93 research outputs found
Interpretable Multi-Task Deep Neural Networks for Dynamic Predictions of Postoperative Complications
Accurate prediction of postoperative complications can inform shared
decisions between patients and surgeons regarding the appropriateness of
surgery, preoperative risk-reduction strategies, and postoperative resource
use. Traditional predictive analytic tools are hindered by suboptimal
performance and usability. We hypothesized that novel deep learning techniques
would outperform logistic regression models in predicting postoperative
complications. In a single-center longitudinal cohort of 43,943 adult patients
undergoing 52,529 major inpatient surgeries, deep learning yielded greater
discrimination than logistic regression for all nine complications. Predictive
performance was strongest when leveraging the full spectrum of preoperative and
intraoperative physiologic time-series electronic health record data. A single
multi-task deep learning model yielded greater performance than separate models
trained on individual complications. Integrated gradients interpretability
mechanisms demonstrated the substantial importance of missing data.
Interpretable, multi-task deep neural networks made accurate, patient-level
predictions that harbor the potential to augment surgical decision-making
Activity and Circadian Rhythm of Sepsis Patients in the Intensive Care Unit
Early mobilization of critically ill patients in the Intensive Care Unit
(ICU) can prevent adverse outcomes such as delirium and post-discharge physical
impairment. To date, no studies have characterized activity of sepsis patients
in the ICU using granular actigraphy data. This study characterizes the
activity of sepsis patients in the ICU to aid in future mobility interventions.
We have compared the actigraphy features of 24 patients in four groups: Chronic
Critical Illness (CCI) sepsis patients in the ICU, Rapid Recovery (RR) sepsis
patients in the ICU, non-sepsis ICU patients (control-ICU), and healthy
subjects. We used several statistical and circadian rhythm features extracted
from the patients' actigraphy data collected over a five-day period. Our
results show that the four groups are significantly different in terms of
activity features. In addition, we observed that the CCI and control-ICU
patients show less regularity in their circadian rhythm compared to the RR
patients. These results show the potential of using actigraphy data for guiding
mobilization practices, classifying sepsis recovery subtype, as well as for
tracking patients' recovery.Comment: 4 pages, IEEE Biomedical and Health Informatics (BHI) 201
Development of Computable Phenotype to Identify and Characterize Transitions in Acuity Status in Intensive Care Unit
Background: In the United States, 5.7 million patients are admitted annually
to intensive care units (ICU), with costs exceeding $82 billion. Although close
monitoring and dynamic assessment of patient acuity are key aspects of ICU
care, both are limited by the time constraints imposed on healthcare providers.
Methods: Using the University of Florida Health (UFH) Integrated Data
Repository as Honest Broker, we created a database with electronic health
records data from a retrospective study cohort of 38,749 adult patients
admitted to ICU at UF Health between 06/01/2014 and 08/22/2019. This repository
includes demographic information, comorbidities, vital signs, laboratory
values, medications with date and timestamps, and diagnoses and procedure codes
for all index admission encounters as well as encounters within 12 months prior
to index admission and 12 months follow-up. We developed algorithms to identify
acuity status of the patient every four hours during each ICU stay. Results: We
had 383,193 encounters (121,800 unique patients) admitted to the hospital, and
51,073 encounters (38,749 unique patients) with at least one ICU stay that
lasted more than four hours. These patients requiring ICU admission had longer
median hospital stay (7 days vs. 1 day) and higher in-hospital mortality (9.6%
vs. 0.4%) compared with those not admitted to the ICU. Among patients who were
admitted to the ICU and expired during hospital admission, more deaths occurred
in the ICU than on general hospital wards (7.4% vs. 0.8%, respectively).
Conclusions: We developed phenotyping algorithms that determined patient acuity
status every four hours while admitted to the ICU. This approach may be useful
in developing prognostic and clinical decision-support tools to aid patients,
caregivers, and providers in shared decision-making processes regarding
resource use and escalation of care.Comment: 21 Pages, that include 6 figures, 3 tables and 1 supplemental Tabl
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