Research plan: The use of clinical knowledge systems such as UpToDate that provide reliable information at the point of care has been shown to improve patient safety and decision-making. With similar content to UpToDate, Mayo Clinic's AskMayoExpert (AME) is an online knowledge system that primarily contains over 5000 (and increasing) specialist-vetted answers to FAQs for point of care use. However, because of the overabundance of clinical resources and guidelines, adding new answers manually to AME and ensuring that it is consistent with evidence is time consuming. This problem is also faced with other systems such as UpToDate. This career grant proposes to investigate the feasibility of using a novel text mining based informatics approach to semi-automate the management of a clinical knowledge system, using AME as the test bed. Although the methods will be applicable to any clinical knowledge system and any topic, they will be evaluated using two important test topics from cardiology (which has the biggest focus in AME) - atrial fibrillation (a topic exhaustively covered in AME) and congestive heart failure (a topic less covered, but is an increasingly complex vast field with knowledge from huge literature). While the existing content of AME is private, the methods and the code we develop to assist in generating the content will be released open-source as part of the Open Health Natural Language Processing (OHNLP) consortium in UIMA framework. Career plan: As most communication of information in clinical practice and biomedical research occurs through the medium of text, the development of methods to render this text computer-interpretable is a prerequisite to the use of this information to improve quality of care and support scientific discovery. The PI's long-term career goal is to become a leader in biomedical informatics (informatics applied to biomedical data), with focus on textual data such as scientific papers and clinical notes. He has BS in Computer Science, PhD in Biomedical Informatics and over a dozen of peer-reviewed publications in biomedical text mining. His career goal is to advance diverse methods and applications of text mining across biomedical informatics (BMI). He will focus on: a) discovering information needs and gaps that can be filled, b) adapting and extending existing text mining algorithms, and c) validating the utility of the applications in the biomedical environment. Rationale: Making the transition from a mentored researcher to an independent researcher requires three main facets of career growth: a) developing a working familiarity with clinical information systems and medical terminologies;b) understanding the information needs of clinicians;and c) training in clinical research. The proposal will translate the PI's knowledgeof the text mining methods to practical experience in an operation clinical environment. Courses listed in the """"""""Career Development/Training Activities"""""""" will educate him more about the environment and train him on clinical research. He will continue sharpening his informatics expertise by attending scientific conferences.
Medical errors are one of the leading causes of death in the United States. It has been observed that point of care access to relevant clinical knowledge support decision making and decreases medical errors, thereby improving patient safety and healthcare costs. The proposed research aims to empower physicians specialized in the area (specialists) in quickly gathering evidence from literature or finding citations supporting or qualifying their expert opinion. It will also generate the answers and suggest updates to the existing answers for their perusal.
|Jonnalagadda, Siddhartha R; Adupa, Abhishek K; Garg, Ravi P et al. (2017) Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials. J Cardiovasc Transl Res 10:313-321|
|Bian, Jiantao; Morid, Mohammad Amin; Jonnalagadda, Siddhartha et al. (2017) Automatic identification of high impact articles in PubMed to support clinical decision making. J Biomed Inform 73:95-103|
|Bui, Duy Duc An; Del Fiol, Guilherme; Hurdle, John F et al. (2016) Extractive text summarization system to aid data extraction from full text in systematic review development. J Biomed Inform 64:265-272|
|Bui, Duy Duc An; Del Fiol, Guilherme; Jonnalagadda, Siddhartha (2016) PDF text classification to leverage information extraction from publication reports. J Biomed Inform 61:141-8|
|Nath, Chinmoy; Huh, Jina; Adupa, Abhishek Kalyan et al. (2016) Website Sharing in Online Health Communities: A Descriptive Analysis. J Med Internet Res 18:e11|
|Del Fiol, Guilherme; Mostafa, Javed; Pu, Dongqiuye et al. (2016) Formative evaluation of a patient-specific clinical knowledge summarization tool. Int J Med Inform 86:126-34|
|Wongchaisuwat, Papis; Klabjan, Diego; Jonnalagadda, Siddhartha Reddy (2016) A Semi-Supervised Learning Approach to Enhance Health Care Community-Based Question Answering: A Case Study in Alcoholism. JMIR Med Inform 4:e24|
|Nath, Chinmoy; Albaghdadi, Mazen S; Jonnalagadda, Siddhartha R (2016) A Natural Language Processing Tool for Large-Scale Data Extraction from Echocardiography Reports. PLoS One 11:e0153749|
|Morid, Mohammad Amin; Fiszman, Marcelo; Raja, Kalpana et al. (2016) Classification of clinically useful sentences in clinical evidence resources. J Biomed Inform 60:14-22|
|Raja, Kalpana; Dasot, Naman; Goyal, Pawan et al. (2016) Towards Evidence-based Precision Medicine: Extracting Population Information from Biomedical Text using Binary Classifiers and Syntactic Patterns. AMIA Jt Summits Transl Sci Proc 2016:203-12|
Showing the most recent 10 out of 20 publications