ArticleIEEE transactions on bio-medical engineering2024
Forecasting Treatment Outcomes Over Time Using Alternating Deep Sequential Models.
Article in IEEE transactions on bio-medical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Large language models forecast patient health trajectories enabling digital twins.NPJ digital medicine · 2025Article
- G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes.Proceedings of machine learning research · 2024Article
- An Artificial Neural Network Predicts Gender Differences of Motor and Non-Motor Symptoms of Patients with Advanced Parkinson's Disease under Levodopa-Carbidopa Intestinal Gel.Medicina (Kaunas, Lithuania) · 2024Observational
- Counterfactual Sepsis Outcome Prediction Under Dynamic and Time-Varying Treatment Regimes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Medical decision making often relies on accurately forecasting future patient trajectories. Conventional approaches for patient progression modeling often do not explicitly model treatments when predicting patient trajectories and outcomes. In this paper, we propose Alternating Transformer (AL-Transformer) to jointly model treatment and clinical outcomes over time as alternating sequential models. We leverage causal convolution in the self-attention mechanism of AL-Transformer to incorporate local spatial information in the sequence, thus enhancing the model's ability to capture local contextual information of the sequence. Additionally, to predict the sparse treatment, a constraint learned by a convolutional neural network (CNN) is used to constrain the sparse treatment output. Experimental results on two datasets from patients with sepsis and respiratory failure extracted from the Medical Information Mart for Intensive Care (MIMIC) database demonstrate the effectiveness of the proposed approach, outperforming existing state-of-the-art methods.
Indexed as
Identifiers
What 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.