Keep on the lookout for the forthcoming seminal book:

L.A. Miner, R Nisbet, J.H. Hilbe, P.S. Bolding, M. Goldstein, N Walton, and G.D.

Miner; Practical Predictive Analytics and Decisioning Systems for Medicine:

Accuracy and Cost-Effectiveness for Healthcare Administration and Delivery

Including Medical Research, (2013 / 2014) Waltham, MA: Elsevier/Academic

Press

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BOOK: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

www.thepredictionbook.com CONFERENCE:

Predictive Analytics World

San Francisco, Chicago, Boston, Washington DC, Toronto, Berlin, and London

www.predictiveanalyticsworld.com

ONLINE PORTAL AND NEWS SITE:

Predictive Analytics Times

www.predictiveanalyticstimes.com

CONFERENCE:

Text Analytics World

San Francisco and Boston

www.textanalyticsworld.com

Online training:

“Predictive Analytics Applied" - View it on-demand www.businessprediction.com

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MISC. BROAD PUBLICATIONS ON HEALTHCARE ANALYTICS:

Nice survey report wrt priority level of healthcare applications of predictive

analytics: http://www.ehcca.com/presentations/predmodel6/riddle_pc2.pdf

For more healthcare applications, see: "9 Ways to Apply Predictive Analytics to

Healthcare" - http://www.icosystem.com/9-ways-to-apply-predictive-analytics-

to-healthcare/

... and also see: "EHR oracles: Merging big data streams could create mission

control for healthcare" - http://medcitynews.com/2013/05/ehr-oracles-merging-

big-data-streams-could-create-mission-control-for-healthcare/

Analytics Magazine's Jan/Feb 2012 edition focuses on healthcare analytics:

http://viewer.zmags.com/publication/644753a2#/644753a2/1

GOOD ARTICLE: http://www.analytics-magazine.org/januaryfebruary-

2012/503-analytics-a-the-future-of-healthcare

http://www.forbes.com/sites/tomgroenfeldt/2012/01/20/big-data-delivers-deep-

views-of-patients-for-better-care/

I am an individual patient, and an individual insurance policyholder. Risk effects

all parties involved.

ACL replacement surgery choice of graft source influences the risk of long term

knee pain when “knee walking”.

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Insured “office workers”

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For the story on exactly how Obama’s campaign used predictive analytics in

2012:

http://bigthink.com/experts-corner/team-obama-mastered-the-science-of-mass-

persuasion-and-won

http://www.predictiveanalyticsworld.com/patimes/obama/

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Predictive modeling learns from data in order to generate a predictive model. For

details on how this work see Chapter 4 of the book "Predictive Analytics: The

Power to Predict Who Will Click, Buy, Lie, or Die"

(http://www.thepredictionbook.com).

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A predictive model generates a predictive score for an individual. For details on

how this work see Chapters 1 and 4 of the book "Predictive Analytics: The Power

to Predict Who Will Click, Buy, Lie, or Die"

(http://www.thepredictionbook.com).

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Marketing targets an individual predicted as likely to buy. For details on how this

work see the Introduction and Chapter 1 of the book "Predictive Analytics: The

Power to Predict Who Will Click, Buy, Lie, or Die"

(http://www.thepredictionbook.com).

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Is prediction an audacious goal? Isn't prediction impossible? For details on how

why predictive analytics predicts well enough, see the Introduction and Chapter

1 of the book "Predictive Analytics: The Power to Predict Who Will Click, Buy,

Lie, or Die" (http://www.thepredictionbook.com).

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A crummy predictive model delivers big value. It’s like a skunk with bling.

Simple arithmetic shows the bottom line profit of direct mail, both in general and

then improved by predictively targeting (and only contacting 25% of the list).

The less simple part is how the predictive scores are generated for each

individual in order to determine exactly who belongs in that 25%. For details on

how this work see Chapter 1 of the book "Predictive Analytics: The Power to

Predict Who Will Click, Buy, Lie, or Die" (http://www.thepredictionbook.com).

Put another way, predicting better than guessing is often sufficient to generate

great value by rendering operations more efficient and effective. For details on

how this works, see the Introduction and Chapter 1 of the book "Predictive

Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die"

(http://www.thepredictionbook.com).

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Each row of training data corresponds to one individual – first the individual’s

facts and figures are listed (predictor variables, aka independent variables), and

then the target variable (aka dependent variable) – ie, the thing you’re trying to

predict – is listed.

A table of such rows composes the training data, on which predictive modeling

operates.

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Urban myth to some, but based on reported results:

http://www.dssresources.com/newsletters/66.php

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Online conversion to paying membership, by email domain. Customers who sign up

with "Hotmail" and "Yahoo" email accounts are far less likely (20 - 25% as likely) to

convert to a paid subscription than users with email addresses that may be more

"permanent," such as ".net" or "EarthLink" email addresses. This insight speaks directly

to business strategy, such as employing incentives for customers to provide permanent

email addresses, or partnering with certain email service providers.

Candian Tire examples, from "What Does Your Credit-Card Company Know

About You?" New York Times, May 12, 2009.

http://www.nytimes.com/2009/05/17/magazine/17credit-t.html

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Correlation does not entail causation. For more information, see Chapter 3 of the

book "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or

Die" (http://www.thepredictionbook.com).

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"Prospective Study of Breakfast Eating and Incident Coronary Heart Disease in a

Cohort of Male US Health Professionals," by Cahill et al.

http://circ.ahajournals.org/content/128/4/337.full.pdf

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The hip bone’s connected to the leg bone, and the leg bone’s…

Your purchases relate to your shopping history, online behavior, and preferred

payment method, and to the actions of your social contacts. Data reveals how to

predict consumer behavior from these elements.

Your health relates to your life choices and environment, and therefore data

captures connections predictive of health based on type of neighborhood and

household characteristics.

Your job satisfaction relates to your salary, evaluations, and promotions, and data

mirrors this reality.

Financial behavior and human emotions are connected, so data may reflect that

relationship as well.

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...and many more, such as Cox Communications, FedEx, Sprint, etc. - see the

book "Predictive Analytics" (www.thepredictionbook) for many case studies,

including a central compendium of 147 mini-case studies, of which 37 are

examples in marketing applications of predictive analytics.

PREMIER Bankcard also lowered delinquency to increase net by over $10

million

More information about First Tennessee Bank and other case studies are available

at http://tinyurl.com/PAExamples

Dan Marks, First Tennessee Bank, "First Tennessee Bank: Analytics Drives

Higher ROI from Marketing Programs," IBM.com, March 9, 2011.

www.ibm.com/smarterplanet/us/en/leadership/firsttenbank/assets/pdf/IBM-

firstTennBank.pdf

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Both marketing and clinical nomenclature:

1. treatment

2. risk

3. outcome

4. effectiveness

5. impact

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Other examples not included in this presentation:

Pulminary embellism diagnosis:

http://www.sigkdd.org/kddcup/index.php?section=2006&method=info

http://ccls.columbia.edu/research/epilepsy-early-warning

"A New Approach to Predicting Epileptic Seizures" -

http://spectrum.ieee.org/biomedical/diagnostics/a-new-approach-to-predicting-

epileptic-seizures

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Also see:

Andrew H. Beck, Ankur R. Sangoi, Samuel Leung, Robert J. Marinelli, Torsten

O. Nielsen, Marc J. van de Vijver, Robert B. West, Matt van de Rijn, and Daphne

Koller, "Systematic Analysis of Breast Cancer Morphology Uncovers Stromal

Features Associated with Survival," Science Magazine Online; Science

Translational Medicine 3, issue 108 (November 9, 2011): 108ra113.

doi:10.1126/scitranslmed.3002564.

http://stm.sciencemag.org/content/3/108/108ra113.

Andrew Myers, "Stanford Team Trains Computer to Evaluate Breast Cancer,"

Stanford School of Medicine, November 9, 2011.

http://med.stanford.edu/ism/2011/november/computer.html.

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See also, wrt diabetes: "Leveraging Disparate Data Sources to Drive Business

Decisions," Swati Abbott, CEO, Blue Health Intelligence -

http://www.predictiveanalyticsworld.com/chicago/2013/agenda.php#day1_350a

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This type of 2-by-2 error table has the very helpful name confusion matrix.

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Kaggle, "Predict HIV Progression," Competition, April 27, 2010.

https://www.kaggle.com/c/hivprogression/details/Background

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Health Prize, "Improve Healthcare, Win $3,000,000," Competition, April 4, 2011.

www.heritagehealthprize.com/c/hhp

Another admissions prediction case study: Scott Zasadil, UPMC Health Plan, "A

Predictive Model for Hospital Readmissions," Predictive Analytics World

Washington, DC, Conference, October 19, 2010, Washington, DC.

www.predictiveanalyticsworld.com/dc/2010/agenda.php#day1-17a. For video

access to this session:

http://www.rmportal.performedia.com/rm/paw10/gallery_01#1358

OTHER READMISSION CASE STUDIES:

http://www.ehcca.com/presentations/predmodel6/kalman_1.pdf

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Riskprediction.org.uk

See also:

Mortality Prediction in Cardiac Surgery Patients. Comparative Performance of

Parsonnet and General Severity Systems. J. Martinez-Alario, MD; I. D. Tuesta,

MD; E. Plasencia, MD; M. Santana, MD; M. L. Mora, MD, PhD.

http://circ.ahajournals.org/content/99/18/2378.long

Predicting Mortality Risk in Patients Undergoing Bariatric Surgery.MARK H.

EBELL, MD, MS, Athens, Georgia. Am Fam Physician. 2008 Jan 15;77(2):220-

221. http://www.aafp.org/afp/2008/0115/p220.html

Predicting mortality in damage control surgery for major abdominal trauma

JOEP TIMMERMANS, M.D. et al.

http://www.ajol.info/index.php/sajs/article/viewFile/53677/42233

EuroSCORE Predicts Long-Term Mortality After Heart Valve Surgery. Ioannis K.

Toumpoulis, MD, Constantine E. Anagnostopoulos, MD, Stavros K. Toumpoulis,

MD, Joseph J. DeRose, Jr, MD, and Daniel G. Swistel, MD.

http://www.columbia.edu/~cea8/Toumpoulis/_pdfs/2005_06_ats_079_1902-

1908.pdf

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http://stream.wsj.com/story/latest-headlines/SS-2-63399/SS-2-267868/

http://ajp.psychiatryonline.org/article.aspx?articleid=177762

http://docserver.ingentaconnect.com/deliver/connect/asma/00956562/v74n7/s12.p

df?expires=1360465524&id=72798881&titleid=8218&accname=Guest+User&c

hecksum=01AF11DF90C899E18B406BAE23D17737

http://www.sciencedirect.com/science/article/pii/S0091743502910546

http://www.sciencedirect.com/science/article/pii/S0749379799001774

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More on mortality prediction:

Solo rockers die younger than those in bands. Although all rock stars face higher

risk, solo rock stars suffer twice the risk of early death as rock band members.

This may be due to the fact that band members benefit from peer support and

solo artists exhibit even riskier behavior (factoid courtesy of public health offices

in the UK).

Men on the Titanic faced much greater risk than women. A woman on the Titanic

was almost four times as likely to survive as a man. Most men died and most

women lived. This may be due to the fact that priority for access to life boats was

given to women.

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Arizona's Petrified Forest National Park

Psychology professor Robert Cialdini

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An example interstitial promotion. If the user accepts the offer, he/she is

allowing the host to pass profile information directly to the sponsor (in addition

to the fields shown).

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A few additional percentage points can be tough to get, in the face of fairly adept

existing systems, but can make a big difference. Consider the insurance business,

where predictive analytics aims to reduce the loss ratio by 2 to 5 points beyond

that attained by standard actuarial methodology, or the engineering of jet engines,

where a 1% increase in efficiency would be a huge bite out of annual fuel

consumption.

The revenue results above are for interstitial ads only; many more ads are

embedded within functional product web pages, and could also be targeted with

only a slight alteration to the analytical system and deployment integration

developed for this project.

The large 25% increase in acceptance rates means formerly less "popular" ads are

now being given a better chance, leading to success; these sponsors are likely to

appreciate the increase in customer leads now coming from advertising with the

client.

Likewise, user satisfaction is likely higher, since users are seeing more ads in

which they are provably more interested.

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IBM’s Watson computer predicts, for an individual Jeopardy! question and

candidate answer, whether it is the correct answer. For more information, see

Chapter 6 of Predictive Analytics (www.thepredictionbook.com)

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Once the tests are complete and results are added to the existing

information that has already been gathered, the Watson Advisor has

identified treatment options for the oncologist to consider along with the

corresponding confidence levels. Watson has also indicated how each of

the treatment options align with the patient’s preference for limiting hair

loss, if possible.

Relevant IBM Case studies: http://www-

01.ibm.com/software/success/cssdb.nsf/industryL2VW?OpenView&Start=

1&Count=10&RestrictToCategory=spss_Healthcare&cty=en_us

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Does contacting the customer make them more likely to respond?

MEDICAL:

Will the patient survive if treated?

"My headache went away!“ Proof of causality by example.

Driving medical decisions with personalized medicine: selecting treatment, e.g.,

treating heart failure with betablockers

Personalized medicine. Naturally, healthcare is where the term treatment

originates. While one medical treatment may deliver better results on average

than another, personalized medicine aims to decide which treatment is best suited

for each patient, since a treatment that helps one patient could hurt another. For

example, to drive beta-blocker treatment decisions for heart failure, researchers

"use two independent data sets to construct a systematic, subject-specific

treatment selection procedure." (Claggett et al 2011) Certain HIV treatment is

shown more effective for younger children. (McKinney et al 1998) Cancer

treatments are targeted by the patient's genes. (Winslow 2011)

Brian Claggett, Lihui Zhao, Lu Tian, Davide Castagno, and L. J. Wei.

"Estimating Subject-Specific Treatment Differences for Risk-Benefit Assessment

with Competing Risk Event-Time Data." Harvard University Biostatistics

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Personalized medicine. While one medical treatment may deliver better results

on average than another, this one-size-fits-all approach commonly implemented

by clinical studies means treatment decisions that help many may in fact hurt

some. In this way, healthcare decisions backfire on occasion, exerting influence

opposite to that intended: they hurt or kill - although they kill fewer than

following no clinical studies at all. Personalized medicine aims to predict which

treatment is best suited for each patient, employing analytical methods to predict

treatment impact (i.e., medical influence) similar to the uplift modeling

techniques used for marketing treatment decisions. For example, to drive beta-

blocker treatment decisions for heart failure, researchers "use two independent

data sets to construct a systematic, subject-specific treatment selection

procedure." A certain HIV treatment is shown to be more effective for younger

children. Treatments for various cancers are targeted by genetic markers - a trend

so significant the Food and Drug Administration is increasingly requiring for new

pharmaceuticals, as the New York Times puts it, "a companion test that could

reliably detect the [genetic] mutation so that the drug could be given to the

patients it is intended to help," those "most likely to benefit.“

Andrew Pollack, "A Push to Tie New Drugs to Testing," New York Times,

December 26, 2011. www.nytimes.com/2011/12/27/health/pressure-to-link-

drugs-and50-companion-diagnostics.html.

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Personalized medicine – evaluation and development of uplift modeling for healthcare applications:

To drive beta-blocker treatment decisions for heart failure, researchers "use two independent data sets to construct a systematic, subject-specific treatment selection procedure."

A certain HIV treatment is shown to be more effective for younger children. Treatments for various cancers are targeted by genetic markers -- a trend so significant the Food and Drug Administration is increasingly requiring for new pharmaceuticals, as the New York Times puts it, "a companion test that could reliably detect the [genetic] mutation so that the drug could be given to the patients it is intended to help, those "most likely to benefit."

Brian Claggett, Lihui Zhao, Lu Tian, Davide Castagno, and L. J. Wei, "Estimating Subject-Specific Treatment Differences for Risk-Benefit Assessment with Competing Risk Event-Time Data," Harvard University Biostatistics Working Paper Series, Working Paper 125, March 2011. biostats.bepress.com/harvardbiostat/paper125.

Ross E. McKinney Jr., MD, George M. Johnson, MD, Kenneth Stanley, MD, Florence H. Yong, MS, Amy Keller, Karen J. O'Donnell, PhD, Pim Brouwers, PhD, Wendy G. Mitchell, MD, Ram Yogev, MD, Diane W. Wara, MD, Andrew Wiznia, MD, Lynne Mofenson, MD, James McNamara, MD, and Stephen A. Spector, MD, the Pediatric AIDS Clinical Trials Group Protocol 300 Study Team, "A Randomized Study of Combined Zidovudine-Lamivudine versus Didanosine Monotherapy in Children with Symptomatic Therapy-Naive HIV-1 Infection," Journal of Pediatrics 133, issue 4 (October 1998): 500-508. www.sciencedirect.com/science/article/pii/S0022347698700575.

Ron Winslow, "Major Shift in War on Cancer: Drug Studies Focus on Genes of Individual Patients; Testing Obstacles Loom," Wall Street Journal, June 5, 2011. http://online.wsj.com/article/SB10001424052702304432304576367802580935000.html.

Ilya Lipkovich, Alex Dmitrienko, Jonathan Denne, and Gregory Enas, "Subgroup Identification Based on Differential Effect Search - A Recursive Partitioning Method for Establishing Response to Treatment in Patient Subpopulations," Statistics in Medicine 30, issue 21 (September 20, 2011): 2601-2621. onlinelibrary.wiley.com/doi/10.1002/sim.4289/abstract.

J. C. Foster, J. M. Taylor, and S. J. Ruberg, "Subgroup Identification from Randomized Clinical Trial Data," U.S. National Library of Medicine National Institute of Health, Statistics in Medicine 30, no. 24 (October 30, 2011): 2867-2880. doi:10.1002/sim.4322. Epub, August 4, 2011. www.ncbi.nlm.nih.gov/pubmed/21815180.

Xiaogang Su, Chih-Ling Tsai, Hansheng Wang, David M. Nickerson, and Bogong Li, "Subgroup Analysis via Recursive Partitioning," Journal of Machine Learning Research 10 (2009): 141-158. http://jmlr.csail.mit.edu/papers/volume10/su09a/su09a.pdf.

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(This paper in turn references all the core technical papers on this topic.)

Free white paper: www.predictiveanalyticsworld.com/signup-uplift-

whitepaper.php

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