Publications by authors named "Prathamesh Parchure"
Importance: Automating the identification of risk for developing hospital delirium with models that use machine learning (ML) could facilitate more rapid prevention, identification, and treatment of delirium. However, there are very few reports on the performance of ML models for delirium risk stratification in live clinical practice.
Objective: To report on development, operationalization, and validation of a multimodal ML model for delirium risk stratification in live clinical practice and its associations with workflow and clinical outcomes.
Importance: Machine learning has potential to transform cancer care by helping clinicians prioritize patients for serious illness conversations. However, models need to be evaluated for unequal performance across racial groups (ie, racial bias) so that existing racial disparities are not exacerbated.
Objective: To evaluate whether racial bias exists in a predictive machine learning model that identifies 180-day cancer mortality risk among patients with solid malignant tumors.
The decision to extubate patients on invasive mechanical ventilation is critical; however, clinician performance in identifying patients to liberate from the ventilator is poor. Machine Learning-based predictors using tabular data have been developed; however, these fail to capture the wide spectrum of data available. Here, we develop and validate a deep learning-based model using routinely collected chest X-rays to predict the outcome of attempted extubation.
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- Malnutrition is often undiagnosed, leading to worse health outcomes and higher costs, prompting the Mount Sinai Health System to implement a machine learning model (MUST-Plus) for detection upon hospital admission.* -
- The study analyzed data from nearly 67,000 adult patients to assess and improve the calibration of MUST-Plus, revealing significant miscalibration across different races and genders, particularly in its predictions.* -
- After logistic recalibration, the model's accuracy improved for all patient subgroups, highlighting the importance of ongoing monitoring and adjustment to reduce healthcare disparities.*
Background: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST-Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition.
View Article and Find Full Text PDFObjectives: To develop and validate a model for prediction of near-term in-hospital mortality among patients with COVID-19 by application of a machine learning (ML) algorithm on time-series inpatient data from electronic health records.
Methods: A cohort comprised of 567 patients with COVID-19 at a large acute care healthcare system between 10 February 2020 and 7 April 2020 observed until either death or discharge. Random forest (RF) model was developed on randomly drawn 70% of the cohort (training set) and its performance was evaluated on the rest of 30% (the test set).