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Scholarly Works (2 results)

Thesis
Peer Reviewed

Generalizable Risk Predictive Deep Learning Models

Amrollahi, Fatemeh
Advisor(s): Nemati, Shamim

UC San Diego Electronic Theses and Dissertations (2023)

The broad adoption of Electronic Health Records (EHRs) accelerated the development and usage of Machine learning (ML) and Deep learning (DL) algorithms in clinical settings. The potential uses of ML and DL algorithms to augment clinical decision-making in domains such as forecasting disease onset and progression, predicting response to treatments, and optimization of treatment protocols are growing. While most existing ML/DL models are trained on single-centered data, multi-center datasets are becoming increasingly available. However, curation of such datasets is often time-consuming and lags behind shifts in disease prevalence and changes in workflow practices, which are known to cause data distribution shifts and degradation in ML/DL model performance.%\cite{piccialli_survey_2021}%

In addition, data privacy concerns and patient confidentiality regulations continue to pose a major barrier to multicenter EHR data access. In this work, we developed algorithms to enable DL models to transfer their knowledge across institutional boundaries and learn from new episodes of patient care without forgetting previously learned patterns. We validated and compared our methods in the context of early prediction of sepsis using data across four geographically distinct healthcare systems. We explore several methods to enhance the generalizability of DL models. We focus on three areas: Continual Learning, Federated Learning, and Generative Adversarial Networks (GANs), introducing new algorithms within each area and comparing their performance against state-of-the-art models. We have validated and compared these methods in one of the most challenging tasks for biomedical researchers: predicting the onset of sepsis in intensive care units.

Cover page: Generalizable Risk Predictive Deep Learning Models

Article
Peer Reviewed

Prediction of Readmission Following Sepsis Using Social Determinants of Health.

UC San Diego Previously Published Works (2024)

OBJECTIVES: To determine the predictive value of social determinants of health (SDoH) variables on 30-day readmission following a sepsis hospitalization as compared with traditional clinical variables. DESIGN: Multicenter retrospective cohort study using patient-level data, including demographic, clinical, and survey data. SETTINGS: Thirty-five hospitals across the United States from 2017 to 2021. PATIENTS: Two hundred seventy-one thousand four hundred twenty-eight individuals in the AllofUs initiative, of which 8909 had an index sepsis hospitalization. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Unplanned 30-day readmission to the hospital. Multinomial logistic regression models were constructed to account for survival in determination of variables associate with 30-day readmission and are presented as adjusted odds rations (aORs). Of the 8909 sepsis patients in our cohort, 21% had an unplanned hospital readmission within 30 days. Median age (interquartile range) was 54 years (41-65 yr), 4762 (53.4%) were female, and there were self-reported 1612 (18.09%) Black, 2271 (25.49%) Hispanic, and 4642 (52.1%) White individuals. In multinomial logistic regression models accounting for survival, we identified that change to nonphysician provider type due to economic reasons (aOR, 2.55 [2.35-2.74]), delay of receiving medical care due to lack of transportation (aOR, 1.68 [1.62-1.74]), and inability to afford flow-up care (aOR, 1.59 [1.52-1.66]) were strongly and independently associated with a 30-day readmission when adjusting for survival. Patients who lived in a ZIP code with a high percentage of patients in poverty and without health insurance were also more likely to be readmitted within 30 days (aOR, 1.26 [1.22-1.29] and aOR, 1.28 [1.26-1.29], respectively). Finally, we found that having a primary care provider and health insurance were associated with low odds of an unplanned 30-day readmission. CONCLUSIONS: In this multicenter retrospective cohort, several SDoH variables were strongly associated with unplanned 30-day readmission. Models predicting readmission following sepsis hospitalization may benefit from the addition of SDoH factors to traditional clinical variables.