Deep Learning Methodologies to Predict Fluid Responsiveness in Hemodynamically Unstable Patients
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Deep Learning Methodologies to Predict Fluid Responsiveness in Hemodynamically Unstable Patients


Deep Learning is a branch of machine learning with a layered structure where each layer gets its input from the previous layer to analyze the data and has shown promising results to aid healthcare providers in various applications. This thesis presents the development and evaluation of various Deep Learning approaches to predict if a hemodynamically unstable patient would be responsive to infusion of intravenous fluids. This thesis also explores various meticulously designed experiments to thoroughly verify the predictive model’s generalization across various carefully curated datasets to represent shocks resulting from different physiologies in pigs. Treatment of patients suffering from shock often involves the infusion of intravenous fluids, also known as fluid bolus therapy (FBT). An increase in cardiac output (CO) of 15% or more after a supply of 500 ml of the fluid bolus indicates fluid responsiveness, and the ground truth labels were designed based on this rule. In addition, the arterial blood pressure (ABP) and central venous pressure (CVP) waveforms were recorded before and after the infusion of each bolus. The period before, during, and after the administration of fluid boluses is known as pre-macrobolus (Premac), macrobolus, post-macrobolus (Postmac), respectively. The deep learning model takes sequences of specific lengths obtained from the ABP and CVP during the premac period and the ground truth labels to classify each bolus to be fluid responsive or not. The models were tuned and trained using nested cross-validation accompanied by grid search algorithms. The results from our experiments suggest that deep learning can offer a satisfactory framework to classify boluses as fluid responsive or fluid non-responsive. In addition, this thesis presents a comprehensive guide to experimentation on various aspects that could potentially affect the performance of deep learning models while classifying one-dimensional data, including input sequence length, model’s architecture, sample weighting in loss functions, normalization, resampling the data, and various methods to sample the data to acquire meaningful inferences from the results. The experiments showcase the restricting nature of small-scale datasets on the deep learning model’s performance. The deep learning model fails to generalize when the training and the test sets contain different physiologies but generalizes better when both the training and the test sets contain a comparable mixture of physiologies.

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