Building artificial intelligence‐based personalized predictive models

Bhandari, Mahendra; Reddiboina, Madhu

doi:10.1111/bju.14746

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…In the constant effort to promote patient-specific care, with the aim of diagnosing and treating different diseases, urologists are forced to manage large datasets and to interpret complex information [ 1 ]. Due to its potential application in accelerating and performing the analysis of this large and nonlinear amount of data, artificial intelligence (AI), has aroused great interest in the scientific community, leading to the development of specific algorithms that are able to consider different variables simultaneously [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Tool to Predict Target Prostate Biopsy Outcomes: An Internal Validation Study

Checcucci

Rosati

Cillis

et al. 2023

JCM

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The aim of this study is to present a personalized predictive model (PPM) with a machine learning (ML) system that is able to identify and classify patients with suspected prostate cancer (PCa) following mpMRI. We extracted all the patients who underwent fusion biopsy (FB) from March 2014 to December 2019, while patients from August 2020 to April 2021 were included as a validation set. The proposed system was based on the following four ML methods: a fuzzy inference system (FIS), the support vector machine (SVM), k-nearest neighbors (KNN), and self-organizing maps (SOMs). Then, a system based on fuzzy logic (FL) + SVM was compared with logistic regression (LR) and standard diagnostic tools. A total of 1448 patients were included in the training set, while 181 patients were included in the validation set. The area under the curve (AUC) of the proposed FIS + SVM model was comparable with the LR model but outperformed the other diagnostic tools. The FIS + SVM model demonstrated the best performance, in terms of negative predictive value (NPV), on the training set (78.5%); moreover, it outperformed the LR in terms of specificity (92.1% vs. 83%). Considering the validation set, our model outperformed the other methods in terms of NPV (60.7%), sensitivity (90.8%), and accuracy (69.1%). In conclusion, we successfully developed and validated a PPM tool using the FIS + SVM model to calculate the probability of PCa prior to a prostate FB, avoiding useless ones in 15% of the cases.

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Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Tool to Predict Target Prostate Biopsy Outcomes: An Internal Validation Study

Checcucci

Rosati

Cillis

et al. 2023

JCM

View full text Add to dashboard Cite

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“…Current advancement in machine learning techniques have made automated predictions more appropriate. Machine learning is the process of deriving complex correlations among factors from the data to provide predictions [7,8]. Clinical data can very highly benefit from the improvement in machine learning techniques [9].…”

Section: Introductionmentioning

confidence: 99%

Pipelined Ensemble Architecture for Mortality Prediction on MIMIC III

D. Malarvizhi,

A. Prakash

2023

J. Adv. Zool.

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Automated healthcare decision support has seen a huge rise with the improved data collection models in hospitals and also improved machine learning based techniques that exhibit high possibilities for automation. Automating healthcare systems as an aide for clinical practitioners can ensure fast and more accurate results for the patients and can also aid in hospital administration. Mortality prediction has been one of the major and critical factors that determines the type of treatment and the level of resources that has to be allocated for a patient. This work presents a pipelined ensemble architecture that can be used for effective prediction of mortality levels of a patient. The pipeline model has been designed in multiple levels to ensure improvement of quality of the medical data and effective prediction. The pipelined architecture model has been compared with existing state-of-the-art model, and the results indicate high performance with 92% accuracy levels, ensuring the model is suitable for use in real time mortality prediction.

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Research on Intelligent Investment Prediction Model of Building Based on Support Vector Machine

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Building artificial intelligence‐based personalized predictive models

Cited by 7 publications

References 5 publications

Machine-Learning-Based Tool to Predict Target Prostate Biopsy Outcomes: An Internal Validation Study

Machine-Learning-Based Tool to Predict Target Prostate Biopsy Outcomes: An Internal Validation Study

Pipelined Ensemble Architecture for Mortality Prediction on MIMIC III

Research on Intelligent Investment Prediction Model of Building Based on Support Vector Machine

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