Reducing the cost of Medical Representatives by identification of misdiagnosis of Psoriatic Arthritis using Classification techniques like Random Forest,KNN and Logistic Regression

1. NAME:DIPTARKA PAL
YEAR: PGDM II
COLLEGE:GLIM GURGAON
ROLL NO: P191B017
REDUCE THE COST OF MEDICAL REPRESENTATIVES FOR XYZ PHARMA BY IDENTIFICATION OF
MISDIAGNOSIS OF PSORIATIC ARTHRITIS USING CLASSIFICATION TECHNIQUES

2. INTRODUCTION
XYZ Pharma is an American multinational pharmaceutical company
headquartered in Washington. XYZ primarily develops medicines to treat arthritis
and cardiovascular diseases.It is a leading manufacturer of Psoriasis(PsA) and
Rheumatoid arthritis(RA) drugs and has operations in almost 30 different
geographies
Problem statement- XYZ is facing a strange problem for past couple of years.It
has observed that Physicians are misclassifying the drugs for PsA and RA and the
company is facing revenue loss as well as loss in time management by Medical
representatives(MR) in visiting those physicians. It is looking for ways to eliminate
those physicians who are misclassifying the PsA drugs so that MR can save their
time and visit only those physicians who are correctly prescribing PsA drugs

3. DIFFERENCES BETWEEN RA AND PSA
Rheumatoid arthritis
a) Often starts in smaller joints like in
fingers or toes and over time it affects
wrists,hips and ankles
b) Usually shows up on the same joints on
both sides of the body
c) Often makes joints feel stiffer in the
morning
d) Can lead to fatigue,low grade
fever,and weight loss
Psoriatic arthritis
a) Can affect joints in the back and pelvis in
addition to ones in fingers and toes
b) Often affects only one side of the body
c) May make your fingers swell up like sausages
d) May make your nail pit and flake
RA PSA
Generic drug(Misdiagnosis)
Misdiagnosis
Good diagnosis
Source:
https://www.webmd.com/rheumat
oid-arthritis/ra-and-psoriatic-
arthritis-difference

4. TARGET
Minimise the cost for
Medical representatives by
ultimately avoiding those
physicians where there has
been a misclassification
OUR
PROCESS
PROBLEM &
SOLUTION
Identification of correct
drug given by a physician
for Psoriatic arthritis
01
Training ML models using
various Classification
techniques like Logistic
Regression and Random
Forest Classifier
02
TABLE OF CONTENTS
03

5. IMPLEMENTATION OF ML IN MISCLASSIFICATION OF DRUGS
●
● We can see that even in the Digital Health Hype Cycle
● which is mainly formulated on the basis of Gartner Hype
● Cycle,Machine Learning is included in the Path of
● Enlightment. AI,ML and Deep Learning are taking the
● healthcare industry by storm.They are not pie in sky tech
● any longer.They are practical tools that help companies
● optimise their service provision,improve the standard of
● care,generate more revenue and decrease risk.
● Even though laws have been stringent for misclassification
● of drugs but still it hurts millions of people every year and
● often it is the drug manufacturing companies are the ones
● who have to bear the brunt.One common misclassification is
● one which happens in case of arthritis in identifying whether
● it is Rheumatoid or Psoriatic arthritis as both of them have
● many common symptom except for certain small differences
● For drug manufacturing companies producing drugs for both
● the diseases the supply of drugs at the correct place at the
● correct time becomes a massive challenge and this is exactly
● where we are going to train the model to remove this fallacy
MACHINE LEARNING
Source: https://www.healthcare.digital/single-post/2019/01/12/The-Digital-
Health-Hype-Cycle-2019

6. OUR SOLUTIONS
INFORMATION
ACTIONABLE
INSIGHTS
Competitors using ML models for
misclassification are also shown
in order to get a better
understanding of the drug market
DATA
Collecting APLD Datasets from
Iqvia,Symphony health mainly
under the category of Prescribers
data analysis
Detailed analysis of entire data
using ML models to understand
misclassification of RA and PsA
All the actionable insights goes
back to the data for model
improvement and preparation of
best fit model
COMPETITOR
ANALYSIS

7. • There is a huge market for PsA drugs
• By 2026 DMARDs market size is going
to grow more than double
• 1 in 3 people in USA have chances of
PsA in near future so for a company
like XYZ it is extremely crucial to have
proper distribution of these drugs
across all geographies
DEMAND ANALYSIS FOR DMARDs IN PsA
Source:https://www.grandviewresearch.com/industry-
analysis/psoriatic-arthritis-psa-treatment-market

8. We use the Anonymous Longitudinal Patient(APLD) dataset in order to perform the classification models.
●Features description: We identify the 16 types of drugs that are mainly used for Rheumatoid(RA) and Psoriatic arthritis(PsA) and create
a column for each one of them.We also include 16 symptoms in accordance to the 16 type of drugs and finally using these features as
our independent variables we try to predict the dependent variable i.e whether a patient is suffering from RA or PsA.
●EDA- EDA was done in order to assess whether there was any class imbalance problem and actions were taken to remove it
●Feature engineering- The training and testing data was divided in 70-30 proportion. Various modelling techniques were used to arrive at
the best possible outcome.The laboratory testing data was already available for all the patients and the predictions done by the model in
the test data was matched with the lab data in order to train the model. Once the best model was chosen it became clear the instances
where misclassification was taking place due to the prescribed medicines by the physicians. The Medical Representatives were instructed
to avoid those physicians and accordingly cut cost and also the time.On the other hand this modelling technique was performed for
different geographies to understand more about the misclassification of arthritis drugs in those places
●Different modelling techniques-
Logistic Regression
Support Vector Classification
Naïve Bayes Classification
K Nearest Neighbour
Decision Tree Classifier-
Random Forest Classifier
CLASSIFICATION TECHNIQUES
MOST EFFECTIVE ML MODELS USED FOR
MISCLASSIFICATION OF ARTHRITIS BY PHARMA
COMPANIES

9. MODEL IMPROVEMENT
 Methods like Bagging,Adaboost were applied
on Random forest models to remove the
problem of overfitting.
 Random forest was the most preferred
classification technique used because its
accuracy was pretty high when compared to
other ML models
METHODS USED FOR MODEL IMPROVEMENT
DURING MISCLASSIFICATION OF ARTHRITIS
Source:https://www.healthcentral.com/condition/psoriati
c-arthritis

10. COMPETITOR ANALYSIS
ASTRAZENECA BenevolentAI-The partnership will focus on
misclassification of drugs for chronic kidney diseases
ELI LILY It has developed a similar classification model in
collaboration with MIT focussing on misclassification of drugs
GSK It has partnered with cloud pharmaceuticals to use neural
network models to prevent misclassification of chronic pulmonary drug
GILEAD PHARMA It has partnered with InSitro to focus on misclassification of NAFLD drugs
Source:https://blog.benchsci.com/pharma-companies-using-artificial-intelligence-in-drug-discovery

11. CHALLENGES AND ROADBLOCKS
1. Inconsistent data collection process- With their extremely rigorous schedules,physicians do not
always take the time to record data in a comprehensive manner.As a result,inconsistencies and
gaps in data are introduced into APLD.These inconsistencies or gaps in data are a major challenge in
robust analytics
2. Varying availability of historical data- APLD is a collection of real time ongoing data.As historical
data is a different concept,there is varying availability of historical data.Data consistency and
formatting can be a challenge when used for analytical purposes,as fields can often be missing or
improperly labelled,and formats vary by system and institution
3. In emerging markets collection of APLD is extremely difficult as most of the drugs are taken without
use of prescriptions and so it becomes almost impossible to track the patient level data.Countries
like India being a large generic manufacturer of drugs many a times generic drugs are prescribed by
the physicians which in turn results in decrease in share of profits by drug manufacturers
Source:https://www.rxdatascience.com/blog/getting-most-out-of-longitudinal-patient-data

12. 1. Since data consistency is a big issue for APLD,so different models are prepared keeping in mind about
different geographies.This helps in removing a lot of noise from the dataset.Formats are also taken
care of when models are prepared in accordance to different geographies
2. Respond Recover Renew
It is always about model improvement and providing actionable insights using the best possible modelling
techniques. Data Information Insights Intelligence Actionable insights
It includes all the way going back to the data and ensuring model improvement everytime so that the next
time when classification of drugs are done best possible results are obtained
OVERCOMING CHALLENGES

13. CONCLUSION
1. Once the test data results were matched with the laboratory data we could identify
the patient id where misclassification of PsA drugs took place
2. Within a particular geography, segmentation was done to look at physicians who
made the maximum number of misclassification
3. Medical representatives(MR) of XYZ Pharma within that geography could then be
instructed to avoid those physicians who made maximum number of
misclassification
4. This in turn could help the MR to focus more on those physicians who prescribed
correct medicines for PsA
5. This can ensure that the supply of PsA drugs from XYZ Pharma are in accordance to
the demand within a particular geography
6. This can help XYZ Pharma get a competitive edge in the PsA drug category