012040103","DOIUrl":"https://doi.org/10.4018/jcmam.2012040103","url":null,"abstract":"A central building block of data privacy is the individual right of information self-determination. Following from that when dealing with shared electronic health records (SEHR), citizens, as the identified individuals of such records, have to be enabled to decide what medical data can be used in which way by medical professionals. In this context individual preferences of privacy have to be reflected by authorization policies to control access to personal health data. There are two potential challenges when enabling patient-controlled access control policy authoring: First, an ordinary citizen neither can be considered a security expert, nor does she or he have the expertise to fully understand typical activities and workflows within the health-care domain. Thus, a citizen is not necessarily aware of implications her or his access control settings have with regards to the protection of personal health data. Both privacy of citizen’s health-data and the overall effectiveness of a health-care information system are at risk if inadequate access control settings are in place. This paper refers to scenarios of a case study previously conducted and shows how privacy and information system effectiveness can be defined and evaluated in the context of SEHR. The paper describes an access control policy analysis method which evaluates a patient-administered access control policy by considering the mentioned evaluation criteria. DOI: 10.4018/jcmam.2012040103 44 International Journal of Computational Models and Algorithms in Medicine, 3(2), 43-62, April-June 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. ing block of privacy is the individual right to decide which data about oneself might be collected and stored and how data is supposed to be processed (OECD, 1980). Since 1983 this right, entitled informational self-determination is a fundamental right in German law1 (law of the German federal constitution (BVerfGE) 65, 1) and further is a substantial part of the European Data Protection Directive 95/46/EC (European Commission, 1995) established all over the European Union (EU) through corresponding national laws. Despite the right of individual users to participate in activities to control privacy-sensitive information, its implementation is difficult and poses at least the following challenges: An ordinary user is typically not considered a security expert and the actual definition or selection of appropriate authorization preferences requires the user to translate her/his mental conception of privacy into enforceable security configurations. Another issue comes with the impact userdefined settings of authorization may have to the user’s privacy or the resulting effectiveness of the health-care information system. Users, as the sole authors of access control statements, have to be made aware about the consequences there settings imply. Although issues regarding","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134448316","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":"Towards HIPAA-Compliant Healthcare Systems in Cloud Computing","authors":"Ruoyu Wu, Gail-Joon Ahn, Hongxin Hu","doi":"10.4018/jcmam.2012040101","DOIUrl":"https://doi.org/10.4018/jcmam.2012040101","url":null,"abstract":"In modern healthcare environments, there is a strong need to create an infrastructure that reduces time-consuming efforts and costly operations to obtain a patient’s complete medical record and uniformly integrates this heterogeneous collection of medical data to deliver it to the healthcare professionals. As a result, healthcare providers are more willing to shift their electronic medical record (EMR) systems to clouds that can remove the geographical distance barriers among providers and patients. Since a shared electronic health record (EHR) essentially represents a virtualized aggregation of distributed clinical records from multiple healthcare providers, sharing of such integrated EHRs should comply with various authorization policies from these data providers. In previous work, the authors present and implement a secure medical data sharing system to support selective sharing of composite EHRs aggregated from various healthcare providers in cloud computing environments. In this paper, the authors point out that when EMR systems are migrated to clouds, it is also critical to ensure that EMR systems are compliant with government regulations such as the Health Insurance Portability and Accountability Act (HIPAA). Also, the authors propose a HIPAA compliance management approach by leveraging logic-based techniques and apply it to the cloud-based EHRs sharing system. The authors also describe evaluation results to demonstrate the feasibility and effectiveness of the approach. DOI: 10.4018/jcmam.2012040101 2 International Journal of Computational Models and Algorithms in Medicine, 3(2), 1-22, April-June 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. result, a patient’s EHRs can be found scattered throughout the entire healthcare sector. From the clinical perspective, in order to deliver quality patient care, it is critical to access the integrated patient care information that is often collected at the point of care to ensure the freshness of time-sensitive data (Grimson et al., 2001). This further requires an efficient, secure and low-cost mechanism for sharing EHRs among multiple healthcare providers. Particularly, in some emergency healthcare situations, immediate exchange of patient’s EHRs is crucial to save lives. However, in current healthcare settings, healthcare providers mostly establish and maintain their own electronic medical record (EMR) systems for storing and managing EHRs. Such self-managed data centers are very expensive for healthcare providers. Besides, the sharing and integration of EHRs among EMR systems managed by different healthcare providers are extremely slow and costly. Thus, a common and open infrastructure platform can play a key role in changing such a situation and improve the healthcare quality. Cloud computing has become a promising computing paradigm drawing extensive attention from both academia and industry (Mell & ","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"147 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114125714","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":"Audit Mechanisms in Electronic Health Record Systems: Protected Health Information May Remain Vulnerable to Undetected Misuse","authors":"J. King, Benjamin H. Smith, L. Williams","doi":"10.4018/jcmam.2012040102","DOIUrl":"https://doi.org/10.4018/jcmam.2012040102","url":null,"abstract":"Inadequate audit mechanisms may result in undetected misuse of data in software-intensive systems. In the healthcare domain, electronic health record (EHR) systems should log the creating, reading, updating, or deleting of privacy-critical protected health information. The objective of this paper is to assess electronic health record audit mechanisms to determine the current degree of auditing for non-repudiation and to assess whether general audit guidelines adequately address non-repudiation. The authors analyzed the audit mechanisms of two open source EHR systems, OpenEMR and Tolven eCHR, and one proprietary EHR system. The authors base the qualitative assessment on a set of 16 general auditable events and 58 black-box test cases for specific auditable events. The authors find that OpenEMR satisfies 62.5% of the general criteria and passes 63.8% of the black-box test cases. Tolven eCHR and the proprietary EHR system each satisfy less than 19% of the general criteria and pass less than 11% of the black-box test cases. DOI: 10.4018/jcmam.2012040102 24 International Journal of Computational Models and Algorithms in Medicine, 3(2), 23-42, April-June 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. trust the privacy practices and accountability of healthcare organizations. Administering software audit mechanisms forms a basis for privacy-driven and accountability-driven policy and regulations, including government regulations (Kent & Souppaya, 2006). Ensuring accountability in an EHR system is essential, since a user should be unable to deny performing certain actions because these actions were recorded by the audit mechanism. The United States Department of Justice’s Global Justice Information Sharing Initiative defines: Non-repudiation – a technique used to ensure that someone performing an action on a computer cannot falsely deny that they performed that action. Non-repudiation provides undeniable proof that a user took a specific action. (Privacy Technology Focus Group, 2006) Audit mechanisms should help ensure privacy of PHI by focusing on recording and detecting inappropriate accesses to PHI to promote non-repudiation. The healthcare field needs specific standards that address the implementation of software audit mechanisms to monitor access and information disclosure, including details of what should be logged, how it should be logged, and how logged information should be monitored. In a previous study, we assessed the audit mechanisms of OpenEMR, OpenMRS, and Tolven eCHR to determine how well the three EHR audit mechanisms address non-repudiation (King, Smith, & Williams, 2012). We based our qualitative assessment on both (1) a set of 16 general auditable events derived from four professional sources of audit guidelines, and (2) set of 58 black-box test cases for specific auditable events derived from the Certification Commission for Health Inform","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124082247","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 Principal Component Analysis Clustering for Distributed Heterogeneous Gene Expression Datasets","authors":"X. Li","doi":"10.4018/jcmam.2011100102","DOIUrl":"https://doi.org/10.4018/jcmam.2011100102","url":null,"abstract":"In this paper, we present approaches to perform principal component analysis (PCA) clustering for distributed heterogeneous genomic datasets with privacy protection. The approaches allow 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. We then further develop a framework for privacy preserving PCA-based gene clustering, which includes two types of participants: data providers and a trusted central site (TCS). Two different methodologies are employed: Collective PCA (C-PCA) and Repeating PCA (R-PCA). The C-PCA requires local sites to transmit a sample of original data to the TCS and can be applied to any heterogeneous datasets. The R-PCA approach requires all local sites have the same or similar number of columns, but releases no original data. Experiments on five independent genomic datasets show that both C-PCA and R-PCA approaches maintain very good accuracy compared with the centralized scenario. DOI: 10.4018/jcmam.2011100102 24 International Journal of Computational Models and Algorithms in Medicine, 2(4), 23-56, October-December 2011 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Gene Expression and DNA Microarray A DNA microarray (Wikipedia, 2010), which is the practical realized technology of the Gene Expression (BioChemWeb.org, 2010), is a 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 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, Sturn, Wolkenhauer, & Trajanoski, 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(a) shows that microarray experiment process in the lab and Figure 1(b) illustrates its gene clustering result. Gene Clustering on Collaborative Datasets on Vertical Partitions Due to the fact that limited technical resources are available of a single research group or institution, researchers are often","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"23 Suppl 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122477768","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":"Social Credential-Based Role Recommendation and Patient Privacy Control in Medical Emergency","authors":"Soon Ae Chun, Joonhee Kwon, Haesung Lee","doi":"10.4018/jcmam.2011100101","DOIUrl":"https://doi.org/10.4018/jcmam.2011100101","url":null,"abstract":"Emerging Health Information Technologies (HIT), such as Electronic Health Records (EHR) and Personal Health Records (PHR) systems, facilitate access to and sharing of patients’ medical data in a distributed environment. The privacy protection of medical information is a pressing issue with the use of these medical technologies. In this paper, the authors present a Patient-controlled Privacy Protection Framework, which allows a patient to specify his or her own privacy policies on their own medical data no matter where they are stored. In addition, the authors extend this basic framework to medical emergency situations, where roles and users may not be limited to an organizational boundary. To enforce patient’s privacy policies even in emergency situations, the authors propose the Situation Role-based Privacy Control model and a social network-based user credential discovery method to recommend a situation role to candidate users. The authors present a mobile prototype system and two experiments to show the feasibility of our approach. DOI: 10.4018/jcmam.2011100101 2 International Journal of Computational Models and Algorithms in Medicine, 2(4), 1-22, October-December 2011 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. of patient data for sharing and for decision support analytics across healthcare providers’ organizational boundaries, urging the use of Health Information Exchange (HIE) standards and an interoperable framework. One of the many major challenges to overcome for EHR systems to be widely adopted for sharing of patient information across different EHR systems in the HIE environment is ensuring patient privacy. With the use of EHR systems, doctors, other healthcare providers, insurance companies, governments, as well as patients could easily access patient information that is stored in various locations. The patient’s privacy should be a paramount priority. Typically, a patient leaves medical records in various providers’ EHR systems. A general practitioner can enter initial checkup notes and his recommendations on his own EHR system. Then a specialist can also record some patient information in his own EHR system, and so do pharmacists, X-ray technicians, etc. In this distributed environment, it is difficult to ensure the consistent privacy control for different health information of the patient. Currently, a patient at the initial visit to a doctor’s office fills out a paper-based form regarding the health information privacy on how his or her own heath information may be shared. It is difficult to ensure that privacy is controlled in the manner the patient desires or to ensure that the healthcare providers honor the privacy specifications of the patient about sharing and using his or her own health data. The patient simply relies that the organization’s policy is executed in good faith, but has no control over who can access what and how her o","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129367415","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 Early Robot Architecture for Cancer Healing","authors":"M. Abbas","doi":"10.4018/jcmam.2011100103","DOIUrl":"https://doi.org/10.4018/jcmam.2011100103","url":null,"abstract":"Treating cancer tumors is a main goal of cancer research. The author of this paper identifies a new manner to treat cancer tumors more effectively using a recommended architecture of a nanorobot called CANBOT. It contains a number of nano-components: an actuator, temperature sensor, chemical sensor, and microcontroller. CANBOT starts its role by moving toward the tumor cells using the actuator. It senses the tumor cell by capturing its image and sensing its chemicals by the chemical sensor. When CANBOT distinguishes the tumor, it verifies the survival of the tumor cells by its temperature sensor. CANBOT increases the temperature of the tumor cell through the warmer. Sensing of the cancer chemicals starts over to detect the remaining existence of cancer cells. The suggested nanorobot injects the cell with the drug from a tiny tank throughout a nano pump with a small pine needle. A nano-microcontroller controls the mechanism of CANBOT formative the role of each one and the appropriate sequences. The position of the proposed nanorobot is simulated with reference to the position of the tumor using an analytical model. The conclusion is drawn that destroying the tumor requires instilling the robot into the cancer tumor directly for effective treatment. DOI: 10.4018/jcmam.2011100103 58 International Journal of Computational Models and Algorithms in Medicine, 2(4), 57-71, October-December 2011 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Shirinzadeh, Freitas, & Kretly, 2007; Sierra, Weir, & Jones, 2005; Cavalcanti & Freitas, 2005; Mathieu, Martel, Yahia, Soulez, & Beaudoin, 2005; Behkam & Sitti, 2006; Xi, Schmidt, & Montemagno, 2005; Lee, Mahapatro, Caron, Requicha, Stauffer, Thompson, & Zhou, 2006; Fukuda, Kawamoto, Arai, & Matsuura, 1995; Freitas, 2005; Patel, Patel, Patel, Patel, & Patel, 2006; Ikeda, Arai, Fukuda, Kim, Negoro, Irie, & Takahashi, 2005; Xu, Vijaykrishnan, Xie, & Irwin, 2004; Park, Lee, & Lee, 2005; Couvreur & Vauthier, 2006; Gao, Wolfgang, Neschen, Morino, Horvath, Shulman, & Fu, 2004). The main objective of this paper is to match targeted therapies to cancer patients resourcefully and untimely using nanorobots. And the differentiated goal is to be able to abnegate tumor tissue in such a way as to abbreviate the bet of causing or allocating a recurrence of the growth in the body. The approach is conscious to be able to treat tumors that cannot be gained access via conventional surgery, such as abysmal brain tumors. Nanorobotics is the appearing technology area beginning apparatuses or robots whose components are at or access to the scale of a nanometer. More characteristically, nanorobotics refers to the nanotechnology engineering discipline of designing and creating nanorobots, with appliances ranging in amplitude from 0.110 micrometers and combined of nanoscale or molecular constituents. Nanomachines are amply in the research-and-developmen","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134100514","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}