Machine learning in the prediction of cancer therapy

Rafique, Raihan; Islam, S. M. Riazul; Kazi, Julhash U.

doi:10.1016/j.csbj.2021.07.003

Cited by 103 publications

(64 citation statements)

References 156 publications

(208 reference statements)

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“…Various machine learning (ML) approaches have been developed to aid in therapy optimization and elucidate the relationships between the (epi)genomic characteristics of cancer cells and the treatment outcome. In particular, classification approaches have been utilized to help identify tumors that may benefit from treatment with particular drugs or their combinations, while regression approaches were designed to quantify the tumors’ degree of sensitivity 1 , 2 . A putative approach to integrating ML methods into the optimization of tumor treatment would be first to distinguish effective from ineffective drugs by classification and then prioritize the effective ones by their efficiency, toxicity, etc., by regression.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method

Lenhof

Eckhart

Gerstner

et al. 2022

Sci Rep

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Machine learning methods trained on cancer cell line panels are intensively studied for the prediction of optimal anti-cancer therapies. While classification approaches distinguish effective from ineffective drugs, regression approaches aim to quantify the degree of drug effectiveness. However, the high specificity of most anti-cancer drugs induces a skewed distribution of drug response values in favor of the more drug-resistant cell lines, negatively affecting the classification performance (class imbalance) and regression performance (regression imbalance) for the sensitive cell lines. Here, we present a novel approach called SimultAneoUs Regression and classificatiON Random Forests (SAURON-RF) based on the idea of performing a joint regression and classification analysis. We demonstrate that SAURON-RF improves the classification and regression performance for the sensitive cell lines at the expense of a moderate loss for the resistant ones. Furthermore, our results show that simultaneous classification and regression can be superior to regression or classification alone.

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Section: Introductionmentioning

confidence: 99%

Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method

Lenhof

Eckhart

Gerstner

et al. 2022

Sci Rep

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“…The machine learning-based modeling in this study provides a convenient tool to predict the outcome of iCCA after treatment, thus, contributing to the best clinical decision. In the context of no mature recommendations, models built for predicting survival by learning from historical medical records can provide a powerful reference for new subjects [15,16]. Machine learning has been increasingly applied in the clinical setting to assist doctors to provide a better recommendation [17,18].…”

Section: Discussionmentioning

confidence: 99%

Machine learning for clinical decision support in intrahepatic cholangiocarcinoma based on a population study of the US SEER database and a Chinese single-center registry

Zhou

Meng

et al. 2022

Preprint

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Background and aims To date, there is still a lack of consensus on the treatment of intrahepatic cholangiocarcinoma (iCCA). This study aims to build a clinical decision support tool based on machine learning of the Surveillance, Epidemiology, and End Results (SEER) database and the Fifth Medical Center of PLA General Hospital in China. Methods A total of 4,398 eligible patients with pathology-proven iCCA from the SEER database and 504 from the hospital data were enrolled for modeling by cross-validation based on the method of machine learning. All models were trained by the open-source Python library 4scikit-survival version 0.16.0 and explained by SHapley Additive exPlanations. Permutation importance was calculated using the library ELI5. Results All of the involved treatment modalities can contribute to a better prognosis. Three models were derived and tested among different data sources with the concordance index in the test datasets of 0.67, 0.69, and 0.73 respectively. The prediction was also consistent with the actual situation in randomly selected real patients. Model 2 trained by the hospital data was selected to develop an online tool because of the advantage of predicting the short-term prognosis.Conclusion The prediction model and tool of this study can be applied to predicting patients’ prognosis after treatment by inputting the patient’s clinical parameters or TNM stages and the treatment option and thus contribute to the optimal clinical decision.

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“…In their study, they stated that machine learning applications have potential opportunities for clinical studies in the field of psychotherapy. In another study, Rafique et al [ 13 ] examined the effect of machine learning on cancer treatment.…”

Section: Related Workmentioning

confidence: 99%

Real-time internet of medical things framework for early detection of Covid-19

Yıldırım

Ci̇ci̇oğlu

Çalhan

2022

Neural Comput & Applic

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The Covid-19 pandemic is a deadly epidemic and continues to affect all world. This situation dragged the countries into a global crisis and caused the collapse of some health systems. Therefore, many technologies are needed to slow down the spread of the Covid-19 epidemic and produce solutions. In this context, some developments have been made with artificial intelligence, machine learning and deep learning support systems in order to alleviate the burden on the health system. In this study, a new Internet of Medical Things (IoMT) framework is proposed for the detection and early prevention of Covid-19 infection. In the proposed IoMT framework, a Covid-19 scenario consisting of various numbers of sensors is created in the Riverbed Modeler simulation software. The health data produced in this scenario are analyzed in real time with Apache Spark technology, and disease prediction is made. In order to provide more accurate results for Covid-19 disease prediction, Random Forest and Gradient Boosted Tree (GBT) Ensemble Learning classifiers, which are formed by Decision Tree classifiers, are compared for the performance evaluation. In addition, throughput, end-to-end delay results and Apache Spark data processing performance of heterogeneous nodes with different priorities are analyzed in the Covid-19 scenario. The MongoDB NoSQL database is used in the IoMT framework to store big health data produced in real time and use it in subsequent processes. The proposed IoMT framework experimental results show that the GBTs classifier has the best performance with 95.70% training, 95.30% test accuracy and 0.970 area under the curve (AUC) values. Moreover, the promising real-time performances of wireless body area network (WBAN) simulation scenario and Apache Spark show that they can be used for the early detection of Covid-19 disease.

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Machine learning in the prediction of cancer therapy

Abstract: Graphical abstract

Cited by 103 publications

References 156 publications

Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method

Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method

Machine learning for clinical decision support in intrahepatic cholangiocarcinoma based on a population study of the US SEER database and a Chinese single-center registry

Real-time internet of medical things framework for early detection of Covid-19

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