Validation of psoriatic arthritis diagnoses in electronic medical records using natural language processing.
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Your vote was cast
Thank you for your feedback
Thank you for your feedback
MetadataShow full item record
CitationSemin. Arthritis Rheum. 2011, 40(5):413-20
AbstractOBJECTIVES: To test whether data extracted from full text patient visit notes from an electronic medical record would improve the classification of psoriatic arthritis (PsA) compared with an algorithm based on codified data. METHODS: From the >1,350,000 adults in a large academic electronic medical record, all 2318 patients with a billing code for PsA were extracted and 550 were randomly selected for chart review and algorithm training. Using codified data and phrases extracted from narrative data using natural language processing, 31 predictors were extracted and 3 random forest algorithms were trained using coded, narrative, and combined predictors. The receiver operator curve was used to identify the optimal algorithm and a cut-point was chosen to achieve the maximum sensitivity possible at a 90% positive predictive value (PPV). The algorithm was then used to classify the remaining 1768 charts and finally validated in a random sample of 300 cases predicted to have PsA. RESULTS: The PPV of a single PsA code was 57% (95% CI 55%-58%). Using a combination of coded data and natural language processing (NLP), the random forest algorithm reached a PPV of 90% (95% CI 86%-93%) at a sensitivity of 87% (95% CI 83%-91%) in the training data. The PPV was 93% (95% CI 89%-96%) in the validation set. Adding NLP predictors to codified data increased the area under the receiver operator curve (P < 0.001). CONCLUSIONS: Using NLP with text notes from electronic medical records improved the performance of the prediction algorithm significantly. Random forests were a useful tool to accurately classify psoriatic arthritis cases to enable epidemiological research.
DescriptionTo access publisher full text version of this article. Please click on the hyperlink in Additional Links field.
RightsArchived with thanks to Seminars in arthritis and rheumatism
- Use of Natural Language Processing Tools to Identify and Classify Periprosthetic Femur Fractures.
- Authors: Tibbo ME, Wyles CC, Fu S, Sohn S, Lewallen DG, Berry DJ, Maradit Kremers H
- Issue date: 2019 Oct
- Natural language processing of clinical notes for identification of critical limb ischemia.
- Authors: Afzal N, Mallipeddi VP, Sohn S, Liu H, Chaudhry R, Scott CG, Kullo IJ, Arruda-Olson AM
- Issue date: 2018 Mar
- NLP based congestive heart failure case finding: A prospective analysis on statewide electronic medical records.
- Authors: Wang Y, Luo J, Hao S, Xu H, Shin AY, Jin B, Liu R, Deng X, Wang L, Zheng L, Zhao Y, Zhu C, Hu Z, Fu C, Hao Y, Zhao Y, Jiang Y, Dai D, Culver DS, Alfreds ST, Todd R, Stearns F, Sylvester KG, Widen E, Ling XB
- Issue date: 2015 Dec
- Development of a Portable Tool to Identify Patients With Atrial Fibrillation Using Clinical Notes From the Electronic Medical Record.
- Authors: Shah RU, Mutharasan RK, Ahmad FS, Rosenblatt AG, Gay HC, Steinberg BA, Yandell M, Tristani-Firouzi M, Klewer J, Mukherjee R, Lloyd-Jones DM
- Issue date: 2020 Oct
- Electronic medical records for discovery research in rheumatoid arthritis.
- Authors: Liao KP, Cai T, Gainer V, Goryachev S, Zeng-treitler Q, Raychaudhuri S, Szolovits P, Churchill S, Murphy S, Kohane I, Karlson EW, Plenge RM
- Issue date: 2010 Aug