ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2012
Prognostic physiology: modeling patient severity in Intensive Care Units using radial domain folding.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Innovative information visualization of electronic health record data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2015Pooled it
- RMS: a ML-based system for ICU respiratory monitoring and resource planning.NPJ digital medicine · 2025Article
- Dynamic Prediction of Patient Outcomes in the Intensive Care Unit: A Scoping Review of the State-of-the-Art.Journal of intensive care medicine · 2023Article
- Using Temporal Data Mining on Patient Data for Clinical Decision Making in the Care of the Sick Newborn.EC paediatrics : open access · 2022Article
- A review of big data applications of physiological signal data.Biophysical reviews · 2019Review
- Big Data and Data Science in Critical Care.Chest · 2018Review
- Automatic recognition of symptom severity from psychiatric evaluation records.Journal of biomedical informatics · 2017Article
- Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Predicting intervention onset in the ICU with switching state space models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2015Article
- Using Visual Analytics to Determine the Utilization of Preoperative Anesthesia Assessments.Applied clinical informatics · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Real-time scalable predictive algorithms that can mine big health data as the care is happening can become the new "medical tests" in critical care. This work describes a new unsupervised learning approach, radial domain folding, to scale and summarize the enormous amount of data collected and to visualize the degradations or improvements in multiple organ systems in real time. Our proposed system is based on learning multi-layer lower dimensional abstractions from routinely generated patient data in modern Intensive Care Units (ICUs), and is dramatically different from most of the current work being done in ICU data mining that rely on building supervised predictive models using commonly measured clinical observations. We demonstrate that our system discovers abstract patient states that summarize a patient's physiology. Further, we show that a logistic regression model trained exclusively on our learned layer outperforms a customized SAPS II score on the mortality prediction task.
Indexed as
Identifiers
23304406PMC3540548W73198697What OpenQuestion holds
Registered trials
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.