TJA

NLP Algorithms for Total Joint Arthroplasty

THA NLP System

Process operative reports to classify a patient’s status of approach, fixation and bearing surface @author Sunyang Fu, Sunghwan Sohn, Hilal Maradit Kremers, Walter Kremers, Ahmad Pahlavan Tafti, Elham Sagheb Hossein Pour, Cody Wyles, Meagan Tibbo, David Lewallen, Daniel Berry

THA module: https://github.com/OHNLP/TJA/tree/master/module/THA_NLP

TKA NLP System

Process operative notes to extract five data elements: (1) category of surgery (total knee arthroplasty, unicompartmental knee arthroplasty, patellofemoral arthroplasty), (2) laterality of surgery, (3) constraint type, (4) presence of patellar resurfacing, and (5) implant model (catalog numbers) @author Elham Sagheb, Sunyang Fu, Sunghwan Sohn, Walter Kremers, Ahmad P. Tafti, Taghi Ramazanian, Cody Wyles, Meagan Tibbo, David Lewallen, Daniel Berry, Hilal Maradit Kremers

TKA module: https://github.com/OHNLP/TJA/tree/master/module/TKA_NLP

PJI NLP System

Process consultation notes, operative notes, pathology reports, and microbiology reports to automated detection of Periprosthetic Joint Infections and Data Elements Using Natural Language Processing. @author Fu S, Wyles CC, Osmon DR, Carvour ML, Sagheb E, Ramazanian T, Kremers WK, Lewallen DG, Berry DJ, Sohn S, Maradit Kremers H.

PJI module: https://github.com/OHNLP/TJA/tree/master/module/PJI_NLP

PPFX NLP System

Process operative reports to identify periprosthetic femur fractures (PPFFx) followed by more complex Vancouver classification.
@author Tibbo ME, Wyles CC, Fu S, Sohn S, Lewallen DG, Berry DJ, Maradit Kremers H.

PPFX module: https://github.com/OHNLP/TJA/tree/master/module/PPFX_NLP

MedTagger

MedTagger contains a suite of programs that the Mayo Clinic NLP program has developed in 2013. It includes three major components: MedTagger for indexing based on dictionaries, MedTaggerIE for information extraction based on patterns, and MedTaggerML for machine learning-based named entity recognition.

MedTagger git repo: https://github.com/OHNLP/MedTagger

Video demo: https://vimeo.com/392331446

Original release: https://github.com/OHNLP/MedTagger/releases

1. Download the latest release from https://github.com/OHNLP/TJA/tree/master/nlp_system
2. Move the .jar file to either THA_NLP or TKA_NLP
3. Modify the INPUTDIR, OUTPUTDIR, and RULEDIR variables in runMedTagger-fit-tja.sh or runMedTagger-fit-tka.sh, as appropriate
   • INPUT_DIR: full directory path of input folder
   • OUTPUT_DIR: full directory path of output folder
   • RULES_DIR: full directory path of ‘Rule’ folder

   Example:

    INPUTDIR="$YOUR_INPUT_DIRECTORY"
    OUTPUTDIR="$YOUR_OUTPUT_DIRECTORY"
    RULEDIR="$YOUR_MEDTAGGER_HOME/medtaggerieresources/covid19"
   

4. Run the batch file

    run_medtagger_unix_mac.sh
   

Publications

THA NLP System: https://pubmed.ncbi.nlm.nih.gov/31567670/

TKA NLP System: https://pubmed.ncbi.nlm.nih.gov/33051119/

PJI NLP System: https://pubmed.ncbi.nlm.nih.gov/32854996/

PPFX NLP System: https://pubmed.ncbi.nlm.nih.gov/31416741/