To achieve maximum health impact of the current WHO HIV model, timely detection of potential HIV virologic failure is critical in preventing adverse clinical trajectories such as treatment failure and immunological deterioration. As such, this paper aims to disseminate various techniques that can be utilized in HIV clinical settings with rich EHR data to proactively anticipate and mitigate the risk of virologic failures before they manifest. A series of statistical learning models consisting of parametric, non-parametric, ensembles and Bayesian approach were trained and evaluated using dataset extracted from an EHR serving over 90,000 HIV patients in Kenya.

1. Author’s name and affiliation
Harnessing the Power of Machine Learning Methods for Enhancing HIV
Care and Treatment Within Resource-Limited Settings
Allan Kimaina1,4
, Jonathan Dick MD2,3,4
, Allison DeLong1,4
, Rami Kantor MD1,4,5,6
, Ann Mwangi1,4
, Hogan Joseph ScD1,4,5
BROWN
Global Health Initiative
Brown University, Providence, RI, USA1
; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA2
; Indiana University School of Medicine, Indianapolis, IN, USA3
; AMPATH
Consortium, Eldoret, Kenya4
; CFAR Core, Providence, RI, USA5
,The Miriam Hospital, Lifespan, Providence, RI USA6
.
Professor Joseph Hogan
D43 PI and mentor
● Deputy Director, Data Science
Initiative
● Carole and Lawrence Sirovich
Professor of Public Health
● Chair and Professor of
Biostatistics, Brown University.
● Director of the Biostatistics
Program for AMPATH
Mr. Allan Kimaina
D43 trainee and presenter
● Program: Masters of Science in
Biostatistics
● University: Brown University
● Track: Health Data Science Track
● Current:
○ Data Manager / Statistician,
AMPATH
○ Fogarty-IeDEA Mentorship
Program (FIMP)
Introduction
Objectives
Results
Conclusion
Methods

2. Author’s name and affiliation
Harnessing the Power of Machine Learning Methods for Enhancing HIV
Care and Treatment Within Resource-Limited Settings
Allan Kimaina1,4
, Jonathan Dick MD2,3,4
, Allison DeLong1,4
, Rami Kantor MD1,4,5,6
, Ann Mwangi1,4
, Hogan Joseph ScD1,4,5
BROWN
Global Health Initiative
Brown University, Providence, RI, USA1
; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA2
; Indiana University School of Medicine, Indianapolis, IN, USA3
; AMPATH
Consortium, Eldoret, Kenya4
; CFAR Core, Providence, RI, USA5
,The Miriam Hospital, Lifespan, Providence, RI USA6
.
Background
● HIV Virologic failure occurs when ART fails to
suppress a person’s viral load (VL) count below
1000 copies of viral RNA per ml
● Delays in efforts to detect and address can lead to
adverse clinical trajectories such as
○Immunological failures
○Clinical failures.
● As such, it is critical to proactively anticipate
and mitigate potential risk of virologic failure
Introduction
Objectives
Results
Conclusion
Methods
Main Objective
● To characterize and compare the predictive accuracy of several statistical learning methods for
predicting viral failure
Source: depts.washington.edu - Viral Load Monitoring

3. Author’s name and affiliation
Harnessing the Power of Machine Learning Methods for Enhancing HIV
Care and Treatment Within Resource-Limited Settings
Allan Kimaina1,4
, Jonathan Dick MD2,3,4
, Allison DeLong1,4
, Rami Kantor MD1,4,5,6
, Ann Mwangi1,4
, Hogan Joseph ScD1,4,5
BROWN
Global Health Initiative
Brown University, Providence, RI, USA1
; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA2
; Indiana University School of Medicine, Indianapolis, IN, USA3
; AMPATH
Consortium, Eldoret, Kenya4
; CFAR Core, Providence, RI, USA5
,The Miriam Hospital, Lifespan, Providence, RI USA6
.
Risk Predictive Modeling: We trained and compared several ML algorithm g(X) using 50 clinically-relevant covariates that
were handpicked and curated by domain experts.
Introduction
Objectives
Results
Conclusion
Methods
Study Design: A retrospective observational study was conducted by analyzing and creating virologic failure risk
prediction models using dataset extracted from AMPATH's EHR serving over 90,000 HIV patients in Kenya.
x2
: TB symptoms
x3
: ARV Line
x4
: health cover
x1
: age
...
x50
: previous VL
VL suppression
VL failure
Y=g(X)
Machine Learning (ML) Model
X
Input Learning algorithm: g(X) Output

4. Author’s name and affiliation
Harnessing the Power of Machine Learning Methods for Enhancing HIV
Care and Treatment Within Resource-Limited Settings
Allan Kimaina1,4
, Jonathan Dick MD2,3,4
, Allison DeLong1,4
, Rami Kantor MD1,4,5,6
, Ann Mwangi1,4
, Hogan Joseph ScD1,4,5
BROWN
Global Health Initiative
Brown University, Providence, RI, USA1
; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA2
; Indiana University School of Medicine, Indianapolis, IN, USA3
; AMPATH
Consortium, Eldoret, Kenya4
; CFAR Core, Providence, RI, USA5
,The Miriam Hospital, Lifespan, Providence, RI USA6
.
Introduction
Objectives
Results
Conclusion
Methods
Conclusion:
High-risk patients can be identified using accurate & well-calibrated risk predictive model and in turn can benefit from
targeted preventative strategies such as:
○ Enhanced adherence monitoring
○ Timely ordering of lab orders such as VL tests
○ Appropriate next-line therapy switching
Results for classification rule based on
g2
(X0
,Y1
), obtained via 10-fold cross
validation using .5 cutoff.
● SL = super learner,
● XGB = XGBoost,
● GBM = gradient boosted machine,
● BART = Bayesian additive regression tree,
● SVM = support vector machine,
● RF = random forest,
● ENET = elastic net,
● OLSL = ordinary least logistic regression,
● CART = classification and regression tree,
● KNN = K nearest neighbors.

5. Author’s name and affiliation
Harnessing the Power of Machine Learning Methods for Enhancing HIV
Care and Treatment Within Resource-Limited Settings
Allan Kimaina1,4
, Jonathan Dick MD2,3,4
, Allison DeLong1,4
, Rami Kantor MD1,4,5,6
, Ann Mwangi1,4
, Hogan Joseph ScD1,4,5
BROWN
Global Health Initiative
Brown University, Providence, RI, USA1
; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA2
; Indiana University School of Medicine, Indianapolis, IN, USA3
; AMPATH
Consortium, Eldoret, Kenya4
; CFAR Core, Providence, RI, USA5
,The Miriam Hospital, Lifespan, Providence, RI USA6
.
Future plans:
Through the Fogarty-IeDEA Mentorship Program (FIMP), we plan to
continue this work by:
● Creating temporal risk predictive models that can be generalized
across countries and care programs using IeDEA data
○ Countries Included:
■ Kenya, Uganda, Tanzania
○ Programs Included:
■ AMPATH, FACES, IDI, Kisesa, Masaka,
Mbarara, Morogoro, Rakai, Tumbi
I will be working under the mentorship of the IeDEA team, including
Prof. Joseph Hogan and,
Many thanks to:
● NIH/FIC HIV Research Training Grant (D43)
● NAMBARI program led by
○ Prof. Ann Mwangi
○ Prof. Joseph Hogan
● AMPATH
● IeDEA
● Brown University
Beverly S. Musick,
M.S.
Professor Constantin
T. Yiannoutsos,
PhD
Dr. Aggrey S.
Semeere,
MD
Professor Kara K.
Wools-Kaloustian,
MD