Literature DB >> 17238732

Prediction of unexpected emergency room visit after extracorporeal shock wave lithotripsy for urolithiasis - an application of artificial neural network in hospital information system.

Chi-Cheng Sun1, Polun Chang.   

Abstract

Extracorporeal shock wave lithotripsy (ESWL) for urolithiasis was developed for more than 30 years. It benefited most patients suffering from acute renal colic. The ESWL may be performed at outpatient based in most hospital in Taiwan. But the post-ESWL emergency room (ER) visits will be a painful experience for patient and the urologist,especially those patients visited ER immediately on the same day of ESWL. Though most guidelines for ESWL suggest the larger stone burden, the higher risk for post-ESWL ER visits,there are about 10% patients will come back to ER due to renal colic post-operatively. We use artificial neural network(ANN) to predict the post-ESWL ER visit for patient with urolithiasis. The result disclosed high sensitivity and specificity of prediction. In conclusion, it will decrease the rate of post-ER visit rate and patients' suffer by using ANN to predict the post-ESWL ER visits.

Entities:  

Mesh:

Year:  2006        PMID: 17238732      PMCID: PMC1839485     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  2 in total

1.  Outpatient-based extracorporeal shock wave lithotripsy using EDAP LT-01.

Authors:  L Grenabo; Y Wang; S Bratell; C Dahlstrand; G Haraldsson; H Hedelin; C Henriksson; G Wikholm; S Pettersson; B F Zachrisson
Journal:  Scand J Urol Nephrol Suppl       Date:  1991

2.  Outpatient extracorporeal lithotripsy of kidney stones: 1,200 treatments.

Authors:  G Vallancien; N Defourmestraux; J P Léo; L Cohen; J Puissan; B Veillon; J M Brisset
Journal:  Eur Urol       Date:  1988       Impact factor: 20.096

  2 in total
  2 in total

1.  Nonlinear logistic regression model for outcomes after endourologic procedures: a novel predictor.

Authors:  Adam O Kadlec; Samuel Ohlander; James Hotaling; Jessica Hannick; Craig Niederberger; Thomas M Turk
Journal:  Urolithiasis       Date:  2014-04-02       Impact factor: 3.436

2.  A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology.

Authors:  Hesham Salem; Daniele Soria; Jonathan N Lund; Amir Awwad
Journal:  BMC Med Inform Decis Mak       Date:  2021-07-22       Impact factor: 2.796

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.