Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing最新文献

Alzheimer's Disease and Related Dementias (ADRD) afflict almost 7 million people in the USA alone. The majority of research in ADRD is conducted using post-mortem samples of brain tissue or carefully recruited clinical trial patients. While these resources are excellent, they suffer from lack of sex/gender, and racial/ethnic inclusiveness. Electronic Health Records (EHR) data has the potential to bridge this gap by including real-world ADRD patients treated during routine clinical care. In this study, we utilize EHR data from a cohort of 70,420 ADRD patients diagnosed and treated at Penn Medicine. Our goal is to uncover important risk features leading to three types of Neuro-Degenerative Disorders (NDD), including Alzheimer's Disease (AD), Parkinson's Disease (PD) and Other Dementias (OD). We employ a variety of Machine Learning (ML) Methods, including uni-variate and multivariate ML approaches and compare accuracies across the ML methods. We also investigate the types of features identified by each method, the overlapping features and the unique features to highlight important advantages and disadvantages of each approach specific for certain NDD types. Our study is important for those interested in studying ADRD and NDD in EHRs as it highlights the strengths and limitations of popular approaches employed in the ML community. We found that the uni-variate approach was able to uncover features that were important and rare for specific types of NDD (AD, PD, OD), which is important from a clinical perspective. Features that were found across all methods represent features that are the most robust.

The emergent abilities of large language models (LLMs) have demonstrated great potential in solving medical questions. They can possess considerable medical knowledge, but may still hallucinate and are inflexible in the knowledge updates. While Retrieval-Augmented Generation (RAG) has been proposed to enhance the medical question-answering capabilities of LLMs with external knowledge bases, it may still fail in complex cases where multiple rounds of information-seeking are required. To address such an issue, we propose iterative RAG for medicine (i-MedRAG), where LLMs can iteratively ask follow-up queries based on previous information-seeking attempts. In each iteration of i-MedRAG, the follow-up queries will be answered by a vanilla RAG system and they will be further used to guide the query generation in the next iteration. Our experiments show the improved performance of various LLMs brought by i-MedRAG compared with vanilla RAG on complex questions from clinical vignettes in the United States Medical Licensing Examination (USMLE), as well as various knowledge tests in the Massive Multitask Language Understanding (MMLU) dataset. Notably, our zero-shot i-MedRAG outperforms all existing prompt engineering and fine-tuning methods on GPT-3.5, achieving an accuracy of 69.68% on the MedQA dataset. In addition, we characterize the scaling properties of i-MedRAG with different iterations of follow-up queries and different numbers of queries per iteration. Our case studies show that i-MedRAG can flexibly ask follow-up queries to form reasoning chains, providing an in-depth analysis of medical questions. To the best of our knowledge, this is the first-of-its-kind study on incorporating follow-up queries into medical RAG.

This paper examines the integration of artificial intelligence (AI) in point-of-care testing (POCT) to enhance diagnostic speed, accuracy, and accessibility, particularly in underserved regions. AI-driven POCT is shown to optimize clinical decision-making, reduce diagnostic times, and offer personalized healthcare solutions, with applications in genome sequencing and infectious disease management. The paper highlights the environmental challenges of AI, including high energy consumption and electronic waste, and proposes solutions such as energy-efficient algorithms and edge computing. It also addresses ethical concerns, emphasizing the reduction of algorithmic bias and the need for equitable access to AI technologies. While AI in POCT can improve healthcare and promote sustainability, collaboration within the POCT ecosystem-among researchers, healthcare providers, and policymakers-is essential to overcome the ethical, environmental, and technological challenges.

The incidence of chronic kidney disease (CKD) is increasing worldwide, but there is no specific treatment available. Therefore, understanding and controlling the risk factors for CKD are essential for preventing disease occurrence. Salt intake raises blood pressure by increasing fluid volume and contributes to the deterioration of kidney function by enhancing the renin-angiotensin system and sympathetic tone. Thus, a low-salt diet is important to reduce blood pressure and prevent kidney diseases. With recent advancements in genetic research, our understanding of the etiology and genetic background of CKD has deepened, enabling the identification of populations with a high genetic predisposition to CKD. It is thought that the impact of lifestyle or environmental factors on disease occurrence or prevention may vary based on genetic factors. This study aims to investigate whether frequency of adding salt has different effects depending on genetic risk for CKD. CKD polygenic risk scores (PRS) were generated using CKDGen Consortium GWAS (N= 765,348) summary statics. Then we applied the CKD PRS to UK Biobank subjects. A total of 331,318 European individuals aged 40-69 without CKD were enrolled in the study between 2006-2010. The average age at enrollment of the participants in this study was 56.69, and 46% were male. Over an average follow-up period of 8 years, 12,279 CKD cases were identified. The group that developed CKD had a higher percentage of individuals who added salt (46.37% vs. 43.04%) and higher CKD high-risk PRS values compared to the group that did not develop CKD (23.53% vs. 19.86%). We classified the individuals into four groups based on PRS: low (0-19%), intermediate (20-79%), high (80-94%), very high (≥ 95%). Incidence of CKD increased incrementally according to CKD PRS even after adjusting for age, sex, race, Townsend deprivation index, body mass index, estimated glomerular filtration rate, smoking, alcohol, physical activity, diabetes mellitus, dyslipidemia, hypertension, coronary artery diseases, cerebrovascular diseases at baseline. Compared to the "never/rarely" frequency of adding salt group, "always" frequency of adding salt group had an increasing incidence of CKD proportionate to the degree of frequency of adding salt. However, the significant association of "always" group on incident CKD disappeared in the low PRS group. This study validated the signal from PRSs for CKD across a large cohort and confirmed that frequency of adding salt contributes to the occurrence of CKD. Additionally, it confirmed that the effect of frequency of "always" adding salt on CKD incidence is greater in those with more than intermediate CKD-PRS. This study suggests that increased salt intake is particularly concerning for individuals with genetic risk factors for CKD, underscoring the clinical importance of reducing salt intake for these individuals.

Background: Ensuring antibiotics are prescribed only when necessary is crucial for maintaining their effectiveness and is a key focus of public health initiatives worldwide. In cases of sinusitis, among the most common reasons for antibiotic prescriptions in children, healthcare providers must distinguish between bacterial and viral causes based on clinical signs and symptoms. However, due to the overlap between symptoms of acute sinusitis and viral upper respiratory infections, antibiotics are often over-prescribed.

Objectives: Currently, there are no electronic health record (EHR)-based methods, such as lab tests or ICD-10 codes, to retroactively assess the appropriateness of prescriptions for sinusitis, making manual chart reviews the only available method for evaluation, which is time-intensive and not feasible at a large scale. In this study, we propose using natural language processing to automate this assessment.

Methods: We developed, trained, and evaluated generative models to classify the appropriateness of antibiotic prescriptions in 300 clinical notes from pediatric patients with sinusitis seen at a primary care practice in the Children's Hospital of Philadelphia network. We utilized standard prompt engineering techniques, including few-shot learning and chain-of-thought prompting, to refine an initial prompt. Additionally, we employed Parameter-Efficient Fine-Tuning to train a medium-sized generative model Llama 3 70B-instruct.

Results: While parameter-efficient fine-tuning did not enhance performance, the combination of few-shot learning and chain-of-thought prompting proved beneficial. Our best results were achieved using the largest generative model publicly available to date, the Llama 3.1 405B-instruct. On our evaluation set, the model correctly identified 94.7% of the 152 notes where antibiotic prescription was appropriate and 66.2% of the 83 notes where it was not appropriate. However, 15 notes that were insufficiently, vaguely, or ambiguously documented by physicians posed a challenge to our model, as none were accurately classified.

Conclusion: Our generative model demonstrated good performance in the challenging task of chart review. This level of performance may be sufficient for deploying the model within the EHR, where it can assist physicians in real-time to prescribe antibiotics in concordance with the guidelines, or for monitoring antibiotic stewardship on a large scale.

Alzheimer's disease (AD) is characterized by cognitive decline and memory loss due to the abnormal accumulation of amyloid-beta (Aβ) plaques and tau tangles in the brain; its onset and progression also depend on genetic factors such as the apolipoprotein E (APOE) genotype. Understanding how these factors affect the brain's neural pathways is important for early diagnostics and interventions. Tractometry is an advanced technique for 3D quantitative assessment of white matter tracts, localizing microstructural abnormalities in diseased populations in vivo. In this work, we applied BUAN (Bundle Analytics) tractometry to 3D diffusion MRI data from 730 participants in ADNI3 (phase 3 of the Alzheimer's Disease Neuroimaging Initiative; age range: 55-95 years, 349M/381F, 214 with mild cognitive impairment, 69 with AD, and 447 cognitively healthy controls). Using along-tract statistical analysis, we assessed the localized impact of amyloid, tau, and APOE genetic variants on the brain's neural pathways. BUAN quantifies microstructural properties of white matter tracts, supporting along-tract statistical analyses that identify factors associated with brain microstructure. We visualize the 3D profile of white matter tract associations with tau and amyloid burden in Alzheimer's disease; strong associations near the cortex may support models of disease propagation along neural pathways. Relative to the neutral genotype, APOE ϵ3/ϵ3, carriers of the AD-risk conferring APOE ϵ4 genotype show microstructural abnormalities, while carriers of the protective ϵ2 genotype also show subtle differences. Of all the microstructural metrics, mean diffusivity (MD) generally shows the strongest associations with AD pathology, followed by axial diffusivity (AxD) and radial diffusivity (RD), while fractional anisotropy (FA) is typically the least sensitive metric. Along-tract microstructural metrics are sensitive to tau and amyloid accumulation, showing the potential of diffusion MRI to track AD pathology and map its impact on neural pathways.

Alzheimer's Disease and Related Dementias (ADRD) afflict almost 7 million people in the USA alone. The majority of research in ADRD is conducted using post-mortem samples of brain tissue or carefully recruited clinical trial patients. While these resources are excellent, they suffer from lack of sex/gender, and racial/ethnic inclusiveness. Electronic Health Records (EHR) data has the potential to bridge this gap by including real-world ADRD patients treated during routine clinical care. In this study, we utilize EHR data from a cohort of 70,420 ADRD patients diagnosed and treated at Penn Medicine. Our goal is to uncover important risk features leading to three types of Neuro-Degenerative Disorders (NDD), including Alzheimer's Disease (AD), Parkinson's Disease (PD) and Other Dementias (OD). We employ a variety of Machine Learning (ML) Methods, including uni-variate and multivariate ML approaches and compare accuracies across the ML methods. We also investigate the types of features identified by each method, the overlapping features and the unique features to highlight important advantages and disadvantages of each approach specific for certain NDD types. Our study is important for those interested in studying ADRD and NDD in EHRs as it highlights the strengths and limitations of popular approaches employed in the ML community. We found that the uni-variate approach was able to uncover features that were important and rare for specific types of NDD (AD, PD, OD), which is important from a clinical perspective. Features that were found across all methods represent features that are the most robust.

Alzheimer's disease (AD) is a neurocognitive disorder that deteriorates memory and impairs cognitive functions. Mild Cognitive Impairment (MCI) is generally considered as an intermediate phase between normal cognitive aging and more severe conditions such as AD. Although not all individuals with MCI will develop AD, they are at an increased risk of developing AD. Diagnosing AD once strong symptoms are already present is of limited value, as AD leads to irreversible cognitive decline and brain damage. Thus, it is crucial to develop methods for the early prediction of AD in individuals with MCI. Recurrent Neural Networks (RNN)-based methods have been effectively used to predict the progression from MCI to AD by analyzing electronic health records (EHR). However, despite their widespread use, existing RNN-based tools may introduce increased model complexity and often face difficulties in capturing long-term dependencies. In this study, we introduced a novel Dynamic deep learning model for Early Prediction of AD (DyEPAD) to predict MCI subjects' progression to AD utilizing EHR data. In the first phase of DyEPAD, embeddings for each time step or visit are captured through Graph Convolutional Networks (GCN) and aggregation functions. In the final phase, DyEPAD employs tensor algebraic operations for frequency domain analysis of these embeddings, capturing the full scope of evolutionary patterns across all time steps. Our experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC) datasets demonstrate that our proposed model outperforms or is in par with the state-of-the-art and baseline methods.

The 2025 Pacific Symposium on Biocomputing (PSB) represents a remarkable milestone, as it is the thirtieth anniversary of PSB. We use this opportunity to analyze the bibliometric output of 30 years of PSB publications in a wide range of analyses with a focus on various eras that represent important disruptive breakpoints in the field of bioinformatics and biocomputing. These include an analysis of paper topics and keywords, flight emissions produced by travel to PSB by authors, citation and co-authorship networks and metrics, and a broad assessment of diversity and representation in PSB authors. We use the results of these analyses to identify insights that we can carry forward to the upcoming decades of PSB.

The primary challenge in reporting cancer cases lies in the labor-intensive and time-consuming process of manually reviewing numerous reports. Current methods predominantly rely on rule-based approaches or custom-supervised learning models, which predict diagnostic codes based on a single pathology report per patient. Although these methods show promising evaluation results, their biased outcomes in controlled settings may hinder adaption to real-world reporting workflows. In this feasibility study, we focused on lung cancer as a test case and developed an agentic retrieval-augmented generation (RAG) system to evaluate the potential of publicly available large language models (LLMs) for cancer registry coding. Our findings demonstrate that: (1) directly applying publicly available LLMs without fine-tuning is feasible for cancer registry coding; and (2) prompt engineering can significantly enhance the capability of pre-trained LLMs in cancer registry coding. The off-the-shelf LLM, combined with our proposed system architecture and basic prompts, achieved a macro-averaged F-score of 0.637 when evaluated on testing data consisting of patients' medical reports spanning 1.5 years since their first visit. By employing chain of thought (CoT) reasoning and our proposed coding item grouping, the system outperformed the baseline by 0.187 in terms of the macro-averaged F-score. These findings demonstrate the great potential of leveraging LLMs with prompt engineering for cancer registry coding. Our system could offer cancer registrars a promising reference tool to enhance their daily workflow, improving efficiency and accuracy in cancer case reporting.