Carmel Kent

Publications (7) View all

• Article: Knowledge-analytics synergy in clinical decision support.

  Noam Slonim, Boaz Carmeli, Abigail Goldsteen, Oliver Keller, Carmel Kent, Ruty Rinott
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  ABSTRACT: Clinical Decision Support (CDS) systems hold tremendous potential for improving patient care. Most existing systems are knowledge-based tools that rely on relatively simple rules. More recent approaches rely on analytics techniques to automatically mine EHR data to reveal meaningful insights. Here, we propose the Knowledge-Analytics Synergy paradigm for CDS, in which we synergistically combine existing relevant knowledge with analytics applied to EHR data. We propose a framework for implementing such a paradigm and demonstrate its principles over real-world clinical and genomic data of hypertensive patients.
  Studies in health technology and informatics 01/2012; 180:703-7.
• Article: Evicase: An Evidence-based Case Structuring Approach for Personalized Healthcare.

  Boaz Carmeli, Paolo Casali, Anna Goldbraich, Abigail Goldsteen, Carmel Kent, Lisa Licitra, Paolo Locatelli, Nicola Restifo, Ruty Rinott, Elena Sini, Michele Torresani, Zeev Waks
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  ABSTRACT: The personalized medicine era stresses a growing need to combine evidence-based medicine with case based reasoning in order to improve the care process. To address this need we suggest a framework to generate multi-tiered statistical structures we call Evicases. Evicase integrates established medical evidence together with patient cases from the bedside. It then uses machine learning algorithms to produce statistical results and aggregators, weighted predictions, and appropriate recommendations. Designed as a stand-alone structure, Evicase can be used for a range of decision support applications including guideline adherence monitoring and personalized prognostic predictions.
  Studies in health technology and informatics 01/2012; 180:604-8.
• Article: Prognostic data-driven clinical decision support - formulation and implications.

  Ruty Rinott, Boaz Carmeli, Carmel Kent, Daphna Landau, Yonatan Maman, Yoav Rubin, Noam Slonim
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  ABSTRACT: Existing Clinical Decision Support Systems (CDSSs) typically rely on rule-based algorithms and focus on tasks like guidelines adherence and drug prescribing and monitoring. However, the increasing dominance of Electronic Health Record technologies and personalized medicine suggest great potential for prognostic data-driven CDSS. A major goal for such systems would be to accurately predict the outcome of patients' candidate treatments by statistical analysis of the clinical data stored at a Health Care Organization. We formally define the concepts involved in the development of such a system, highlight an inherent difficulty arising from bias in treatment allocation, and propose a general strategy to address this difficulty. Experiments over hypertension clinical data demonstrate the validity of our approach.
  Studies in health technology and informatics 01/2011; 169:140-4.
• Conference Proceeding: Semantic Warehousing of Diverse Biomedical Information.

  Stefano Bianchi, Anna Burla, Costanza Conti, Ariel Farkash, Carmel Kent, Yonatan Maman, Amnon Shabo
  Next Generation Information Technologies and Systems, 7th International Conference, NGITS 2009, Haifa, Israel, June 16-18, 2009. Revised Selected Papers; 01/2009
• Article: Biomedical data integration - capturing similarities while preserving disparities.

  Stefano Bianchi, Anna Burla, Costanza Conti, Ariel Farkash, Carmel Kent, Yonatan Maman, Amnon Shabo
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  ABSTRACT: One of the challenges of healthcare data processing, analysis and warehousing is the integration of data gathered from disparate and diverse data sources. Promoting the adoption of worldwide accepted information standards along with common terminologies and the use of technologies derived from semantic web representation, is a suitable path to achieve that. To that end, the HL7 V3 Reference Information Model (RIM) [1] has been used as the underlying information model coupled with the Web Ontology Language (OWL) [2] as the semantic data integration technology. In this paper we depict a biomedical data integration process and demonstrate how it was used for integrating various data sources, containing clinical, environmental and genomic data, within Hypergenes, a European Commission funded project exploring the Essential Hypertension [3] disease model.
  Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:4654-7.