About

Yindalon Aphinyanaphongs, MD, PhD, is the Director of Translational Clinical Informatics for DataCore at NYU Grossman School of Medicine. Her role involves leading efforts in clinical informatics with a focus on translational applications. Her background includes medical training and doctoral research, which contribute to her expertise in data-driven clinical informatics. She is part of the Health Tech Hub team, working on innovative health technology initiatives that integrate clinical data and informatics to improve healthcare delivery and research.

Research topics

• Medicine
• Internal medicine
• Artificial Intelligence
• Machine Learning
• Computer Science
• Software engineering
• Psychology
• Gastroenterology
• Immunology
• Emergency medicine
• Data science
• Virology
• Biology
• Intensive care medicine

Selected publications

• Enhancing the prediction of hospital discharge disposition with extraction-based language model classification

  npj Health Systems · 2026-01-09

  articleOpen accessSenior author

  Early identification of inpatient discharges to skilled nursing facilities (SNFs) facilitates care transition planning. Predictive information in admission history and physical notes (H&Ps) is dispersed across long documents. Language models adeptly predict clinical outcomes from text but have limitations: token length constraints, noisy inputs, and opaque outputs. Therefore, we developed extraction-based language model classification (ELC): generative language models distill H&Ps into task-relevant categories ("Structured Extracted Data") before summarizing them into a concise narrative ("AI Risk Snapshot"). We hypothesized that language models utilizing AI Risk Snapshots to predict SNF discharges would perform the best. In this retrospective observational study, nine language models predicted SNF discharges from unstructured predictors (raw H&P text, truncated assessment and plan) and ELC-derived predictors (Structured Extracted Data, AI Risk Snapshots). ELC substantially reduced input length (AI Risk Snapshot median 141 tokens vs raw H&P median 2,120 tokens) and improved average AUROC and AUPRC across models. The best performance was achieved by Bio+Clinical BERT fine-tuned on AI Risk Snapshots (AUROC = .851). AI Risk Snapshots enhanced interpretability by aligning with nurse case managers' risk assessments and facilitating prompt design. Structuring and summarizing H&Ps via ELC thus mitigates the practical limitations of language models and improves SNF discharge prediction.

  PublisherOA PDFDOI

• Large Language Models Predict Functional Outcomes after Acute Ischemic Stroke

  arXiv (Cornell University) · 2026-01-18

  articleOpen access

  Accurate prediction of functional outcomes after acute ischemic stroke can inform clinical decision-making and resource allocation. Prior work on modified Rankin Scale (mRS) prediction has relied primarily on structured variables (e.g., age, NIHSS) and conventional machine learning. The ability of large language models (LLMs) to infer future mRS scores directly from routine admission notes remains largely unexplored. We evaluated encoder (BERT, NYUTron) and generative (Llama-3.1-8B, MedGemma-4B) LLMs, in both frozen and fine-tuned settings, for discharge and 90-day mRS prediction using a large, real-world stroke registry. The discharge outcome dataset included 9,485 History and Physical notes and the 90-day outcome dataset included 1,898 notes from the NYU Langone Get With The Guidelines-Stroke registry (2016-2025). Data were temporally split with the most recent 12 months held out for testing. Performance was assessed using exact (7-class) mRS accuracy and binary functional outcome (mRS 0-2 vs. 3-6) accuracy and compared against established structured-data baselines incorporating NIHSS and age. Fine-tuned Llama achieved the highest performance, with 90-day exact mRS accuracy of 33.9% [95% CI, 27.9-39.9%] and binary accuracy of 76.3% [95% CI, 70.7-81.9%]. Discharge performance reached 42.0% [95% CI, 39.0-45.0%] exact accuracy and 75.0% [95% CI, 72.4-77.6%] binary accuracy. For 90-day prediction, Llama performed comparably to structured-data baselines. Fine-tuned LLMs can predict post-stroke functional outcomes from admission notes alone, achieving performance comparable to models requiring structured variable abstraction. Our findings support the development of text-based prognostic tools that integrate seamlessly into clinical workflows without manual data extraction.

  PublisherOA PDF

• Large Language Models Predict Functional Outcomes after Acute Ischemic Stroke

  Open MIND · 2026-01-18

  preprint

  Accurate prediction of functional outcomes after acute ischemic stroke can inform clinical decision-making and resource allocation. Prior work on modified Rankin Scale (mRS) prediction has relied primarily on structured variables (e.g., age, NIHSS) and conventional machine learning. The ability of large language models (LLMs) to infer future mRS scores directly from routine admission notes remains largely unexplored. We evaluated encoder (BERT, NYUTron) and generative (Llama-3.1-8B, MedGemma-4B) LLMs, in both frozen and fine-tuned settings, for discharge and 90-day mRS prediction using a large, real-world stroke registry. The discharge outcome dataset included 9,485 History and Physical notes and the 90-day outcome dataset included 1,898 notes from the NYU Langone Get With The Guidelines-Stroke registry (2016-2025). Data were temporally split with the most recent 12 months held out for testing. Performance was assessed using exact (7-class) mRS accuracy and binary functional outcome (mRS 0-2 vs. 3-6) accuracy and compared against established structured-data baselines incorporating NIHSS and age. Fine-tuned Llama achieved the highest performance, with 90-day exact mRS accuracy of 33.9% [95% CI, 27.9-39.9%] and binary accuracy of 76.3% [95% CI, 70.7-81.9%]. Discharge performance reached 42.0% [95% CI, 39.0-45.0%] exact accuracy and 75.0% [95% CI, 72.4-77.6%] binary accuracy. For 90-day prediction, Llama performed comparably to structured-data baselines. Fine-tuned LLMs can predict post-stroke functional outcomes from admission notes alone, achieving performance comparable to models requiring structured variable abstraction. Our findings support the development of text-based prognostic tools that integrate seamlessly into clinical workflows without manual data extraction.

  DOI

• Generalist Foundation Models Are Not Clinical Enough for Hospital Operations

  Research Square · 2026-03-19

  preprintOpen access
  PublisherOA PDFDOI

• Generalist Large Language Models Outperform Clinical Tools on Medical Benchmarks

  ArXiv.org · 2025-12-01

  preprintOpen access

  Specialized clinical AI assistants are rapidly entering medical practice, often framed as safer or more reliable than general-purpose large language models (LLMs). Yet, unlike frontier models, these clinical tools are rarely subjected to independent, quantitative evaluation, creating a critical evidence gap despite their growing influence on diagnosis, triage, and guideline interpretation. We assessed two widely deployed clinical AI systems (OpenEvidence and UpToDate Expert AI) against three state-of-the-art generalist LLMs (GPT-5, Gemini 3 Pro, and Claude Sonnet 4.5) using a 1,000-item mini-benchmark combining MedQA (medical knowledge) and HealthBench (clinician-alignment) tasks. Generalist models consistently outperformed clinical tools, with GPT-5 achieving the highest scores, while OpenEvidence and UpToDate demonstrated deficits in completeness, communication quality, context awareness, and systems-based safety reasoning. These findings reveal that tools marketed for clinical decision support may often lag behind frontier LLMs, underscoring the urgent need for transparent, independent evaluation before deployment in patient-facing workflows.

  PublisherOA PDFDOI

• The TRIPOD-LLM reporting guideline for studies using large language models: a Korean translation

  The Ewha Medical Journal · 2025-07-31 · 1 citations

  articleOpen access

  대형 언어 모델(large language model, LLM)의 활용이 의료 분야에서 빠르게 확대되면서, 표준화된 보고 지침의 필요성이 커지고 있다. 이 논문에서는 LLM을 활용한 연구를 위한 다변수 예측모델의 투명한 보고(TRIPOD-LLM) 지침을 제시하였다. TRIPOD-LLM은 기존 TRIPOD와 인공지능(artificial intelligence) 확장 지침을 기반으로 하며, 바이오 메디컬 분야에서 LLM이 가지는 고유한 도전 과제들을 반영하고 있다. 이 지침은 제목부터 논의까지 주요 내용을 포괄하는 19개 주요 항목과 50개 세부 항목으로 구성되어 있다. 다양한 LLM 연구설계와 작업에 적용할 수 있도록 모듈형 형식을 도입하였고, 모든 연구에 공통적으로 적용할 수 있는 14개 주요 항목과 32개 세부 항목을 포함한다. 이 지침은 신속한 델파이(Delphi) 과정과 전문가 합의를 거쳐 개발하였으며, 투명성과 인간 감독, 과업 특이적 성과(task-specific performance) 보고의 중요성을 강조한다. 또한 지침의 손쉬운 작성과 제출용 PDF 생성을 지원하는 인터랙티브 웹사이트(https://tripod-llm.vercel.app/)를 소개한다. TRIPOD-LLM은 ‘생명력 있는 문서’로서, 연구현장의 변화에 맞추어 지속적으로 개정될 예정이다. 이 지침을 통해 LLM 연구의 보고 수준을 높이고, 재현성과 임상 적용 가능성을 강화하는 데 기여하려고 한다.

  PublisherDOI

• The TRIPOD-LLM reporting guideline for studies using large language models

  Nature Medicine · 2025-01-01 · 312 citations

  reviewOpen access
  PublisherOA PDFDOI

• Automating the Referral of Bone Metastases Patients With and Without the Use of Large Language Models

  Neurosurgery · 2025-08-15 · 2 citations

  article

  BACKGROUND AND OBJECTIVES: Bone metastases, affecting more than 4.8% of patients with cancer annually, and particularly spinal metastases require urgent intervention to prevent neurological complications. However, the current process of manually reviewing radiological reports leads to potential delays in specialist referrals. We hypothesized that natural language processing (NLP) review of routine radiology reports could automate the referral process for timely multidisciplinary care of spinal metastases. METHODS: We assessed 3 NLP models-a rule-based regular expression (RegEx) model, GPT-4, and a specialized Bidirectional Encoder Representations from Transformers (BERT) model (NYUTron)-for automated detection and referral of bone metastases. Study inclusion criteria targeted patients with active cancer diagnoses who underwent advanced imaging (computed tomography, MRI, or positron emission tomography) without previous specialist referral. We defined 2 separate tasks: task of identifying clinically significant bone metastatic terms (lexical detection), and identifying cases needing a specialist follow-up (clinical referral). Models were developed using 3754 hand-labeled advanced imaging studies in 2 phases: phase 1 focused on spine metastases, and phase 2 generalized to bone metastases. Standard McRae's line performance metrics were evaluated and compared across all stages and tasks. RESULTS: In the lexical detection, a simple RegEx achieved the highest performance (sensitivity 98.4%, specificity 97.6%, F1 = 0.965), followed by NYUTron (sensitivity 96.8%, specificity 89.9%, and F1 = 0.787). For the clinical referral task, RegEx also demonstrated superior performance (sensitivity 92.3%, specificity 87.5%, and F1 = 0.936), followed by a fine-tuned NYUTron model (sensitivity 90.0%, specificity 66.7%, and F1 = 0.750). CONCLUSION: An NLP-based automated referral system can accurately identify patients with bone metastases requiring specialist evaluation. A simple RegEx model excels in syntax-based identification and expert-informed rule generation for efficient referral patient recommendation in comparison with advanced NLP models. This system could significantly reduce missed follow-ups and enhance timely intervention for patients with bone metastases.

  PublisherDOI
• RETRACTED

  RETRACTED: Repurposing the Scientific Literature with Vision-Language Models

  Research Square · 2025-01-29 · 4 citations

  preprint
  PublisherDOI

• Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithms

  Scientific Reports · 2025-04-05 · 4 citations

  articleOpen access

  Early detection of pancreatic cancer (PC) remains challenging largely due to the low population incidence and few known risk factors. However, screening in at-risk populations and detection of early cancer has the potential to significantly alter survival. In this study, we aim to develop a predictive model to identify patients at risk for developing new-onset PC at two and a half to three year time frame. We used the Electronic Health Records (EHR) of a large medical system from 2000 to 2021 (N = 537,410). The EHR data analyzed in this work consists of patients' demographic information, diagnosis records, and lab values, which are used to identify patients who were diagnosed with pancreatic cancer and the risk factors used in the machine learning algorithm for prediction. We identified 73 risk factors of pancreatic cancer with the Phenome-wide Association Study (PheWAS) on a matched case-control cohort. Based on them, we built a large-scale machine learning algorithm based on EHR. A temporally stratified validation based on patients not included in any stage of the training of the model was performed. This model showed an AUROC at 0.742 [0.727, 0.757] which was similar in both the general population and in a subset of the population who has had prior cross-sectional imaging. The rate of diagnosis of pancreatic cancer in those in the top 1 percentile of the risk score was 6 folds higher than the general population. Our model leverages data extracted from a 6-month window of time in the electronic health record to identify patients at nearly sixfold higher than baseline risk of developing pancreatic cancer 2.5-3 years from evaluation. This approach offers an opportunity to define an enriched population entirely based on static data, where current screening may be recommended.

  PublisherOA PDFDOI

Frequent coauthors

• Vincent J. Major

  NYU Langone Health

  38 shared

• Constantin F. Aliferis

  University of Minnesota

  28 shared

• Neil Jethani

  26 shared

• Jonathan Austrian

  New York University

  20 shared

• Rajesh Ranganath

  Courant Institute of Mathematical Sciences

  18 shared

• Narges Razavian

  NYU Langone Health

  15 shared

• Alisa Surkis

  13 shared

• Lawrence D. Fu

  NYU Langone Health

  12 shared