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Comprehend Medical: A Named Entity Recognition and Relationship Extraction Web Service
Comprehend Medical is a stateless and Health Insurance Portability and Accountability Act (HIPAA) eligible Named Entity Recognition …
Parminder Bhatia
,
Busra Celikkaya
,
Mohammed Khalilia
,
Selvan Senthivel
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DOI
Joint Entity Extraction and Assertion Detection for Clinical Text
Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task …
Parminder Bhatia
,
Busra Celikkaya
,
Mohammed Khalilia
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DOI
Comprehend Medical
Amazon Comprehend Medical is a HIPAA-eligible natural language processing (NLP) service that uses machine learning that has been pre-trained to understand and extract health data from medical text, such as prescriptions, procedures, or diagnoses.
Slides
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured …
Mengqi Jin
,
Mohammad Taha Bahadori
,
Aaron Colak
,
Parminder Bhatia
,
Busra Celikkaya
,
Ram Bhakta
,
Selvan Senthivel
,
Mohammed Khalilia
,
Daniel Navarro
,
Borui Zhang
,
Tiberiu Doman
,
Arun Ravi
,
Matthieu Liger
,
Taha Kass-hout
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The Digital Slide Archive: A Software Platform for Management, Integration, and Analysis of Histology for Cancer Research
Tissue-based cancer studies can generate large amounts of histology data in the form of glass slides. These slides contain important …
David A Gutman
,
Mohammed Khalilia
,
Sanghoon Lee
,
Michael Nalisnik
,
Zach Mullen
,
Jonathan Beezley
,
Deepak R Chittajallu
,
David Manthey
,
Lee A D Cooper
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DOI
Identifying Patients at Risk of High Healthcare Utilization
Clinical predictive modeling involves two challenging tasks, model development and model deployment. In this paper we demonstrate a …
Lincoln Sheets
,
Lori Popejoy
,
Mohammed Khalilia
,
Greg Petroski
,
Jerry C. Parker
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Clinical predictive modeling development and deployment through FHIR web services
Clinical predictive modeling involves two challenging tasks, model development and model deployment. In this paper we demonstrate a …
Mohammed Khalilia
,
Myung Choi
,
Amelia Henderson
,
Sneha Iyengar
,
Mark Braunstein
,
Jimeng Sun
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Cloud-based Predictive Modeling System and its Application to Asthma Readmission Prediction
The predictive modeling process is time consuming and requires clinical researchers to handle complex electronic health record (EHR) …
Robert Chen
,
Hang Su
,
Mohammed Khalilia
,
Sizhe Lin
,
Yue Peng
,
Tod Davis
,
Daniel A Hirsh
,
Elizabeth Searles
,
Javier Tejedor-Sojo
,
Michael Thompson
,
Jimeng Sun
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Comparing Aging in Place to Home Health Care: Impact of Nurse Care Coordination On Utilization and Costs
For the chronically ill, care delivered in the home is a lifeline to the self-management of chronic conditions. Currently, 90% of …
Lori L. Popejoy
,
Frank Stetzer
,
Lanis Hicks
,
Marilyn J. Rantz
,
Colleen Galambos
,
Mihail Popescu
,
Mohammed A. Khalilia
,
Karen D. Marek
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Quantifying care coordination using natural language processing and domain-specific ontology
Objective This research identifies specific care coordination activities used by Aging in Place (AIP) nurse care coordinators and home …
Lori L Popejoy
,
Mohammed A Khalilia
,
Mihail Popescu
,
Colleen Galambos
,
Vanessa Lyons
,
Marilyn Rantz
,
Lanis Hicks
,
Frank Stetzer
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