journal
https://read.qxmd.com/read/38301750/clarus-an-interactive-explainable-ai-platform-for-manual-counterfactuals-in-graph-neural-networks
#21
JOURNAL ARTICLE
Jacqueline Michelle Metsch, Anna Saranti, Alessa Angerschmid, Bastian Pfeifer, Vanessa Klemt, Andreas Holzinger, Anne-Christin Hauschild
BACKGROUND: Lack of trust in artificial intelligence (AI) models in medicine is still the key blockage for the use of AI in clinical decision support systems (CDSS). Although AI models are already performing excellently in systems medicine, their black-box nature entails that patient-specific decisions are incomprehensible for the physician. Explainable AI (XAI) algorithms aim to "explain" to a human domain expert, which input features influenced a specific recommendation. However, in the clinical domain, these explanations must lead to some degree of causal understanding by a clinician...
January 30, 2024: Journal of Biomedical Informatics
https://read.qxmd.com/read/38278312/clinical-natural-language-processing-for-secondary-uses
#22
EDITORIAL
Yanjun Gao, Diwakar Mahajan, Ozlem Uzuner, Meliha Yetisgen
No abstract text is available yet for this article.
January 24, 2024: Journal of Biomedical Informatics
https://read.qxmd.com/read/38272433/cmbee-a-constraint-based-multi-task-learning-framework-for-biomedical-event-extraction
#23
JOURNAL ARTICLE
Jingyue Hu, Buzhou Tang, Nan Lyu, Yuxin He, Ying Xiong
OBJECTIVE: Event extraction plays a crucial role in natural language processing. However, in the biomedical domain, the presence of nested events adds complexity to event extraction compared to single events, and these events usually have strong semantic relationships and constraints. Previous approaches ignored the binding connections between these complex nested events. This study aims to develop a unified framework based on event constraint information that jointly extract biomedical event triggers and arguments and enhance the performance of nested biomedical event extraction...
January 23, 2024: Journal of Biomedical Informatics
https://read.qxmd.com/read/38272432/adjusting-for-false-discoveries-in-constraint-based-differential-metabolic-flux-analysis
#24
JOURNAL ARTICLE
Bruno G Galuzzi, Luca Milazzo, Chiara Damiani
One of the critical steps to characterize metabolic alterations in multifactorial diseases, as well as their heterogeneity across different patients, is the identification of reactions that exhibit significantly different usage (or flux) between cohorts. However, since metabolic fluxes cannot be determined directly, researchers typically use constraint-based metabolic network models, customized on post-genomics datasets. The use of random sampling within the feasible region of metabolic networks is becoming more prevalent for comparing these networks...
January 23, 2024: Journal of Biomedical Informatics
https://read.qxmd.com/read/38253228/useful-blunders-can-automated-speech-recognition-errors-improve-downstream-dementia-classification
#25
JOURNAL ARTICLE
Changye Li, Weizhe Xu, Trevor Cohen, Serguei Pakhomov
OBJECTIVES: We aimed to investigate how errors from automatic speech recognition (ASR) systems affect dementia classification accuracy, specifically in the "Cookie Theft" picture description task. We aimed to assess whether imperfect ASR-generated transcripts could provide valuable information for distinguishing between language samples from cognitively healthy individuals and those with Alzheimer's disease (AD). METHODS: We conducted experiments using various ASR models, refining their transcripts with post-editing techniques...
January 20, 2024: Journal of Biomedical Informatics
https://read.qxmd.com/read/38244958/one-shot-distributed-algorithms-for-addressing-heterogeneity-in-competing-risks-data-across-clinical-sites
#26
JOURNAL ARTICLE
Dazheng Zhang, Jiayi Tong, Ronen Stein, Yiwen Lu, Naimin Jing, Yuchen Yang, Mary R Boland, Chongliang Luo, Robert N Baldassano, Raymond J Carroll, Christopher B Forrest, Yong Chen
OBJECTIVE: To characterize the interplay between multiple medical conditions across sites and account for the heterogeneity in patient population characteristics across sites within a distributed research network, we develop a one-shot algorithm that can efficiently utilize summary-level data from various institutions. By applying our proposed algorithm to a large pediatric cohort across four national Children's hospitals, we replicated a recently published prospective cohort, the RISK study, and quantified the impact of the risk factors associated with the penetrating or stricturing behaviors of pediatric Crohn's disease (PCD)...
January 18, 2024: Journal of Biomedical Informatics
https://read.qxmd.com/read/38244957/semantics-enabled-biomedical-literature-analytics
#27
EDITORIAL
Halil Kilicoglu, Faezeh Ensan, Bridget McInnes, Lucy Lu Wang
No abstract text is available yet for this article.
January 18, 2024: Journal of Biomedical Informatics
https://read.qxmd.com/read/38244956/strategies-for-secondary-use-of-real-world-clinical-and-administrative-data-for-outcome-ascertainment-in-pragmatic-clinical-trials
#28
JOURNAL ARTICLE
Cynthia Hau, Patricia A Woods, Amanda S Guski, Srihari I Raju, Liang Zhu, Patrick R Alba, William C Cushman, Peter A Glassman, Areef Ishani, Addison A Taylor, Ryan E Ferguson, Sarah M Leatherman
BACKGROUND: Pragmatic trials are gaining popularity as a cost-effective way to examine treatment effectiveness and generate timely comparative evidence. Incorporating supplementary real-world data is recommended for robust outcome monitoring. However, detailed operational guidelines are needed to inform effective use and integration of heterogeneous databases. OBJECTIVE: Lessons learned from the Veterans Affairs (VA) Diuretic Comparison Project (DCP) are reviewed, providing adaptable recommendations to capture clinical outcomes from real-world data...
January 18, 2024: Journal of Biomedical Informatics
https://read.qxmd.com/read/38199300/few-shot-learning-based-oral-cancer-diagnosis-using-a-dual-feature-extractor-prototypical-network
#29
JOURNAL ARTICLE
Zijun Guo, Sha Ao, Bo Ao
A large global health issue is cancer, wherein early diagnosis and treatment have proven to be life-saving. This holds true for oral cancer, thus emphasizing the significance of timely intervention. Deep learning techniques have gained traction in early cancer detection, exhibiting promising outcomes in accurate diagnosis. However, collecting a substantial amount of training data poses a challenge for deep learning models in cancer diagnosis. To address this limitation, this study proposes an oral cancer diagnosis approach based on a few-shot learning framework that circumvents the need for extensive training data...
January 8, 2024: Journal of Biomedical Informatics
https://read.qxmd.com/read/38191012/counterfactual-formulation-of-patient-specific-root-causes-of-disease
#30
JOURNAL ARTICLE
Eric V Strobl
OBJECTIVE: Root causes of disease intuitively correspond to root vertices of a causal model that increase the likelihood of a diagnosis. This description of a root cause nevertheless lacks the rigorous mathematical formulation needed for the development of computer algorithms designed to automatically detect root causes from data. We seek a definition of patient-specific root causes of disease that models the intuitive procedure routinely utilized by physicians to uncover root causes in the clinic...
January 6, 2024: Journal of Biomedical Informatics
https://read.qxmd.com/read/38191011/multiwd-multi-label-wellness-dimensions-in-social-media-posts
#31
JOURNAL ARTICLE
Muskan Garg, Xingyi Liu, M S V P J Sathvik, Shaina Raza, Sunghwan Sohn
BACKGROUND: Halbert L. Dunn's concept of wellness is a multi-dimensional aspect encompassing social and mental well-being. Neglecting these dimensions over time can have a negative impact on an individual's mental health. The manual efforts employed in in-person therapy sessions reveal that underlying factors of mental disturbance if triggered, may lead to severe mental health disorders. OBJECTIVE: In our research, we introduce a fine-grained approach focused on identifying indicators of wellness dimensions and mark their presence in self-narrated human-writings on Reddit social media platform...
January 6, 2024: Journal of Biomedical Informatics
https://read.qxmd.com/read/38191010/primis-privacy-preserving-medical-image-sharing-via-deep-sparsifying-transform-learning-with-obfuscation
#32
JOURNAL ARTICLE
Isaac Shiri, Behrooz Razeghi, Sohrab Ferdowsi, Yazdan Salimi, Deniz Gündüz, Douglas Teodoro, Slava Voloshynovskiy, Habib Zaidi
OBJECTIVE: The primary objective of our study is to address the challenge of confidentially sharing medical images across different centers. This is often a critical necessity in both clinical and research environments, yet restrictions typically exist due to privacy concerns. Our aim is to design a privacy-preserving data-sharing mechanism that allows medical images to be stored as encoded and obfuscated representations in the public domain without revealing any useful or recoverable content from the images...
January 6, 2024: Journal of Biomedical Informatics
https://read.qxmd.com/read/38163514/retrieval-augmentation-of-large-language-models-for-lay-language-generation
#33
JOURNAL ARTICLE
Yue Guo, Wei Qiu, Gondy Leroy, Sheng Wang, Trevor Cohen
The complex linguistic structures and specialized terminology of expert-authored content limit the accessibility of biomedical literature to the general public. Automated methods have the potential to render this literature more interpretable to readers with different educational backgrounds. Prior work has framed such lay language generation as a summarization or simplification task. However, adapting biomedical text for the lay public includes the additional and distinct task of background explanation: adding external content in the form of definitions, motivation, or examples to enhance comprehensibility...
December 30, 2023: Journal of Biomedical Informatics
https://read.qxmd.com/read/38160758/development-of-a-3-step-theory-of-suicide-ontology-to-facilitate-3st-factor-extraction-from-clinical-progress-notes
#34
JOURNAL ARTICLE
Esther L Meerwijk, Gabrielle A Jones, Asqar S Shotqara, Sofia Reyes, Suzanne R Tamang, Hyrum S Eddington, Ruth M Reeves, Andrea K Finlay, Alex H S Harris
OBJECTIVE: Suicide risk prediction algorithms at the Veterans Health Administration (VHA) do not include predictors based on the 3-Step Theory of suicide (3ST), which builds on hopelessness, psychological pain, connectedness, and capacity for suicide. These four factors are not available from structured fields in VHA electronic health records, but they are found in unstructured clinical text. An ontology and controlled vocabulary that maps psychosocial and behavioral terms to these factors does not exist...
December 29, 2023: Journal of Biomedical Informatics
https://read.qxmd.com/read/38142903/learning-from-vertically-distributed-data-across-multiple-sites-an-efficient-privacy-preserving-algorithm-for-cox-proportional-hazards-model-with-variable-selection
#35
JOURNAL ARTICLE
Guanhong Miao, Lei Yu, Jingyun Yang, David A Bennett, Jinying Zhao, Samuel S Wu
OBJECTIVE: To develop a lossless distributed algorithm for regularized Cox proportional hazards model with variable selection to support federated learning for vertically distributed data. METHODS: We propose a novel distributed algorithm for fitting regularized Cox proportional hazards model when data sharing among different data providers is restricted. Based on cyclical coordinate descent, the proposed algorithm computes intermediary statistics by each site and then exchanges them to update the model parameters in other sites without accessing individual patient-level data...
December 22, 2023: Journal of Biomedical Informatics
https://read.qxmd.com/read/38135173/the-pacific-ontology-for-heterogeneous-data-management-in-cardiology
#36
JOURNAL ARTICLE
Amel Raboudi, Pierre-Yves Hervé, Marianne Allanic, Philippe Boutinaud, Jean-Joseph Christophe, Hüseyin Firat, Elie Mousseaux, Mathieu Pernot, Pierre Prot, Alfonso Sartorius-Carvajal, Frédérique Chézalviel-Guilbert, Jean-Sébastien Hulot
With the emergence of health data warehouses and major initiatives to collect and analyze multi-modal and multisource data, data organization becomes central. In the PACIFIC-PRESERVED (PhenomApping, ClassIFication, and Innovation for Cardiac Dysfunction - Heart Failure with PRESERVED LVEF Study, NCT04189029) study, a data driven research project aiming at redefining and profiling the Heart Failure with preserved Ejection Fraction (HFpEF), an ontology was developed by different data experts in cardiology to enable better data management in a complex study context (multisource, multiformat, multimodality, multipartners)...
December 20, 2023: Journal of Biomedical Informatics
https://read.qxmd.com/read/38122841/fine-tuning-coreference-resolution-for-different-styles-of-clinical-narratives
#37
JOURNAL ARTICLE
Yuxiang Liao, Hantao Liu, Irena Spasić
OBJECTIVE: Coreference resolution (CR) is a natural language processing (NLP) task that is concerned with finding all expressions within a single document that refer to the same entity. This makes it crucial in supporting downstream NLP tasks such as summarization, question answering and information extraction. Despite great progress in CR, our experiments have highlighted a substandard performance of the existing open-source CR tools in the clinical domain. We set out to explore some practical solutions to fine-tune their performance on clinical data...
December 18, 2023: Journal of Biomedical Informatics
https://read.qxmd.com/read/38104851/identifying-the-joint-signature-of-brain-atrophy-and-gene-variant-scores-in-alzheimer-s-disease
#38
JOURNAL ARTICLE
Federica Cruciani, Antonino Aparo, Lorenza Brusini, Carlo Combi, Silvia F Storti, Rosalba Giugno, Gloria Menegaz, Ilaria Boscolo Galazzo
The joint modeling of genetic data and brain imaging information allows for determining the pathophysiological pathways of neurodegenerative diseases such as Alzheimer's disease (AD). This task has typically been approached using mass-univariate methods that rely on a complete set of Single Nucleotide Polymorphisms (SNPs) to assess their association with selected image-derived phenotypes (IDPs). However, such methods are prone to multiple comparisons bias and, most importantly, fail to account for potential cross-feature interactions, resulting in insufficient detection of significant associations...
December 15, 2023: Journal of Biomedical Informatics
https://read.qxmd.com/read/38101690/deep-learning-uncertainty-quantification-for-clinical-text-classification
#39
JOURNAL ARTICLE
Alina Peluso, Ioana Danciu, Hong-Jun Yoon, Jamaludin Mohd Yusof, Tanmoy Bhattacharya, Adam Spannaus, Noah Schaefferkoetter, Eric B Durbin, Xiao-Cheng Wu, Antoinette Stroup, Jennifer Doherty, Stephen Schwartz, Charles Wiggins, Linda Coyle, Lynne Penberthy, Georgia D Tourassi, Shang Gao
INTRODUCTION: Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network's confidence, in-depth analyses are needed to establish whether they are well calibrated. METHOD: In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries...
December 13, 2023: Journal of Biomedical Informatics
https://read.qxmd.com/read/38101689/improving-severity-classification-of-hebrew-pet-ct-pathology-reports-using-test-time-augmentation
#40
JOURNAL ARTICLE
Seffi Cohen, Edo Lior, Moshe Bocher, Lior Rokach
Classifying medical reports written in Hebrew is challenging due to the ambiguity and complexity of the language. This study proposes Text Test Time Augmentation (TTTA), a novel method to improve the classification accuracy of cancer severity levels from PET-CT diagnostic reports in Hebrew. Hebrew, being a morphologically rich language, often leads to each word having multiple ambiguous interpretations. TTTA leverages test-time augmentation to enhance text information retrieval and model robustness. During training and testing phases, this method generates and evaluates sets of augmentations to enhance the semantics extracted from each report...
December 13, 2023: Journal of Biomedical Informatics
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