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Case Study

Increasing utilization of medical machines by leveraging ML models to predict patient readmission rates

Client Background

The client is one of the leading healthcare providers for patients with chronic and acute renal disease

Client Need

  • Help prevent the hospitalization/re-hospitalization of patients post dialysis by leveraging predictive analytics
  • Build a model to predict the likelihood of hospitalization for high-risk patients within 30, 60 & 90 days of treatment at the hospital
  • Facilitate optimal utilization of slots and related resources allocated to patients with higher propensity of hospitalization

Our Solution

  • Analyzed data captured during a patient visit, including demographic details, vitals diagnosis, lab tests, and medical conditions currently associated with the patient
  • Developed classification machine learning models to predict patient hospitalization rate using advanced ML and DL algorithm
  • Analyzed important features and derived new ones to augment the model performance
  • Interpreted the reason behind hospitalization of each patients predicted positive by the model using model interpretability techniques

Tools & Technologies

Python, Anaconda, Spyder, Sokit Learn

Key Benefits

  • Predicted the likelihood of patients’ readmission with 72% ROC-AUC score
  • Increased utilization of dialysis slots by 70%
  • Helped client improve their rating by keeping a check on hospitalization of CKD patients
  • Made efficient utilization of resources by providing better care to high-risk patients
  • Substantial medical cost savings with the help of preventive care
Key benefits from machine learning - Innova Solutions

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