Validation of Machine Learning-Based Individualized Treatment for Depressive Disorder Using Target Trial Emulation

This study aims to develop and validate the use of machine learning-based prediction models to select individualized pharmacological treatment for patients with depressive disorder. This study used data from Taiwan’s National Health Insurance Research Database. Patients with incident depressive disorders were included in this study. The study outcome was treatment failure, which was defined as psychiatric hospitalization, self-harm hospitalization, emergency visits, or treatment change. Prediction models based on the Super Learner ensemble were trained separately for the initial and the next-step treatments if the previous treatments failed. An individualized treatment strategy was developed for selecting the drug with the lowest probability of treatment failure for each patient as the model-selected regimen. We emulated clinical trials to estimate the effectiveness of individualized treatments. The area under the curve of the prediction model using Super Learner was 0.627 and 0.751 for the initial treatment and the next-step treatment, respectively. Model-selected regimens were associated with reduced treatment failure rates, with a 0.84-fold (95% confidence interval (CI) 0.82–0.86) decrease for the initial treatment and a 0.82-fold (95% CI 0.80–0.83) decrease for the next-step. In emulation of clinical trials, the model-selected regimen was associated with a reduced treatment failure rate.

Diagnosis and Classification of Diabetes Mellitus Type 1 and Type 2 Through Super Learner

Diabetes Mellitus (DM) plays a significant role in increasing the associated health problems worldwide by acting as a Comorbid condition. Moreover, it is a progressive illness without severe external symptoms leading to a fatal impact on the human body if left unnoticed or untreated. This research work aims to associate an individual’s lifestyle and ethnic background in assessing the risk of Diabetes acting as a comorbid condition. A detailed assessment of lockdown impact with rapid modification in individual’s lifestyle due to the pandemic gives specific insight into individuals becoming susceptible to Diabetes Mellitus. An ensemble of ML algorithms is utilized in predicting the risk of individuals turning Diabetic. The ensemble of the ML model is trained on the Pima Indian dataset and Vanderbilt biostatistics diabetes dataset providing the impact of Type 1 diabetes mellitus. The proposed super learner model provides the highest classification accuracy of T1DM & T2DM with 97% compared to an ensemble of algorithms in identifying and classifying the individuals as being susceptible to DM due to the lifestyle and ethnic background.