Mortality Prediction in Patients with Acute Kidney Injury (AKI)
Dates: February 24, 2025 - March 7, 2025
Time: 6:00PM - 8:30PM
KES: 2,500
Overview
Acute kidney injury (AKI) happens when the kidneys suddenly
can't filter waste products from the blood. When the kidneys can't filter
wastes, harmful levels of wastes may build up. The blood's chemical makeup may
get out of balance. Acute kidney injury used to be called acute kidney failure.
Acute kidney injury is most common in people who are in the hospital, mostly in
people who need intensive care.
Read more here
Objective
The
objective is to predict in-hospital mortality in the ICU of a retrospective
cohort of patients with AKI. The data comes from the MIMIC-III database.Each
registry includes a set of variables that summarize the clinical trajectory of
the patients during their stay in the ICU. This information is detailed in the
table shown below. The target variable, in-hospital mortality (IHM), is defined
as a binary variable where the value ’1’ indicates the death of the patient in
the ICU.
Course Outline
The live classes will be from 6:00pm - 8:30pm EAT (UTC +
0300hrs)
|
Day |
Date |
Objective |
Delivery |
|
Day 1 Mon |
24/Feb/2025 |
Onboarding -
Course Overview -
Mentees to join google classroom |
Online (Live Session) |
|
Day 2 Tue |
25/Feb/2025 |
-
Introduction to Machine Learning -
Applications of ML in Healthcare -
Understand Acute Kidney Injury Mortality Prediction -
Python basics |
Online (Live Session) -
Theory and Practical |
|
Day 3 Wed |
26/Feb/2025 |
-
Python basics assignment -
Practical -
Data preprocessing - Reading |
Offline (Submit Assignment to google classroom) |
|
Day 4 Thur |
27/Feb/2025 |
-
Assignment - Potential
applications of ML in existing Healthcare Projects in your country (Part of
capstone Project) |
Offline (Submit Assignment to google classroom) |
|
Day 5 Fri |
28/Feb/2025 |
-
AKI Project - Data
loading - Data
Preprocessing - Feature
Engineering - Feature
Selection |
Online (Live Session) -
Practical |
|
Day 6 Sat |
29/Feb/2025 |
-
Assignment - Reading - Classical ML - Ensemble ML - Neural Networks |
Offline (Submit Assignment to google classroom) |
|
Day 7 Tue |
4/March/2025 |
-
AKI project - Model
Development - Model Fine
tuning - Model
Deployment |
Online (Live Session) -
Practical |
|
Day 8 Wed |
5/March/2025 |
-
Capstone Project - Ideate of a healthcare problem that can be solved with
ML in your County - Document ML process |
Offline (Submit Assignment to google classroom) |
|
Day 9 Fri |
7/March/2025 |
-
Capstone Project presentations |
Online (Live Session) |
Trainer
David Nene
AI Engineer - Health
Innovations (Gen AI, LLMs, Computer Vision)
MSc. Data Science
& Analytics - www.strathmore.edu
Technical mentor www.neuromatch.io,
www.mexa.app
Founder www.advernet.africa
LinkedIn: www.linkedin.com/in/david-nene
About the trainer
David
Nene is an AI Engineer specializing in health innovations, with expertise in
Generative AI, Large Language Models (LLMs), and Computer Vision. He holds an
MSc in Data Science & Analytics from Strathmore University and serves as a
technical mentor at Neuromatch. With a strong background in AI-driven
healthcare solutions, David has worked on predictive modeling for medical
applications, including disease diagnosis, drug prescription optimization, and
ICU mortality prediction. As the founder of Advernet Africa, he is passionate
about leveraging AI to drive impactful and sustainable healthcare innovations
in Africa.