ArticleBMC medical informatics and decision making2021
Predicting cardiovascular health trajectories in time-series electronic health records with LSTM models.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 38 citations in OpenAlex.
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Multi-output LSTM-based prediction of postoperative delirium: integrating baseline and perioperative data for enhanced risk stratification in older spine surgery patients.BioData mining · 2026Article
- RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data.Patterns (New York, N.Y.) · 2025Article
- Systematic data management for effective AI-driven decision support systems in robotic rehabilitation.Scientific reports · 2025Article
- Applications of flexible materials in health management assisted by machine learning.RSC advances · 2025Review
- Association of bone fracture with 30-year body mass index (BMI) trajectories: findings from the Framingham Heart Study : Bone fracture and 30-year BMI trajectories.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2024Article
- Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning.Diabetes & metabolism journal · 2024Article
- Cardiovascular disease prediction model based on patient behavior patterns in the context of deep learning: a time-series data analysis perspective.Frontiers in psychiatry · 2024Article
- Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review.Journal of healthcare informatics research · 2023Article
- Subtyping Patients With Chronic Disease Using Longitudinal BMI Patterns.IEEE journal of biomedical and health informatics · 2023Article
- Estimation of Life's Essential 8 Score with Incomplete Data of Individual Metrics.medRxiv : the preprint server for health sciences · 2023Article
- Estimation of life's essential 8 score with incomplete data of individual metrics.Frontiers in cardiovascular medicine · 2023Article
- Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model.BMC medicine · 2022Article
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Authors and funding
5 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiovascular disease (CVD) is the leading cause of death in the United States (US). Better cardiovascular health (CVH) is associated with CVD prevention. Predicting future CVH levels may help providers better manage patients' CVH. We hypothesized that CVH measures can be predicted based on previous measurements from longitudinal electronic health record (EHR) data.
methodsThe Guideline Advantage (TGA) dataset was used and contained EHR data from 70 outpatient clinics across the United States (US). We studied predictions of 5 CVH submetrics: smoking status (SMK), body mass index (BMI), blood pressure (BP), hemoglobin A1c (A1C), and low-density lipoprotein (LDL). We applied embedding techniques and long short-term memory (LSTM) networks - to predict future CVH category levels from all the previous CVH measurements of 216,445 unique patients for each CVH submetric.
resultsThe LSTM model performance was evaluated by the area under the receiver operator curve (AUROC): the micro-average AUROC was 0.99 for SMK prediction; 0.97 for BMI; 0.84 for BP; 0.91 for A1C; and 0.93 for LDL prediction. Model performance was not improved by using all 5 submetric measures compared with using single submetric measures.
conclusionsWe suggest that future CVH levels can be predicted using previous CVH measurements for each submetric, which has implications for population cardiovascular health management. Predicting patients' future CVH levels might directly increase patient CVH health and thus quality of life, while also indirectly decreasing the burden and cost for clinical health system caused by CVD and cancers.
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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 Socratic graph.