A primer on artificial intelligence for the paediatric cardiologist

2020 ◽  
Vol 30 (7) ◽  
pp. 934-945
Author(s):  
Addison Gearhart ◽  
Sharib Gaffar ◽  
Anthony C. Chang
Keyword(s):  
Artificial Intelligence ◽  
Mortality Prediction ◽  
Genetic Profile ◽  
Paediatric Cardiology ◽  
Intelligence System ◽  
History Of ◽  
Complex Decision Making ◽  
Barriers To Implementation ◽  
Complex Decision ◽  
Decision Making Model

AbstractThe combination of pediatric cardiology being both a perceptual and a cognitive subspecialty demands a complex decision-making model which makes artificial intelligence a particularly attractive technology with great potential. The prototypical artificial intelligence system would autonomously impute patient data into a collaborative database that stores, syncs, interprets and ultimately classifies the patient’s profile to specific disease phenotypes to compare against a large aggregate of shared peer health data and outcomes, the current medical body of literature and ongoing trials to offer morbidity and mortality prediction, drug therapy options targeted to each patient’s genetic profile, tailored surgical plans and recommendations for timing of sequential imaging. The focus of this review paper is to offer a primer on artificial intelligence and paediatric cardiology by briefly discussing the history of artificial intelligence in medicine, modern and future applications in adult and paediatric cardiology across selected concentrations, and current barriers to implementation of these technologies.

Blood, Treasure, and Time

The Last Card ◽  
2019 ◽  
pp. 277-295
Author(s):  
Richard K. Betts
Keyword(s):  
Decision Making ◽  
Iraq War ◽  
Decision Making Process ◽  
War In Iraq ◽  
History Of ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Significant Question

This chapter places the surge decision in the context of the broader history of the Iraq War and offers a modestly positive appraisal. Indeed, the surge is only important in how it affected the overall project of the American war in Iraq. The significant question is whether the history of the surge offers lessons beyond itself, or at least whether the surge was decisive for achieving US objectives. The chapter then examines various dilemmas and challenges that the war occasioned—of relating strategy to both operations and politics; of promoting democratization in Iraq while also seeking some control over Iraqi decision making; and of seeking to exert presidential command over a complex decision-making process. It argues that the surge decision reflected a “delicate and skillful exercise in leadership” given civil-military tensions, but questions how well the surge answered the broader strategic questions surrounding American involvement.

2. The History of the Practice of Strategy from Antiquity to Napoleon

2018 ◽  
pp. 19-35
Author(s):  
Beatrice Heuser
Keyword(s):  
Middle Ages ◽  
Napoleonic Wars ◽  
Early Modern Europe ◽  
Louis Xiv ◽  
Philip Ii ◽  
War Of Independence ◽  
The First World War ◽  
History Of ◽  
Complex Decision Making ◽  
Complex Decision

This chapter discusses the history of the practice of strategy from Antiquity to the First World War. After introducing the reader to the various definitions of strategy, the chapter considers sources of Antiquity about warfare, from ancient Greece and Rome to the time of Rome's Constantinopolitan (Byzantine) successors. Justinian I and Heraclius. It then examines episodes of European history since antiquity for which historians claim to have found evidence of the practice of strategy. In particular, it looks at the West European Middle Ages, which saw the rise of complex decision-making involving multiple tools — strategy. It also analyses the transformation of warfare and of strategy in early modern Europe, covering case studies that span the wars involving Philip II of Spain, Louis XIV of France, and Frederick II of Prussia, as well as the American War of Independence and the Napoleonic Wars.

scholarly journals AN INTEGRATED DECISION-MAKING MODEL FOR EFFICIENCY ANALYSIS OF THE FORKLIFTS IN WAREHOUSING SYSTEMS

2021 ◽  
Vol 19 (3) ◽  
pp. 537
Author(s):  
Eldina Mahmutagić ◽  
Željko Stević ◽  
Zdravko Nunić ◽  
Prasenjit Chatterjee ◽  
Ilija Tanackov
Keyword(s):  
Decision Making ◽  
Daily Basis ◽  
Data Envelopment ◽  
Business Applications ◽  
Decision Making Problem ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Warehousing Systems ◽  
Using Data ◽  
Decision Making Model

In the logistics world, special attention should be given to warehousing systems, cost rationalization, and improvement of all the factors that affect efficiency and contribute to smooth functioning of logistics subsystems. In real time industrial practice, the issue of evaluating and selecting the most appropriate forklift involves a complex decision-making problem that should be formulated through an efficient analytical model. The forklifts efficiency plays a very important role in the company. The forklifts are being used on a daily basis and no logistical processes could be done without them. Therefore, it has been decided to determine their efficiency, which will contribute to the optimization of the process in this logistics subsystem. This study puts forward an integrated forklift selection model using Data Envelopment Analysis (DEA), Full Consistency Method (FUCOM) and Measurement Alternatives and Ranking According to the Compromise Solution (MARCOS) methods. Five input parameters (regular servicing costs, fuel costs, exceptional servicing costs, total number of all minor accidents and damage caused by forklifts) and one output parameter (number of operating hours) were first identified to assess efficiency of eight forklifts in a warehousing system of the Natron-Hayat company using the DEA model. This step allows sorting of efficient forklifts which are subsequently evaluated and ranked using FUCOM and MARCOS methods. A sensitivity analysis is also performed in order to check reliability and accuracy of the results. The findings of this research clearly show that the proposed decision-making model can significantly contribute to all spheres of business applications.

scholarly journals Advantages of Applying Artificial Intelligent System to Medical Neurology (Preprint)

2020 ◽  
Author(s):  
Zhenqiang Fu ◽  
Jingtao Wang ◽  
Jingtao Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Factors ◽  
Cox Model ◽  
Stroke Recurrence ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Stroke Risk Factors ◽  
Svm Model ◽  
History Of

BACKGROUND Cerebral stroke is a common cardiovascular disease in neurology. The current imaging detection method and psychological nerve scoring method are characterized by low sensitivity and high subjectivity. Machine learning in artificial intelligence system has high accuracy in the diagnosis and treatment of diseases and is applied in the field of neurology. At present, there are few researches on machine learning and stroke diagnosis. OBJECTIVE The study aimed to explore the predictive value of artificial intelligence system in stroke disease, and to provide reference for the application of artificial intelligence system in the field of medical neurology. METHODS A retrospective analysis was performed on 763 patients with stroke confirmed by the neurology department of XXX Hospital from January 2014 to December 2019 (183 of whom had recurrent stroke). Basic data and data of all subjects were collected. Univariate and multivariate Cox and Logistic regression model algorithm were respectively used to predict stroke risk factors. Receiver Operating Characteristic (ROC) curve was used to detect the accuracy and sensitivity of Cox and Logistic models. According to the Support Vector Machines (SVM) algorithm in machine learning, data were filled and preprocessed by means of mean value method, median method, linear regression method and normalized Expected Maximum (EM). The influencing factors were selected by conservative mean method, and the risk factors for stroke recurrence were predicted by SVM model. Area under the Curve (AUC) of ROC curve was used to analyze and compare the prediction results of the three models. RESULTS Multivariate Cox model and Logistic model analysis showed that family history of stroke, systolic blood pressure, history of heart disease, total cholesterol, disease progression, dietary habits and history of hypertension were the main risk factors for stroke recurrence. The sensitivity and specificity of Cox model were 0.754 and 0.805 respectively. The AUC of Logistic model was 0.889. In the SVM model data filling algorithm, the median AUC was 0.874, which was significantly higher than other algorithms (P<0.05). The top 10 risk factors of stroke patients predicted by SVM model included both clinically established risk factors and some potential risk factors. The prediction results of stroke risk factors showed 0.873SVM>0.861Logistic>0.853Cox. CONCLUSIONS Artificial intelligence system has obvious advantages in the prediction of stroke disease, which provides reference for the application of artificial intelligence system in the field of medical neurology. CLINICALTRIAL

Cancer patients’ views and understanding of genome sequencing: a qualitative study

2020 ◽  
Vol 57 (10) ◽  
pp. 671-676
Author(s):  
Nicci Bartley ◽  
Megan Best ◽  
Chris Jacobs ◽  
Ilona Juraskova ◽  
Ainsley J Newson ◽  
...  
Keyword(s):  
Decision Making ◽  
Qualitative Study ◽  
Genome Sequencing ◽  
Decisional Conflict ◽  
Deliberation Process ◽  
History Of ◽  
Decision Making Processes ◽  
Complex Decision Making ◽  
Baseline Knowledge ◽  
Complex Decision

BackgroundLittle is known about knowledge of, and attitudes towards, genome sequencing (GS) among individuals with a personal history of cancer who decide to undergo GS. This qualitative study aimed to investigate baseline knowledge and attitudes among individuals previously diagnosed with a cancer of likely genetic origin who have consented to GS.MethodsSemistructured interviews were conducted with purposively selected participants (n=20) from the longitudinal Psychosocial Issues in Genomic Oncology study, within a month of consenting to GS and prior to receiving any results. Participants were adults with a cancer of likely genetic aetiology who are undertaking GS as part of a larger genetic study.ResultsAnalysis identified three main themes: limited understanding of genomics; multifactorial motivation; and complex decision making. While motivations such as obtaining health information about self and family appear to be the main drivers for undertaking GS, these motivations are sometimes based on limited knowledge of the accuracy and utility of GS, creating unrealistic expectations. This in turn can prolong the deliberation process and lead to ongoing decisional conflict.ConclusionUnderstanding the degree and nature of patient understanding of GS, as well as their attitudes and decision-making processes, will enable healthcare professionals to better manage patient expectations and appropriately engage and support patients to make an informed decision when pursuing GS.

A Hybrid AI-Based Conceptual Decision-Making Model for Sustainable Maintenance Strategy Selection

2019 ◽  
pp. 63-93
Author(s):  
Soumava Boral ◽  
Sanjay K. Chaturvedi ◽  
V. N. A. Naikan ◽  
Ian M. Howard
Keyword(s):  
Decision Making ◽  
Case Based Reasoning ◽  
Strategy Selection ◽  
Maintenance Strategy ◽  
Critical Systems ◽  
Optimal Maintenance ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Decision Making Model ◽  
Hybrid Artificial Intelligence

Selection of optimal maintenance strategy for critical systems/machinery is considered as a complex decision-making task that takes into account several available maintenance alternatives that are evaluated in terms of a set of different conflicting qualitative and quantitative factors. In the last few decades, progress has been made in different sustainable-based decision-making problems, where environmental, social, and economic factors played a pivotal role to arrive at the best decision. In this chapter, a hybrid artificial intelligence (AI)-based conceptual decision-making model is described by taking advantages of both expert system and case-based reasoning methodology to solve sustainable maintenance strategy selection problems. Adding to this, a flowchart of the model is suitably described by hypothetical examples of a sustainable maintenance strategy selection program.

Wildland fire risk research in Canada

2020 ◽  
Vol 28 (2) ◽  
pp. 164-186 ◽  
Author(s):  
Lynn M. Johnston ◽  
Xianli Wang ◽  
Sandy Erni ◽  
Stephen W. Taylor ◽  
Colin B. McFayden ◽  
...  
Keyword(s):  
Wildland Fire ◽  
Fire Risk ◽  
Wide Range ◽  
Risk Framework ◽  
History Of ◽  
Complex Decision Making ◽  
Current Context ◽  
Complex Decision ◽  
Future Work ◽  
Wildland Fire Risk

Despite increasing concern about wildland fire risk in Canada, there is little synthesis of knowledge that could contribute to the development of a comprehensive risk framework for a wide range of values, which is an essential need for the country. With dramatic variability in costs and losses from this natural hazard, there must be more support for complex decision-making under the uncertainty of how to assess and manage risk to coexist with wildland fire. A long history of Canadian wildland fire research offers solid foundational knowledge related to risk, but the key knowledge gaps must be addressed to fully consider risk in a comprehensive manner. We provide a review of the current context in which risk is variably defined, and recommend use of the general paradigm where risk is the product of both the likelihood and the potential impacts of wildland fire. We then synthesize research related to wildland fire risk from the Canadian scientific literature. With this review, we aim to provide a better understanding of research challenges, limitations, and opportunities for future work on fire risk within the country.

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