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1.发明申请SYSTEMS AND METHODS FOR DISEASE KNOWLEDGE MODELING AND CLINICAL DECISION SUPPORT 审中-公开疾病知识建模与临床决策支持系统与方法公开(公告)号：US20150081323A1
公开(公告)日：2015-03-19
申请号：US14390019
申请日：2013-04-02
发明人： David B. Jackson , Alexander Zien , Stephan Brock , Alexander Picker , Guillaume Taglang , Bernhard Sulzer , Martin Stein
IPC分类号： G06F19/00
CPC分类号： G16H50/20 , G06F19/30 , G06F19/3481 , G16B20/00 , G16B50/00
摘要： Systems and methods are described herein for disease knowledge modeling and clinical treatment decision support. Disease or indication information, including identification of biomolecular entities associated with the indication may be culled through data mining to create a knowledge model of the indication. In some embodiments, the knowledge model may comprise a network of associations between molecular entities, including drug targets and biomarkers, genes, pathways. The model is used for prioritizing treatment decisions, for treatments comprising one or more medications associated with one or more molecular entities in the model. The priority of a suggested treatment depends on at least one property of one or more medications of the suggested treatment.
摘要翻译： 本文描述了用于疾病知识建模和临床治疗决策支持的系统和方法。 疾病或指示信息，包括与指示相关联的生物分子实体的识别可以通过数据挖掘来剔除，以创建指示的知识模型。 在一些实施方案中，知识模型可以包括分子实体之间的关联网络，包括药物靶标和生物标志物，基因，途径。 该模型用于优先考虑治疗决定，包括与模型中的一个或多个分子实体相关联的一种或多种药物的治疗。 建议治疗的优先级取决于建议治疗的一种或多种药物的至少一种性质。
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公开(公告)号：US10672514B2
公开(公告)日：2020-06-02
申请号：US14763027
申请日：2013-10-01
发明人： Alexander Zien , David B. Jackson , Martin Stein , Guillaume Taglang , Stephan Brock , Alexander Picker , Theodoros Soldatos , Bernhard Sulzer
摘要： The present disclosure relates to systems and methods for bioinformatics and data processing. In particular, in a first aspect, the present disclosure relates to methods and systems for generating a personalized treatment guideline for a patient and for selecting a treatment for a patient. In another aspect, the present disclosure relates to methods and systems for selecting patients for a clinical trial of a treatment. The invention resolves cases in which patients have more than one “actionable” aberration by combining the patient-specific molecular information and the treatment-specific molecular information further with a clinico-molecular disease model, specifically a scoring of genes and/or proteins that represents several aspects of their involvement into the disease. In this way, treatments and patients can be prioritized that are most likely to impact or impacted by the disease mechanism, respectively.
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