77 research outputs found
A New Species of Leucothoid Amphipod, Anamixis bananarama, sp. n., from Shallow Coral Reefs in French Polynesia (Crustacea, Amphipoda, Leucothoidae)
Both leucomorph and anamorph developmental stages of Anamixis bananarama sp. n., are illustrated and described from shallow back reef environments of Moorea, French Polynesia. Distinguished by vestigial fi rst gnathopods that persist in post-transformational adult males, this is the second species in the genus to exhibit this unusual character. In other features such as coxae and second gnathopods A. bananarama sp. n. resembles other Pacific Plate endemics of Anamixis known from the region. Specific host association is not documented but suspected to be small calcareous asconoid sponges associated with coral rubble
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
A multi-cohort study on prediction of acute brain dysfunction states using selective state space models
Assessing acute brain dysfunction (ABD), including delirium and coma in the
intensive care unit (ICU), is a critical challenge due to its prevalence and
severe implications for patient outcomes. Current diagnostic methods rely on
infrequent clinical observations, which can only determine a patient's ABD
status after onset. Our research attempts to solve these problems by harnessing
Electronic Health Records (EHR) data to develop automated methods for ABD
prediction for patients in the ICU. Existing models solely predict a single
state (e.g., either delirium or coma), require at least 24 hours of observation
data to make predictions, do not dynamically predict fluctuating ABD conditions
during ICU stay (typically a one-time prediction), and use small sample size,
proprietary single-hospital datasets. Our research fills these gaps in the
existing literature by dynamically predicting delirium, coma, and mortality for
12-hour intervals throughout an ICU stay and validating on two public datasets.
Our research also introduces the concept of dynamically predicting critical
transitions from non-ABD to ABD and between different ABD states in real time,
which could be clinically more informative for the hospital staff. We compared
the predictive performance of two state-of-the-art neural network models, the
MAMBA selective state space model and the Longformer Transformer model. Using
the MAMBA model, we achieved a mean area under the receiving operator
characteristic curve (AUROC) of 0.95 on outcome prediction of ABD for 12-hour
intervals. The model achieves a mean AUROC of 0.79 when predicting transitions
between ABD states. Our study uses a curated dataset from the University of
Florida Health Shands Hospital for internal validation and two publicly
available datasets, MIMIC-IV and eICU, for external validation, demonstrating
robustness across ICU stays from 203 hospitals and 140,945 patients.Comment: 22 pages, 8 figures, To be publishe
CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attention
Advancements in medical imaging and endovascular grafting have facilitated
minimally invasive treatments for aortic diseases. Accurate 3D segmentation of
the aorta and its branches is crucial for interventions, as inaccurate
segmentation can lead to erroneous surgical planning and endograft
construction. Previous methods simplified aortic segmentation as a binary image
segmentation problem, overlooking the necessity of distinguishing between
individual aortic branches. In this paper, we introduce Context Infused
Swin-UNet (CIS-UNet), a deep learning model designed for multi-class
segmentation of the aorta and thirteen aortic branches. Combining the strengths
of Convolutional Neural Networks (CNNs) and Swin transformers, CIS-UNet adopts
a hierarchical encoder-decoder structure comprising a CNN encoder, symmetric
decoder, skip connections, and a novel Context-aware Shifted Window
Self-Attention (CSW-SA) as the bottleneck block. Notably, CSW-SA introduces a
unique utilization of the patch merging layer, distinct from conventional Swin
transformers. It efficiently condenses the feature map, providing a global
spatial context and enhancing performance when applied at the bottleneck layer,
offering superior computational efficiency and segmentation accuracy compared
to the Swin transformers. We trained our model on computed tomography (CT)
scans from 44 patients and tested it on 15 patients. CIS-UNet outperformed the
state-of-the-art SwinUNetR segmentation model, which is solely based on Swin
transformers, by achieving a superior mean Dice coefficient of 0.713 compared
to 0.697, and a mean surface distance of 2.78 mm compared to 3.39 mm.
CIS-UNet's superior 3D aortic segmentation offers improved precision and
optimization for planning endovascular treatments. Our dataset and code will be
publicly available
Acute kidney injury prediction for non-critical care patients: a retrospective external and internal validation study
Background: Acute kidney injury (AKI), the decline of kidney excretory
function, occurs in up to 18% of hospitalized admissions. Progression of AKI
may lead to irreversible kidney damage. Methods: This retrospective cohort
study includes adult patients admitted to a non-intensive care unit at the
University of Pittsburgh Medical Center (UPMC) (n = 46,815) and University of
Florida Health (UFH) (n = 127,202). We developed and compared deep learning and
conventional machine learning models to predict progression to Stage 2 or
higher AKI within the next 48 hours. We trained local models for each site (UFH
Model trained on UFH, UPMC Model trained on UPMC) and a separate model with a
development cohort of patients from both sites (UFH-UPMC Model). We internally
and externally validated the models on each site and performed subgroup
analyses across sex and race. Results: Stage 2 or higher AKI occurred in 3%
(n=3,257) and 8% (n=2,296) of UFH and UPMC patients, respectively. Area under
the receiver operating curve values (AUROC) for the UFH test cohort ranged
between 0.77 (UPMC Model) and 0.81 (UFH Model), while AUROC values ranged
between 0.79 (UFH Model) and 0.83 (UPMC Model) for the UPMC test cohort.
UFH-UPMC Model achieved an AUROC of 0.81 (95% confidence interval [CI] [0.80,
0.83]) for UFH and 0.82 (95% CI [0.81,0.84]) for UPMC test cohorts; an area
under the precision recall curve values (AUPRC) of 0.6 (95% CI, [0.05, 0.06])
for UFH and 0.13 (95% CI, [0.11,0.15]) for UPMC test cohorts. Kinetic estimated
glomerular filtration rate, nephrotoxic drug burden and blood urea nitrogen
remained the top three features with the highest influence across the models
and health centers. Conclusion: Locally developed models displayed marginally
reduced discrimination when tested on another institution, while the top set of
influencing features remained the same across the models and sites
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