Truth in Data Science | Jaya Tripathi | TEDxYouth@BHS

Summary

Jaya Tripathi, a principal scientist at Mitel, discusses her role as a data scientist, emphasizing the interdisciplinary nature of the field. She highlights the importance of domain expertise, statistical methods, and machine learning in extracting knowledge from data. Tripathi shares her experience with hypothesis testing in medical research, where she predicts prescription fraud by analyzing data from the Prescription Drug Monitoring Program. She also explores data through visualization techniques like histograms, clustering, and geospatial analysis, leading to significant findings such as the identification of potential fraud and the prediction of an HIV epidemic in Indiana. She concludes by advocating for passion in research, the importance of asking questions, and the scientific method in data analysis.

Takeaways

• 🔬 Jaya Tripathi is a principal scientist at Mitel, focusing on data science and innovation programs.
• 📊 Data science involves interdisciplinary approaches, including domain expertise, math, statistics, visualization, and graph analytics.
• 🔍 The concept of absolute truth does not exist in data science, especially in medical research, due to the variability of observable properties.
• ⚖️ Hypothesis testing is a key method in scientific research, where one begins with a hypothesis, makes assumptions, and tests them through models and data analysis.
• 💊 Tripathi's research involved predicting prescription fraud by analyzing data elements like travel distance and drug combinations.
• 📈 The CRISP-DM process was used for data mining, emphasizing the importance of understanding and preparing data before modeling.
• 📊 Visualization techniques, such as histograms and clustering, were used to explore data and identify patterns that could inform machine learning models.
• 🗺 Geospatial analysis, including heat maps, was used to identify hotspots for drug use, which led to significant findings and interventions.
• 🔎 The importance of not just accepting the obvious explanation in data analysis was highlighted, encouraging deeper exploration to uncover underlying issues.
• 📚 Tripathi suggests further reading on the Simpsons paradox, UC-Berkeley gender bias study, and the man in the cave allegory to understand data science concepts better.

Q & A

What is Jaya Tripathi's profession and where does she work?

Jaya Tripathi is a Principal Scientist at Mitel Corporation in Bedford, Massachusetts.

What is the purpose of the innovation program at Mitel Corporation?

The innovation program at Mitel Corporation is an internal research program that fosters creativity and is funded to support research initiatives.

What does Jaya Tripathi consider as the 'sexiest job' of the 21st century?

Jaya Tripathi refers to a quote from the Harvard Business Review, which states that a data scientist has the 'sexiest job' of the 21st century.

What interdisciplinary techniques does a data scientist like Jaya Tripathi use?

Data scientists use a combination of domain expertise, math, statistics, visualization techniques, and graph analytics, among other scientific methods and algorithms, to extract knowledge from data.

Why does Jaya Tripathi believe the concept of absolute truth does not exist in data science, particularly in medical research?

Jaya Tripathi explains that in medical research, data is based on observable properties that can vary over time or across different data sets, and the concept of absolute truth can introduce biases, which contradicts the scientific method.

What is the significance of hypothesis testing in Jaya Tripathi's research?

Hypothesis testing is a method of scientific research where Jaya Tripathi starts with a question, makes assumptions, and creates tests to validate those assumptions. It helps in building models and determining the confidence of results, leading to the acceptance or rejection of initial assumptions.

Can you explain the CRISP-DM process Jaya Tripathi mentioned for data mining?

The CRISP-DM process is a cross-industry standard for data mining that Jaya Tripathi follows. It involves understanding the data, preparing it by removing duplicates and correcting errors, modeling, and finally evaluating and validating the results.

What was Jaya Tripathi's hypothesis regarding prescription fraud by physicians?

Jaya Tripathi hypothesized that she could predict prescription fraud by physicians based on data elements such as the distance traveled by the person, certain combinations of drugs prescribed, and other related factors.

How did Jaya Tripathi use geospatial analysis in her research?

Jaya Tripathi used geospatial analysis, specifically heat maps, to visualize data on drug distribution and usage across different regions. This helped in identifying hotspots for further investigation, such as the HIV epidemic in Scott County, Indiana.

What is Jaya Tripathi's advice for conducting good research?

Jaya Tripathi suggests that one must be passionate about the research topic, make observations, ask interesting questions, formulate hypotheses, gather the correct data, test predictions, and generalize findings with other datasets.

Outlines

00:00

🔬 Introduction to Data Science and Research Methodology

Jaya Tripathi, a principal scientist at Mitel, introduces herself and her work in the field of data science. She discusses her role in federally funded research and development centers, focusing on an internal innovation program. Tripathi emphasizes the importance of data in forming opinions and decisions, quoting Sherlock Holmes and highlighting the 'sexiest job' of the 21st century as described by the Harvard Business Review. She outlines the interdisciplinary nature of data science, which includes domain expertise, mathematics, statistics, and various analytical techniques. Tripathi also touches on the concept of truth in data science, contrasting it with absolute truths found in mathematics and religious texts, and explains how evidence-based research forms the basis of medical research.

05:02

📊 Data Mining and Hypothesis Testing in Medical Research

The speaker delves into the process of data mining using the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology. She discusses the importance of understanding and preparing data, including data quality assessment and handling duplicates or missing values. Tripathi then focuses on the modeling phase, where she employs predictive machine learning methods such as support vector machines, neural networks, and random forests. She emphasizes the iterative nature of this process, adjusting features and models based on outcomes. The speaker shares her experience with hypothesis testing, using her own research on prescription fraud as an example. She explains how she formulated a hypothesis, gathered data from sources like the Prescription Drug Monitoring Program (PDMP), and tested her assumptions to predict fraudulent prescriptions by physicians.

10:03

🔎 Exploratory Data Analysis and Visualization Techniques

Tripathi presents her approach to exploratory data analysis, beginning with basic visualizations like histograms to understand the distribution of data. She discusses gender and age differences in ADHD drug prescriptions, noting the higher prevalence in male children and the potential for deeper investigation beyond surface-level explanations. The speaker also explores clustering as an unsupervised learning technique to identify patterns in data without prior assumptions. She uses this method to predict 'bad doctors' based on overlapping prescriptions and highlights the importance of not stopping at the first plausible explanation. Tripathi further discusses the use of geospatial analysis and heat maps to identify hotspots of drug activity, which led to the discovery of an HIV epidemic in Scott County, Indiana. She concludes by emphasizing the value of these techniques in guiding resource allocation and educational outreach.

15:04

🎓 Conclusion and Recommendations for Research

In her concluding remarks, Tripathi stresses the importance of passion in research and the process of making observations, asking questions, and formulating hypotheses. She advises on the necessity of gathering appropriate data to test predictions and the iterative process of accepting or rejecting hypotheses. The speaker also suggests generalizing findings across different datasets and offers further reading materials, including the Simpson's paradox and the man in the cave allegory, to deepen understanding of data science concepts and their implications.

Keywords

💡Principal Scientist

A Principal Scientist is a senior research professional who leads scientific projects and teams, often with a significant degree of autonomy and responsibility for the direction and success of research initiatives. In the context of the video, Jaya Tripathi, the speaker, holds this title at Mitel Corporation, indicating her significant role in driving scientific research and innovation within the company.

💡Federally Funded Research and Development Centers (FFRDCs)

FFRDCs are unique independent organizations that assist the U.S. government with scientific research and analysis, technical expertise, and scientific knowledge. They are funded by the government but operate autonomously, allowing for objective research. In the video, the speaker's organization operates several FFRDCs, emphasizing its commitment to advancing research in the public interest.

💡Innovation Program

An Innovation Program typically refers to an initiative designed to foster creativity and new ideas within an organization. It often provides resources and support for research and development. In the video, the speaker's work has been funded by such a program, highlighting the importance of internal support for driving innovation and research in data science.

💡Data Scientist

A Data Scientist is a professional who analyzes and interprets complex digital data, such as those used in scientific research, to help businesses make decisions. They are skilled in statistics, data analysis, and often programming. The speaker identifies as a Data Scientist, emphasizing the video's focus on the role of data in scientific research and its potential for uncovering insights.

💡Hypothesis Testing

Hypothesis testing is a scientific method used to make decisions about claims based on data. It involves formulating a hypothesis, gathering data, and then testing the hypothesis to determine its validity. The speaker discusses hypothesis testing as a key method in her research, showing how it's used to explore and validate assumptions about data.

💡Evidence-Based

Evidence-based practices refer to actions or decisions made based on the best available evidence, often from data or research. In the context of the video, the speaker mentions that medical research is evidence-based, emphasizing the importance of data in understanding and addressing medical phenomena.

💡Machine Learning

Machine Learning is a subset of artificial intelligence that provides systems the ability to learn and improve from experience without being explicitly programmed. The speaker uses machine learning techniques, such as support vector machines and random forests, to predict and analyze data, showcasing the application of advanced analytics in data science.

💡Predictive Analytics

Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. The speaker discusses using predictive analytics to forecast prescription fraud by physicians, demonstrating how predictive models can be used to analyze and prevent fraudulent activities.

💡Geospatial Analysis

Geospatial analysis involves the examination of data in relation to its geographical context. In the video, the speaker uses geospatial analysis to create heat maps that visualize data distribution across geographic areas, such as the prevalence of certain drugs. This technique helps identify patterns and anomalies that could inform policy decisions or resource allocation.

💡Data Visualization

Data visualization is the graphical representation of information and data. It helps in understanding data by providing a visual context, making it easier to interpret. The speaker uses various types of visualizations, such as histograms and heat maps, to explore and communicate findings from her data analysis, highlighting the importance of visual tools in data science.

Highlights

Jaya Tripathi, a principal scientist at Mitel, discusses her work funded by the innovation program.

Emphasizes the importance of data in decision-making, quoting 'without data, you're just another person with an opinion'.

Cites Sherlock Holmes on the mistake of theorizing before having data.

Data scientists are described as having the 'sexiest job of the 21st century' according to Harvard Business Review.

Interdisciplinary approach of a data scientist includes domain expertise, math, statistics, and visualization techniques.

Discusses the concept of truth in data science, contrasting it with absolute truth in mathematics.

Explains the scientific method's role in data science, particularly in hypothesis testing.

Shares a personal project predicting prescription fraud by analyzing data elements like travel distance and drug combinations.

Describes the CRISP-DM process for data mining, emphasizing the importance of understanding and preparing data.

Details the use of predictive machine learning methods like support vector machines and random forests in modeling.

Highlights the iterative process of model building, feature selection, and validation in data science.

Exploration of data through visualization techniques like histograms reveals insights into prescription patterns.

Uncovers a potential case of prescription fraud by further investigating data that initially seemed explained.

Discusses the significance of not just looking for the obvious explanation and delving deeper into data.

Uses clustering to identify patterns in data, suggesting potential inputs for machine learning models.

Presents a hypothesis on the correlation between certain drug combinations and cash payments, validated through data visualization.

Demonstrates the use of geospatial heat maps to identify potential hotspots for further investigation, leading to significant findings.

Concludes with advice on the importance of passion in research and the process of making observations, formulating hypotheses, and generalizing findings.

Transcripts

[Music]

okay good so I'm Jaya Tripathi and I am

a principal scientist at the Mitel

corporation here in Bedford

Massachusetts next please

just a line bought minor so minor works

in the public interest and we operate

several FFRDCs federally funded research

and development centers so one of the

programs we have is an internal research

program that fosters creativity it's

called the innovation program and the

work that I've been doing for the last

seven or eight years was funded by this

research program the innovation program

next please just a couple of quotes on

data because that's what I do I'm a data

scientist then I really like one without

data you're just another person with an

opinion I don't see the slides here

that's okay you're just another person

with an opinion there's another one that

I like next please from so this is from

Sherlock Holmes a study in scarlet from

Sir Arthur Conan Doyle's it is a mistake

to theorize before one has data next

okay so Howard says I have the sexiest

job this is a quote from the Harvard

Business Review a data scientist the

sexiest job of the 20th century so

that's me

next so what does a data scientist do

what is someone like me do first it's an

interdisciplinary approach so we use a

lot of the techniques some of which are

you must know your domain you must have

domain expertise you use math statistics

visualization techniques graph analytics

and so on so essentially we use all

these scientific methods algorithms

subject matter expertise to try and

extract knowledge from data and explain

the phenomenon that you're trying to

address next ways

so you are familiar with truth in an

informal way in in terms of ethics

philosophy religion you've read about it

in the Bible and in math we there is a

concept of absolute truth we use proofs

like reductio ad absurdum or a an

example which is proof by contradiction

to establish the absolute truth however

this notion of absolute truth does not

exist in data science particularly when

applied to medical research why is that

why because in medical research it's

based on observable properties of the

phenomenon something you measure you

must have heard the term evidence-based

and what you measure and what you

observe today may be different in a

different data set or at a different

point in time also if you subscribe to

the notion of absolute truth right it

violates the scientific method because

you're bringing in preconceived notions

and biases lastly the truth in

scientific method is relatively the

context or the model and that gave me

different pathways or different models

in which to approach your problem next

please

so one method of scientific research

that I want to talk about is hypothesis

testing and there's a quote there from

Edward Teller that I like so fact is a

simple statement that everyone believes

right if innocent it is innocent until

found guilty with the hypotheses it's

guilty until you found effective so you

begin with the hypothesis right you ask

yourself an interesting question you

begin with the hypothesis you make some

assumptions and then you create tests to

test those assumptions and you build

models and then how confident are you

that the result is not due to chance and

once you're confident you've sticked

your hypotheses otherwise you revert to

the alternate hypothesis and you accept

or reject the initial assumption in

favor of the augment position and then

you go on and try and expand your

hypotheses to other data sets to

generalize so here was i have done

several projects one of which was my

hypothesis was that if i I can predict a

particular kind of

committed by prescribers of physicians a

particular kind of prescription fraud

solely and looking at data sets that

have certain data elements data elements

have to do with the distance the person

traveled certain combinations of drugs

and so on so that was my policies and

the first thing I did was to go and see

data that would be suitable to test my

father sees one of which was the PDMP

prescription drug monitoring programs

all the states have them they are

prescriptions reported by pharmacies

once a controlled substance is filled so

an oxycodone or hydrocodone a benzo and

benzodiazepine and so on next please so

here is once I have my policies I seek

the appropriate data and this is an

industry standard for data mining it's

called the crisp cross industry standard

process for data mining and the first

step is understanding your data well you

really need to spend a lot of time

understanding the data well do the data

quality assist assessment and then the

next stage its preparation removing

duplicates and spurious data are missing

values and so on part that I would say I

spent the most time on is on modeling so

there's several different techniques

there but I used something predictive

machine learning methods examples of

which are support vector machines you

all must have heard of artificial neural

networks random forests and so on and

that's a cyclical process so you try

certain essentially you're trying to

predict some things in this case I'm

trying to predict who the bad doctors

are and so you you tries different

features to feed into your model and

then if that doesn't work out you come

back and change your features and so on

so it's the cyclical profit process and

in the end is evaluation and validation

next slide please

okay so I'm just going to show the next

three slides and then I'll end after

that I'm going to show you some examples

of exploration so before I get into

the modeling and the machine learning I

did some exploration on the data and

visualization histogram you all are

familiar with histograms two basic

concepts on this chat here I have male

children on the upper graph and females

on the lower one and I'm just doing an

age gender histogram on the

prescriptions that were fed remember

these are all controlled substance

prescriptions what do you see what it

since the audience is small enough it's

okay for you to shout out what do you

see that's different in the two graphs

correct so on the on the right side

sorry your name is yeah and she pointed

out the difference mainly is in children

under the age of 10 years right and so

it's worth exploring it when a histogram

is something that's very simple to do

and should always do your basic Union

bivariate statistics in the beginning so

I look at it and then I see that most of

the drugs almost all the drugs were ADHD

drugs ritalin and adderal and you look

up literature and you research it and

you find that there is a two point

something times greater prevalence of

that diagnosis in male children than

female children so at this point a lot

of data scientists would have said well

that explains it you know there's a

that's a roughly two point five times

over there's a 2.5 times greater

prevalence of ADHD and that explains it

but something in me wanted to delve

further so I look further and and I

think this this is the point that I'm

trying to make is don't just look for

the obvious explanation it's worth it to

spend a little bit more time and delve

further so look further and they found

actually most of these prescriptions

were filled by a pharmacy in a different

state more than 100 miles away and there

were several pharmacies much closer and

without revealing much more let's just

say that this led to a case next slide

please

so here I am before I'm getting into

complex machine learning models or doing

any

so intelligence applications this is me

just trying to get a feel for the data

so essentially after I did the data

quality is you look for distributions

you look to see if your data is Gaussian

because certain models require certain

distributions and in this case I'm just

hypothesizing and and doing certain

visualizations so the the upper-right

one I am doing clustering

so what clustering is it's a method of

unsupervised machine learning where what

is the data saying about itself you have

no bias no prior knowledge of the data

can if I'm trying to make two classes

for example it's called a binary

classifier to classes bad guys good guys

for example that's as simple as

classifier can the data distribute

itself into two groups and I'm not

saying which one's bad which one's good

but it's just the data so I'm Here I am

trying to classify the people based on

who did multiple drugs at the same time

so not one drug in January and for 30

days then another one in August but

overlaps and overlaps across different

pharmacological groups and do you guys

see that needs clustering into two

groups that's promising

and so that tells me this is an input

that I should use in my input vector

when I do machine learning so this is

Annie tree unique uniquely separable

data here this is another hypothesis I

have so in this case I'm looking for

these dots are all prescribers or

doctors red and blue the red ones are

the ones that prescribe the certain

combination of drugs for which there is

no medical legitimacy by that I mean

there are certain combinations of drugs

which no doctor would ever give why

because it leads to 12 or more times

greater chance of respiratory depression

coma or even death and so I have

prophesized that most of those guys who

did this particular combination of drugs

there was cash payments involvement

that's a hypothesis right there just

guess sometimes the Prophecy's based on

some some sort of expert knowledge and

sometimes it is

we're trying up so I wanted to show you

here so the blue dots and and the red do

you see so what's my hypothesis correct

or wrong what do you see what do you see

from that crap okay

so I'll give you the answer it's you

could more or less say that it's kind of

a linear along the 45-degree line right

and so here I have Medicare payments and

you have cash so my policies was the

people who patients who went to doctors

who gave the sort of combination that's

a no-no had some sort of cash payments

mixed in to go under the radar and I

would say that's not entirely true

hypotheses right because it's it's along

the 45-degree line however there is

something happening because it's below

this this vertical line here most of the

people who did that also had a lot of

prescriptions there's a logarithmic

scale so had a lot of prescriptions

these examples of visualizing the data

next please

so here's a technique called geospatial

or heat maps and hotspots you must have

seen this all the time this is the map

of Indiana because that's where I got

the data and this is normalized by

livable land area and by population

because if you don't normalize the

population Indianapolis in the center

will always turn out bright red because

there's lots of people there so you have

to normalize that population and zcta

for the most part is the same as the US

postal zip code postal service construct

is more or less the same thing zip code

tabulation area zcta but I take zcta

because it gives me access to other

demographic information so I can tie in

the zip codes or the Z CTAs with income

over those deaths education and so on so

I can do that and so here this is a heat

map on a particular drug called opana so

I did heat maps on many different drugs

the top 10 drugs

them on polypharmacy combinations of

drugs and also overlaid with other

datasets from CDC and other places so

what's that sticks out here the the Reds

the red areas Hartford City and Scott

Scott Scott's bar is no I I don't really

live in Indiana's I gave it to the

Health and Human Services and the health

commissioner from Indiana and asked him

to look into it and we don't have any

plausible explanation for why why this

is read these you know largely rural

areas and no one knows what going on

so guess what two months after I gave

the state of Indiana this chart there

was a huge HIV epidemic that broke out

in Scotts County

this is relatively large percent of

population at HIV was in 2014 and the

CDC and the federal government had to

intervene and give aid and so on and at

the conclusion of the investigation it

turned out that the HIV epidemic could

be attributed to people sharing needles

from a panel so here's a technique

that's validated kind of this is my kind

of truth it's validated because there's

a hot map heat map that said you know

explore this region further and States

typically have limited resources so you

if you not in this particular example

but other examples for example if you

want to see where law enforcement query

should be police could decide where to

place the drug diversion officers and

along the heat maps you could focus your

education outreach programs and then in

the red spot areas and so on next please

so in conclusion first we you can't do

really good research unless you really

passionate about the topic so pick a

topic that that the dreamy speaks to you

that you're passionate about make

observations ask interesting questions

formulating hypotheses and develop the

predictions that you can test gather the

correct data test your predictions

accept or reject your hypotheses and if

excepted and see if you can generalize

it invalidate with other datasets for

further reading

may I suggest reading about the Simpsons

paradox uc-berkeley gender bias study or

how many of you are familiar with it

good and then the man in the cave

allegory from Plato's Republic thank you

for having me

you