ArticleNPJ digital medicine2023
Zero-shot interpretable phenotyping of postpartum hemorrhage using large language models.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed, 60 citations in OpenAlex.
- SPELL: A scalable NLP method using regular expressions and large language models for clinical information extraction.Computer methods and programs in biomedicine · 2026Article
- Trends in postpartum hemorrhage, associated risk factors, and severe maternal outcomes in a multihospital health system, 2016-2023: a retrospective cohort study.American journal of obstetrics & gynecology MFM · 2026Article
- Digital phenotyping with large language models to detect depressive state changes in patients.NPJ digital medicine · 2026Article
- A generative approach for semantic auditing of electronic health records.NPJ digital medicine · 2026Article
- irAE-GPT: leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets.EBioMedicine · 2026Article
- Comparative Evaluation of Proprietary and Open-Source Large Language Models for Systematic Multi-source Information Extraction in Interventional Oncology.Cardiovascular and interventional radiology · 2026Article
- HEAL: health-enhanced adaptive LoRaWAN for AI-based real-time soldier monitoring and status prediction.Scientific reports · 2026Article
- Improving classification of myocardial infarction with machine learning in a diverse population.American journal of epidemiology · 2026Article
- Review
- Automated abstraction of clinical parameters of multiple myeloma from real-world clinical notes using large language models.BMC medical informatics and decision making · 2026Article
- Phecoder: semantic retrieval for auditing and expanding ICD-based phenotypes in EHR biobanks.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence for postpartum hemorrhage: a systematic review.Frontiers in global women's health · 2026Review
- Auditable large language model curation of clinical notes refines glucagon-like peptide-1 initiation, persistence ascertainment, and compounding use beyond prescription records.Biology methods & protocols · 2026Article
- External validation of a machine learning model to predict postpartum hemorrhage in a US northeastern healthcare system.Pregnancy (Hoboken, N.J.) · 2026Article
- Surgical indicators for obstetrics and family planning in routine health information systems: a landscape analysis.Health policy and planning · 2025Article
- Accuracy of Large Language Models to Identify Stroke Subtypes Within Unstructured Electronic Health Record Data.Stroke · 2025Article
- Predicting Postpartum Hemorrhage Using Clinical Features Extracted With Large Language Models.O&G open · 2025Article
- Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models.Proceedings of machine learning research · 2025Article
- In-Context Learning with Large Language Models: A Simple and Effective Approach to Improve Radiology Report Labeling.Healthcare informatics research · 2025Article
- Evaluation and Bias Analysis of Large Language Models in Generating Synthetic Electronic Health Records: Comparative Study.Journal of medical Internet research · 2025Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Many areas of medicine would benefit from deeper, more accurate phenotyping, but there are limited approaches for phenotyping using clinical notes without substantial annotated data. Large language models (LLMs) have demonstrated immense potential to adapt to novel tasks with no additional training by specifying task-specific instructions. Here we report the performance of a publicly available LLM, Flan-T5, in phenotyping patients with postpartum hemorrhage (PPH) using discharge notes from electronic health records (n = 271,081). The language model achieves strong performance in extracting 24 granular concepts associated with PPH. Identifying these granular concepts accurately allows the development of interpretable, complex phenotypes and subtypes. The Flan-T5 model achieves high fidelity in phenotyping PPH (positive predictive value of 0.95), identifying 47% more patients with this complication compared to the current standard of using claims codes. This LLM pipeline can be used reliably for subtyping PPH and outperforms a claims-based approach on the three most common PPH subtypes associated with uterine atony, abnormal placentation, and obstetric trauma. The advantage of this approach to subtyping is its interpretability, as each concept contributing to the subtype determination can be evaluated. Moreover, as definitions may change over time due to new guidelines, using granular concepts to create complex phenotypes enables prompt and efficient updating of the algorithm. Using this language modelling approach enables rapid phenotyping without the need for any manually annotated training data across multiple clinical use cases.
Identifiers
What Socratic 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 Socratic graph.