A collaborative distributed privacy-sensitive decision support system for monitoring heterogeneous data sources

2005 ◽  
Author(s):  
H. Kargupta ◽  
K. Sarkar ◽  
Dipti Aswath ◽  
W.D. Handy
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Heterogeneous Data Sources

scholarly journals A Description of Clinical Decision Support Framework Prediction in Heterogeneous Data Sources

2019 ◽  
Vol 8 (6S) ◽  
pp. 425-427
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Heterogeneous Data ◽  
Scientific Discipline ◽  
Work Environments ◽  
Data Sources ◽  
Heterogeneous Data Sources ◽  
Decision Support Framework ◽  
Different Sources

To keep pace with the updates in obliging scientific discipline, thriving recuperating knowledge is being assembled incessantly. Regardless, inferable from the not too appalling gathering of its categories and sources, therapeutic knowledge has over up being significantly hugger-mugger in numerous specialist's work environments that it currently wants Clinical call Support (CDS) system for its affiliation. To reasonably utilize the party flourishing knowledge, we tend to propose a CDS structure which will distort mixed thriving knowledge from totally different sources, for example, take a goose at workplace check works out as planned, important info of patients and action records into a joined depiction of options everything thought-about. Victimization the electronic roaring healing knowledge therefore created, multi-name delineation was accustomed endorse a layout of afflictions and so facilitate consultants in diagnosis or treating their patients' therapeutic problems a lot of competently. Once the ace sees the contamination of a patient, the running with organize is to contemplate the conceivable complexities of that disarray, which may impel a lot of infections

scholarly journals A Clinical Decision Support Framework for Heterogeneous Data Sources

2018 ◽  
Vol 22 (6) ◽  
pp. 1824-1833 ◽  
Author(s):  
Mengxing Huang ◽  
Huirui Han ◽  
Hao Wang ◽  
Lefei Li ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Heterogeneous Data Sources ◽  
Decision Support Framework

Patient-Centered Clinical Trials Decision Support Using Linked Open Data

2018 ◽  
pp. 1765-1782
Author(s):  
Bonnie MacKellar ◽  
Christina Schweikert ◽  
Soon Ae Chun
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Clinical Trials ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Linked Data ◽  
Open Data ◽  
Data Sources ◽  
Patient Centered

Patients often want to participate in relevant clinical trials for new or more effective alternative treatments. The clinical search system made available by the NIH is a step forward to support the patient's decision making, but, it is difficult to use and requires the patient to sift through lengthy text descriptions for relevant information. In addition, patients deciding whether to pursue a given trial often want more information, such as drug information. The authors' overall aim is to develop an intelligent patient-centered clinical trial decision support system. Their approach is to integrate Open Data sources related to clinical trials using the Semantic Web's Linked Data framework. The linked data representation, in terms of RDF triples, allows the development of a clinical trial knowledge base that includes entities from different open data sources and relationships among entities. The authors consider Open Data sources such as clinical trials provided by NIH as well as the drug side effects dataset SIDER. The authors use UMLS (Unified Medical Language System) to provide consistent semantics and ontological knowledge for clinical trial related entities and terms. The authors' semantic approach is a step toward a cognitive system that provides not only patient-centered integrated data search but also allows automated reasoning in search, analysis and decision making using the semantic relationships embedded in the Linked data. The authors present their integrated clinical trial knowledge base development and a prototype, patient-centered Clinical Trial Decision Support System that include capabilities of semantic search and query with reasoning ability, and semantic-link browsing where an exploration of one concept leads to other concepts easily via links which can provide visual search for the end users.

Patient Representation Learning from Heterogeneous Data Sources and Knowledge Graphs using Deep Collective Matrix Factorization: Evaluation Study (Preprint)

2021 ◽  
Author(s):  
Sajit Kumar ◽  
Alicia Nanelia Tan Li Shi ◽  
Ragunathan Mariappan ◽  
Adithya Rajagopal ◽  
Vaibhav Rajan
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Matrix Factorization ◽  
Representation Learning ◽  
Clinical Decision ◽  
Heterogeneous Data ◽  
Biomedical Literature ◽  
Data Sources ◽  
Heterogeneous Data Sources ◽  
Knowledge Graphs

BACKGROUND Patient Representation Learning aims to learn features, also called representations, from input sources automatically, often in an unsupervised manner, for use in predictive models. This obviates the need for cumbersome, time- and resource-intensive manual feature engineering, especially from unstructured data such as text, images or graphs. Most previous techniques have used neural network based autoencoders to learn patient representations, primarily from clinical notes in Electronic Medical Records (EMR). Knowledge Graphs (KG), with clinical entities as nodes and their relations as edges, can be extracted automatically from biomedical literature, and provide complementary information to EMR data that have been found to provide valuable predictive signals. OBJECTIVE We evaluate the efficacy of Collective Matrix Factorization (CMF) - both classical variants and a recent neural architecture called Deep CMF (DCMF) - in integrating heterogeneous data sources from EMR and KG to obtain patient representations for Clinical Decision Support Tasks. METHODS Using a recent formulation of obtaining graph representations through matrix factorization, within the context of CMF, we infuse auxiliary information during patient representation learning. We also extend the DCMF architecture to create a task-specific end-to-end model that learns to simultaneously find effective patient representations and predict. We compare the efficacy of such a model to that of first learning unsupervised representations and then independently learning a predictive model. We evaluate patient representation learning using CMF-based methods and autoencoders for two clinical decision support tasks on a large EMR dataset. RESULTS Our experiments show that DCMF provides a seamless way to integrate multiple sources of data to obtain patient representations, both in unsupervised and supervised settings. Its performance in single-source settings is comparable to that of previous autoencoder-based representation learning methods. When DCMF is used to obtain representations from a combination of EMR and KG, where most previous autoencoder-based methods cannot be used directly, its performance is superior to that of previous non-neural methods for CMF. Infusing information from KGs into patient representations using DCMF was found to improve downstream predictive performance. CONCLUSIONS Our experiments indicate that DCMF is a versatile model that can be used to obtain representations from single and multiple data sources, and to combine information from EMR data and Knowledge Graphs. Further, DCMF can be used to learn representations in both supervised and unsupervised settings. Thus, DCMF offers an effective way of integrating heterogeneous data sources and infusing auxiliary knowledge into patient representations.

scholarly journals A Digital Twin Decision Support System for the Urban Facility Management Process

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8460
Author(s):  
Armir Bujari ◽  
Alessandro Calvio ◽  
Luca Foschini ◽  
Andrea Sabbioni ◽  
Antonio Corradi
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Smart City ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Facility Management ◽  
Activity Level ◽  
Digital Twin ◽  
Area Of Interest

The ever increasing pace of IoT deployment is opening the door to concrete implementations of smart city applications, enabling the large-scale sensing and modeling of (near-)real-time digital replicas of physical processes and environments. This digital replica could serve as the basis of a decision support system, providing insights into possible optimizations of resources in a smart city scenario. In this article, we discuss an extension of a prior work, presenting a detailed proof-of-concept implementation of a Digital Twin solution for the Urban Facility Management (UFM) process. The Interactive Planning Platform for City District Adaptive Maintenance Operations (IPPODAMO) is a distributed geographical system, fed with and ingesting heterogeneous data sources originating from different urban data providers. The data are subject to continuous refinements and algorithmic processes, used to quantify and build synthetic indexes measuring the activity level inside an area of interest. IPPODAMO takes into account potential interference from other stakeholders in the urban environment, enabling the informed scheduling of operations, aimed at minimizing interference and the costs of operations.

Patient-Centered Clinical Trials Decision Support using Linked Open Data

2014 ◽  
Vol 6 (3) ◽  
pp. 31-48 ◽  
Author(s):  
Bonnie MacKellar ◽  
Christina Schweikert ◽  
Soon Ae Chun
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Clinical Trials ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Linked Data ◽  
Open Data ◽  
Data Sources ◽  
Patient Centered

Patients often want to participate in relevant clinical trials for new or more effective alternative treatments. The clinical search system made available by the NIH is a step forward to support the patient's decision making, but, it is difficult to use and requires the patient to sift through lengthy text descriptions for relevant information. In addition, patients deciding whether to pursue a given trial often want more information, such as drug information. The authors' overall aim is to develop an intelligent patient-centered clinical trial decision support system. Their approach is to integrate Open Data sources related to clinical trials using the Semantic Web's Linked Data framework. The linked data representation, in terms of RDF triples, allows the development of a clinical trial knowledge base that includes entities from different open data sources and relationships among entities. The authors consider Open Data sources such as clinical trials provided by NIH as well as the drug side effects dataset SIDER. The authors use UMLS (Unified Medical Language System) to provide consistent semantics and ontological knowledge for clinical trial related entities and terms. The authors' semantic approach is a step toward a cognitive system that provides not only patient-centered integrated data search but also allows automated reasoning in search, analysis and decision making using the semantic relationships embedded in the Linked data. The authors present their integrated clinical trial knowledge base development and a prototype, patient-centered Clinical Trial Decision Support System that include capabilities of semantic search and query with reasoning ability, and semantic-link browsing where an exploration of one concept leads to other concepts easily via links which can provide visual search for the end users.

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