Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies

Onay, Aytun; Onay, Melih; Abul, Osman

doi:10.1016/j.cmpb.2017.02.004

Cited by 15 publications

(15 citation statements)

References 51 publications

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“…Some studies also used AI to predict drug interactions by analyzing EHR data [ 88 ], unstructured discharge notes [ 90 ], and clinical charts [ 99 , 104 ]. One study also used AI to identify drugs that were withdrawn from the commercial markets by the FDA [ 100 ].…”

Section: Resultsmentioning

confidence: 99%

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

2020

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Background Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. Objective The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. Methods We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. Results We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. Conclusions This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.

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Section: Resultsmentioning

confidence: 99%

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

2020

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“…The effective hyperplane is considered as a classifier line and the sample inputs which are nearest to the classification line are called support vector. The expression for the training set of the SVM classifier is [14,15]…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Intelligent Plant Disease Identification System Using Machine Learning

Alagumariappan

Dewan

Muthukrishnan

et al. 2020

7th International Electronic Conference on Sensors and Applications

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Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India’s gross domestic product (GDP). Therefore, protecting and enhancing the agricultural sector helps in the development of India’s economy. In this work, a real-time decision support system integrated with a camera sensor module was designed and developed for identification of plant disease. Furthermore, the performance of three machine learning algorithms, such as Extreme Learning Machine (ELM) and Support Vector Machine (SVM) with linear and polynomial kernels was analyzed. Results demonstrate that the performance of the extreme learning machine is better when compared to the adopted support vector machine classifier. It is also observed that the sensitivity of the support vector machine with a polynomial kernel is better when compared to the other classifiers. This work appears to be of high social relevance, because the developed real-time hardware is capable of detecting different plant diseases.

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“…Work has been carried out to predict compounds that will bind to this receptor [114] . In general, based on an equal number of drugs approved or withdrawn for the treatment of CNS pathologies, possible discriminative fragments have been studied that allow the search for other similar compounds for the treatment of CNS pathologies [115] .…”

Section: Biological Problems Asses By Machine Learning In Drug Discoverymentioning

confidence: 99%

A review on machine learning approaches and trends in drug discovery

Carracedo-Reboredo

Liñares-Blanco

Rodríguez-Fernández

et al. 2021

Computational and Structural Biotechnology Journal

268

120

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Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies

Cited by 15 publications

References 51 publications

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

Intelligent Plant Disease Identification System Using Machine Learning

A review on machine learning approaches and trends in drug discovery

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