MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...最新文献
Lors d’une opération de construction d’un immeuble ou d’une partie d’immeuble telle que l’assainissement non collectif (ANC), les constructeurs doivent s’assurer que l’ouvrage livré répond bien aux critères d’un patrimoine immobilier du bâtiment et tout particulièrement avec une durée de vie en service d’au moins 50 ans. Pour cela, la première étape de cette opération, appelée phase de conception, est indispensable avant toute opération de travaux. Pour satisfaire cette opération, il est important que le maître d’ouvrage (MOA) prenne attache auprès d’un constructeur en charge de la conception qui sera là pour l’accompagner dans la définition d’une installation cohérente avec les données techniques de l’immeuble et de la parcelle. Le projet de construction proposé, répondant aux exigences du patrimoine immobilier, devra mentionner clairement les coûts d’investissement ainsi que les coûts d’entretien et de maintenance sur la durée de vie en service. Il est impératif de garder à l’esprit que le MOA ne peut se prononcer que sur des aspects de confort ou d’esthétisme, la pertinence technique étant de la responsabilité du constructeur malgré la foultitude de solutions techniques proposées sur le marché. Cette étape doit faire l’objet d’un contrôle par le service public d’assainissement non collectif (Spanc). La mauvaise qualité de l’étude ou le non-respect de la législation en vigueur ne peut que conduire le Spanc à considérer le dossier comme incomplet ou à formuler un avis défavorable. A contrario, lorsque le Spanc émet un avis favorable, le MOA peut alors entreprendre les devis auprès de constructeurs en charge de la pose. Rappelons que l’avis du Spanc est nécessaire, mais non suffisant. Ainsi une étude de qualité et aboutie, décrivant l’ouvrage dans sa globalité, permettra de limiter autant que faire se peut les sinistres sur l’installation d’ANC.
{"title":"Assainissement non collectif : la mission de conception,\u0000étape clé pour la pérennité des ouvrages","authors":"A. Decout, B. Aumettre, J.-M. Dumel, A. Lakel","doi":"10.36904/tsm/202112119","DOIUrl":"https://doi.org/10.36904/tsm/202112119","url":null,"abstract":"Lors d’une opération de construction d’un immeuble ou d’une partie d’immeuble telle que\u0000l’assainissement non collectif (ANC), les constructeurs doivent s’assurer que l’ouvrage livré répond bien aux\u0000critères d’un patrimoine immobilier du bâtiment et tout particulièrement avec une durée de vie en service\u0000d’au moins 50 ans. Pour cela, la première étape de cette opération, appelée phase de conception, est\u0000indispensable avant toute opération de travaux. Pour satisfaire cette opération, il est important que le maître\u0000d’ouvrage (MOA) prenne attache auprès d’un constructeur en charge de la conception qui sera là pour\u0000l’accompagner dans la définition d’une installation cohérente avec les données techniques de l’immeuble et\u0000de la parcelle. Le projet de construction proposé, répondant aux exigences du patrimoine immobilier, devra\u0000mentionner clairement les coûts d’investissement ainsi que les coûts d’entretien et de maintenance sur la\u0000durée de vie en service. Il est impératif de garder à l’esprit que le MOA ne peut se prononcer que sur des\u0000aspects de confort ou d’esthétisme, la pertinence technique étant de la responsabilité du constructeur\u0000malgré la foultitude de solutions techniques proposées sur le marché. Cette étape doit faire l’objet d’un\u0000contrôle par le service public d’assainissement non collectif (Spanc). La mauvaise qualité de l’étude ou le\u0000non-respect de la législation en vigueur ne peut que conduire le Spanc à considérer le dossier comme\u0000incomplet ou à formuler un avis défavorable. A contrario, lorsque le Spanc émet un avis favorable, le MOA\u0000peut alors entreprendre les devis auprès de constructeurs en charge de la pose. Rappelons que l’avis du\u0000Spanc est nécessaire, mais non suffisant. Ainsi une étude de qualité et aboutie, décrivant l’ouvrage dans sa\u0000globalité, permettra de limiter autant que faire se peut les sinistres sur l’installation d’ANC.","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74336660","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}
This paper focuses on the quantitative and qualitative systematic analysis of the multimodal semiosphere pertaining to the linguistic landscape of the selected urban spaces in Banská Bystrica. It examines public urban communication in the form of text-based public signs (road signs, billboards, street names, place names, commercial shop signs, public signs, etc.) as information units to which passers-by are exposed. This paper explores both local features and commonalities that may show the patterns of globalisation, as well as general areal and historical patterns.
{"title":"THE LINGUISTIC LANDSCAPE OF BANSKÁ BYSTRICA","authors":"Jana Lauková","doi":"10.33543/1202135140","DOIUrl":"https://doi.org/10.33543/1202135140","url":null,"abstract":"This paper focuses on the quantitative and qualitative systematic analysis of the multimodal semiosphere pertaining to the linguistic landscape of the selected urban spaces in Banská Bystrica. It examines public urban communication in the form of text-based public signs (road signs, billboards, street names, place names, commercial shop signs, public signs, etc.) as information units to which passers-by are exposed. This paper explores both local features and commonalities that may show the patterns of globalisation, as well as general areal and historical patterns.","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86607425","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}
As SNOMED usage becomes more ingrained within applications, its range of concept descriptors, and particularly its synonym adequacy, becomes more important. A simulated clinical scenario involving various term-based concept searches is used to assess whether SNOMED's concept descriptors provide sufficient differentiation to enable possible concept selection between similar terms. Four random samples from different SNOMED concept populations are utilized. Of particular interest are concepts mapped duplicately into UMLS concepts due to shared term patterns. While overall synonym problems are rare (1%), some concept populations exhibited a high rate of potential problems for clinical use (17-62%). The vast majority of issues are due to SNOMED's inherent structure and fine granularity. Many findings hint at a lack of clear delineation between reference and interface terminological qualities. Closer attention should be given to practical clinical use-case scenarios. Reducing SNOMED's structural complexity may alleviate many of the described findings and encourage clinical adoption.
{"title":"Clinical clarity versus terminological order: the readiness of SNOMED CT concept descriptors for primary care","authors":"Zhe He, M. Halper, Y. Perl, Gai Elhanan","doi":"10.1145/2389672.2389674","DOIUrl":"https://doi.org/10.1145/2389672.2389674","url":null,"abstract":"As SNOMED usage becomes more ingrained within applications, its range of concept descriptors, and particularly its synonym adequacy, becomes more important. A simulated clinical scenario involving various term-based concept searches is used to assess whether SNOMED's concept descriptors provide sufficient differentiation to enable possible concept selection between similar terms. Four random samples from different SNOMED concept populations are utilized. Of particular interest are concepts mapped duplicately into UMLS concepts due to shared term patterns. While overall synonym problems are rare (1%), some concept populations exhibited a high rate of potential problems for clinical use (17-62%). The vast majority of issues are due to SNOMED's inherent structure and fine granularity. Many findings hint at a lack of clear delineation between reference and interface terminological qualities. Closer attention should be given to practical clinical use-case scenarios. Reducing SNOMED's structural complexity may alleviate many of the described findings and encourage clinical adoption.","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"78 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78028373","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":"Session details: Electronic health systems interoperability and integration","authors":"C. Tao","doi":"10.1145/3251598","DOIUrl":"https://doi.org/10.1145/3251598","url":null,"abstract":"","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89513288","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}
C. Tao, Yuji Zhang, Guoqian Jiang, M. Bouamrane, C. Chute
Semantic MEDLINE provides comprehensive resources with structured annotations that have a potential to facilitate translational studies in the biomedical domain. It is computationally challenging, however, to perform queries directly from the data in the current Semantic MEDLINE database. In this research, we propose a domain pattern driven approach to optimize the Semantic MEDLINE data organization and representation for translational science studies using the Resource Description Framework (RDF) and Semantic Web technologies.
{"title":"Optimizing semantic MEDLINE for translational science studies using semantic web technologies","authors":"C. Tao, Yuji Zhang, Guoqian Jiang, M. Bouamrane, C. Chute","doi":"10.1145/2389672.2389683","DOIUrl":"https://doi.org/10.1145/2389672.2389683","url":null,"abstract":"Semantic MEDLINE provides comprehensive resources with structured annotations that have a potential to facilitate translational studies in the biomedical domain. It is computationally challenging, however, to perform queries directly from the data in the current Semantic MEDLINE database. In this research, we propose a domain pattern driven approach to optimize the Semantic MEDLINE data organization and representation for translational science studies using the Resource Description Framework (RDF) and Semantic Web technologies.","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"67 1","pages":"53-58"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85343218","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}
This paper proposes a temporal data mining method to construct and maintain a clinical pathway used for schedule management of clinical care. The method consists of the following four steps: first, histories of nursing orders are extracted from hospital information system. Second, orders are classified into several groups by using clustering and multidimensional scaling method. Third, by using the information on groups, feature selection is applied to the data and important features for classification are extracted. Finally, original temporal data are split into several groups and the first step will be repeated. After the grouping results are stable, a new pathway is constructed based on the induced results. The method was applied to a dataset whose patients had an operation of cataracts. The results show that the reuse of stored data will give a powerful tool for maintenance of clinical pathway, which can be viewed as data-oriented management of nursing schedule.
{"title":"Construction and maintenance of clinical pathways using data mining methods","authors":"S. Tsumoto, H. Iwata, S. Hirano","doi":"10.1145/2389672.2389682","DOIUrl":"https://doi.org/10.1145/2389672.2389682","url":null,"abstract":"This paper proposes a temporal data mining method to construct and maintain a clinical pathway used for schedule management of clinical care. The method consists of the following four steps: first, histories of nursing orders are extracted from hospital information system. Second, orders are classified into several groups by using clustering and multidimensional scaling method. Third, by using the information on groups, feature selection is applied to the data and important features for classification are extracted. Finally, original temporal data are split into several groups and the first step will be repeated. After the grouping results are stable, a new pathway is constructed based on the induced results. The method was applied to a dataset whose patients had an operation of cataracts. The results show that the reuse of stored data will give a powerful tool for maintenance of clinical pathway, which can be viewed as data-oriented management of nursing schedule.","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"25 1","pages":"47-52"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82216942","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}
More and more biomedical documents are digitally written and stored. To make the most of the rich resources, it is crucial to precisely locate the information pertinent to users' interests. One of the obstacles in finding information in natural language text is negations, which deny or reverse the meaning of a sentence or clause. This is especially problematic in the biomedical domain since scientific findings and clinical records often contain negated expressions to explicitly state negative effects or the absence of symptoms. Ignoring such negated expressions result in more irrelevant information and may even lead to false conclusions. Therefore, identifying negative words and their scopes are important sub-tasks in biomedical information processing. This paper reports on our ongoing work on a hybrid approach to negation identification combining statistical and heuristic approaches. Our approach is evaluated on three types of biomedical documents in comparison with an existing machine learning approach. In addition, the empirical results are manually analyzed to better understand the nature of the problems.
{"title":"A hybrid approach to finding negated and uncertain expressions in biomedical documents","authors":"Kazuki Fujikawa, Kazuhiro Seki, K. Uehara","doi":"10.1145/2389672.2389685","DOIUrl":"https://doi.org/10.1145/2389672.2389685","url":null,"abstract":"More and more biomedical documents are digitally written and stored. To make the most of the rich resources, it is crucial to precisely locate the information pertinent to users' interests. One of the obstacles in finding information in natural language text is negations, which deny or reverse the meaning of a sentence or clause. This is especially problematic in the biomedical domain since scientific findings and clinical records often contain negated expressions to explicitly state negative effects or the absence of symptoms. Ignoring such negated expressions result in more irrelevant information and may even lead to false conclusions. Therefore, identifying negative words and their scopes are important sub-tasks in biomedical information processing. This paper reports on our ongoing work on a hybrid approach to negation identification combining statistical and heuristic approaches. Our approach is evaluated on three types of biomedical documents in comparison with an existing machine learning approach. In addition, the empirical results are manually analyzed to better understand the nature of the problems.","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"58 1","pages":"67-74"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76979050","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":"Session details: Ontology-based application on clinical data","authors":"Guoqian Jiang","doi":"10.1145/3251597","DOIUrl":"https://doi.org/10.1145/3251597","url":null,"abstract":"","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72836591","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}
Currently, research in Machine Learning (ML) mainly focuses on the ability to process very large amounts of data and build accurate models. Problems related to complexity, heterogeneity, and semantics of healthcare data are often out of the main focus. Healthcare is particularly rich in background knowledge. Surprisingly, few ML methods used in healthcare can handle these sources of background knowledge, and instead treat healthcare data as a set of numbers without particular meaning. This paper explores an approach that can fill in this gap. A medical ontology (i.e., UMLS) is proposed to provide background knowledge for the ML method to understand healthcare data. The ontology-guided ML-based rule induction method is described and illustrated to analyze the clinical data supplemented with an ontology-based background knowledge.
{"title":"Clinical data analysis using ontology-guided rule learning","authors":"Hua Min, Janusz Wojtusiak","doi":"10.1145/2389672.2389676","DOIUrl":"https://doi.org/10.1145/2389672.2389676","url":null,"abstract":"Currently, research in Machine Learning (ML) mainly focuses on the ability to process very large amounts of data and build accurate models. Problems related to complexity, heterogeneity, and semantics of healthcare data are often out of the main focus. Healthcare is particularly rich in background knowledge. Surprisingly, few ML methods used in healthcare can handle these sources of background knowledge, and instead treat healthcare data as a set of numbers without particular meaning. This paper explores an approach that can fill in this gap. A medical ontology (i.e., UMLS) is proposed to provide background knowledge for the ML method to understand healthcare data. The ontology-guided ML-based rule induction method is described and illustrated to analyze the clinical data supplemented with an ontology-based background knowledge.","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"6 1","pages":"17-22"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85301559","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}
Manuel Quesada-Martínez, J. Fernández-breis, R. Stevens
The increasing interest in biomedical ontologies has provoked the development of a significant number of ontologies, and many more are expected to be produced in the near future. A significant proportion of such ontologies have not been created by computer scientists or ontology engineers, but by domain experts. Many such ontologies are rich in implicit knowledge, but are really just plain taxonomies and controlled vocabularies, with little axiomatization. Many of these ontologies have much information within the labels of the classes. There is a great deal of knowledge about the entities described within such labels and text definitions held on classes; these are useful for human users, but not for machine processing. In previous work we proposed a process for enriching ontologies, which included the analysis of such labels, the identification of lexical patterns and the design of corresponding knowledge patterns. However, this process relied on manual intervention. In this paper we present a method to analyze and extract unused information contained in the structure of the labels in biomedical ontologies. The aim of this method is to improve the source ontology. The first step is the identification of lexical patterns based on repetitions of sets of words. Second, such lexical patterns will be examined in existing biomedical ontologies to identify whether those patterns are referencing existing ontological entities. Finally, the results obtained with relevant biomedical ontologies are presented and discussed.
{"title":"Extraction and analysis of the structure of labels in biomedical ontologies","authors":"Manuel Quesada-Martínez, J. Fernández-breis, R. Stevens","doi":"10.1145/2389672.2389675","DOIUrl":"https://doi.org/10.1145/2389672.2389675","url":null,"abstract":"The increasing interest in biomedical ontologies has provoked the development of a significant number of ontologies, and many more are expected to be produced in the near future. A significant proportion of such ontologies have not been created by computer scientists or ontology engineers, but by domain experts. Many such ontologies are rich in implicit knowledge, but are really just plain taxonomies and controlled vocabularies, with little axiomatization.\u0000 Many of these ontologies have much information within the labels of the classes. There is a great deal of knowledge about the entities described within such labels and text definitions held on classes; these are useful for human users, but not for machine processing. In previous work we proposed a process for enriching ontologies, which included the analysis of such labels, the identification of lexical patterns and the design of corresponding knowledge patterns. However, this process relied on manual intervention.\u0000 In this paper we present a method to analyze and extract unused information contained in the structure of the labels in biomedical ontologies. The aim of this method is to improve the source ontology. The first step is the identification of lexical patterns based on repetitions of sets of words. Second, such lexical patterns will be examined in existing biomedical ontologies to identify whether those patterns are referencing existing ontological entities. Finally, the results obtained with relevant biomedical ontologies are presented and discussed.","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"13 1","pages":"7-16"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73508389","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}
MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：