Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470250
S. N. Nagabhushan, T. Ahn, M. Srikanth, T. Park, Ajit S. Bopardikar, R. Narayanan
Personalized genomic medicine aims to revolutionize healthcare by applying our growing understanding of the molecular basis of disease for effective diagnosis and personalized therapy. Computational research in this arena has major challenges such as handling large volume of highly heterogeneous data sets. To extract knowledge, researchers must integrate data from several sources and efficiently query these large and diverse data sets. This presents daunting informatics challenges such as suitable data representation for computational inference (knowledge representation), linking heterogeneous data sets (data integration) and keeping track of the source of the data to be aggregated. Many of these challenges can be categorized as data integration problems. In this paper, we present relevant methodologies from the field of data integration as potential solution for such challenges encountered by computational biologist while handling diversified data. The work presented in the paper represents the first crucial step towards identifying cancer biomarkers leading to cancer pathways signatures and personalized medicine.
{"title":"A data aggregation framework for cancer subtype discovery","authors":"S. N. Nagabhushan, T. Ahn, M. Srikanth, T. Park, Ajit S. Bopardikar, R. Narayanan","doi":"10.1109/BIBMW.2012.6470250","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470250","url":null,"abstract":"Personalized genomic medicine aims to revolutionize healthcare by applying our growing understanding of the molecular basis of disease for effective diagnosis and personalized therapy. Computational research in this arena has major challenges such as handling large volume of highly heterogeneous data sets. To extract knowledge, researchers must integrate data from several sources and efficiently query these large and diverse data sets. This presents daunting informatics challenges such as suitable data representation for computational inference (knowledge representation), linking heterogeneous data sets (data integration) and keeping track of the source of the data to be aggregated. Many of these challenges can be categorized as data integration problems. In this paper, we present relevant methodologies from the field of data integration as potential solution for such challenges encountered by computational biologist while handling diversified data. The work presented in the paper represents the first crucial step towards identifying cancer biomarkers leading to cancer pathways signatures and personalized medicine.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91132374","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}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470346
Zhaohui Liang, Gang Zhang, Ziping Li, Jian Yin, W. Fu
The inheritance of clinical experience of veteran doctors of Chinese medicine (CM) plays a key role in the development and effectiveness enhancement of Chinese medicine in the history. The clinical experience are classified as the patterns of disease diagnosis and Chinese medical Zheng diagnosis, the identification of core elements of Zheng, the treatment experience and relation of herbal medicine formulae, Zheng and disease, and the common law of diagnosis and treatment in real practice. The source of the experience mainly originates from literature and manuscripts of CM masters, which are being electronically recorded during the last two decades. As a result, it makes feasible to apply data mining to the knowledge discovery through the experience of veteran CM doctors. However, the current focus on this field is limited to the published literature such as journal papers, conference proceedings and textbooks, but the paper based manuscripts personally written by the veteran doctors are usually neglected. In this paper, we established a database for Dr Situ Ling, who is a deceased famous CM acupuncture master in southern China. The study objective is to discover the acupuncture point selection patterns which require profession knowledge and experience from senior CM doctors. It is believed these patterns are deposited as underlying knowledge with various middle level concepts that can be analyzed and discover by a serial of algorithms. Thus in this work, we formularized the patterns of acupuncture point selection as a learning task with deep architecture, which attempts to capture either existent or underlying concepts so as to simulate the planning process of the combined diagnosis of western medicine and Chinese medicine. The Restricted Boltzmann Machines (RBM) was used as the main model for deep learning to process to medical record data with international standard diagnosis (ICD-10) previously made by trained doctors. Then the ICD-10 based diagnosis dataset was introduced into our framework to enhance the concepts diversity. After applying this model, the learning accuracy based on the medical record database of Dr Situ Ling was raised up to 75%. Thus this model can serve as a solution to discover the acupuncture point selection patterns of CM acupuncture veteran doctors. Furthermore, the data mining study model linked by international diagnosis standard (i.e. ICD-10), point selection patterns, and clinical symptoms will provide useful cues to reveal the essence of Zheng diagnosis through experience of CM veteran doctors.
{"title":"Deep learning for acupuncture point selection patterns based on veteran doctor experience of Chinese medicine","authors":"Zhaohui Liang, Gang Zhang, Ziping Li, Jian Yin, W. Fu","doi":"10.1109/BIBMW.2012.6470346","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470346","url":null,"abstract":"The inheritance of clinical experience of veteran doctors of Chinese medicine (CM) plays a key role in the development and effectiveness enhancement of Chinese medicine in the history. The clinical experience are classified as the patterns of disease diagnosis and Chinese medical Zheng diagnosis, the identification of core elements of Zheng, the treatment experience and relation of herbal medicine formulae, Zheng and disease, and the common law of diagnosis and treatment in real practice. The source of the experience mainly originates from literature and manuscripts of CM masters, which are being electronically recorded during the last two decades. As a result, it makes feasible to apply data mining to the knowledge discovery through the experience of veteran CM doctors. However, the current focus on this field is limited to the published literature such as journal papers, conference proceedings and textbooks, but the paper based manuscripts personally written by the veteran doctors are usually neglected. In this paper, we established a database for Dr Situ Ling, who is a deceased famous CM acupuncture master in southern China. The study objective is to discover the acupuncture point selection patterns which require profession knowledge and experience from senior CM doctors. It is believed these patterns are deposited as underlying knowledge with various middle level concepts that can be analyzed and discover by a serial of algorithms. Thus in this work, we formularized the patterns of acupuncture point selection as a learning task with deep architecture, which attempts to capture either existent or underlying concepts so as to simulate the planning process of the combined diagnosis of western medicine and Chinese medicine. The Restricted Boltzmann Machines (RBM) was used as the main model for deep learning to process to medical record data with international standard diagnosis (ICD-10) previously made by trained doctors. Then the ICD-10 based diagnosis dataset was introduced into our framework to enhance the concepts diversity. After applying this model, the learning accuracy based on the medical record database of Dr Situ Ling was raised up to 75%. Thus this model can serve as a solution to discover the acupuncture point selection patterns of CM acupuncture veteran doctors. Furthermore, the data mining study model linked by international diagnosis standard (i.e. ICD-10), point selection patterns, and clinical symptoms will provide useful cues to reveal the essence of Zheng diagnosis through experience of CM veteran doctors.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88724394","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}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470349
D. Guo, Jian Li, Gang Zhang, Weixiang Lu, Shaojian Xu, Jun Liu
At present, more and more patients suffering from knee OA (Ostarthritis) are treated with complementary and alternative medicine, such as herbal drugs, herbal patches, acupuncture and manipulation etc, as an effective therapy. However, traditional statistical methods data gathered from randomized controlled trials (RCT) which were considered as the golden standard for therapy effectiveness failed to confirm those therapies efficacy. Whether we can accurately predict these therapeutic effects on the basis of a prospective, five-center, parallel-group, randomized controlled trial by means of other innovative ways is the question. According to this question, our team adopted several commonly used data mining algorithms to study it, such as KNN (k-Nearest Neighbor algorithm), j48 (decision tree), ANN (Artificial Neural Network). By means of modeling analysis of the patients' Traditional Chinese Medicine (TCM) symptoms questionnaire, Western Ontario and McMaster Universities Index of OA (WOMAC) total score and SF-36 assessment to predict the therapeutic effect which a patient can achieve after adopting one of those TCM therapies. Then we comprehensively analysed the effect and characteristic of every therapy schedule.
{"title":"Research on optimal Traditional Chinese Medicine treatment of knee ostarthritis with data mining algorithms","authors":"D. Guo, Jian Li, Gang Zhang, Weixiang Lu, Shaojian Xu, Jun Liu","doi":"10.1109/BIBMW.2012.6470349","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470349","url":null,"abstract":"At present, more and more patients suffering from knee OA (Ostarthritis) are treated with complementary and alternative medicine, such as herbal drugs, herbal patches, acupuncture and manipulation etc, as an effective therapy. However, traditional statistical methods data gathered from randomized controlled trials (RCT) which were considered as the golden standard for therapy effectiveness failed to confirm those therapies efficacy. Whether we can accurately predict these therapeutic effects on the basis of a prospective, five-center, parallel-group, randomized controlled trial by means of other innovative ways is the question. According to this question, our team adopted several commonly used data mining algorithms to study it, such as KNN (k-Nearest Neighbor algorithm), j48 (decision tree), ANN (Artificial Neural Network). By means of modeling analysis of the patients' Traditional Chinese Medicine (TCM) symptoms questionnaire, Western Ontario and McMaster Universities Index of OA (WOMAC) total score and SF-36 assessment to predict the therapeutic effect which a patient can achieve after adopting one of those TCM therapies. Then we comprehensively analysed the effect and characteristic of every therapy schedule.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81291222","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}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470274
Daniel Veltri, Amarda Shehu
Concerns over antibacterial resistance have antimicrobial peptides (AMPs) garnering attention as potential targets for new antibacterial drugs [1]. Wet-lab development of AMP-based drugs hinge on understanding the relationship between AMP sequence and activity [1]. In support of such efforts, we devise a method to highlight position-based physico-chemical features related to activity. We do so in a focused analysis of the mature peptide fragments of cathelicidins; a populous sequence-diverse family of well-studied a-helical AMPs [1]. We employ features based on the AAIndex [2], an extensive collection of documented physico-chemical amino acid properties, and Support Vector Machine (SVM) to recognize cathelicidins from a set of carefully designed decoy sequences. Our results demonstrate that these features are very useful in elucidating specific residue positions and properties related to AMP activity.
{"title":"Physico-chemical features for recognition of antimicrobial peptides","authors":"Daniel Veltri, Amarda Shehu","doi":"10.1109/BIBMW.2012.6470274","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470274","url":null,"abstract":"Concerns over antibacterial resistance have antimicrobial peptides (AMPs) garnering attention as potential targets for new antibacterial drugs [1]. Wet-lab development of AMP-based drugs hinge on understanding the relationship between AMP sequence and activity [1]. In support of such efforts, we devise a method to highlight position-based physico-chemical features related to activity. We do so in a focused analysis of the mature peptide fragments of cathelicidins; a populous sequence-diverse family of well-studied a-helical AMPs [1]. We employ features based on the AAIndex [2], an extensive collection of documented physico-chemical amino acid properties, and Support Vector Machine (SVM) to recognize cathelicidins from a set of carefully designed decoy sequences. Our results demonstrate that these features are very useful in elucidating specific residue positions and properties related to AMP activity.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84664837","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}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470326
Masood Zamani, S. C. Kremer
In this study, we evaluate the performance of a protein secondary structure prediction model using a new amino acid "codon" encoding inspired by genetic codon mappings. The dimensionality of the binary codon encoding is less than that of an orthogonal encoding which requires less computations. Protein secondary structure prediction is an important step for machine learning techniques ultimately applied for protein 3D structure prediction. In the proposed model, one-stage binary support vector machines are employed, and the efficiency of the codon encoding to that of a commonly used orthogonal encoding are compared without incorporating protein evolutionary and structural information for an unbiased comparison. The performance of the classification model is measured according to Q3 and segment overlap (SOV) scores. The scores are compared with those of the prediction methods using an orthogonal encoding and protein sequence profiles. The experimental results indicate higher prediction accuracy based on Q3 SOV scores when sequence profiles are not used. Also, the relative classification scores of the proposed method are comparable with the methods incorporating protein global and evolutionary information. The experimental result implies the encoding scheme is able to integrate the evolutionary information into the prediction model since the encoding is based on genetic codon mappings which are the building blocks of amino acid formations at the primary level of biological processes. The codon encoding is worthwhile to be investigated using more complex learning architectures with the profiles and structural properties of proteins.
{"title":"Protein secondary structure prediction using support vector machines and a codon encoding scheme","authors":"Masood Zamani, S. C. Kremer","doi":"10.1109/BIBMW.2012.6470326","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470326","url":null,"abstract":"In this study, we evaluate the performance of a protein secondary structure prediction model using a new amino acid \"codon\" encoding inspired by genetic codon mappings. The dimensionality of the binary codon encoding is less than that of an orthogonal encoding which requires less computations. Protein secondary structure prediction is an important step for machine learning techniques ultimately applied for protein 3D structure prediction. In the proposed model, one-stage binary support vector machines are employed, and the efficiency of the codon encoding to that of a commonly used orthogonal encoding are compared without incorporating protein evolutionary and structural information for an unbiased comparison. The performance of the classification model is measured according to Q3 and segment overlap (SOV) scores. The scores are compared with those of the prediction methods using an orthogonal encoding and protein sequence profiles. The experimental results indicate higher prediction accuracy based on Q3 SOV scores when sequence profiles are not used. Also, the relative classification scores of the proposed method are comparable with the methods incorporating protein global and evolutionary information. The experimental result implies the encoding scheme is able to integrate the evolutionary information into the prediction model since the encoding is based on genetic codon mappings which are the building blocks of amino acid formations at the primary level of biological processes. The codon encoding is worthwhile to be investigated using more complex learning architectures with the profiles and structural properties of proteins.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90592923","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}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470207
Sameh Saleh, Brian S. Olson, Amarda Shehu
Obtaining a structural characterization of the biologically active (native) state of a protein is a long standing problem in computational biology. The high dimensionality of the conformational space and ruggedness of the associated energy surface are key challenges to algorithms in search of an ensemble of low-energy decoy conformations relevant for the native state. As the native structure does not often correspond to the global minimum energy, diversity is key. We present a memetic evolutionary algorithm to sample a diverse ensemble of conformations that represent low-energy local minima in the protein energy surface. Conformations in the algorithm are members of an evolving population. The molecular fragment replacement technique is employed to obtain children from parent conformations. A greedy search maps a child conformation to its nearest local minimum. Resulting minima and parent conformations are merged and truncated back to the initial population size based on potential energies. Results show that the additional minimization is key to obtaining a diverse ensemble of decoys, circumvent premature convergence to sub-optimal regions in the conformational space, and approach the native structure with IRMSDs comparable to state-of-the-art decoy sampling methods.
{"title":"A population-based evolutionary algorithm for sampling minima in the protein energy surface","authors":"Sameh Saleh, Brian S. Olson, Amarda Shehu","doi":"10.1109/BIBMW.2012.6470207","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470207","url":null,"abstract":"Obtaining a structural characterization of the biologically active (native) state of a protein is a long standing problem in computational biology. The high dimensionality of the conformational space and ruggedness of the associated energy surface are key challenges to algorithms in search of an ensemble of low-energy decoy conformations relevant for the native state. As the native structure does not often correspond to the global minimum energy, diversity is key. We present a memetic evolutionary algorithm to sample a diverse ensemble of conformations that represent low-energy local minima in the protein energy surface. Conformations in the algorithm are members of an evolving population. The molecular fragment replacement technique is employed to obtain children from parent conformations. A greedy search maps a child conformation to its nearest local minimum. Resulting minima and parent conformations are merged and truncated back to the initial population size based on potential energies. Results show that the additional minimization is key to obtaining a diverse ensemble of decoys, circumvent premature convergence to sub-optimal regions in the conformational space, and approach the native structure with IRMSDs comparable to state-of-the-art decoy sampling methods.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89416358","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}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470254
Xiong Liu, Kaizhi Tang, S. Harper, B. Harper, J. Steevens, R. Xu
Nanomaterial environmental impact (NEI) modeling is critical for industry and policymakers to assess the unintended biological effects (e.g. mortality, malformation, growth inhibition) resulting from the application of engineered nanomaterials. The scope of NEI modeling covers nanomaterial physical, chemical and manufacturing properties, exposure and study scenarios, environmental and ecosystem responses, biological responses, and their interactions. In this paper, we introduce a data mining approach to modeling the biological effects of nanomaterials. Data mining techniques can assist analysts in developing risk assessment models for nanomaterials. Using an experimental dataset on the toxicity of nanomaterials to embryonic zebrafish, we conducted case studies on modeling the overall effect/impact of nanomaterials and the specific toxic end-points such as mortality, delayed development, and morpholigcal malformations and behavioral abnormalities. The results show that different biological effects have different modeling accuracy given the same set of algorithms and data. The results also show that the weighting scheme for different biological effects has a significant influence on modeling the overall biological effect. These results provide insights into the understanding and modeling of nanomaterial biological effects.
{"title":"Predictive modeling of nanomaterial biological effects","authors":"Xiong Liu, Kaizhi Tang, S. Harper, B. Harper, J. Steevens, R. Xu","doi":"10.1109/BIBMW.2012.6470254","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470254","url":null,"abstract":"Nanomaterial environmental impact (NEI) modeling is critical for industry and policymakers to assess the unintended biological effects (e.g. mortality, malformation, growth inhibition) resulting from the application of engineered nanomaterials. The scope of NEI modeling covers nanomaterial physical, chemical and manufacturing properties, exposure and study scenarios, environmental and ecosystem responses, biological responses, and their interactions. In this paper, we introduce a data mining approach to modeling the biological effects of nanomaterials. Data mining techniques can assist analysts in developing risk assessment models for nanomaterials. Using an experimental dataset on the toxicity of nanomaterials to embryonic zebrafish, we conducted case studies on modeling the overall effect/impact of nanomaterials and the specific toxic end-points such as mortality, delayed development, and morpholigcal malformations and behavioral abnormalities. The results show that different biological effects have different modeling accuracy given the same set of algorithms and data. The results also show that the weighting scheme for different biological effects has a significant influence on modeling the overall biological effect. These results provide insights into the understanding and modeling of nanomaterial biological effects.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84102087","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}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470243
Timoteus B. Ziminski, A. D. L. R. Algarin, R. Saripalle, S. Demurjian, E. Jackson
Interactions between prescription medications, over-the-counter drugs, and nutritional supplements can have negative consequences for patients. There is a need for the reconciliation across this spectrum spurred on by the adoption of electronic medical records by healthcare providers and the usage of personal health records by patients. In such a setting, unifying information from multiple sources through automated reconciliation can address adverse medication interactions, track adverse medication reactions, and avoid overmedication. This requires mitigating the integration issues of multiple data sources and systems. In this paper, we leverage Harvard University's SMART framework to perform medication reconciliation across different data sources, with the long-term goal of providing robust decision support for overmedication and adverse interactions. Our prototype application SMARTSync provides ontology-backed recognition of interactions, decision support, and is able to warn a patient (or notify a provider) of potential medication problems.
{"title":"SMARTSync: Towards patient-driven medication reconciliation","authors":"Timoteus B. Ziminski, A. D. L. R. Algarin, R. Saripalle, S. Demurjian, E. Jackson","doi":"10.1109/BIBMW.2012.6470243","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470243","url":null,"abstract":"Interactions between prescription medications, over-the-counter drugs, and nutritional supplements can have negative consequences for patients. There is a need for the reconciliation across this spectrum spurred on by the adoption of electronic medical records by healthcare providers and the usage of personal health records by patients. In such a setting, unifying information from multiple sources through automated reconciliation can address adverse medication interactions, track adverse medication reactions, and avoid overmedication. This requires mitigating the integration issues of multiple data sources and systems. In this paper, we leverage Harvard University's SMART framework to perform medication reconciliation across different data sources, with the long-term goal of providing robust decision support for overmedication and adverse interactions. Our prototype application SMARTSync provides ontology-backed recognition of interactions, decision support, and is able to warn a patient (or notify a provider) of potential medication problems.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86853865","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}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470355
Benjie Qi, Qingfeng Luo, Mingzhi Wan
With needle scalpel, we have treated 145 cases of traumatic ankylosis, including 35 cases of knees, 32 cases of ankles, 38 cases of shoulders, 40 cases of elbows. And we find the treatment is easy to operate, with few side effects, but significant effect.
{"title":"Clinical curative effect of needle scalpel for traumatic ankylosis","authors":"Benjie Qi, Qingfeng Luo, Mingzhi Wan","doi":"10.1109/BIBMW.2012.6470355","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470355","url":null,"abstract":"With needle scalpel, we have treated 145 cases of traumatic ankylosis, including 35 cases of knees, 32 cases of ankles, 38 cases of shoulders, 40 cases of elbows. And we find the treatment is easy to operate, with few side effects, but significant effect.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85582969","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}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470336
K. Bao, Han-guo Peng, P. Su
Arteriovenous fistula (AVF) is so important to hemodialysis patients that it is called their lifeline. In modern society, it is particularly urgent to improve AVF surgeon's micrological technique as patients' vascular condition tending to deteriorate because of increasing population aging and prevalence of hypertension and diabetes. So this paper is made to discuss how to improve the AVF surgeon's micrological technique from the following three aspects: enhancing the capability of hand movement control, establishing the concept of main-secondary hand and surgery airspace.
{"title":"Discussion on the improvement of the arteriovenous fistula surgeon's micrological technique","authors":"K. Bao, Han-guo Peng, P. Su","doi":"10.1109/BIBMW.2012.6470336","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470336","url":null,"abstract":"Arteriovenous fistula (AVF) is so important to hemodialysis patients that it is called their lifeline. In modern society, it is particularly urgent to improve AVF surgeon's micrological technique as patients' vascular condition tending to deteriorate because of increasing population aging and prevalence of hypertension and diabetes. So this paper is made to discuss how to improve the AVF surgeon's micrological technique from the following three aspects: enhancing the capability of hand movement control, establishing the concept of main-secondary hand and surgery airspace.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80295709","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}
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