Hayoung Jeong’s research while affiliated with Georgia Institute of Technology and other places

Unsupervised clustering of intensive care unit (ICU) medications may identify unique medication clusters (i.e., pharmacophenotypes) in critically ill adults. We performed an unsupervised analysis with Restricted Boltzmann Machine of 991 medications profiles of patients managed in the ICU to explore pharmacophenotypes that correlated with ICU complications (e.g., mechanical ventilation) and patient-centered outcomes (e.g., length of stay, mortality). Six unique pharmacophenotypes were observed, with unique medication profiles and clinically relevant differences in ICU complications and patient-centered outcomes. While pharmacophenotypes 2 and 4 had no statistically significant difference in ICU length of stay, duration of mechanical ventilation, or duration of vasopressor use, their mortality differed significantly (9.0% vs. 21.9%, p < 0.0001). Pharmacophenotype 4 had a mortality rate of 21.9%, compared with the rest of the pharmacophenotypes ranging from 2.5 to 9%. Phenotyping approaches have shown promise in classifying the heterogenous syndromes of critical illness to predict treatment response and guide clinical decision support systems but have never included comprehensive medication information. This first-ever machine learning approach revealed differences among empirically-derived subgroups of ICU patients that are not typically revealed by traditional classifiers. Identification of pharmacophenotypes may enable enhanced decision making to optimize treatment decisions.

The recognition of Acute Respiratory Distress Syndrome (ARDS) may be delayed or missed entirely among critically ill patients. This study focuses on the development of a predictive algorithm for Hypoxic Respiratory Failure and associated risk of ARDS by utilizing routinely collected bedside monitoring. Specifically, the algorithm aims to predict onset over time. Uniquely, and favorable to robustness, the algorithm utilizes routinely collected, non-invasive cardiorespiratory waveform signals. This is a retrospective, Institutional-Review-Board-approved study of 2,078 patients at a tertiary hospital system. A modified Berlin criteria was used to identify 128 of the patients to have the condition during their encounter. A prediction horizon of 6 to 36 hours was defined for model training and evaluation. Xtreme Gradient Boosting algorithm was evaluated against signal processing and statistical features derived from the waveform and clinical data. Waveform-derived cardiorespiratory features, namely measures relating to variability and multi-scale entropy were robust and reliable features that predicted onset up to 36 hours before the clinical definition is met. The inclusion of structured data from the medical record, namely oxygenation patterns, complete blood counts, and basic metabolics further improved model performance. The combined model with 6-hour prediction horizon achieved an area under the receiver operating characteristic of 0.79 as opposed to the first 24-hour Lung Injury Prediction Score of 0.72.

Clinical decision support systems (CDSS) that are developed based on artificial intelligence and machine learning (AI/ML) approaches carry transformative potentials in improving the way neonatal care is practiced. From the use of the data available from electronic health records to physiological sensors and imaging modalities, CDSS can be used to predict clinical outcomes (such as mortality rate, hospital length of state, or surgical outcome) or early warning signs of diseases in neonates. However, only a limited number of clinical decision support systems for neonatal care are currently deployed in healthcare facilities or even implemented during pilot trials (or prospective studies). This is mostly due to the unresolved challenges in developing a real-time supported clinical decision support system, which mainly consists of three phases: model development, model evaluation, and real-time deployment. In this review, we introduce some of the pivotal challenges and factors we must consider during the implementation of real-time supported CDSS.

Unsupervised clustering of intensive care unit (ICU) medications may identify unique medication clusters (i.e., pharmacophenotypes) in critically ill adults. We performed an unsupervised analysis with Restricted Boltzmann Machine of 991 medications profiles of patients managed in the ICU to explore pharmacophenotypes that correlated with ICU complications (e.g., mechanical ventilation) and patient-centered outcomes (e.g., length of stay, mortality). Six unique pharmacophenotypes were observed, with unique medication profiles and clinically relevant differences in ICU complications and patient-centered outcomes. While pharmacophenotypes 2 and 4 had no statistically significant difference in ICU length of stay, duration of mechanical ventilation, or duration of vasopressor use, their mortality differed significantly (9.0% vs. 21.9%, p < 0.0001). Pharmacophenotype 4 had a mortality rate of 21.9%, compared with the rest of the pharmacophenotypes ranging from 2.5-9%. Phenotyping approaches have shown promise in classifying the heterogenous syndromes of critical illness to predict treatment response and guide clinical decision support systems but have never included comprehensive medication information. This first-ever machine learning approach revealed differences among empirically-derived subgroups of ICU patients that are not typically revealed by traditional classifiers. Identification of pharmacophenotypes may enable enhanced decision making to optimize treatment decisions.