Predicting Length of Stay of Patients with Lung Cancer and Mental Illness

For determining the Correlation between lung cancer patients who have undergone lobectomy and have a mental illness, the team developed multiple machine learning models. For this study, we split the data into 80% training data (4464 sample data) and 20% test data (1117 sample data).

We divided this problem statement into two areas of evaluation:

  • Predicting LOS of a patient with both lung cancer and mental illness using only Diagnosis codes.
  • Predicting LOS of a patient with both lung cancer and mental illness using both Diagnosis codes and Socio-demographic features.

The following algorithms were then developed:

  • SGDRegressor
  • GradientBoostingRegressor
  • LinearRegression
  • KNeighborsRegressor
  • RandomForestRegressor
  • SVR
  • TensorFlow

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Determining Correlation between Mental Illness and Lung Cancer using Machine Learning

DATA DISTRIBUTION

For any machine learning algorithm to be designed, it is important to understand the variability of the data and skewness, as well as the assumptions that we can make to build machine learning models. Here are some key statistical distribution models of the dataset we used for our study:

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Exploratory Analysis of Mental Illness Data amongst Lung Cancer Patients

During the course of our study, we specifically focused on lung cancer patients who have undergone lobectomy (lung cancer surgery) and analyze if any specific mental illness/psychiatric diagnoses or groups of diagnoses increase perioperative death risk.

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Analyzing Severe Mental Illness in Lung Cancer Patients

Lung cancer is the number one cause of cancer-related deaths worldwide. Patients with severe mental illness (SMI) are a group who are overrepresented in the lung cancer population. SMI refers to psychological problems, including mood disorders, major depression, schizophrenia, bipolar disorder, and substance abuse disorders, that inhibit a person’s ability to engage in functional and occupational activities.

Cancer patients diagnosed with SMI may not adhere to treatment plans and may have reduced access to healthcare. Individuals with SMI may have advanced tumor growth at diagnosis due to factors such as limited access to healthcare and healthcare systems. The aggregation of inadequate healthcare and increased risk for somatic disorders in patients with SMI can explain higher mortality rates. Many research papers have indicated that cancer represents a significant proportion of excess mortality for people with mental illness. Mental illness is typically associated with suicide, but much of the excess mortality rates associated with mental illness are due to cardiovascular or respiratory diseases and cancer. (more…)

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Predicting hospital readmissions and underlying risk factors of Lung Cancer with Machine Learning

Readmission after pulmonary lobectomy is a frequent challenge for hospitals, healthcare plans, and insurance providers. Readmission is a condition when a patient is admitted to a hospital for any reason within 30 days of discharge from their hospital. Re-occurring problems and readmissions have been a major issue in the healthcare system. Readmissions are often costly; however, their findings can be incredibly beneficial for both the public and healthcare industries. With this in consideration, to improve Americans’ healthcare, Hospital Readmissions Reduction Program (HRRP) was brought in motion by the Centers for Medicare & Medicaid Services (CMS). This program penalizes hospitals with excessive readmissions.

Allwyn is developing a machine learning based approach to reduce readmissions by recommending data-driven preventive actions prior to a lobectomy procedure. This approach can be used by various organizations such as hospitals or healthcare companies to take proactive measures and circumvent readmissions by predicting:

  • The probability of a patient’s readmission
  • Underlying risk factors

We will be sharing the challenges with Data Exploration and Engineering, followed by our Strategy and its impact. Follow us on LinkedIn as we share our approach in the coming weeks.

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