Predicting Individual Patient Platelet Demand in a Large Tertiary Care Hospital Using Machine Learning

Optimizing platelet transfusion through a personalized deep learning risk assessment system for demand management

ABSTRACT

Platelet demand management (PDM) is a resource-consuming task for physicians and transfusion managers of large hospitals. Inpatient numbers and institutional standards play significant roles in PDM. However, reliance on these factors alone commonly results in platelet shortages. Using data from multiple sources, we developed, validated, tested, and implemented a patient-specific approach to support PDM that uses a deep learning-based risk score to forecast platelet transfusions for each hospitalized patient in the next 24 hours. The models were developed using retrospective electronic health record data of 34 809 patients treated between 2017 and 2022. Static and time-dependent features included demographics, diagnoses, procedures, blood counts, past transfusions, hematotoxic medications, and hospitalization duration. Using an expanding window approach, we created a training and live-prediction pipeline with a 30-day input and 24-hour forecast. Hyperparameter tuning determined the best validation area under the precision-recall curve (AUC-PR) score for long short-term memory deep learning models, which were then tested on independent data sets from the same hospital. The model tailored for hematology and oncology patients exhibited the best performance (AUC-PR, 0.84; area under the receiver operating characteristic curve [ROC-AUC], 0.98), followed by a multispecialty model covering all other patients (AUC-PR, 0.73). The model specific to cardiothoracic surgery had the lowest performance (AUC-PR, 0.42), likely because of unexpected intrasurgery bleedings. To our knowledge, this is the first deep learning-based platelet transfusion predictor enabling individualized 24-hour risk assessments at high AUC-PR. Implemented as a decision-support system, deep-learning forecasts might improve patient care by detecting platelet demand earlier and preventing critical transfusion shortages.

LayNet-A multi-layer architecture to handle imbalance in medical imaging data

In an imbalanced dataset, a machine learning classifier using traditional imbalance handling methods may achieve good accuracy, but in highly imbalanced datasets, it may over-predict the majority class and ignore the minority class. In the medical domain, failing to correctly estimate the minority class might lead to a false negative, which is concerning in cases of life-threatening illnesses and infectious diseases like Covid-19. Currently, classification in deep learning has a single layered architecture where a neural network is employed. This paper proposes a multilayer design entitled LayNet to address this issue. LayNet aims to lessen the class imbalance by dividing the classes among layers and achieving a balanced class distribution at each layer. To ensure that all the classes are being classified, minor classes are combined to form a single new 'hybrid' class at higher layers. The final layer has no hybrid class and only singleton(distinct) classes. Each layer of the architecture includes a separate model that determines if an input belongs to one class or a hybrid class. If it fits into the hybrid class, it advances to the following layer, which is further categorized within the hybrid class. The method to divide the classes into various architectural levels is also introduced in this paper. The Ocular Disease Intelligent Recognition Dataset, Covid-19 Radiography Dataset, and Retinal OCT Dataset are used to evaluate this methodology. The LayNet architecture performs better on these datasets when the results of the traditional single-layer architecture and the proposed multilayered architecture are compared.