Multi-Institutional Validation Study of Neural Networks to Predict Duration of Stay After Laparoscopic Radical/Simple or Partial Nephrectomy

Parekattıl, Sıjo J.; Gill, Inderbir S.; Castle, Erik P.; Burgess, Scott V.; Walls, Melissa M.; Thomas, Raju; Kumar, Udaya; Purifoy, Jody Ann; Ng, Christopher; Kang, Young Mi; Fuchs, Gerhard; Weise, Erik S.; Winfield, Howard N.; Lallas, Costas D.; Andrews, Paul

doi:10.1097/01.ju.0000173921.67597.e8

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…The high-risk group identified by this model can benefit from systemic treatment post cystectomy to improve their disease related morbidity and mortality [ 95 , 96 ]. The 5 years survival post cystectomy was the output of 2 other ANN with a high prediction efficacy of 77% and 90% respectively (Table 6 ) [ 97 , 99 ].…”

Section: Resultsmentioning

confidence: 99%

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

Salem

Soria

Lund

et al. 2021

BMC Med Inform Decis Mak

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Background Testing a hypothesis for ‘factors-outcome effect’ is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified. Methods The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system. Results The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible. Conclusion ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.

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Section: Resultsmentioning

confidence: 99%

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

Salem

Soria

Lund

et al. 2021

BMC Med Inform Decis Mak

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“…Research on clinical pathways mostly deals with representing them in a formal way [3,4] by documenting, collecting and representing variances [5,6], and developing decision support tools that would supplement a pathway with a predictor of LOS [7,8]. Widely adopted methodologies to model clinical pathways and the clinical guidelines they implement [9] include PERT/CPM models [3], Petri nets, skeletal plans and decision rules in the Arden syntax (see [4] for a review).…”

Section: Introductionmentioning

confidence: 99%

“…The methodologies for using variance records allow tracking variances associated with a clinical pathway [5,6], but are usually limited to variance frequencies, while their impact on LOS is not evaluated [5]. Finally, predictors of LOS are often implemented with neural network models; thus they may be difficult to comprehend by the patient management teams, although they may have acceptable predictive accuracy [7,8].…”

Section: Introductionmentioning

confidence: 99%

Using a Bayesian belief network model to categorize length of stay for radical prostatectomy patients

Michalowski

Wilk

Thijssen

et al. 2006

Health Care Manage Sci

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A clinical pathway implements best medical practices and represents sequencing and timing of interventions by clinicians for a particular clinical presentation. We used a Bayesian belief network (BBN) to model a clinical pathway for radical prostatectomy and to categorize patient's length of stay (LOS) as being met or delayed given the patient's outcomes and activities. A BBN model constructed from historical data collected as part of a retrospective chart study represents probabilistic dependencies between specific events from the pathway and identifies events directly affecting LOS. Preliminary evaluation of a BBN model on an independent test sample of patients' data shows that model reliably categorizes LOS for the second and third day after the surgery (with overall accuracy of 82 and 84%, respectively).

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Current World Literature

2006

Current Opinion in Urology

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Multi-Institutional Validation Study of Neural Networks to Predict Duration of Stay After Laparoscopic Radical/Simple or Partial Nephrectomy

Abstract: The LapNx model provides 72% accuracy in predicting the DOS at all 6 institutions. The LapPNx model provided fair accuracy only at CCF and Tulane University Medical School. These models may streamline the delivery of care and continued testing will allow for further refinement.

Cited by 4 publications

References 11 publications

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

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

Using a Bayesian belief network model to categorize length of stay for radical prostatectomy patients

Current World Literature

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