Ms3 Predicting Optimal Treatment Regimens for Hr+/Her2- Breast Cancer Based on Electronic Health Records Using Random Forest

Cui, Zhanglin Lin; Kadziola, Zbigniew; Faries, Douglas E.; Lipkovich, Ilya; Ratitch, Bohdana; Li, X.; Sheffield, KM; Carter, G. Cuyun

doi:10.1016/j.jval.2020.04.046

Cited by 1 publication

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“…Through this approach, a study was conducted to examine indoor environments in homes and schools for health outcomes using machine learning and logistic regression methods [20]. Using the RF approach, an exploratory study was conducted to predict optimal treatment regimens for cancer, which highlighted the role of machine learning in providing recommendations to specialists for selecting appropriate treatments that improve outcomes for breast cancer patients [21].…”

Section: Related Workmentioning

confidence: 99%

Building a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning

Lafraxo,

Hami,

Merrakchi

et al. 2023

Int. J. Onl. Eng.

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This study aimed to build a recommender system that predicts the shape of bacteria for biological requests of urine cytobacteriological examination (UCBE) using machine learning techniques, to reduce the time taken to identify the shape of bacteria (Cocci or Bacilli). We used different methods and techniques in the process: Unified Modelling Language (UML) was used for digital design architecture, Rstudio tool with R programming language for system development, and Random Forest (RF) algorithm for the prediction. Experimental results showed that the time needed to identify the shape of bacteria is decreased, and bacilli bacteria are better recognized by the algorithm with an error rate of 3%. In addition to that, the proposed recommender system allows biologists to validate and correct the prediction and improve the accuracy of the classification algorithm used in the future.

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Section: Related Workmentioning

confidence: 99%

Building a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning

Lafraxo,

Hami,

Merrakchi

et al. 2023

Int. J. Onl. Eng.

View full text Add to dashboard Cite

show abstract

Ms3 Predicting Optimal Treatment Regimens for Hr+/Her2- Breast Cancer Based on Electronic Health Records Using Random Forest

Abstract: ance in the pre-treatment covariates, reduces bias and reduces variance and deserves consideration in outcomes research studies.

Cited by 1 publication

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Building a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning

Building a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning

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