A Decision-Making tool based on historical data for service time prediction in outpatient scheduling

AbstractDefensive repositioning strategies (shifts) have become more prevalent in Major League Baseball in recent years. In 2018, batters faced some form of the shift in 34% of their plate appearances (Sawchik, Travis. 2019. “Don’t Worry, MLB–Hitters Are Killing The Shift On Their Own.” FiveThirtyEight, January 17, 2019. Also available at fivethirtyeight.com/features/dont-worry-mlb-hitters-are-killing-the-shift-on-their-own/). Most teams use a shift that overloads one side of the infield and adjusts the positioning of the outfield. In this work we describe a mathematical approach to the positioning of players over the entire field of play without the limitations of traditional positions or current methods of shifting. The model uses historical data for individual batters, and it leaves open the possibility of fewer than four infielders. The model also incorporates risk penalties for positioning players too far from areas of the field in which extra-base hits are more likely. This work is meant to serve as a decision-making tool for coaches and managers to best use their defensive assets. Our simulations show that an optimal positioning with three infielders lowered predicted batting average on balls in play (BABIP) by 5.9% for right-handers and by 10.3% for left-handers on average when compared to a standard four-infielder placement of players.

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An Evaluation of Medicare's Hospital Compare as a Decision-Making Tool for Patients and Hospitals

e-Service Journal ◽

10.2979/eservicej.12.1.02 ◽

2020 ◽

Vol 12 (1) ◽

pp. 42

Author(s):

Sen

Keyword(s):

Decision Making ◽

Hospital Compare ◽

Decision Making Tool

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Decision aids for alternative techniques in urban storm management

Water Science & Technology ◽

10.2166/wst.1995.0011 ◽

1995 ◽

Vol 32 (1) ◽

pp. 41-48 ◽

Cited By ~ 3

Author(s):

Y. Azzout ◽

S. Barraud ◽

F. N. Cres ◽

E. Alfakih

Keyword(s):

Decision Making ◽

Decision Aids ◽

Decision Making Process ◽

Urban Stormwater ◽

Elimination Phase ◽

Poor Understanding ◽

Preliminary Study ◽

Alternative Techniques ◽

Urban Storm ◽

Decision Making Tool

The choice of alternative techniques in urban stormwater drainage (infiltration and detention systems), in the course of a project, is most often made with a poor understanding of site constraints, and the possibilities afforded by these techniques. This gives rise to extra costs and also subsequent malfunctioning. To arrive at feasible choices, we have formalised the decision-making process, taking account of the multiple criteria and the large number of partners involved. At present, we are developing a decision-making tool for alternative techniques in urban stormwater management at the preliminary study stage. The first phase makes it possible to eliminate solutions which are unworkable (elimination phase). It is aimed at the designer. Work on the next phase (the decision-making phase), which is more complex, is in progress. It will make it possible, in collaboration with all the partners involved, to choose a stormwater drainage strategy which will best suit the objectives and the wishes of the partners. It uses multi-criteria methods.

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How To Identify And Prioritize Watershed Pollution Sources? ------ Case Studies of Transform Water Quality Data into Decision Making Tool

Proceedings of the Water Environment Federation ◽

10.2175/193864716819714267 ◽

2016 ◽

Vol 2016 (8) ◽

pp. 3265-3273

Author(s):

Ting Lu ◽

James Schlaman ◽

Leisha Pica ◽

MaryLynn Lodor ◽

Gérald Checco ◽

...

Keyword(s):

Water Quality ◽

Decision Making ◽

Case Studies ◽

Pollution Sources ◽

Quality Data ◽

Water Quality Data ◽

Decision Making Tool

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Applying Deep Learning to Audit Procedures: An Illustrative Framework

Accounting Horizons ◽

10.2308/acch-52455 ◽

2019 ◽

Vol 33 (3) ◽

pp. 89-109 ◽

Cited By ~ 9

Author(s):

Ting (Sophia) Sun

Keyword(s):

Decision Making ◽

Deep Learning ◽

Data Warehouse ◽

Visual Recognition ◽

Prediction Models ◽

Historical Data ◽

Structured Data ◽

Text Understanding ◽

Audit Data ◽

Learning Functions

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

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