ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2019
Assessing Contribution of Higher Order Clinical Risk Factors to Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage Patients.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed, 10 citations in OpenAlex.
- Machine learning modeling for outcome prediction of hospitalized patients with aneurysmal subarachnoid hemorrhage.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study.Journal of medical Internet research · 2024Article
- Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms.Scientific reports · 2023Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The goal of this study was to investigate the application of machine learning models capable of capturing multiplica tive and temporal clinical risk factors for outcome prediction inpatients with aneurysmal subarachnoid hemorrhage (aSAH). We examined a cohort of 575 aSAH patients from Emory Healthcare, identified via digital subtraction angiog- raphy. The outcome measure was the modified Ranking Scale (mRS) after 90 days. Predictions were performed with longitudinal clinical and imaging risk factors as inputs into a regularized Logistic Regression, a feedforward Neural Network and a multivariate time-series prediction model known as the long short-term memory (LSTM) architecture. Through extraction of higher-order risk factors, the LSTM model achieved an AUC of 0.89 eight days into hospitaliza tion, outperforming other techniques. Our preliminary findings indicate the proposed model has the potential to aid treatment decisions and effective imaging resource utilization in high-risk patients by providing actionable predictions prior to the development of neurological deterioration.
Indexed as
Identifiers
32308881PMC7153066W3026086344What 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.