18 citations · 45 across the 10 of their papers we have counts for
13 papers
Closing Ambient Clinical Documentation Gaps with Automated Provider Queries
Joseph Paul Cohen, Raj Shah, Han-Chin Shing +4
Provider queries are clarifying requests sent by clinical documentation specialists to physicians to close gaps in the clinical note and ensure accurate billing. Prior work automat…
Toward Reliable Clinical Coding with Language Models: Verification and Lightweight Adaptation
Zhangdie Yuan, Han-Chin Shing, Mitch Strong +1
Accurate clinical coding is essential for healthcare documentation, billing, and decision-making. While prior work shows that off-the-shelf LLMs struggle with this task, evaluation…
Fact-Controlled Diagnosis of Hallucinations in Medical Text Summarization
Suhas BN, Han-Chin Shing, Lei Xu +9
Hallucinations in large language models (LLMs) during summarization of patient-clinician dialogues pose significant risks to patient care and clinical decision-making. However, the…
TN-Eval: Rubric and Evaluation Protocols for Measuring the Quality of Behavioral Therapy Notes
Raj Sanjay Shah, Lei Xu, Qianchu Liu +3
Behavioral therapy notes are important for both legal compliance and patient care. Unlike progress notes in physical health, quality standards for behavioral therapy notes remain u…
CriSPO: Multi-Aspect Critique-Suggestion-guided Automatic Prompt Optimization for Text Generation
Han He, Qianchu Liu, Lei Xu +4
Existing automatic prompt engineering methods are typically designed for discriminative tasks, where new task prompts are iteratively refined with limited feedback from a single me…
Entity Anchored ICD Coding
Jay DeYoung, Han-Chin Shing, Luyang Kong +2
Medical coding is a complex task, requiring assignment of a subset of over 72,000 ICD codes to a patient's notes. Modern natural language processing approaches to these tasks have…
Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes
Han-Chin Shing, Chaitanya Shivade, Nima Pourdamghani +4
The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information. This paper introduces the tas…
Neural Inverse Text Normalization
Monica Sunkara, Chaitanya Shivade, Sravan Bodapati +1
While there have been several contributions exploring state of the art techniques for text normalization, the problem of inverse text normalization (ITN) remains relatively unexplo…
Receptivity of an AI Cognitive Assistant by the Radiology Community: A Report on Data Collected at RSNA
Karina Kanjaria, Anup Pillai, Chaitanya Shivade +4
Due to advances in machine learning and artificial intelligence (AI), a new role is emerging for machines as intelligent assistants to radiologists in their clinical workflows. But…
Leveraging Medical Visual Question Answering with Supporting Facts
Tomasz Kornuta, Deepta Rajan, Chaitanya Shivade +2
In this working notes paper, we describe IBM Research AI (Almaden) team's participation in the ImageCLEF 2019 VQA-Med competition. The challenge consists of four question-answering…
Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models
Oren Melamud, Chaitanya Shivade
Large-scale clinical data is invaluable to driving many computational scientific advances today. However, understandable concerns regarding patient privacy hinder the open dissemin…
Lessons from Natural Language Inference in the Clinical Domain
Alexey Romanov, Chaitanya Shivade
State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilitie…
Addressing Limited Data for Textual Entailment Across Domains
Chaitanya Shivade, Preethi Raghavan, Siddharth Patwardhan
We seek to address the lack of labeled data (and high cost of annotation) for textual entailment in some domains. To that end, we first create (for experimental purposes) an entail…