Title: Interactive machine learning methods for clinical natural language processing
Awarding Agency: NIH/NLM, 2R01LM010681
PI: Hua Xu
Duration: 09/29/2014 – 09/28/2018
Budget: $1,947,052 (total cost)
Project Description: In this study, we propose to investigate interactive machine learning (IML) methods to address the challenges in clinical NLP about building annotated corpora and combining domain knowledge and statistical learning methods. We will conduct IML studies to three NLP related tasks including word sense disambiguation, named entity recognition, and clinical phenotyping.
Title: Informatics Tools for Pharmacogenomic Discovery using Practice-based Data
Awarding Agency: NIH/NIGMS, R01GM103859
PI: Josh Denny (contact), Jyotishman Pathak, and Hua Xu
Duration: 09/18/2014 – 05/31/2018
UTH Budget: $572,180 (total cost)
Project Description: In this study, we will collaborate with i2b2 to extend its informatics framework to the pharmacogenomics domain. We will develop informatics tools to facility pharmacogenomic studies by using EHRs and linked biobanks.
Title: BioCADDIE: Biomedical and healthCAre Data Discovery and Indexing Engine center
Awarding Agency: NIH, U24HL126126
PI: Lucila Ohno-Machado, UCSD
UTH Subcontract PI: Hua Xu
Duration: 09/29/2014 – 09/28/2017
UTH Budget: $1,553,694 (total cost for UTH)
Project Description: BioCADDIE is a consortium of data producers, curators, publishers, and consumers who will work together to develop practical, sustainable solutions to the problem of biomedical and healthcare data discovery. This project is to develop an NIH BD2K Data Discovery Index Coordination Consortium.
Title: Patient Medical History Representation, Extraction, and Inference from EHR Data
Awarding Agency: NIH/NLM
Grant Number: 1R01LM011829
PI: Cui Tao
Co-Is: Hua Xu, Elmer Bernstam
Duration: 09/01/2014 – 08/31/2018
UTH Budget: $1,358,868
This proposed project fills in the current gaps among ontologies, Natural Language Processing (NLP), and EHR-based clinical research for temporal data representation, normalization, extractions, and reasoning. We propose to develop novel approaches for automatic temporal data representation, normalization and reasoning for large, diverse, and heterogeneous EHR data and prepare the integrated data for further analysis. We will build new reasoning and extraction capacities on our TIMER (Temporal Information Modeling, Extracting, and Reasoning) framework to provide an end-to-end, open-source, standard-conforming software package. TIMER will be built on strong prior work by our team. We will develop new features in our CNTRO (Clinical Narrative Temporal Relation Ontology) for semantically defining the time domain and representing temporal data in complex EHR data. On top of the new developed CNTRO semantics, we will implement temporal relation reasoning capacities to automatically normalize temporal expressions, compute and infer temporal relations, and resolve ambiguities. We will leverage existing NLP tools and work on top of these tools to develop new extraction approaches to fill in the current gaps between NLP approaches and ontology-based reasoning approaches. We will adapt the SHARPn EHR data normalization pipeline and cTAKES for extracting and normalizing clinical event mentions from clinical narratives. We will explore an innovative approach for temporal relation extraction and event coreference, and make it work with the TIMER framework.
Title: Learning from patient safety events: a case-based toolkit
Awarding Agency: AHRQ
Grant Number: R01HS022895
PI: Yang Gong
Co-Is: Jing Wang, Hua Xu, Nnaemeka Okafor, Jiajie Zhang
Duration: 9/30/2014 – 9/29/2019
UTH Budget: $1,246,715
Timely reporting and effective learning from medical incidents is considered an effective way in developing strategies for reducing medical errors. Utilizing an innovative user-centered, learning-supportive, and ontological approach combining with case-based reasoning and natural language processing techniques, we propose to develop a knowledgebase and learning toolkit that can systematically collect and analyze incident reports, linking historical reports with WebM&M, the highest quality of voluntary reports and expert reviews on patient safety. We envision that the innovative approach will facilitate timely, quality reporting and learning from the incidents and ultimately cultivating a just and learning culture of patient safety.