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Timothy Tschampel Phones & Addresses

  • Carlsbad, CA
  • Reston, VA
  • Arlington, VA
  • 4667 4Th St, La Mesa, CA 91941 (703) 724-7816
  • Ashburn, VA
  • Fairfax Station, VA
  • Eden, NY
  • Alexandria, VA
  • Hudson, FL
  • Fx Station, VA
  • Louisville, KY

Publications

Us Patents

Patient Context Vectors: Low Dimensional Representation Of Patient Context Towards Enhanced Rule Engine Semantics And Machine Learning

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US Patent:
20200381090, Dec 3, 2020
Filed:
May 29, 2020
Appl. No.:
16/888199
Inventors:
- Cardiff CA, US
Carmelo Velez - Encinitas CA, US
Timothy Tschampel - Ashburn VA, US
Assignee:
Computer Technology Associates, Inc. - Cardiff CA
International Classification:
G16H 10/60
G16H 50/30
G16H 50/20
G06N 20/00
Abstract:
A PCV generation process using deep learning networks and multi-task learning wherein what knowledge is already known can be used to learn new knowledge such as the addition of CPT and medication information to augment patient PCVs based on ICD codes and expressions of history in free text notes.

Disease Specific Ontology-Guided Rule Engine And Machine Learning For Enhanced Critical Care Decision Support

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US Patent:
20190057774, Feb 21, 2019
Filed:
Aug 15, 2018
Appl. No.:
15/998436
Inventors:
- Cardiff CA, US
Timothy Tschampel - Ashburn VA, US
Emilia Apostolova - Chicago IL, US
Adam Boris - Cardiff CA, US
Assignee:
Computer Technology Associates, Inc. - Cardiff CA
International Classification:
G16H 50/20
G16H 50/50
G16H 70/60
G06F 15/18
Abstract:
A disease-specific ontology crafted by a consensus of expert clinicians may be used to semantically characterize/provide semantic meaning to dynamically changing patient electronic medical record (EMR) data in critical care settings. Hierarchical, directed node-edge-node graphs (concept maps or Vmaps) developed with an end-user friendly graphical user interface and ontology editor, can be used to represent structured clinical reasoning and serve as the first step in disease-specific ontology building. Disease domain Vmaps reflecting expert clinical reasoning associated with management of acute illnesses encountered in critical care settings (e.g. ICUs) that extend core clinical ontologies, developed and reviewed by experts, are in turn extended with existing medical ontologies and automatically translated to a domain ontology processing engine. Semantically-enhanced EMR data derived from the ontology processing engine is incorporated into both real-time ‘track and trigger” rule engines and machine learning training algorithms using aggregated data. The resulting rule engines and machine-learnt models provide enhanced diagnostic and prognostic information respectively, to assist in clinical dual modes of reasoning (analytical rules and models based on experiential data) to assist in decisions associated with the specific disease in acute critical care settings.
Timothy R Tschampel from Carlsbad, CA, age ~47 Get Report