Technik in der Medizin ist zwar kein neues Phänomen, führt aber heute mehr denn je zur Diskussion um Sinn und Wert der Medizin bzw. des Arztseins. Mit ihrer naturwissenschaftlichen und damit gleichzeitig technischen Fundierung seit der zweiten Hälfte des 19. Jahrhunderts hatte die Medizin zunächst in der Diagnostik, nachfolgend in der Therapie einen bislang nicht gekannten Aufschwung erfahren. Dies führte einerseits zu der Auffassung und dem Anspruch der Patienten, jede Erkrankung sei heilbar und jedes Organ ersetzbar. Andererseits artikulierte sich aber auch Unbehagen an der „Apparatemedizin“. Neben ethischen Bedenken bei einer auf biomedizinische Technik fokussierten Medizin stellt sich heute vor allem (wieder) die Frage nach dem Verhältnis von Patient und Arzt und der Rolle des Arztes an sich. / The use of technology in medicine is not a new phenomenon, but it today leads more than ever before to discussions on the purpose and value of medicine and what it means to be a doctor. With its scientific and simultaneously technical foundations, medicine has, since the second half of the nineteenth century, been experiencing a previously unheard-of boom – first in diagnostics, and subsequently in therapy. This led on the one hand to a belief and expectation among patients that every illness was curable, and every organ replaceable. On the other hand, reservations were also expressed about such “gadgetry medicine”. Alongside ethical concerns regarding a medicine focused on biomedical technology, the question of the patient-doctor relationship and the role of the doctor has today (once more) come to the fore.

Zugl.: Berlin, Techn. Universiẗat, Diss., 2005.

Technik in der Medizin ist zwar kein neues Phänomen, führt aber heute mehr denn je zur Diskussion um Sinn und Wert der Medizin bzw. des Arztseins. Mit ihrer naturwissenschaftlichen und damit gleichzeitig technischen Fundierung seit der zweiten Hälfte des 19. Jahrhunderts hatte die Medizin zunächst in der Diagnostik, nachfolgend in der Therapie einen bislang nicht gekannten Aufschwung erfahren. Dies führte einerseits zu der Auffassung und dem Anspruch der Patienten, jede Erkrankung sei heilbar und jedes Organ ersetzbar. Andererseits artikulierte sich aber auch Unbehagen an der „Apparatemedizin“. Neben ethischen Bedenken bei einer auf biomedizinische Technik fokussierten Medizin stellt sich heute vor allem (wieder) die Frage nach dem Verhältnis von Patient und Arzt und der Rolle des Arztes an sich. / The use of technology in medicine is not a new phenomenon, but it today leads more than ever before to discussions on the purpose and value of medicine and what it means to be a doctor. With its scientific and simultaneously technical foundations, medicine has, since the second half of the nineteenth century, been experiencing a previously unheard-of boom – first in diagnostics, and subsequently in therapy. This led on the one hand to a belief and expectation among patients that every illness was curable, and every organ replaceable. On the other hand, reservations were also expressed about such “gadgetry medicine”. Alongside ethical concerns regarding a medicine focused on biomedical technology, the question of the patient-doctor relationship and the role of the doctor has today (once more) come to the fore.

This paper describes the evaluation of screen printed materials fabricated with an additive manufacturing process for flexible biomedical applications. Five different conductive polymeric thick film pastes, printed on a polyimide substrate have been investigated. For the intended biocompatible applications, the cytotoxicity of the used materials was tested through adherent cell test. Furthermore, the electrical resistance, the printed structure thickness, the surface energy and roughness have been examined. Additionally, the mechanical resilience of the printed materials was tested through a bending test. During the bending the electrical resistance of printed meander structures could be monitored indicating failures. Two out of five materials were qualified as non-toxic, all of the materials are useable for flexible electronics, as they provide good electrical and mechanical properties.

BACKGROUND: Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. RESULTS: We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions. In addition, we provide an analysis on the following aspects: (1) How do definitions mined from the Web and literature differ from the ones mined from manually created definitions, e.g., MeSH? (2) How do different feature representations, e.g., the restrictions of relations' domain and range, impact on the generated definition quality?, (3) How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, (4) What is the influence of the learning data size to the task? We discuss all of these settings in detail and show that the suggested approach can achieve success rates of over 90%. In addition, the results show that the choice of corpora, lexical features, learning algorithm and data size do not impact the performance as strongly as semantic types do. Semantic types limit the domain and range of a predicted relation, and as long as relations' domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. CONCLUSIONS: The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github.com/alifahsyamsiyah/learningDL.

BACKGROUND: The complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts. This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them. The graph represents concepts as vertices and relations as edges, stemming from structured (ontologies) and unstructured (textual) data. In this graph, path patterns, i.e. sequences of relations, are mined using distant supervision that potentially characterize a biomedical relation. RESULTS: It is possible to identify characteristic path patterns of biomedical relations from this representation using machine learning. For experimental evaluation two frequent biomedical relations, namely \"has target\", and \"may treat\", are chosen. Results suggest that relation discovery using indirect knowledge is possible, with an AUC that can reach up to 0.8, a result which is a great improvement compared to the random classification, and which shows that good predictions can be prioritized by following the suggested approach. CONCLUSIONS: Analysis of the results indicates that the models can successfully learn expressive path patterns for the examined relations. Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts.

Background Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. Results We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions. In addition, we provide an analysis on the following aspects: (1) How do definitions mined from the Web and literature differ from the ones mined from manually created definitions, e.g., MeSH? (2) How do different feature representations, e.g., the restrictions of relations’ domain and range, impact on the generated definition quality?, (3) How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, (4) What is the influence of the learning data size to the task? We discuss all of these settings in detail and show that the suggested approach can achieve success rates of over 90%. In addition, the results show that the choice of corpora, lexical features, learning algorithm and data size do not impact the performance as strongly as semantic types do. Semantic types limit the domain and range of a predicted relation, and as long as relations’ domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. Conclusions The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github.com/alifahsyamsiyah/learningDL.

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BACKGROUND: Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. RESULTS: We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions. In addition, we provide an analysis on the following aspects: (1) How do definitions mined from the Web and literature differ from the ones mined from manually created definitions, e.g., MeSH? (2) How do different feature representations, e.g., the restrictions of relations' domain and range, impact on the generated definition quality?, (3) How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, (4) What is the influence of the learning data size to the task? We discuss all of these settings in detail and show that the suggested approach can achieve success rates of over 90%. In addition, the results show that the choice of corpora, lexical features, learning algorithm and data size do not impact the performance as strongly as semantic types do. Semantic types limit the domain and range of a predicted relation, and as long as relations' domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. CONCLUSIONS: The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github.com/alifahsyamsiyah/learningDL.

Background Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. Results We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions. In addition, we provide an analysis on the following aspects: (1) How do definitions mined from the Web and literature differ from the ones mined from manually created definitions, e.g., MeSH? (2) How do different feature representations, e.g., the restrictions of relations’ domain and range, impact on the generated definition quality?, (3) How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, (4) What is the influence of the learning data size to the task? We discuss all of these settings in detail and show that the suggested approach can achieve success rates of over 90%. In addition, the results show that the choice of corpora, lexical features, learning algorithm and data size do not impact the performance as strongly as semantic types do. Semantic types limit the domain and range of a predicted relation, and as long as relations’ domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. Conclusions The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github.com/alifahsyamsiyah/learningDL.