Post on 28-Mar-2019
WELCOME!
NetHope Solutions Center
ICT4D Webinar Series:
AI and Machine Learning in Health
Programs
November 20, 2018
Speakers:
Neil Sahota, United Nations AI Subject Matter Expert, Faculty at University of California, Irvine, and IBM Master Inventor
Steve Hellen,
Director of ICT4D
and GIS,
Catholic Relief
Services
Moderator: Sonja Ruetzel, Catholic Relief Services
Dr Anuraj Shankar, Senior Research Scientist, Harvard T.H. Chan School of Public Health
Toby Norman, CEO, SimprintsTechnology
Discussion:
Toby Norman, CEO, Simprints Technology
What are the opportunities and risks of deploying AI in digital health care?
Opportunity: Learning from our dataRisk: Learning from our biases
Error ~ 0.8% Error ~ 35%
https://www.media.mit.edu/articles/facial-recognition-is-accurate-if-you-re-a-white-guy/
AI is great… for white males
www.simprints.com
Opportunity: Truly customized technologyRisk: Vendor lock-in / no interoperability
www.simprints.com
Conclusion: For every complex problem there is an answer
that is clear, simple, and wrong
1. Learning vs. bias
1. Mass data vs. privacy
1. Customization vs. openness
Discussion:
Bridging the gap of data collection and analytics
Dr Anuraj Shankar, Senior Research Scientist, Harvard T.H. Chan School of Public Health
Open Smart Register Platform
www.smartregister.org
OpenSRP is a scalable Open-source Smart Register Platform that enables FHWs
to digitally register and track the health of all their patients through a single,
user-friendly mobile/tablet application, adaptable to different use cases.
AH Shankar, AI and ML in Health Programs 11 |
Challenges for AI and ML in Public Health
Use case is not clear
The right data is not collected
Assumption that AI/ML can ”clean up” bad data
The “Human Gap”
– Supervisors do not understand the analysis
– Workers don’t trust the conclusions
“Humans help humans, technology is simply a tool”
AH Shankar, AI and ML in Health Programs 12 |
Case study:
Optimize use of screening data to improve efficiency of
community health workers to detect tuberculosis cases
▪ cough
▪ fever
▪ haemoptysis
▪ night sweats
▪ weight loss
▪ fatigue
▪ shortness of breath
▪ chest pain
▪ diabetes
▪ family diabetes
▪ TB exposure
▪ kidney failure
▪ asthma
▪ COPD
▪ HIV
▪ active smoker
Self reported symptoms and conditions by verbal screening
Septiandri AA, Aditiawarman, Tjiong R, Burhan E, Shankar AH (2017). Cost-Sensitive
Machine Learning Classification for mass tuberculosis screening. In Latent Tuberculosis
Infection and Epidemiology of Disease, American Thoracic Society, A3991.
AH Shankar, AI and ML in Health Programs 13 |
Analytic approach to optimize accuracy of diagnosis
Data preparation▪ Cleaning
▪ Standardize data
▪ Deal with missing data
Select additional data▪ Clinic location
Apply analytic approaches▪ Support vector machine
▪ XGboost
▪ Logistic regression
Analytic method Accuracy Sensitivity Specificity
WHO Score
approach
30.5 91.6 20.6
XGboost w/out 40.5 92.9 31.9
Xgboost with clinic 48.2 92.1 41.1
SVM w/out clinic 37.7 94.3 28.3
SVM with clinic 45.9 94.3 38.1
Log Reg w/out clinic 48.8 88.6 42.2
Log Reg with clinic 51.0 93.0 44.2
AH Shankar, AI and ML in Health Programs 14 |
Implications of the analysis for active TB screening
Increasing the specificity by 20% or more, by considering clinic
location, means saving more money and time from doing
unnecessary tests.
This can be achieved without losing any sick people in the
screening process.
Uniform Manifold Approximation and Projection (UMAP)
visualization underscores case associations with clinics
Each dot is a
person screened
Each color is a
clinic
Each purple
dot is a
person
screened
Each yellow
dot is a TB
case
AH Shankar, AI and ML in Health Programs 15 |
Child’s baseline data, weight, height,
weight for age, height for age, weight for
height, exclusive breastfeeding, Vitamin
A (for child)
Immunizations
Child’s
baseline data,
assessment
results
Ris
k
Fla
g
Risk
FlagSDIDTKHOME
INVENTORYKARANA
Vitamin A
(for mother)
Pregnanc
y status
Immunizations
Weight, Height, Head
Circumference Risk Flag
MIDWIF
E APP
ECD
APP
VACCINATOR
APP
NUTRITION
APP
Ris
k
Fla
g
OpenSRP Indonesia cross-cadre data sharing:
enables system wide AI/ML analysis
AH Shankar, AI and ML in Health Programs 16 |
Indonesia – OpenSRP
AI/ML to improve client-centric care
Increase workforce productivity by
identifying incorrect or bogus data,
assess predictors of worker
productivity and client outcomes,
identify ways to improve
coordination among health
workers.
Increase client health by identifying
specific client care seeking
patterns and tendencies. Assessing
what types of interactions are
effective, what locations are high
risk
Clients
Pustu
(Village based health post)
Puskesmas
(Community Health Center)
Polindes (Village-based Community
Birth Center)
ECD CenterPosyandu
(Community Health Extension Clinic)
Discussion:
How is CRS using AI and machine learning in global programs?
Steve Hellen,
Director of ICT4D
and GIS,
Catholic Relief Services
Results: High confidence predictions of those at greatest risk are available 1-2 months in advance. Early warning information is continuously shared with communities to help them plan and target responses.
Solution: A low burden, high frequency data collection protocol gathers information about shocks, household characteristics, and food security. Machine learning algorithms identify predictors of food insecurity and examine household resilience.
Problem: The increased severity of natural disasters exacerbates food insecurity. Severe floods in 2015 followed by drought displaced hundreds of thousands in Malawi. The ability to respond to shocks varies significantly by household. Resilience is difficult to measure or predict.
Predicting Resilience in Malawi
Discussion:
What are the key questions you should ask yourself before starting an AI-based initiative? What are the small things you can do to make a difference?
Neil Sahota, United Nations AI Subject Matter Expert, Faculty at University of California, Irvine, and IBM Master Inventor
Speakers:
Neil Sahota, United Nations AI Subject Matter Expert, Faculty at University of California, Irvine, and IBM Master Inventor
Steve Hellen,
Director of ICT4D
and GIS,
Catholic Relief
Services
Moderator: Sonja Ruetzel, Catholic Relief Services
Dr Anuraj Shankar, Senior Research Scientist, Harvard T.H. Chan School of Public Health
Toby Norman, CEO, SimprintsTechnology
THANK YOU!
See you at the next ICT4D Webinar:
December 12: Digital financial tools for humanitarian response
www.ict4dconference.org
Call for Speaker ends November 30!
11th ICT4D Conference, April 30 to May 3, 2019 – Kampala, Uganda