Review in Critical care clinics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
5 authors.
Joshua Pei LeSchool of Medicine, University of Limerick, Castletroy, Co, Limerick V94 T9PX, Ireland.
Supreeth Prajwal ShashikumarDivision of Biomedical Informatics, University of California San Diego, San Diego, CA, USA.
Atul MalhotraDivision of Pulmonary, Critical Care and Sleep Medicine, University of California San Diego, San Diego, CA, USA.
Shamim NematiDivision of Biomedical Informatics, University of California San Diego, San Diego, CA, USA.
Gabriel WardiDivision of Pulmonary, Critical Care and Sleep Medicine, University of California San Diego, San Diego, CA, USA; Department of Emergency Medicine, University of California San Diego, 200 W Arbor Drive, San Diego, CA 92103, USA. Electronic address: gwardi@health.ucsd.edu.
Funding
Is Obstructive Sleep Apnea Important in the Development of Alzheimer's DiseaseR01AG063925 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MALHOTRA, ATUL · 2020 to 2024
$3.7M
Underlying mechanisms of obesity-induced obstructive sleep apneaR01HL148436 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Atul Malhotra · 2020 to 2026
$3.4M
Sleep Apnea Endophenotypes: One Size Does Not Fit AllR01HL154926 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MALHOTRA, ATUL · 2021 to 2025
$3.3M
The cardiovascular consequences of sleep apnea plus COPD (Overlap syndrome)R01HL166485 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Atul Malhotra · 2023 to 2026
$3.1M
VentNet: A Real-Time Multimodal Data Integration Model for Prediction of Respiratory Failure in Patients with COVID-19R01HL157985 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MALHOTRA, ATUL, NEMATI, SHAMIM · 2022 to 2025
$2.9M
GeneRAlizable Sepsis Phenotyping (GRASP) using Electronic Health Records and Continuous Monitoring SensorsR35GM143121 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NEMATI, SHAMIM · 2021 to 2025
$2.2M
Training in the Next Generation in Respiratory ScienceT32HL134632 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MALHOTRA, ATUL, POWELL, FRANK L. · 2017 to 2021
$2.1M
RAAB-AI: Reducing Automation and Anchoring Bias in AI SystemsR01LM013998 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2022 to 2026
$1.7M
Developing a Diverse Next Generation of Leaders in Respiratory ScienceT32HL166127 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Laura Elise Crotty Alexander, Atul Malhotra · 2023 to 2026
$1.6M
AIVIS: Next Generation Vigilant Information Seeking Artificial Intelligence-based Clinical Decision Support for SepsisR42AI177108 · NIAID · HEALCISIO, INC. · PI JOSEF, CHRISTOPHER · 2023 to 2024
$1.3M
Implementation of Continuum of Care Sepsis Phenotyping and Risk StratificationK23GM146092 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Gabriel Wardi · 2022 to 2026
$898k
Enhanced Metadata Design, Architecture, and Learning (MeDAL) for Development of Generalizable Deep Learning-based Predictive Analytics from Electronic Health RecordsR56LM013517 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NEMATI, SHAMIM · 2020 to 2020
Syndromic conditions, such as sepsis, are commonly encountered in the intensive care unit. Although these conditions are easy for clinicians to grasp, these conditions may limit the performance of machine-learning algorithms. Individual hospital practice patterns may limit external generalizability. Data missingness is another barrier to optimal algorithm performance and various strategies exist to mitigate this. Recent advances in data science, such as transfer learning, conformal prediction, and continual learning, may improve generalizability of machine-learning algorithms in critically ill patients. Randomized trials with these approaches are indicated to demonstrate improvements in patient-centered outcomes at this point.
Indexed as
AlgorithmsSepsisHumansIntensive Care UnitsMachine LearningCritical careData missingnessData scienceMachine learningSepsisSyndrome
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape. · full record | OpenQuestion