Expert Analysis on Implementation of Machine Learning on Lobectomy Data.

Our research has enabled us to develop models suitable for targeting and capturing nearly eight readmitted patients out of every 10. Our final model revealed a combination of demographic and diagnosis related features. These combinations further allowed us to analyze the likelihood of someone being readmitted when going through a lobectomy procedure.

This has helped us understand which variables contribute the most to the model.

Circulatory system diseases (I00-I99), certain infectious and parasitic diseases (A00-B99), neoplasms (C00-D49), musculoskeletal system and connective tissue diseases (M00-M99) were among the top contributing factors to the predictive ability of our model in the medical factors.

By understanding the likelihood of a patient’s readmission, pre/post-operative interventions such as weight loss, home monitoring programs, or additional medical procedures can be introduced into a patient’s hospital care cycle, which would improve their outcome and reduce the relative costs for them, healthcare provider, and the hospital.

Likewise, our approach can target different medical procedures for any dataset with similar information but not necessarily all the features used in our models.


One of the key limitations we faced in our research was the ICD10 data being available only from Q415 to Q417. This limited us only to research the existing data from a two-year period.

Similar research done on readmission cases covers a decade’s worth data.

Acquisition of more data can enable us further optimizing the models based on the desired target metric and help with class imbalance. The study is limited to the non-medical factors that are being collected in the NRD, and depending on healthcare information providers, the final model is subject to change.

Next Steps

  • Refine the readmission predictive analysis model on a smaller subset of medical and non-medical features and perform more real-world data validation.
  • Refine the model by applying to more massive data sets from other sources.
  • Working with the medical community on possible preventive actions to reduce readmissions.

The Healthcare industry is one of the primary adopters of Machine Learning initiatives in the past decade. Applications of ML goes beyond this prescriptive analysis and can even contribute to highly sensitive AI operations.

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