Informs 2015

INFORMS Philadelphia – 2015

215

3 - Engineering Technician Location Analytics

Dwayne Otis, Air Products and Chemicals, 7201 Hamilton

Boulevard, Allentown, PA, 18195, United States of America,

otisdk@airproducts.com

In this talk we present a model whose objective is to determine, within a given

physical region, the near-optimal required number of technicians and their

assigned home base locations in order to satisfy defined customer service levels.

Ideally, the workload for each technician would be balanced and the total cost to

meet the desired service level is minimized. Furthermore, we examine additional

areas where this general technique could be applied.

MC31

31-Room 408, Marriott

Data Mining in Healthcare

Sponsor: Data Mining

Sponsored Session

Chair: Ramin Moghaddass, Assistant Professor, University of Miami,

1251 Memorial Dr, Coral Gables, FL, 33146, United States of America,

ramin@miami.edu

1 - Influential Neighbor Analysis with a Hierarchical Bayesian Model

Ramin Moghaddass, Assistant Professor, University of Miami,

1251 Memorial Dr, Coral Gables, FL, 33146,

United States of America,

ramin@miami.edu

, Cynthia Rudin

For a doctor with enough experience, almost every patient would have

similarities to key cases seen in the past and each new patient could be viewed as

a mixture of important pieces of these key past cases. Because doctors often tend

to reason this way, an efficient computationally aided diagnostic tool might be

helpful in locating key past cases of interest that could assist with diagnosis. We

develop a model to mimic the type of logical thinking that physicians use when

considering past cases.

2 - Falling Rule Lists

Fulton Wang, MIT, 5 Cambridge Center #792, Cambridge, MA,

02142, United States of America,

fultonwang@gmail.com,

Cynthia Rudin

Falling rule lists are classification models consisting of an ordered list of if-then

rules, where (i) the order of rules determines which example should be classified

by each rule, and (ii) the estimated probability of success decreases monotonically

down the list. These kinds of rule lists are inspired by healthcare applications

where patients would be stratified into risk sets and the highest at-risk patients

should be considered first.

3 - Machine Learning for Clinical Decision Support for Heart

Failure(HF) Readmission

Wei Jiang, PhD Student, Johns Hopkins University,

6606 Copper Ridge Drive Apt. 201, Baltimore, MD, 21209,

United States of America,

wjiang1990@gmail.com,

Sean Barnes,

Matthew Toerper, Scott Levin, Eric Hamrock, Sauleh Siddiqui,

Stephanie Cabral

Predicting risk of HF readmission have gained increasing attention.Previous

studies mainly used administrative data. We will focus on using clinical data from

EMR for predicting HF readmission by creating structured data from unstructured

clinical data and combining it with administrative data. Then we use classification

models such as random forest and support vector machine for predicting purpose.

In the end, we will demonstrate the value of clinical data in predicting HF

readmission.

4 - Or’s of and’s for Interpretable Machine Learning:

Prediction and Explanation

Tong Wang, Graduate Student, MIT, 70 Pacific Street, apt 242A,

Cambridge, MA, 02139, United States of America,

tongwang@mit.edu,

Cynthia Rudin

We present a form of interpretable models for data prediction and explanation.

The model is comprised of a small number of disjunctions of conjunctions (or’s of

and’s). We apply OA models to solve two machine learning tasks, classification

and causal effect analysis. We formed two integer linear programs to construct the

optimal OA models that can achieve good performance and interpretability, by

incorporating regularizations on the sparseness. We show that regularizations

reduce computation.

MC32

32-Room 409, Marriott

Statistical Innovations in Computational Biology

and Genomics

Cluster: Big Data Analytics in Computational Biology/Medicine

Invited Session

Chair: Mingyao Li, Statistical Innovations in Computational Biology

and Genomics, University of Pennsylvania, 213 Blockley Hall, 423

Guardian Drive, Philadelphia, PA, 19104, United States of America,

mingyao@mail.med.upenn.edu

1 - Prediction using Multinomial Inverse Regression in

Microbiome Studies

Hongzhe Li, Professor of Biostatistics, University of Pennsylvania,

215 Blockley Hall, Philadelphia, PA, United States of America,

hongzhe@mail.med.upenn.edu

Next-generation sequencing technologies allow 16S rRNA gene surveys or

metagenome shotgun sequencing in order to characterize taxonomic composition.

The data can be summarized as k-mer counts. We consider the regression problem

for for such high dimensional in order to build a model for predicting the clinical

outcomes based on microbiome data and demonstrate its applications.

2 - Computational Validation of NGS Variant Calls using

Genotype Data

Margaret Taub, Assistant Scientist, Johns Hopkins Department of

Biostatistics, 615 N Wolfe St, E3527, Baltimore, MD, United

States of America,

mtaub@jhsph.edu

, Suyash Shringarpure,

Ingo Ruczinski, Rasika Mathias, Kathleen Barnes

We performed a comparison of different variant calling algorithms on 642

samples whole-genome sequenced to an average depth of 30x, focusing on

characteristics of variants called by different subsets of callers. We developed a

classifier which uses genotyping array data, often collected for all sequenced

individuals, as a gold standard to improve calibration of variant calls. We found

little difference in quality between single- and multi-sample calling methods at

30x coverage.

MC33

33-Room 410, Marriott

Topics in Health Systems

Sponsor: Health Applications