Machine Learning and Multi Drug Resistant(MDR) Infections case study

Data Science for Healthcare
June 8, 2017

© AlgoAnalytics All rights reserved
Outline
Our Healthcare Solutions
MDR Organism Infection Mortality
Prediction Case Study
About AlgoAnalytics

Aniruddha Pant
CEO and Founder of AlgoAnalytics
PhD, Control systems, University of
California at Berkeley, USA 2001
• 20+ years in application of advanced mathematical techniques
to academic and enterprise problems.
• Experience in application of machine learning to various
business problems.
• Experience in financial markets trading; Indian as well as global
markets.
Highlights
• Experience in cross-domain application of basic scientific
process.
• Research in areas ranging from biology to financial markets to
military applications.
• Close collaboration with premier educational institutes in India,
USA & Europe.
• Active involvement in startup ecosystem in India.
Expertise
• Vice President, Capital Metrics and Risk Solutions
• Head of Analytics Competency Center, Persistent Systems
• Scientist and Group Leader, Tata Consultancy Services
Prior Experience
• Work at the intersection of mathematics and other
domains
• Harness data to provide insight and solutions to our
clients
Analytics Consultancy
• +30 data scientists with experience in mathematics
and engineering
• Team strengths include ability to deal with
structured/ unstructured data, classical ML as well as
deep learning using cutting edge methodologies
Led by Aniruddha Pant
• Develop advanced mathematical models or solutions
for a wide range of industries:
• Financial services, Retail, economics, healthcare,
BFSI, telecom, …
Expertise in Mathematics and Computer
Science
• Work closely with domain experts – either from the
clients side or our own – to effectively model the
problem to be solved
Working with Domain Specialists
About AlgoAnalytics

AlgoAnalytics - One Stop AI Shop
•We use structured data to
design our predictive analytics
solutions like churn,
recommender system
•We use techniques like
clustering, Recurrent Neural
Networks,
Structured
Data
•We use text data analytics for
designing solutions like
sentiment analysis, news
summarization and many more
•We use techniques like natural
language processing, word2vec,
deep learning, TF-IDF
Text Data
•Image data is used for predicting
existence of particular
pathology, image recognition
and many others
•We use techniques like deep
learning – convolutional neural
network, artificial neural
networks and technologies like
TensorFlow
Image Data
•We use sound data to design
factory solutions like air leakage
detection, identification of
empty and loaded strokes from
press data, engine-compressor
fault detection
•We use techniques like deep
learning
Sound Data
BFSI
•Dormancy Analysis
•Recommender System
•Credit/Collection Score
Retail
•Churn Analysis
•Recommender System
•Image Analytics
Healthcare
•Medical Image Diagnostics
•Work flow optimization
•Cash flow forecasting
Legal
•Contracts Management
•Structured Document decomposition
•Document similarity in text analytics
Internet of Things
•Predictive in ovens
•Air leakage detection
•Engine/compressor fault detection
Others
•Algorithmic trading strategies
•Risk sensing – network theory
•Network failure model

Multi Drug Resistant Infections Mortality Case Study

Multi Drug Resistant: What’s it all about?
 Multi-Drug Resistant(MDR) infections are increasing in hospitalized patients
 The use of preventive measures aimed at reducing the spread of multi-resistant bacteria is essential
 Large amounts of antibiotics used for human therapy result in the selection of pathogenic bacteria resistant to multiple drugs
Using Machine learning techniques and building models, better approaches
and preventive ways can be introduced to lower mortality rates & costs
MDR Bacteria thus creates a burden on medical care in hospitals, especially for patients admitted to intensive care units (ICU).
The incidence of ICU-HAI (Hospital Acquired Infections) is 5–10-times higher than HAI rates in general wards.
HAI in the ICU has been associated with increased morbidity, mortality, and costs
(Ref: International Journal of Infectious Diseases Vol 31, February 2015, Pages 31–34)

Data visualization
• This graph demonstrates the
distribution of Age with respect to the
Sex of the patient
• Median Female Age = 59 years
• Mean Female Age = 55.13 years
• Median Male Age = 54 years
• Mean Male Age = 54.98 years
Age with Sex
Data Sample tested on 117 patients
(Undisclosed source)

Data visualization
• This plot demonstrates the
proportion of infection caused by
various MDR organisms.
• Total number of Organisms = 12
• Most common Organisms =
Acinetobacter, Klebsiella,
Pseudomonas etc.
Percentage Break-Up of Organisms

Data Visualization
• Percentage of patients wrt specimen sent to the
laboratory which turned out to be positive for
multi drug resistant (MDR)organism infection and
relative percentage of deaths in that group
Percentage of Deaths wrt to Invasive procedure
performed
Percentage of Deaths wrt Lab Specimen Results
Percentage of patients who had undergone
invasive procedures in the ICU eg Central
Venous Catheter, arterial line, intubation etc.
and relative percentage of deaths in that group

Data Visualization
This plot demonstrates the proportions of
outcome(Death/Discharge) with respect to specimen
sent to laboratory and MDR organism found.
This plot demonstrates the proportions of outcomes
(Death/Discharge) with respect to most frequently
prescribed antibiotics and MDR organisms.
Outcome Based on Organism and Specimen Sent to Lab Outcome Based on Organism and Antibiotics

Mortality prediction: Model Building and Results
Ensemble
Results
nnet
knn
Random
Forest
knn:
• An object is classified by a majority
vote of its neighbors
• The neighbors are taken from a set
of objects for which the object
property value is known.
• low calculation time
Nnet:
• Information processing paradigm
inspired from biological nervous
system
• It has a remarkable ability to extract
meaning from complicated and
imprecise data
RandomForest:
• Random Forest operates by
constructing a multitude of decision
trees
• It runs efficiently on large data bases
• It has an effective method for
estimating missing data and
maintains accuracy when a large
proportion of the data are missing
ROC
Parameter Value
Accuracy 0.725
Kappa 0.45
ROC 0.73
Sensitivity 0.72
Specificity 0.73
PPV 0.75
NPV 0.71
Result Table
Top 10 Important Features:
• Age
• Organism
• Working Diagnosis
• Intubation
• Culture Positive
• Metronidazole
• Source
• Sex
• Cvc
• Intubation
No. of data points = 117
No. of features = 78

Other Work Done in Healthcare
Medical diagnostics – Detecting serious disorders or
diseases through image analytics. We have developed
solutions in diabetic retinopathy, brain MRI scan,
sonography and others
Cash Flow Forecasting – Forecasting of cash flows based
on claims history, reimbursement analysis and potential
denials to forecast cash
Work Flow Optimization– Using historical data for staffing
to reduce costs, Having the right clinician at right time at
right place
Efficient Use of Hospital Resources – Prevent bottlenecks
in urgent care by analyzing patient flow during peak times
Grant problem - Predict likelihood that a particular
proposal will receive grant using text analytics

Technology

Interested in Knowing More?
Contact: info@algoanalytics.com
June 8, 2017

Machine Learning and Multi Drug Resistant(MDR) Infections case study

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