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General General

Artificial Intelligence in Pharmacovigilance: Scoping Points to Consider.

In Clinical therapeutics

Artificial intelligence (AI), a highly interdisciplinary science, is an increasing presence in pharmacovigilance (PV). A better understanding of the scope of artificial intelligence in pharmacovigilance (AIPV) may be advantageous to more sharply defining, for example, which terms, methods, tasks, and data sets are suitably subsumed under the application of AIPV. Accordingly, this article explores relevant points to consider regarding defining the scope of AIPV and offers a potential working definition of the scope of AIPV.

Hauben Manfred, Hartford Craig G

2021-Jan-18

AI, artificial intelligence, machine learning, pharmacovigilance, technology

Cardiology Cardiology

Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review.

In Journal of the American College of Cardiology ; h5-index 167.0

The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clinical experts in some of these tasks. Machine learning has the potential to benefit patients and cardiologists, but only if clinicians take an active role in bringing these new algorithms into practice. The aim of this review is to introduce clinicians who are not data science experts to key concepts in machine learning that will allow them to better understand the field and evaluate new literature and developments. The current published data in machine learning for cardiovascular disease is then summarized, using both a bibliometric survey, with code publicly available to enable similar analysis for any research topic of interest, and select case studies. Finally, several ways that clinicians can and must be involved in this emerging field are presented.

Quer Giorgio, Arnaout Ramy, Henne Michael, Arnaout Rima

2021-Jan-26

artificial intelligence, bibliometric analysis, cardiology, deep learning, literature search, machine learning

General General

COVID-19 and healthcare system in China: challenges and progression for a sustainable future.

In Globalization and health

With the ongoing COVID-19 outbreak, healthcare systems across the world have been pushed to the brink. The approach of traditional healthcare systems to disaster preparedness and prevention has demonstrated intrinsic problems, such as failure to detect early the spread of the virus, public hospitals being overwhelmed, a dire shortage of personal protective equipment, and exhaustion of healthcare workers. Consequently, this situation resulted in manpower and resource costs, leading to the widespread and exponential rise of infected cases at the early stage of the epidemic. To limit the spread of infection, the Chinese government adopted innovative, specialized, and advanced systems, including empowered Fangcang and Internet hospitals, as well as high technologies such as 5G, big data analysis, cloud computing, and artificial intelligence. The efficient use of these new forces helped China win its fight against the virus. As the rampant spread of the virus continues outside China, these new forces need to be integrated into the global healthcare system to combat the disease. Global healthcare system integrated with new forces is essential not only for COVID-19 but also for unknown infections in the future.

Sun Shuangyi, Xie Zhen, Yu Keting, Jiang Bingqian, Zheng Siwei, Pan Xiaoting

2021-Jan-21

COVID-19, Epidemic: healthcare system, High-tech, Internet hospitals: post-epidemic era

General General

Erratum: Iyer, A., et al. Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells. J. Clin. Med. 2020, 9, 1206.

In Journal of clinical medicine

In the published manuscript [...].

Iyer Arvind, Gupta Krishan, Sharma Shreya, Hari Kishore, Lee Yi Fang, Ramalingam Neevan, Yap Yoon Sim, West Jay, Bhagat Ali Asgar, Subramani Balaram Vishnu, Sabuwala Burhanuddin, Zea Tan Tuan, Thiery Jean Paul, Jolly Mohit Kumar, Ramalingam Naveen, Sengupta Debarka

2021-Jan-19

General General

Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses.

In Polymers

Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.

Hagiwara Naruki, Sekizaki Shoma, Kuwahara Yuji, Asai Tetsuya, Akai-Kasaya Megumi

2021-Jan-19

PEDOT:PSS, artificial synapse, conductive polymer wire, resistance change memory

General General

An Entropy Metric for Regular Grammar Classification and Learning with Recurrent Neural Networks.

In Entropy (Basel, Switzerland)

Recently, there has been a resurgence of formal language theory in deep learning research. However, most research focused on the more practical problems of attempting to represent symbolic knowledge by machine learning. In contrast, there has been limited research on exploring the fundamental connection between them. To obtain a better understanding of the internal structures of regular grammars and their corresponding complexity, we focus on categorizing regular grammars by using both theoretical analysis and empirical evidence. Specifically, motivated by the concentric ring representation, we relaxed the original order information and introduced an entropy metric for describing the complexity of different regular grammars. Based on the entropy metric, we categorized regular grammars into three disjoint subclasses: the polynomial, exponential and proportional classes. In addition, several classification theorems are provided for different representations of regular grammars. Our analysis was validated by examining the process of learning grammars with multiple recurrent neural networks. Our results show that as expected more complex grammars are generally more difficult to learn.

Zhang Kaixuan, Wang Qinglong, Giles C Lee

2021-Jan-19

complexity analysis, entropy, recurrent neural network, regular grammar classification