Browsing by Subject "natural language processing"

Now showing 1 - 5 of 5
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    Identification of Patients with Family History of Pancreatic Cancer - Investigation of an NLP System Portability
    (IOS, 2015) Mehrabi, Saeed; Krishnan, Anand; Roch, Alexandra M.; Schmidt, Heidi; Li, DingCheng; Kesterson, Joe; Beesley, Chris; Dexter, Paul; Schmidt, Max; Palakal, Mathew; Liu, Hongfang; Department of BioHealth Informatics, School of Informatics and Computing
    In this study we have developed a rule-based natural language processing (NLP) system to identify patients with family history of pancreatic cancer. The algorithm was developed in a Unstructured Information Management Architecture (UIMA) framework and consisted of section segmentation, relation discovery, and negation detection. The system was evaluated on data from two institutions. The family history identification precision was consistent across the institutions shifting from 88.9% on Indiana University (IU) dataset to 87.8% on Mayo Clinic dataset. Customizing the algorithm on the the Mayo Clinic data, increased its precision to 88.1%. The family member relation discovery achieved precision, recall, and F-measure of 75.3%, 91.6% and 82.6% respectively. Negation detection resulted in precision of 99.1%. The results show that rule-based NLP approaches for specific information extraction tasks are portable across institutions; however customization of the algorithm on the new dataset improves its performance.
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    Identifying and Characterizing a Chronic Cough Cohort Through Electronic Health Records
    (Elsevier, 2021-06) Weiner, Michael; Dexter, Paul R.; Heithoff, Kim; Roberts, Anna R.; Liu, Ziyue; Griffith, Ashley; Hui, Siu; Schelfhout, Jonathan; Dicpinigaitis, Peter; Doshi, Ishita; Weaver, Jessica P.; Medicine, School of Medicine
    Background Chronic cough (CC) of 8 weeks or more affects about 10% of adults and may lead to expensive treatments and reduced quality of life. Incomplete diagnostic coding complicates identifying CC in electronic health records (EHRs). Natural language processing (NLP) of EHR text could improve detection. Research Question Can NLP be used to identify cough in EHRs, and to characterize adults and encounters with CC? Study Design and Methods A Midwestern EHR system identified patients aged 18 to 85 years during 2005 to 2015. NLP was used to evaluate text notes, except prescriptions and instructions, for mentions of cough. Two physicians and a biostatistician reviewed 12 sets of 50 encounters each, with iterative refinements, until the positive predictive value for cough encounters exceeded 90%. NLP, International Classification of Diseases, 10th revision, or medication was used to identify cough. Three encounters spanning 56 to 120 days defined CC. Descriptive statistics summarized patients and encounters, including referrals. Results Optimizing NLP required identifying and eliminating cough denials, instructions, and historical references. Of 235,457 cough encounters, 23% had a relevant diagnostic code or medication. Applying chronicity to cough encounters identified 23,371 patients (61% women) with CC. NLP alone identified 74% of these patients; diagnoses or medications alone identified 15%. The positive predictive value of NLP in the reviewed sample was 97%. Referrals for cough occurred for 3.0% of patients; pulmonary medicine was most common initially (64% of referrals). Limitations Some patients with diagnosis codes for cough, encounters at intervals greater than 4 months, or multiple acute cough episodes may have been misclassified. Interpretation NLP successfully identified a large cohort with CC. Most patients were identified through NLP alone, rather than diagnoses or medications. NLP improved detection of patients nearly sevenfold, addressing the gap in ability to identify and characterize CC disease burden. Nearly all cases appeared to be managed in primary care. Identifying these patients is important for characterizing treatment and unmet needs.
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    Measuring the quality of colonoscopy: Where are we now and where are we going?
    (Elsevier, 2015-09) Imler, Timothy D.; Department of Medicine, IU School of Medicine
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    Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks
    (arXiv, 2019) Nuthakki, Siddhartha; Neela, Sunil; Gichoya, Judy W.; Purkayastha, Saptarshi; BioHealth Informatics, School of Informatics and Computing
    Coding diagnosis and procedures in medical records is a crucial process in the healthcare industry, which includes the creation of accurate billings, receiving reimbursements from payers, and creating standardized patient care records. In the United States, Billing and Insurance related activities cost around $471 billion in 2012 which constitutes about 25% of all the U.S hospital spending. In this paper, we report the performance of a natural language processing model that can map clinical notes to medical codes, and predict final diagnosis from unstructured entries of history of present illness, symptoms at the time of admission, etc. Previous studies have demonstrated that deep learning models perform better at such mapping when compared to conventional machine learning models. Therefore, we employed state-of-the-art deep learning method, ULMFiT on the largest emergency department clinical notes dataset MIMIC III which has 1.2M clinical notes to select for the top-10 and top-50 diagnosis and procedure codes. Our models were able to predict the top-10 diagnoses and procedures with 80.3% and 80.5% accuracy, whereas the top-50 ICD-9 codes of diagnosis and procedures are predicted with 70.7% and 63.9% accuracy. Prediction of diagnosis and procedures from unstructured clinical notes benefit human coders to save time, eliminate errors and minimize costs. With promising scores from our present model, the next step would be to deploy this on a small-scale real-world scenario and compare it with human coders as the gold standard. We believe that further research of this approach can create highly accurate predictions that can ease the workflow in a clinical setting.
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    Provider-specific quality measurement for ERCP using natural language processing
    (Elsevier, 2017) Imler, Timothy D.; Sherman, Stuart; Imperiale, Thomas F.; Xu, Huiping; Ouyang, Fangqian; Beesley, Christopher; Hilton, Charity; Coté, Gregory A.; Department of Medicine, IU School of Medicine
    Background and Aims Natural language processing (NLP) is an information retrieval technique that has been shown to accurately identify quality measures for colonoscopy. There are no systematic methods by which to track adherence to quality measures for ERCP, the highest risk endoscopic procedure widely used in practice. Our aim was to demonstrate the feasibility of using NLP to measure adherence to ERCP quality indicators across individual providers. Methods ERCPs performed by 6 providers at a single institution from 2006 to 2014 were identified. Quality measures were defined using society guidelines and from expert opinion, and then extracted using a combination of NLP and data mining (eg, ICD9-CM codes). Validation for each quality measure was performed by manual record review. Quality measures were grouped into preprocedure (5), intraprocedure (6), and postprocedure (2). NLP was evaluated using measures of precision and accuracy. Results A total of 23,674 ERCPs were analyzed (average patient age, 52.9 ± 17.8 years, 14,113 were women [59.6%]). Among 13 quality measures, precision of NLP ranged from 84% to 100% with intraprocedure measures having lower precision (84% for precut sphincterotomy). Accuracy of NLP ranged from 90% to 100% with intraprocedure measures having lower accuracy (90% for pancreatic stent placement). Conclusions NLP in conjunction with data mining facilitates individualized tracking of ERCP providers for quality metrics without the need for manual medical record review. Incorporation of these tools across multiple centers may permit tracking of ERCP quality measures through national registries.