How We Built an AI Model That Predicts Patient Risk with 92% Accuracy
Overview of Our Predictive AI Model for Patient Risk Scoring Case Study
A leading hospital network (under NDA) partnered with our AI agent development company to improve early intervention for patients suffering from chronic illnesses such as heart disease, diabetes, and respiratory conditions. Their existing process relied mostly on manual reviews of patient records, which made it hard to spot early signs of health deterioration.
To solve this, we developed a custom AI model that continuously analyzed patient history, real-time vitals, lab reports, and medication patterns. The system generated accurate risk scores that flagged high-risk patients before their conditions worsened. Doctors and care teams could now focus their attention where it was needed most, take preventive steps earlier.
Healthcare
AI Model Development
Our Process of Delivering a Reliable AI Model for Clinical Risk Prediction
We followed a rigorous approach focused on medical accuracy and real-time usability:
Data Collection and Preprocessing
We gathered patient EHRs, lab results, admission history, vitals, and medication logs. Missing values were handled carefully, and data was anonymized to protect privacy.
Model Development and Training
We trained a classification model using gradient boosting and logistic regression. We focused on precision and recall to reduce false alarms and ensure reliability.
Model Validation with Clinical Teams
Our AI engineers tested the model using past data and worked with doctors to validate results. Adjustments were made to reflect clinical realities and risk thresholds.
Integration into Hospital Workflow
The AI model was deployed into the hospital’s internal system with a simple interface. Doctors received a risk score and suggested actions which got updated every hour.
