ArticleNPJ digital medicine2022
Predictive structured-unstructured interactions in EHR models: A case study of suicide prediction.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Life events extraction from healthcare notes for veteran acute suicide risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Joint models targeting U.S. Army soldiers at high-risk of post-separation unemployment, homelessness, and suicide-related behaviors.Npj mental health research · 2026Article
- Using transfer learning to improve prediction of suicide risk in acute care hospitals.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Prediction of neoadjuvant chemotherapy efficacy in patients with HER2-low breast cancer based on ultrasound radiomics.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Neighborhood-level social determinants of suicidality in youth with schizophrenia: An EHR-based study.Schizophrenia research · 2025Article
- Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study.JMIR medical informatics · 2025Article
- Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review.Information (Basel) · 2025Article
- Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation.European journal of radiology open · 2024Article
- Scalable incident detection via natural language processing and probabilistic language models.Scientific reports · 2024Article
- Extracting Critical Information from Unstructured Clinicians' Notes Data to Identify Dementia Severity Using a Rule-Based Approach: Feasibility Study.JMIR aging · 2024Article
- Optimising the use of electronic medical records for large scale research in psychiatry.Translational psychiatry · 2024Review
- Combining clinical notes with structured electronic health records enhances the prediction of mental health crises.Cell reports. Medicine · 2023Article
- Evidence, ethics and the promise of artificial intelligence in psychiatry.Journal of medical ethics · 2023Article
- Target-based fusion using social determinants of health to enhance suicide prediction with electronic health records.PloS one · 2023Article
- Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records.Frontiers in psychiatry · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Clinical risk prediction models powered by electronic health records (EHRs) are becoming increasingly widespread in clinical practice. With suicide-related mortality rates rising in recent years, it is becoming increasingly urgent to understand, predict, and prevent suicidal behavior. Here, we compare the predictive value of structured and unstructured EHR data for predicting suicide risk. We find that Naive Bayes Classifier (NBC) and Random Forest (RF) models trained on structured EHR data perform better than those based on unstructured EHR data. An NBC model trained on both structured and unstructured data yields similar performance (AUC = 0.743) to an NBC model trained on structured data alone (0.742, p = 0.668), while an RF model trained on both data types yields significantly better results (AUC = 0.903) than an RF model trained on structured data alone (0.887, p < 0.001), likely due to the RF model's ability to capture interactions between the two data types. To investigate these interactions, we propose and implement a general framework for identifying specific structured-unstructured feature pairs whose interactions differ between case and non-case cohorts, and thus have the potential to improve predictive performance and increase understanding of clinical risk. We find that such feature pairs tend to capture heterogeneous pairs of general concepts, rather than homogeneous pairs of specific concepts. These findings and this framework can be used to improve current and future EHR-based clinical modeling efforts.
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.