ArticleProceedings of the ... Innovative Applications of Artificial Intelligence Conference. Innovative Applications of Artificial Intelligence Conference2012
Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records.
Article in Proceedings of the ... Innovative Applications of Artificial Intelligence Conference. Innovative Applications of Artificial Intelligence Conference, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed.
- Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial.Circulation. Arrhythmia and electrophysiology · 2018Trial
- Multi-domain identification of myocardial infarction incidence using explainable AI: the overlooked role of periodontal health.Scientific reports · 2026Article
- A machine learning-based predictive model for the occurrence of lower extremity deep vein thrombosis after laparoscopic surgery in abdominal surgery.Frontiers in surgery · 2025Article
- Development of decision tree classification algorithms in predicting mortality of COVID-19 patients.International journal of emergency medicine · 2024Article
- A deep learning-powered diagnostic model for acute pancreatitis.BMC medical imaging · 2024Article
- Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning.Frontiers in cellular and infection microbiology · 2024Article
- Automated Family Histories Significantly Improve Risk Prediction in an EHR.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.Journal of Korean medical science · 2023Review
- Enhancing Kidney Transplant Care through the Integration of Chatbot.Healthcare (Basel, Switzerland) · 2023Review
- DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.BMC bioinformatics · 2023Article
- Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework.JMIR AI · 2023Article
- Predicting benzodiazepine prescriptions: A proof-of-concept machine learning approach.Frontiers in psychiatry · 2023Article
- Comparison of the modified Singapore myocardial infarction registry risk score with GRACE 2.0 in predicting 1-year acute myocardial infarction outcomes.Scientific reports · 2022Article
- Operationalizing a real-time scoring model to predict fall risk among older adults in the emergency department.Frontiers in digital health · 2022Article
- Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?NPJ digital medicine · 2021Article
- Temporal bias in case-control design: preventing reliable predictions of the future.Nature communications · 2021Article
- An Explainable Multimodal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data.Frontiers in artificial intelligence · 2021Article
- Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes.Frontiers in pharmacology · 2021Article
- Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data.BMC medical informatics and decision making · 2020Article
- Using Machine Learning to Predict Primary Care and Advance Workforce Research.Annals of family medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Electronic health records (EHRs) are an emerging relational domain with large potential to improve clinical outcomes. We apply two statistical relational learning (SRL) algorithms to the task of predicting primary myocardial infarction. We show that one SRL algorithm, relational functional gradient boosting, outperforms propositional learners particularly in the medically-relevant high recall region. We observe that both SRL algorithms predict outcomes better than their propositional analogs and suggest how our methods can augment current epidemiological practices.
Identifiers
25360347PMC4211289What 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.