The rise of artificial intelligence in healthcare applications

Bohr, Adam; Memarzadeh, Kaveh

doi:10.1016/b978-0-12-818438-7.00002-2

Cited by 839 publications

(530 citation statements)

References 52 publications

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“…Nowadays, artificial intelligence (AI) research in healthcare is accelerating rapidly, with potential applications across almost every domain of medicine [ 34 – 36 ]. As an important branch of AI, machine learning allows computers to train models using large numbers of examples and may detect difficult-to-recognize patterns from complex dataset [ 37 ]. Unlike conventional regression-based approaches, machine learning algorithms are capable of capturing higher-order, non-linear inter-actions between predictors [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection

Yang

Yuan³

et al. 2021

J Transl Med

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Background Surgical resection is the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC) and the survival of patients after radical resection is closely related to relapse. We aimed to develop models to predict the risk of relapse using machine learning methods based on multiple clinical parameters. Methods Data were collected and analysed of 262 PDAC patients who underwent radical resection at 3 institutions between 2013 and 2017, with 183 from one institution as a training set, 79 from the other 2 institution as a validation set. We developed and compared several predictive models to predict 1- and 2-year relapse risk using machine learning approaches. Results Machine learning techniques were superior to conventional regression-based analyses in predicting risk of relapse of PDAC after radical resection. Among them, the random forest (RF) outperformed other methods in the training set. The highest accuracy and area under the receiver operating characteristic curve (AUROC) for predicting 1-year relapse risk with RF were 78.4% and 0.834, respectively, and for 2-year relapse risk were 95.1% and 0.998. However, the support vector machine (SVM) model showed better performance than the others for predicting 1-year relapse risk in the validation set. And the k neighbor algorithm (KNN) model achieved the highest accuracy and AUROC for predicting 2-year relapse risk. Conclusions By machine learning, this study has developed and validated comprehensive models integrating clinicopathological characteristics to predict the relapse risk of PDAC after radical resection which will guide the development of personalized surveillance programs after surgery.

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Section: Discussionmentioning

confidence: 99%

Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection

Yang

Yuan³

et al. 2021

J Transl Med

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“…Concepts of artificial intelligence and machine learning have been introduced in the middle of the 20 th century. The versatility of their application in various fields, including healthcare, development of new medicines, and (bio)medicine in general, has been growing ever since (2). ML can be based on supervised and unsupervised learning algorithms.…”

Section: Overview Of the Available Ai Methodologiesmentioning

confidence: 99%

Review of machine learning algorithms' application in pharmaceutical technology

Đuriš¹,

Kurćubić²,

Ibrić³

2021

Arhiv za farmaciju

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Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.

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“…Machine learning for health care is a key discipline aimed to translate large health data sets into operative knowledge in different medical fields [ 3 - 7 ]. The methods of this artificial intelligence paradigm can be classified as supervised or unsupervised, based on the underlying strategy used [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study

Benito‐León¹,

Castillo²,

Estirado³

et al. 2021

J Med Internet Res

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Background Early detection and intervention are the key factors for improving outcomes in patients with COVID-19. Objective The objective of this observational longitudinal study was to identify nonoverlapping severity subgroups (ie, clusters) among patients with COVID-19, based exclusively on clinical data and standard laboratory tests obtained during patient assessment in the emergency department. Methods We applied unsupervised machine learning to a data set of 853 patients with COVID-19 from the HM group of hospitals (HM Hospitales) in Madrid, Spain. Age and sex were not considered while building the clusters, as these variables could introduce biases in machine learning algorithms and raise ethical implications or enable discrimination in triage protocols. Results From 850 clinical and laboratory variables, four tests—the serum levels of aspartate transaminase (AST), lactate dehydrogenase (LDH), C-reactive protein (CRP), and the number of neutrophils—were enough to segregate the entire patient pool into three separate clusters. Further, the percentage of monocytes and lymphocytes and the levels of alanine transaminase (ALT) distinguished cluster 3 patients from the other two clusters. The highest proportion of deceased patients; the highest levels of AST, ALT, LDH, and CRP; the highest number of neutrophils; and the lowest percentages of monocytes and lymphocytes characterized cluster 1. Cluster 2 included a lower proportion of deceased patients and intermediate levels of the previous laboratory tests. The lowest proportion of deceased patients; the lowest levels of AST, ALT, LDH, and CRP; the lowest number of neutrophils; and the highest percentages of monocytes and lymphocytes characterized cluster 3. Conclusions A few standard laboratory tests, deemed available in all emergency departments, have shown good discriminative power for the characterization of severity subgroups among patients with COVID-19.

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The rise of artificial intelligence in healthcare applications

Cited by 839 publications

References 52 publications

Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection

Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection

Review of machine learning algorithms' application in pharmaceutical technology

Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study

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