Mo1724 ARTIFICIAL INTELLIGENCE IS A POWERFUL TOOL TO DETERMINE THE NEED FOR ADDTIONAL SURGERY AFTER ENDOSCOPIC RESECTION OF T1 COLORECTAL CANCER −ANALYSIS BASED ON A BIG DATA FOR MACHINE LEARNING−

Abstract Background and study aims Decisions concerning additional surgery after endoscopic resection of T1 colorectal cancer (CRC) are difficult because preoperative prediction of lymph node metastasis (LNM) is problematic. We investigated whether artificial intelligence can predict LNM presence, thus minimizing the need for additional surgery. Patients and methods Data on 690 consecutive patients with T1 CRCs that were surgically resected in 2001 – 2016 were retrospectively analyzed. We divided patients into two groups according to date: data from 590 patients were used for machine learning for the artificial intelligence model, and the remaining 100 patients were included for model validation. The artificial intelligence model analyzed 45 clinicopathological factors and then predicted positivity or negativity for LNM. Operative specimens were used as the gold standard for the presence of LNM. The artificial intelligence model was validated by calculating the sensitivity, specificity, and accuracy for predicting LNM, and comparing these data with those of the American, European, and Japanese guidelines. Results Sensitivity was 100 % (95 % confidence interval [CI] 72 % to 100 %) in all models. Specificity of the artificial intelligence model and the American, European, and Japanese guidelines was 66 % (95 %CI 56 % to 76 %), 44 % (95 %CI 34 % to 55 %), 0 % (95 %CI 0 % to 3 %), and 0 % (95 %CI 0 % to 3 %), respectively; and accuracy was 69 % (95 %CI 59 % to 78 %), 49 % (95 %CI 39 % to 59 %), 9 % (95 %CI 4 % to 16 %), and 9 % (95 %CI 4 % – 16 %), respectively. The rates of unnecessary additional surgery attributable to misdiagnosing LNM-negative patients as having LNM were: 77 % (95 %CI 62 % to 89 %) for the artificial intelligence model, and 85 % (95 %CI 73 % to 93 %; P < 0.001), 91 % (95 %CI 84 % to 96 %; P < 0.001), and 91 % (95 %CI 84 % to 96 %; P < 0.001) for the American, European, and Japanese guidelines, respectively. Conclusions Compared with current guidelines, artificial intelligence significantly reduced unnecessary additional surgery after endoscopic resection of T1 CRC without missing LNM positivity.

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Blockchain Disrupting Fintech and the Banking System

Proceedings ◽

10.3390/proceedings2021074024 ◽

2021 ◽

Vol 74 (1) ◽

pp. 24

Author(s):

Eduard Alexandru Stoica ◽

Daria Maria Sitea

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Financial Institutions ◽

New World ◽

Great Part ◽

Banking System ◽

Daily Activities ◽

Blockchain Technology ◽

Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

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Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18157890 ◽

2021 ◽

Vol 18 (15) ◽

pp. 7890

Author(s):

Bruce Mellado ◽

Jianhong Wu ◽

Jude Dzevela Kong ◽

Nicola Luigi Bragazzi ◽

Ali Asgary ◽

...

Keyword(s):

Public Health ◽

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Developed Countries ◽

Machine Learning Techniques ◽

Multiple Data ◽

Learning Techniques ◽

Unequal Access ◽

Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

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Innovative Approach to Build a Scalable Hybrid Model for Prescriptive Maintenance

10.2118/208031-ms ◽

2021 ◽

Author(s):

Richard Büssow ◽

Bruno Hain ◽

Ismael Al Nuaimi

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Hybrid Model ◽

Domain Knowledge ◽

Spare Parts ◽

Good Prediction ◽

First Principle ◽

Plant Data ◽

Rotating Equipment

Abstract Objective and Scope Analysis of operational plant data needs experts in order to interpret detected anomalies which are defined as unusual operation points. The next step on the digital transformation journey is to provide actionable insights into the data. Prescriptive Maintenance defines in advance which kind of detailed maintenance and spare parts will be required. This paper details requirements to improve these predictions for rotating equipment and show potential to integrate the outcome into an operational workflow. Methods, Procedures, Process First principle or physics-based modelling provides additional insights into the data, since the results are directly interpretable. However, such approaches are typically assumed to be expensive to build and not scalable. Identification of and focus on the relevant equipment to be modeled in a hybrid model using a combination of first principle physics and machine learning is a successful strategy. The model is trained using a machine learning approach with historic or current real plant data, to predict conditions which have not occurred before. The better the Artificial Intelligence is trained, the better the prediction will be. Results, Observations, Conclusions The general aim when operating a plant is the actual usage of operational data for process and maintenance optimization by advanced analytics. Typically a data-driven central oversight function supports operations and maintenance staff. A major lesson-learned is that the results of a rather simple statistical approach to detect anomalies fall behind the expectations and are too labor intensive. It is a widely spread misinterpretation that being able to deal with big data is sufficient to come up with good prediction quality for Prescriptive Maintenance. What big data companies are normally missing is domain knowledge, especially on plant critical rotating equipment. Without having domain knowledge the relevant input into the model will have shortcomings and hence the same will apply to its predictions. This paper gives an example of a refinery where the described hybrid model has been used. Novel and Additive Information First principle models are typically expensive to build and not scalable. This hybrid model approach, combining first principle physics based models with artificial intelligence and integration into an operational workflow shows a new way forward.

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Prior endoscopic resection does not affect the outcome of secondary surgery for T1 colorectal cancer, a systematic review and meta-analysis

International Journal of Colorectal Disease ◽

10.1007/s00384-021-04049-8 ◽

2021 ◽

Author(s):

Jianhua Nian ◽

Liang Tao ◽

Weiguang Zhou

Keyword(s):

Colorectal Cancer ◽

Systematic Review ◽

Endoscopic Resection ◽

Meta Analysis ◽

Secondary Surgery ◽

T1 Colorectal Cancer

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Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

Journal of Business Strategy ◽

10.1108/jbs-05-2021-0090 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Pooya Tabesh

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Making ◽

Big Data ◽

Digital Transformation ◽

Decision Processes ◽

Decision Makers ◽

Content Type ◽

Human Decision ◽

Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

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Data Science: Big Data, Machine Learning, and Artificial Intelligence

Journal of the American College of Radiology ◽

10.1016/j.jacr.2018.01.029 ◽

2018 ◽

Vol 15 (3) ◽

pp. 497-498 ◽

Cited By ~ 16

Author(s):

Ruth C. Carlos ◽

Charles E. Kahn ◽

Safwan Halabi

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Data Science

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Machinic Empathy and Mental Health: The Relational Ethics of Machine Empathy and Artificial Intelligence in Her

ReFocus: The Films of Spike Jonze ◽

10.3366/edinburgh/9781474447621.003.0009 ◽

2019 ◽

pp. 158-175

Author(s):

Frances Shaw

Keyword(s):

Mental Health ◽

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Health Research ◽

Mental Health Treatment ◽

Mental Health Research ◽

Relational Ethics ◽

Health Treatment

This paper situates a discussion of Her within contemporary developments in empathic machine learning for mental health treatment and therapy. Her simultaneously hooks into and critiques a particular imaginary about what artificial intelligence can do when combined with big data. Shaw threads the representation of empathy and artificial intelligence in the film into discussions of contemporary mental health research, in particular possibilities for the automation of treatment, whether through machine learning or guided interventions. Her provides some useful ways to think through utopian, dystopian, and ambivalent readings of such applications of technology in a broader sense, raising questions about sincerity and loss of human connectivity, relational ethics and automated empathy.

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