scholarly journals Predicting Mortality with Applied Machine Learning: Can We Get There?

2019 ◽  
Vol 8 (1) ◽  
pp. 115-119
Author(s):  
Emily S. Patterson ◽  
C.J. Hansen ◽  
Theodore T. Allen ◽  
Qiwei Yang ◽  
Susan D. Moffatt-Bruce
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Real Time ◽  
Drug Administration ◽  
Patient Population ◽  
Food And Drug Administration ◽  
Hospital Environment ◽  
Clinical Settings ◽  
Applied Machine Learning

There is growing interest in using AI-based algorithms to support clinician decision-making. An important consideration is how transparent complex algorithms can be for predictions, particularly with respect to imminent mortality in a hospital environment. Understanding the basis of predictions, the process used to generate models and recommendations, how to generalize models based on one patient population to another, and the role of oversight organizations such as the Food and Drug Administration are important topics. In this paper, we debate opposing positions regarding whether these algorithms are ‘ready yet’ for use today in clinical settings for physicians, patients and caregivers. We report voting results from participating audience members in attendance at the conference debate for each of these positions obtained real-time from a smartphone-based platform.

scholarly journals Poly-ADP-ribose polymerase inhibitor use in ovarian cancer: expanding indications and novel combination strategies

2019 ◽  
Vol 29 (5) ◽  
pp. 956-968 ◽  
Author(s):  
Emily Hinchcliff ◽  
Shannon Neville Westin ◽  
Graziela Dal Molin ◽  
Christopher J LaFargue ◽  
Robert L. Coleman
Keyword(s):  
Ovarian Cancer ◽  
Drug Administration ◽  
Food And Drug Administration ◽  
Parp Inhibitors ◽  
Parp Inhibition ◽  
Combination Therapies ◽  
Combination Strategies ◽  
Polymerase Inhibitor

The use of poly(ADP-ribose) polymerase (PARP) inhibition is transforming care for the treatment of ovarian cancer, with three different PARP inhibitors (PARPi) gaining US Food and Drug Administration approval since 2014. Given the rapidly expanding use of PARPi, this review aims to summarize the key evidence for their use and therapeutic indications. Furthermore, we provide an overview of the development of PARPi resistance and the emerging role of PARPi combination therapies, including those with anti-angiogenic and immunotherapeutic agents.

Modelling near-real-time order arrival demand in e-commerce context: a machine learning predictive methodology

2020 ◽  
Vol 120 (6) ◽  
pp. 1149-1174 ◽  
Author(s):  
K.H. Leung ◽  
Daniel Y. Mo ◽  
G.T.S. Ho ◽  
C.H. Wu ◽  
G.Q. Huang
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Real Time ◽  
Service Providers ◽  
Series Data ◽  
Illustrative Case ◽  
Implementation Framework ◽  
Inference System ◽  
Content Type

PurposeAccurate prediction of order demand across omni-channel supply chains improves the management's decision-making ability at strategic, tactical and operational levels. The paper aims to develop a predictive methodology for forecasting near-real-time e-commerce order arrivals in distribution centres, allowing third-party logistics service providers to manage the hour-to-hour fast-changing arrival rates of e-commerce orders better.Design/methodology/approachThe paper proposes a novel machine learning predictive methodology through the integration of the time series data characteristics into the development of an adaptive neuro-fuzzy inference system. A four-stage implementation framework is developed for enabling practitioners to apply the proposed model.FindingsA structured model evaluation framework is constructed for cross-validation of model performance. With the aid of an illustrative case study, forecasting evaluation reveals a high level of accuracy of the proposed machine learning approach in forecasting the arrivals of real e-commerce orders in three different retailers at three-hour intervals.Research limitations/implicationsResults from the case study suggest that real-time prediction of individual retailer's e-order arrival is crucial in order to maximize the value of e-order arrival prediction for daily operational decision-making.Originality/valueEarlier researchers examined supply chain demand, forecasting problem in a broader scope, particularly in dealing with the bullwhip effect. Prediction of real-time, hourly based order arrivals has been lacking. The paper fills this research gap by presenting a novel data-driven predictive methodology.

The Statistics of Nutrition Sampling and Analysis

1974 ◽  
Vol 57 (5) ◽  
pp. 1181-1189
Author(s):  
Howard R Roberts
Keyword(s):  
Drug Administration ◽  
Natural Variation ◽  
Food And Drug Administration ◽  
Nutrient Content ◽  
Nutrition Labeling ◽  
Critical Importance ◽  
Analytical Methodology ◽  
Labeling Regulations ◽  
Analytical Variation

Abstract It is widely recognized that there is natural variation in the nutrient content of food products. When one attempts to measure nutrient levels, another source of variation becomes apparent—that inherent in the measurement process itself. Analytical variation is, of course, apparent when different methods are used but can also occur with the same method because of differences among laboratories and/or analysts. Both in its own right and more especially with regard to evaluating compliance with labeling regulations, method variability is of critical importance. In order to appreciate the role of analytical methodology, the nutrition labeling regulations and the procedures by which the Food and Drug Administration will assess compliance must first be thoroughly understood. This paper is directed toward that understanding.

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