JAMIA 是 AMIA 首屈一指的生物医学和健康信息学同行评审期刊。JAMIA 涵盖该领域的所有活动，包括临床护理、临床研究、转化科学、实施科学、成像、教育、消费者健康、公共卫生和政策。JAMIA 的文章描述了有助于推进生物医学科学和促进健康的创新信息学研究和系统。案例报告、观点和评论还可以帮助读者与实施、政策和教育中最重要的信息学发展保持联系。

The Journal of the American Medical Informatics Association: JAMIA is the premier peer-reviewed journal of the American Medical Informatics Association (AMIA) in the field of biomedical and health informatics. It covers all activities in this domain, including clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. Articles published in JAMIA describe innovative informatics research and systems that advance biomedical science and promote health. Case reports, viewpoints, and commentaries also help readers stay informed about the most significant developments in informatics related to implementation, policy, and education.

《Journal of the American Medical Informatics Association : JAMIA》是美国医学信息学协会（AMIA）最具权威的生物医学与健康信息学同行评审期刊。该期刊涵盖了该领域的所有活动，包括临床护理、临床研究、转化科学、实施科学、成像、教育、消费者健康、公共卫生和政策等。JAMIA发表的文章展示了推动生物医学科学和促进健康的创新信息学研究和系统。案例报告、观点和评论也帮助读者了解实施、政策和教育中最重要的信息学发展。

Journal of the American Medical Informatics Association : JAMIA

The rapid advancement of artificial intelligence (AI) has led to significant transformations in health and healthcare. As AI technologies continue to evolve, there is an urgent need to establish a unified framework that guides the design, implementation, and evaluation of AI-driven interventions across individual and population health contexts.

Philip R O Payne

Kevin B Johnson

Thomas M Maddox

Peter J Embi

Kenneth D Mandl

Deven McGraw

Suchi Saria

Laura Adams

JAMIA Journal of the American Medical Informatics Association

Toward an artificial intelligence code of conduct for health and healthcare: implications for the biomedical informatics community.

To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions.

Siru Liu

Aileen P Wright

Barron L Patterson

Jonathan P Wanderer

Robert W Turer

Scott D Nelson

Allison B McCoy

Dean F Sittig

Adam Wright

Journal of the American Medical Informatics Association

Using AI-generated suggestions from ChatGPT to optimize clinical decision support

Alzheimer's disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support difficult. The objective of this study was to develop a novel multimodal deep learning framework to aid medical professionals in AD diagnosis.We present a Multimodal Alzheimer's Disease Diagnosis framework (MADDi) to accurately detect the presence of AD and mild cognitive impairment (MCI) from imaging, genetic, and clinical data. MADDi is novel in that we use cross-modal attention, which captures interactions between modalities-a method not previously explored in this domain. We perform multi-class classification, a challenging task considering the strong similarities between MCI and AD. We compare with previous state-of-the-art models, evaluate the importance of attention, and examine the contribution of each modality to the model's performance.MADDi classifies MCI, AD, and controls with 96.88% accuracy on a held-out test set. When examining the contribution of different attention schemes, we found that the combination of cross-modal attention with self-attention performed the best, and no attention layers in the model performed the worst, with a 7.9% difference in F1-scores.Our experiments underlined the importance of structured clinical data to help machine learning models contextualize and interpret the remaining modalities. Extensive ablation studies showed that any multimodal mixture of input features without access to structured clinical information suffered marked performance losses.This study demonstrates the merit of combining multiple input modalities via cross-modal attention to deliver highly accurate AD diagnostic decision support.

Michal Golovanevsky

Carsten Eickhoff

Ritambhara Singh

Multimodal attention-based deep learning for Alzheimer’s disease diagnosis

Abstract Objectives This article aims to enhance the performance of larger language models (LLMs) on the few-shot biomedical named entity recognition (NER) task by developing a simple and effective method called Retrieving and Chain-of-Thought (RT) framework and to evaluate the improvement after applying RT framework. Materials and Methods Given the remarkable advancements in retrieval-based language model and Chain-of-Thought across various natural language processing tasks, we propose a pioneering RT framework designed to amalgamate both approaches. The RT approach encompasses dedicated modules for information retrieval and Chain-of-Thought processes. In the retrieval module, RT discerns pertinent examples from demonstrations during instructional tuning for each input sentence. Subsequently, the Chain-of-Thought module employs a systematic reasoning process to identify entities. We conducted a comprehensive comparative analysis of our RT framework against 16 other models for few-shot NER tasks on BC5CDR and NCBI corpora. Additionally, we explored the impacts of negative samples, output formats, and missing data on performance. Results Our proposed RT framework outperforms other LMs for few-shot NER tasks with micro-F1 scores of 93.50 and 91.76 on BC5CDR and NCBI corpora, respectively. We found that using both positive and negative samples, Chain-of-Thought (vs Tree-of-Thought) performed better. Additionally, utilization of a partially annotated dataset has a marginal effect of the model performance. Discussion This is the first investigation to combine a retrieval-based LLM and Chain-of-Thought methodology to enhance the performance in biomedical few-shot NER. The retrieval-based LLM aids in retrieving the most relevant examples of the input sentence, offering crucial knowledge to predict the entity in the sentence. We also conducted a meticulous examination of our methodology, incorporating an ablation study. Conclusion The RT framework with LLM has demonstrated state-of-the-art performance on few-shot NER tasks.

Mingchen Li

Huixue Zhou

Han Yang

Rui Zhang

RT: a Retrieving and Chain-of-Thought framework for few-shot medical named entity recognition

Abstract Objectives Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects. Materials and Methods A gradient-boosted decision tree ML method was used to analyze synthetic datasets that replicate the complexity of clinical scenarios involving high-risk medical devices. We designed this process to detect learning effects using a risk-adjusted cumulative sum method, quantify the excess adverse event rate attributable to operator inexperience, and adjust for these alongside patient factors in evaluating device safety signals. To maintain integrity, we employed blinding between data generation and analysis teams. Synthetic data used underlying distributions and patient feature correlations based on clinical data from the Department of Veterans Affairs between 2005 and 2012. We generated 2494 synthetic datasets with widely varying characteristics including number of patient features, operators and institutions, and the operator learning form. Each dataset contained a hypothetical study device, Device B, and a reference device, Device A. We evaluated accuracy in identifying learning effects and identifying and estimating the strength of the device safety signal. Our approach also evaluated different clinically relevant thresholds for safety signal detection. Results Our framework accurately identified the presence or absence of learning effects in 93.6% of datasets and correctly determined device safety signals in 93.4% of cases. The estimated device odds ratios’ 95% confidence intervals were accurately aligned with the specified ratios in 94.7% of datasets. In contrast, a comparative model excluding operator learning effects significantly underperformed in detecting device signals and in accuracy. Notably, our framework achieved 100% specificity for clinically relevant safety signal thresholds, although sensitivity varied with the threshold applied. Discussion A machine learning framework, tailored for the complexities of post-market device evaluation, may provide superior performance compared to standard parametric techniques when operator learning is present. Conclusion Demonstrating the capacity of ML to overcome complex evaluative challenges, our framework addresses the limitations of traditional statistical methods in current post-market surveillance processes. By offering a reliable means to detect and adjust for learning effects, it may significantly improve medical device safety evaluation.

Jejo D Koola

Karthik Ramesh

Jialin Mao

Minyoung Ahn

Sharon E Davis

Usha Govindarajulu

Amy M Perkins

Dax Westerman

Henry Ssemaganda

Theodore Speroff

Lucila Ohno-Machado

Craig R Ramsay

Art Sedrakyan

Frederic S Resnic

Michael E Matheny

A machine learning framework to adjust for learning effects in medical device safety evaluation

用于调整医疗设备安全性评估中学习效应的机器学习框架

Abstract Background Mild cognitive impairment and early-stage dementia significantly impact healthcare utilization and costs, yet more than half of affected patients remain underdiagnosed. This study leverages audio-recorded patient-nurse verbal communication in home healthcare settings to develop an artificial intelligence-based screening tool for early detection of cognitive decline. Objective To develop a speech processing algorithm using routine patient-nurse verbal communication and evaluate its performance when combined with electronic health record (EHR) data in detecting early signs of cognitive decline. Method We analyzed 125 audio-recorded patient-nurse verbal communication for 47 patients from a major home healthcare agency in New York City. Out of 47 patients, 19 experienced symptoms associated with the onset of cognitive decline. A natural language processing algorithm was developed to extract domain-specific linguistic and interaction features from these recordings. The algorithm’s performance was compared against EHR-based screening methods. Both standalone and combined data approaches were assessed using F1-score and area under the curve (AUC) metrics. Results The initial model using only patient-nurse verbal communication achieved an F1-score of 85 and an AUC of 86.47. The model based on EHR data achieved an F1-score of 75.56 and an AUC of 79. Combining patient-nurse verbal communication with EHR data yielded the highest performance, with an F1-score of 88.89 and an AUC of 90.23. Key linguistic indicators of cognitive decline included reduced linguistic diversity, grammatical challenges, repetition, and altered speech patterns. Incorporating audio data significantly enhanced the risk prediction models for hospitalization and emergency department visits. Discussion Routine verbal communication between patients and nurses contains critical linguistic and interactional indicators for identifying cognitive impairment. Integrating audio-recorded patient-nurse communication with EHR data provides a more comprehensive and accurate method for early detection of cognitive decline, potentially improving patient outcomes through timely interventions. This combined approach could revolutionize cognitive impairment screening in home healthcare settings.

Maryam Zolnoori

Ali Zolnour

Sasha Vergez

Sridevi Sridharan

Ian Spens

Maxim Topaz

James M Noble

Suzanne Bakken

Julia Hirschberg

Kathryn Bowles

Nicole Onorato

Margaret V McDonald

Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications

超越电子健康记录数据：利用自然语言处理和机器学习从患者-护士口头交流中揭示认知洞察

Abstract Objective This article describes the implementation of preemptive clinical pharmacogenomics (PGx) testing linked to an automated clinical decision support (CDS) system delivering actionable PGx information to clinicians at the point of care at UCSF Health, a large Academic Medical Center. Methods A multidisciplinary team developed the strategic vision for the PGx program. Drug–gene interactions of interest were compiled, and actionable alleles identified. A genotyping platform was selected and validated in-house. Following HIPAA protocols, genotype results were electronically transferred and stored in electronic health records (EHRs). CDS was developed and integrated with electronic prescribing. Results We developed a customized PGx program for 56 medications and 15 genes. Two hundred thirty-three pharmacogenomic prescribing alerts and 15 pharmacogenomic testing prompts, approved by clinicians, were built into EHR to deliver actionable clinical PGx information to clinicians. Conclusions Our multidisciplinary team successfully implemented preemptive PGx testing linked to point-of-care CDS to guide clinicians with precise medication decision-making.

Bani Tamraz

Jaekyu Shin

Raman Khanna

Jessica Van Ziffle

Susan Knowles

Susan Stregowski

Eunice Wan

Rajesh Kamath

Christopher Collins

Choeying Phunsur

Benjamin Tsai

Patsy Kong

Clari Calanoc

Aleta Pollard

Rajeev Sawhney

Jennifer Pleiman

Walter Patrick Devine

Rhiannon Croci

Aparna Sashikanth

Lisa Kroon

Russell Cucina

Aleks Rajkovic

Clinical implementation of preemptive pharmacogenomics testing for personalized medicine at an academic medical center

学术医学中心个性化医疗的预防性药物基因组学检测的临床应用

We aim to use large language models (LLMs) to detect mentions of nuanced psychotherapeutic outcomes and impacts than previously considered in transcripts of interviews with adolescent depression. Our clinical authors previously created a novel coding framework containing fine-grained therapy outcomes beyond the binary classification (eg, depression vs control) based on qualitative analysis embedded within a clinical study of depression. Moreover, we seek to demonstrate that embeddings from LLMs are informative enough to accurately label these experiences.

Alison W Xin

Dylan M Nielson

Karolin Rose Krause

Guilherme Fiorini

Nick Midgley

Francisco Pereira

Juan Antonio Lossio-Ventura

Using large language models to detect outcomes in qualitative studies of adolescent depression

使用大型语言模型检测青少年抑郁质性研究中的结果

Abstract Objectives To assess the potential to adapt an existing technology regulatory model, namely the Clinical Laboratory Improvement Amendments (CLIA), for clinical artificial intelligence (AI). Materials and Methods We identify overlap in the quality management requirements for laboratory testing and clinical AI. Results We propose modifications to the CLIA model that could make it suitable for oversight of clinical AI. Discussion In national discussions of clinical AI, there has been surprisingly little consideration of this longstanding model for local technology oversight. While CLIA was specifically designed for laboratory testing, most of its principles are applicable to other technologies in patient care. Conclusion A CLIA-like approach to regulating clinical AI would be complementary to the more centralized schemes currently under consideration, and it would ensure institutional and professional accountability for the longitudinal quality management of clinical AI.

Brian R Jackson

Mark P Sendak

Anthony Solomonides

Suresh Balu

Regulation of artificial intelligence in healthcare: Clinical Laboratory Improvement Amendments (CLIA) as a model

医疗保健中人工智能的监管：以临床实验室改进修正案（CLIA）为模型

Abbreviation	J Am Med Inform Assoc
Journal Impact	4.82
Quartiles(Global)	MEDICAL INFORMATICS(Q1)

ISSN	1067-5027, 1527-974X
h-index	177
Top Journals	Yes

Publication Information	Publisher: Oxford University Press，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1994，Proportion of original research papers: 86.64%，Self Citation Rate:8.50%， Gold OA Rate: 32.94%
Average review cycle	网友分享经验：一般,3-8周
Average recruitment ratio	网友分享经验：很难

Journal of the American Medical Informatics Association : JAMIA

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Toward an artificial intelligence code of conduct for health and healthcare: implications for the biomedical informatics community.

Using AI-generated suggestions from ChatGPT to optimize clinical decision support

Multimodal attention-based deep learning for Alzheimer’s disease diagnosis

RT: a Retrieving and Chain-of-Thought framework for few-shot medical named entity recognition

A machine learning framework to adjust for learning effects in medical device safety evaluation

Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications

Clinical implementation of preemptive pharmacogenomics testing for personalized medicine at an academic medical center

Using large language models to detect outcomes in qualitative studies of adolescent depression

Toward an artificial intelligence code of conduct for health and healthcare: implications for the biomedical informatics community.

Regulation of artificial intelligence in healthcare: Clinical Laboratory Improvement Amendments (CLIA) as a model