AMIA ... Annual Symposium Proceedings / AMIA Symposium. AMIA Symposium[JOURNAL]
Sun
200 papers
Facilitating Clinical Information Extraction with Synthetic Data and Ontology using Large Language Models.
Hu Y, He H, Chen Q … +3 more , Jiang X, Roberts K, Xu H
AMIA Annu Symp Proc · 2024 · PMID 41726483
The rapid growth of unstructured clinical text in electronic health records necessitates robust information extraction systems, yet their development is hindered by the scarcity of high-quality annotated data. This study...
The rapid growth of unstructured clinical text in electronic health records necessitates robust information extraction systems, yet their development is hindered by the scarcity of high-quality annotated data. This study explores the potential of large language models to generate synthetic data for clinical named entity recognition and examines its impact on model performance. We propose a novel framework that integrates self-verified synthetic data generation with domain-specific semantic mapping using SNOMED-CT. By leveraging GPT-4o-mini for synthetic data creation and refining its quality through iterative verification and anomaly detection, we systematically evaluate the influence of synthetic data quality and quantity on fine-tuning LLaMA-3-8B. Experimental results across four datasets (MTSamples, UTP, MIMIC-III, and i2b2) demonstrate that self-verification and semantic mapping significantly enhance synthetic data utility, improving model generalizability. Our findings highlight the importance of balancing human-annotated and synthetic data, with a 1:1 ratio emerging as the optimal configuration for performance gains. This study advances clinical NLP by providing a scalable approach to mitigating annotation challenges while improving model performance.
Which decisions affect cohort distribution in COVID-19 data analytics?
Haghighathoseini A, Wojtusiak J, Ngana LP … +2 more , Min H, Menon NM
AMIA Annu Symp Proc · 2024 · PMID 41726482
Data analytics has emerged as a crucial tool for understanding the multifaceted impacts of the COVID-19 pandemic. By collecting and analyzing extensive datasets, researchers have gained valuable insights into the virus's...
Data analytics has emerged as a crucial tool for understanding the multifaceted impacts of the COVID-19 pandemic. By collecting and analyzing extensive datasets, researchers have gained valuable insights into the virus's transmission, severity, and the effectiveness of public health measures. Yet, many contradictive and non-reproducible results have been published. The significance of cohort representativeness in this context cannot be overstated, as diverse cohorts provide a comprehensive understanding of how various demographic and clinical factors influence COVID-19 outcomes. This study investigates the impact of decision-making processes on cohort diversity, focusing on demographic categories including sex, race, and ethnicity. Results show that decisions made during data preprocessing and cohort construction increase variability in demographic distribution. Specifically, the difference in female representation varied by 0.77% to 2.68%, in Black race from 1.17% to 5.15%, and in Hispanic or Latino ethnicity from 5.84% to 8.21%. It highlights how arbitrary decisions can lead to varying data including changes to data distribution. Seemingly unrelated to demographics factors, including timing and provider selection, significantly influence patient distribution and outcomes, underscoring the necessity for informed, data-driven strategies. The findings emphasize the importance of strategic, evidence-based decision-making to enhance consistency, optimize resource utilization, and effectively serve diverse populations, ultimately contributing to more equitable health outcomes and informed public health policies.
Toward Integrating Machine Learning-powered Polysocial Risk Scores into Electronic Health Record Workflows.
He X, Huang Y, Hu Y … +5 more , Pappa M, Miller N, Gregory ME, Guo JS, Bian J
AMIA Annu Symp Proc · 2024 · PMID 41726481
Social determinants of health (SDoH) account for 80% of modifiable factors driving health disparities. Health systems play a critical role in addressing patients' unmet social needs essential to health outcomes. To integ...
Social determinants of health (SDoH) account for 80% of modifiable factors driving health disparities. Health systems play a critical role in addressing patients' unmet social needs essential to health outcomes. To integrate social risk management into patient health care, we developed an electronic health record (EHR)-based machine learning-powered pipeline to identify and address unmet social needs associated with hospitalization risk. By quantifying social risk via a polysocial risk score, this tool enables healthcare providers to identify patients at high social risk and prioritize targeted SDoH interventions. However, gaps exist regarding integrating our polysocial risk score tool into clinical flow. Therefore, in this study, through participatory design sessions with healthcare providers and social workers following user-centered design (UCD) principles, we initiated the integration of this predictive model into EHR workflows. This preliminary work lays the foundation for a comprehensive formal user-centered design process to enhance social risk assessment and intervention implementation.
RAG vs Reddit: Decoding Autism Conversations on Reddit with LLMs and Topic Modeling.
Wattegama D, Black B, Moen M … +1 more , Shyu CR
AMIA Annu Symp Proc · 2024 · PMID 41726480
Social media platforms like Reddit have become vital spaces for autistic individuals and caregivers to seek advice, share experiences, and discuss challenges. Simultaneously, Large Language Models (LLMs) are increasingly...
Social media platforms like Reddit have become vital spaces for autistic individuals and caregivers to seek advice, share experiences, and discuss challenges. Simultaneously, Large Language Models (LLMs) are increasingly used to provide medical guidance. This study examines autism-related discussions on Reddit, comparing them with clinician-patient discussions and evaluating the effectiveness of an autism-specific Retrieval-Augmented Generation (RAG) system. We applied BERTopic to identify key discussion themes in r/autism and r/autism_parenting, revealing significant discussions around behavioral challenges, and practical support. Comparing clinical messages from the University of Missouri Thompson Center for Autism and Neurodevelopment, we found caregivers in clinical settings focused more on medication management, whereas online discussions emphasized non-traditional therapies. We then assessed LLM-generated responses against Reddit peer advice, discussing the differences in accuracy, relevance, empathy and helpfulness. This work underscores the potential of RAG systems in enhancing autism-related guidance while emphasizing the importance of community-driven insights in healthcare conversations.
A Reinforcement Learning (RL)-Motivated Simulation Framework for Evaluating Vancomycin Dosing Strategies.
Mao B, Xie Z, Rasmy L … +2 more , Nigo M, Zhi D
AMIA Annu Symp Proc · 2024 · PMID 41726479
Achieving and maintaining the therapeutic range in vancomycin treatment is important for optimal outcomes. While guidelines and best practices based on empirical studies exist, the theoretical best dosing strategies unde...
Achieving and maintaining the therapeutic range in vancomycin treatment is important for optimal outcomes. While guidelines and best practices based on empirical studies exist, the theoretical best dosing strategies under various conditions remain illusive. We developed an RL-based simulation framework using a deep learning two-compartment pharmacokinetic model (PK-RNN-2CM) and introduced the area under the time-concentration curve (AUC) reward score, which translates clinical guidelines into an RL reward. Ground truth time-concentration curves were generated from patient-specific data, and simulated curves were produced under different dosing strategies with optional noise perturbations to mimic real-world settings. Evaluation metrics included 24-hour AUC assessments and RMSE. Results indicated that while the low-dosing AUC target (low-doser) and the high-dosing AUC target (high-doser) performed comparably in noise-free conditions, the low-doser achieved slightly higher AUC reward scores under noisy conditions, whereas the high-doser exhibited greater stability. This framework opens new approaches for optimizing vancomycin dosing.
Mapping Documentation Burden: Analyzing Centrality and Clusters among Flowsheet Measures and Templates Through Network Analysis.
Wang P, Finnegan A, Yen PY … +1 more , Rossetti SC
AMIA Annu Symp Proc · 2024 · PMID 41726478
This study applied network analysis (NA) to identify documentation burden in nursing flowsheets, quantifying and visualizing the interconnectedness of nursing flowsheet templates and measures. By analyzing centrality met...
This study applied network analysis (NA) to identify documentation burden in nursing flowsheets, quantifying and visualizing the interconnectedness of nursing flowsheet templates and measures. By analyzing centrality metrics and edge weights, we highlighted an outdated flowsheet structure and the need for a modern data structure. Using centrality metrics, we identified key templates and measures contributing to documentation burden, including highly repetitive nodes, central hubs, and intermediary nodes that facilitated communication in the network. Our findings showed that certain templates and measures exhibited consistently high centralities across multiple metrics, indicating opportunities for streamlining documentation structures. We also analyzed the communities in both networks to reveal the functional clusters of template and measure documentation. These insights provided a data-driven approach to understand documentation burden and inform opportunities to optimize documentation structures, particularly with increasing ambient technology integrations.
Is Tree-of-Thought Prompting Strategy Better than Chain-of-Thought? Vaping Cessation Analysis Using Large Language Models.
Aust L, Fu A, Huang M
AMIA Annu Symp Proc · 2024 · PMID 41726477
Vapingis gainingpopularity amongadolescents andposes severe risks to users. Social media platforms such as Reddit and X offer insights into user behaviors and attitudes regarding vaping. In previous studies, our team exp...
Vapingis gainingpopularity amongadolescents andposes severe risks to users. Social media platforms such as Reddit and X offer insights into user behaviors and attitudes regarding vaping. In previous studies, our team explored the ability of large language models (LLMs) to perform binary classification at a sentence level to determine if LLMs can be used to identify users for a vaping cessation application. Maintaining the same goal, this study expands to compare OpenAI's GPT-o1 and GPT-o3-mini, Google's Gemini 2.0 Flash and Gemma 2, Meta's LLAMA 3.3, Deepseek's R1, and xAI's Grok-2 against human annotators to examine which models best perform binary classification to identify quit intention and multiclass classification to detect quit stages. We tested these models with emerging and prompts together with a simple prompt to see which strategy performed best. To our knowledge, this is investigation of prompting. Our initial results indicate that tree-of-thought and chain-of-thought prompting do not boost performance.
Trustworthy and Uncertainty-Aware AI for Predicting Respiratory Complications Following Total Hip and Knee Arthroplasty.
Rezvani F, Towsen K, Menezes Z … +7 more , Einhorn A, Davis J, Gupta P, Plate JF, Fox C, Myers N, Tafti AP
AMIA Annu Symp Proc · 2024 · PMID 41726476
Total hip and knee arthroplasty (THA/TKA) are among the fastest-growing surgeries in the United States, where they are designed to restore mobility and improve quality of life in individuals with joint disorders. Despite...
Total hip and knee arthroplasty (THA/TKA) are among the fastest-growing surgeries in the United States, where they are designed to restore mobility and improve quality of life in individuals with joint disorders. Despite their benefits, these procedures may carry significant risks, including, but not limited to, major respiratory complications. Prompt identification of patients at increased risk is essential for optimizing preoperative treatment, reducing adverse outcomes, and increasing patient safety. In this study, we propose an uncertainty-aware and trustworthy artificial intelligence (AI) framework to predict the likelihood of major respiratory complications, including unplanned intubation, failure to wean from ventilation, and postoperative pneumonia occurring during the index hospitalization and within 30 days following both primary and revision THA and TKA procedures. Unlike traditional risk models, our framework explicitly quantifies prediction uncertainty while maintaining high interpretability, enabling proactive and personalized clinical interventions. We assessed four machine learning (ML) models, including Random Forest (RF), XGBoost (XGB), Logistic Regression (LR), and Artificial Neural Networks (ANNs) to predict three postoperative respiratory outcomes. The ML models demonstrated strong predictive performance, with RF achieving an F1-score of 0.87 for respiratory complications in THA, while ANNs outperformed other models in TKA, also attaining an F1-score of 0.87.
Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset.
Luo Y, Skandari R, Martinez C … +2 more , Kilic A, Padman R
AMIA Annu Symp Proc · 2024 · PMID 41726475
Decisions about managing patients on the heart transplant waitlist are currently made by committees of doctors who consider multiple factors, but the process remains largely ad-hoc. With the growing volume of longitudina...
Decisions about managing patients on the heart transplant waitlist are currently made by committees of doctors who consider multiple factors, but the process remains largely ad-hoc. With the growing volume of longitudinal patient, donor, and organ data collected by the United Network for Organ Sharing (UNOS) since 2018, there is increasing interest in analytical approaches to support clinical decision-making at the time of organ availability. In this study, we benchmark machine learning models that leverage longitudinal waitlist history data for time-dependent, time-to-event modeling of waitlist mortality. We train on 23,807 patient records with 77 variables and evaluate both survival prediction and discrimination at a 1-year horizon. Our best model achieves a C-Index of 0.94 and AUROC of 0.89, significantly outperforming previous models. Key predictors align with known risk factors while also revealing novel associations. Our findings can support urgency assessment and policy refinement in heart transplant decision making.
Cultural Prompting Improves the Empathy and Cultural Responsiveness of GPT-Generated Therapy Responses.
Xie SJ, Zhai S, Liang Y … +4 more , Li J, Fan X, Cohen T, Yuwen W
AMIA Annu Symp Proc · 2024 · PMID 41726474
Large Language Model (LLM)-based conversational agents offer promising solutions for mental health support, but lack cultural responsiveness for diverse populations. This study evaluated the effectiveness of cultural pro...
Large Language Model (LLM)-based conversational agents offer promising solutions for mental health support, but lack cultural responsiveness for diverse populations. This study evaluated the effectiveness of cultural prompting in improving cultural responsiveness and perceived empathy of LLM-generated therapeutic responses for Chinese American family caregivers. Using a randomized controlled experiment, we compared GPT-4o and Deepseek-V3 responses with and without cultural prompting. Thirty-six participants evaluated input-response pairs on cultural responsiveness (competence and relevance) and perceived empathy. Results showed that cultural prompting significantly enhanced GPT-4o's performance across all dimensions, with GPT-4o with cultural prompting being the most preferred, while improvements in DeepSeek-V3 responses were not significant. Mediation analysis revealed that cultural prompting improved empathy through improving cultural responsiveness. This study demonstrated that prompt-based techniques can effectively enhance the cultural responsiveness of LLM-generated therapeutic responses, highlighting the importance of cultural responsiveness in delivering empathetic AI-based therapeutic interventions to culturally and linguistically diverse populations.
Unified Resource Browser: An Interactive and Reconfigurable Web Framework for Biomedical Metadata Exploration, Visualization, and Management.
Lin S, Tong L, Smith KA … +10 more , Ng L, Mollenkopf T, Huang Y, Abeysinghe R, Chou WC, Byrd AT, Cui L, Zhang GQ, Li X, Tao S
AMIA Annu Symp Proc · 2024 · PMID 41726473
We present Unified Resource Browser, a web-based framework designed to optimize the configuration, exploration, visualization, and dissemination of complex biomedical metadata. As a core component of Neuroanatomy-Anchore...
We present Unified Resource Browser, a web-based framework designed to optimize the configuration, exploration, visualization, and dissemination of complex biomedical metadata. As a core component of Neuroanatomy-Anchored Information Management Platform (NIMP), Unified Resource Browser enables researchers to efficiently access donor information, brain tissue samples, and sequencing data through structured resource tables and advanced search and filtering capabilities. The platform allows users to customize data views, share configurations via direct links, and seamlessly export data for further analysis. Integrated visualization tools offer immediate insights through customizable charts, enhancing data interpretation. By improving the accessibility and usability of biomedical data resources, Unified Resource Browser fosters collaborative research and advances discoveries in brain structure and function.
Contextual Phenotyping of Pediatric Sepsis Cohort Using Large Language Models.
Nagori A, Gautam A, Wiens MO … +6 more , Nguyen V, Mugisha NK, Kabakyenga J, Kissoon N, Ansermino JM, Kamaleswaran R
AMIA Annu Symp Proc · 2024 · PMID 41726472
The clustering of patient subgroups is essential for personalized care and efficient use of resources. Traditional clustering methods struggle with high-dimensional heterogeneous healthcare data and lack contextual under...
The clustering of patient subgroups is essential for personalized care and efficient use of resources. Traditional clustering methods struggle with high-dimensional heterogeneous healthcare data and lack contextual understanding. This study evaluates clustering based on the Large Language Model (LLM) against classical methods using a pediatric sepsis dataset from a low-income country (LIC), containing 2,686 records with 28 numerical variables and 119 categorical variables. Patient records were serialized into text with and without a clustering objective. Embeddings were generated using quantized LLAMA 3.1 8B, DeepSeek-R1-Distill-Llama-8B with low-rank adaptation(LoRA), and Stella-En-400M-V5 models. K-means clustering was applied to these embeddings. Classical comparisons included K-Medoids clustering on UMAP and FAMD-reduced mixed data. Silhouette scores and statistical tests evaluated the quality and distinctiveness of the cluster. Stella-En-400M-V5 achieved the highest Silhouette Score (0.86). LLAMA 3.1 8B with the clustering objective performed better with a higher number of clusters, identifying subgroups with distinct nutritional, clinical, and socioeconomic profiles. LLM-based methods outperformed classical techniques by capturing richer context and prioritizing key features. These results highlight the potential of LLMs for contextual phenotyping and informed decision making in resource-limited settings.
Coding Fairness: Detecting Demographic-Related Coding Discrepancies in ICD Code Assignments.
Yin Y, Nelson SJ, Shao Y … +3 more , Faselis C, Ahmed A, Zeng-Treitler Q
AMIA Annu Symp Proc · 2024 · PMID 41726471
Coded clinical data are crucial in biomedical informatics research. While it is well known that electronic medical records often contain coding errors, numerous studies rely on International Classification of Diseases (I...
Coded clinical data are crucial in biomedical informatics research. While it is well known that electronic medical records often contain coding errors, numerous studies rely on International Classification of Diseases (ICD) codes for phenotyping in cohort assembly, statistical analysis, and AI modeling. Although fairness hasbecome an important focus in AI research, the potential biases embedded in coded clinical data have received less attention. In this study, we employed a race- and sex-agnostic AI phenotyping model to assess coding fairness across 203 ICD code blocks within the Veterans Health Administration Clinical Data Warehouse. Our findings revealed variability in coding consistency across demographic subgroups, including sex, race, and ethnicity. Notably, over 50% of the code blocks exhibitedstatisticallysignificant differences in discrepancies between AI-generated and ICD-based phenotypesacross these demographic groups. These results suggest the need to recognize and address demographic-related coding discrepancies to ensure coding fairness.
From Food to Clinic: Mapping FoodOn to the UMLS to Enable Nutritional Decision Support.
Sarkar IN
AMIA Annu Symp Proc · 2024 · PMID 41726470
Food and nutrition knowledge is recognized as a fundamental factor for the health and well-being of communities; however, its integration into biomedical and health knowledge systems is limited by the absence of standard...
Food and nutrition knowledge is recognized as a fundamental factor for the health and well-being of communities; however, its integration into biomedical and health knowledge systems is limited by the absence of standardized ontologies that encapsulate food-related concepts. This study mapped FoodOn, an open-source food ontology, to the Unified Medical Language System (UMLS) metathesaurus, a compendium of biomedical ontologies. As the first systematic mapping of a food ontology to the UMLS, the results of this study provide an ontological foundation for incorporating dietary data into clinical and public health workflows. The findings suggest that expanding the representation of food concepts in biomedical ontologies could enhance the potential to incorporate food and nutritional into clinical decision-making and research. Furthermore, this work lays the groundwork for integrating food-based therapies from traditional medicine systems (e.g., Ayurveda and Traditional Chinese Medicine) into contemporary clinical knowledge frameworks to support more holistic approaches to health care.
DeepJ: Graph Convolutional Transformers with Differentiable Pooling for Patient Trajectory Modeling.
Li D, Yao Z, Liang M … +1 more , Liu M
AMIA Annu Symp Proc · 2024 · PMID 41726469
In recent years, graph learning has gained significant interest for modeling complex interactions among medical events in structured Electronic Health Record (EHR) data. However, existing graph-based approaches often wor...
In recent years, graph learning has gained significant interest for modeling complex interactions among medical events in structured Electronic Health Record (EHR) data. However, existing graph-based approaches often work in a static manner, either restricting interactions within individual encounters or collapsing all historical encounters into a single snapshot. As a result, when it is necessary to identify meaningful groups of medical events spanning longitudinal encounters, existing methods are inadequate in modeling interactions cross encounters while accounting for temporal dependencies. To address this limitation, we introduce Deep Patient Journey (DeepJ), a novel graph convolutional transformer model with differentiable graph pooling to effectively capture intra-encounter and inter-encounter medical event interactions. DeepJ can identify groups of temporally and functionally related medical events, offering valuable insights into key event clusters pertinent to patient outcome prediction. DeepJ significantly outperformed five state-of-the-art baseline models while enhancing interpretability, demonstrating its potential for improved patient risk stratification.
An Information Extraction Approach to Detecting Novelty of Biomedical Publications.
Peng X, Ondov B, He H … +2 more , Hu Y, Xu H
AMIA Annu Symp Proc · 2024 · PMID 41726468
Scientific novelty plays a critical role in shaping research impact, yet it remains inconsistently defined and difficult to quantify. Existing approaches often reduce novelty to a single measure, failing to distinguish t...
Scientific novelty plays a critical role in shaping research impact, yet it remains inconsistently defined and difficult to quantify. Existing approaches often reduce novelty to a single measure, failing to distinguish the specific types of contributions (such as new concepts or relationships) that drive influence. In this study, we introduce a semantic measure of novelty based on the emergence of new biomedical entities and relationships within the conclusion sections of research articles. Leveraging transformer-based named entity recognition (NER) and relation extraction (RE) tools, we identify novel findings and classify articles into four categories: No Novelty, Entity-only Novelty, Relation-only Novelty, and Entity-Relation Novelty. We evaluate this framework using citation counts and Journal Impact Factors (JIF) as proxies for research influence. Our results show that Entity-Relation Novelty articles receive the highest citation impact, with relation novelty more closely aligned with high-impact journals. These findings offer a scalable framework for assessing novelty and guiding future research evaluation.
My Kidney T.R.E.K. - Thinking, Reflecting, and Empowering Kidney Transplant Patients, through technology.
Dunbar JC, Pratt W, Jeffs L … +4 more , Ng C, Sayed S, Smith J, Pollack AH
AMIA Annu Symp Proc · 2024 · PMID 41726467
Adolescents and young adults with kidney transplants face unique challenges as they transition toward independent self-management. These youth need tools that not only support skill-building but also foster reflection, a...
Adolescents and young adults with kidney transplants face unique challenges as they transition toward independent self-management. These youth need tools that not only support skill-building but also foster reflection, a key component of self-management. To address this need, we created a digital prototype designed to help users reflect on their transplant journey through storytelling and evaluated it in a study with 23 participants (13 youth and 10 caregivers) using. Participants found value in engaging with the prototype, which helped them reflect on their past experiences, gain insight into their current journey, and envision their future. Youth, in particular, reported increased self-awareness and confidence in managing their health. Based on these findings, we present design recommendations for future digital health tools aimed at supporting self-management in youth with chronic conditions.
Translating Nursing Data into Computational Metrics: An Evaluation Guideline for Inpatient Intravenous and Subcutaneous Insulin Management.
Varkhedi V, Cato K, Albers D … +7 more , Tiase VL, Joshi S, Thate J, Connell K, Hull W, Finnegan A, Rossetti SC
AMIA Annu Symp Proc · 2024 · PMID 41726466
A challenge in utilizing electronic health record data for artificial intelligence models is contextualization, including understanding differences between missing data and missed care. Our team aims to develop knowledge...
A challenge in utilizing electronic health record data for artificial intelligence models is contextualization, including understanding differences between missing data and missed care. Our team aims to develop knowledge graphs and computational models that account for these contexts, such as when data is missing (nurses being unable to document), but acceptable nursing care was delivered. We developed evaluation guidelines for intravenous and subcutaneous insulin management to establish a binary variable derived from EHR data representing minimally acceptable safe and quality nursing care for use in computational modeling. These guidelines were developed by our nurse informatics team based on best practices and validated by three nurse subject matter experts. The resulting evaluation guidelines are agnostic to institutional policies and focus on evaluating minimally acceptable safe and quality levels of care to inform inferences about missing data versus missed nursing care. Future work includes data-driven validations and expanding to other clinical scenarios.
Data-Driven Evidence-Based Patient-Centered Optimal Initiation Time for Dialysis Treatment.
Lee EK, Liu D, Hoffman J
AMIA Annu Symp Proc · 2024 · PMID 41726465
In this study, we propose a novel decision-making framework based on natural-language processing, machine learning and stochastic modeling for the purpose of providing a data-driven perspective to optimize the initiation...
In this study, we propose a novel decision-making framework based on natural-language processing, machine learning and stochastic modeling for the purpose of providing a data-driven perspective to optimize the initiation time for dialysis treatment. When to start dialysis treatment is an important decision for end-stage chronic kidney disease (CKD) care. In the absence of a national guideline, determining the best time to start dialysis remains a challenge. Our decision support framework for optimizing the initiation time includes (a) a comprehensive, efficient "pipeline" for extracting, de-identifying, and standardizing EHR data; (b) an informatics toolkit that couples natural language processing and event mapping, clustering and machine learning to uncover the disease prognosis and treatment effects, and deduce the symptoms and utility rewards for each disease-action stage; (c) a first-of-its-kind, personalized, dialysis-timing stochastic model to determine the optimal initiation time; and (d) a clinical practice guideline (CPG) for systematic testing and implementation in the clinical setting. We evaluate the results using utility rewards for each decision process and published financial costs for each CKD disease and treatment stage. Compared to current clinical policy, the optimal initiation-time policies offer a potential 20.0% to 54.7% mortality reduction, an increase of 6.4% to 14.7% in overall utility reward and a reduction of 9.6% to 16.7% in overall cost. Working with nephrologists and a CKD/ESRD care team, a CPG was developed and tested in the clinic. Initial usage shows promising results. Follow-on clinical trials will gauge the overall effectiveness and impact on patient care of our approach. The new CPG has the potential to become a national standard.
Variogram Modeling of Spatially Variant Early Response to Therapy in Advanced Non-Small Cell Lung Cancer.
Yaseen F, MHI, Hippe DS … +5 more , Soni PV, Wang S, Duan C, Gennari JH, Bowen SR
AMIA Annu Symp Proc · 2024 · PMID 41726464
Predicting heterogeneity in treatment response for non-small cell lung cancer (NSCLC) at multiple scales, both between patients and spatially within each patient, can support clinical decisions that personalize oncologic...
Predicting heterogeneity in treatment response for non-small cell lung cancer (NSCLC) at multiple scales, both between patients and spatially within each patient, can support clinical decisions that personalize oncologic management. In this study, we evaluated different variogram models of voxel-level spatial correlation in tumor response in locally advanced NSCLC and metastatic NSCLC from two different clinical trials. The Stable model achieved the lowest root mean squared error (RMSE) on average (mean: 5.2-5.5%), followed by the Matérn model (mean: 5.8-7.4%), both of which performed better than most other models. In contrast, the Exponential model had the highest RMSE (mean: 9.4-15.6%). These results remained consistent across two different cohorts of NSCLC. Given the robust performance of the Stable model, it may generalize for modeling spatial response in other clinical settings beyond NSCLC and should be further studied.
About
- Frequency
- Sun
- Papers found
- 200
- RSS feed
- Subscribe