Healthcare
❯
Predict patient outcome
❯Medical Assistants
❯Predicting relapse of a dialysis patient during treatment
Predicting relapse of a dialysis patient during treatment
For:
Dialysis nurses, dialysis patients, partner hospitals
Scope:
Build an AI solution to augment dialysis nurses
Goal:
Improved Customer Experience
Instant triaging of wounds
For:
Wound nurses, diabetes patients, hospitals
Scope:
Build an AI solution to augment triaging decisions by wound nurses.
Goal:
Improved Employee Efficiency
Prediction of Multidrug-Resistant TB from CT Pulmonary Images Based on Deep Learning Techniques
For:
Clinicians and medical professionals predicting multidrug-resistant (MDR) patients from drug-sensitive (DS) ones based on CT lung images.
Goal:
Improved Employee Efficiency
Applying machine learning to predict patient risk for in-hospital new infections
For:
Hospitals
Goal:
Improved Customer Experience, Anticipate Risks
Predicting relapse of a dialysis patient during treatment
For:
Dialysis nurses, dialysis patients, partner hospitalsGoal:
Improved Customer ExperienceProblem addressed
Use AI to predict if a patient may relapse during dialysis to reduce patient
trauma
Scope of use case
Build an AI solution to augment dialysis nurses
Description
The private dialysis clinic was relying solely on the discretion
of trained nurses to make a call whether or not a patient can
get started for a dialysis session or is expected to be taken to
a hospital ahead of the treatment due to possible relapse.
This created inconsistencies in the patients experience and
10 % of the patients would relapse and suffer trauma in the
middle of their sessions. The deep learning model was able
to provide a more consistent call about the likelihood of
relapse, upon which the trained nurses can decide
proactively for or against starting the dialysis session.