Proceedings. IEEE International Conference on Bioinformatics and Biomedicine最新文献

{"title":"Parsing Clinical Trial Eligibility Criteria for Cohort Query by a Multi-Input Multi-Output Sequence Labeling Model.","authors":"Shubo Tian, Pengfei Yin, Hansi Zhang, Arslan Erdengasileng, Jiang Bian, Zhe He","doi":"10.1109/bibm58861.2023.10385876","DOIUrl":"10.1109/bibm58861.2023.10385876","url":null,"abstract":"<p><p>To enable electronic screening of eligible patients for clinical trials, free-text clinical trial eligibility criteria should be translated to a computable format. Natural language processing (NLP) techniques have the potential to automate this process. In this study, we explored a supervised multi-input multi-output (MIMO) sequence labelling model to parse eligibility criteria into combinations of fact and condition tuples. Our experiments on a small manually annotated training dataset showed that that the performance of the MIMO framework with a BERT-based encoder using all the input sequences achieved an overall lenient-level AUROC of 0.61. Although the performance is suboptimal, representing eligibility criteria into logical and semantically clear tuples can potentially make subsequent translation of these tuples into database queries more reliable.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"4426-4430"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11251129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Practical Approach to Disease Risk Prediction: Focus on High-Risk Patients via Highest-<i>k</i> Loss.","authors":"Hongyi Yang, Rich Gonzalez, Brahmajee K Nallamothu, Keith D Aaronson, Kevin R Ward, Alfred O Hero, Sardar Ansari","doi":"10.1109/bibm58861.2023.10385816","DOIUrl":"10.1109/bibm58861.2023.10385816","url":null,"abstract":"<p><p>Disease risk prediction models play an important role in preventing disease developments in modern healthcare. However, the lack of focus on high-risk patients has hindered the large-scale practical application of these models, especially considering the limitation of medical resources available for following up on patients who are deemed high-risk. In this study, we propose a novel and practical approach that focuses on minimizing the number of false positive observations among high-risk patients by introducing the <i>Highest</i>-<i>k Loss</i>. The solution is to estimate the weights of the highest <math><mi>k</mi></math> scores with a differentiable estimation of the sorting operation and apply the weights to the loss function. We extracted 253,680 survey responses from a public dataset of the U.S. health survey system to define a diabetes prediction task. This study employs nested cross-validation as well as an aggregated model applied to an independent test set to systematically evaluate the proposed method. Compared with traditional binary cross entropy loss and Focal loss, the Highest- <math><mi>k</mi></math> loss improved the precision (positive predictive value) for the highest 1% scores by 0.05 (95% CI: 0.041-0.055), the highest 5% scores by 0.03 (95% CI: 0.024-0.032), and the highest 10% scores by 0.02 (95% CI: 0.016-0.021). The introduced Highest- <math><mi>k</mi></math> loss function addresses the problem of prevailing risk prediction models and offers a practical solution that focuses on patients with the <math><mi>k</mi></math> highest predictive scores who can realistically receive an intervention as opposed to the entire patient population.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"3226-3233"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11821551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building Prediction Models for 30-Day Readmissions Among ICU Patients Using Both Structured and Unstructured Data in Electronic Health Records.","authors":"Alex Moerschbacher, Zhe He","doi":"10.1109/bibm58861.2023.10385612","DOIUrl":"10.1109/bibm58861.2023.10385612","url":null,"abstract":"<p><p>ICU readmissions are associated with poor outcomes for patients and poor performance of hospitals. Patients who are readmitted have an increased risk of in-hospital deaths; hospitals with a higher read-mission rate have a reduced profitability, due to an increase in cost and reduced payments from Medicare and Medicaid programs. Predicting a patient's likelihood of being readmitted to the ICU can help reduce early discharges, the risk of in-hospital deaths, and help in-crease profitability. In this study, we built and evaluated multiple machine learning models to predict 30-day readmission rates of ICU patients in the MIMIC-III database. We used both the structured data including demographics, laboratory tests, comorbidities, and unstructured discharge summaries as the predictors and evaluated different combinations of features. The best performing model in this study Logistic Regression achieved an AUROC of 75.7%. This study shows the potential of leveraging machine learning and deep learning for predicting ICU readmissions.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"4368-4373"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11271049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating Sex-Specific Disease Dynamics in Incident Dementia.","authors":"Muskan Garg, Xingyi Liu, Maria Vassilaki, Ronald C Petersen, Jennifer St Sauver, Sunghwan Sohn","doi":"10.1109/bibm58861.2023.10385324","DOIUrl":"10.1109/bibm58861.2023.10385324","url":null,"abstract":"<p><p>Dementia is among the leading causes of cognitive and functional loss and disability in older adults. Past studies suggested sex differences in health conditions and progression of cognitive decline. Existing studies on the temporal trajectory of health conditions for patient characterization after dementia diagnosis are scarce and ambiguous. Thus, there's limited and unclear research on how health conditions change over time after a dementia diagnosis. To this end, we aim to analyze the shift in medical conditions and examine sex-specific changes in patterns of chronic health conditions after dementia diagnosis. We centered our analysis on a 15-year window around the point of dementia diagnosis, encompassing the 5 years leading up to the diagnosis and the 10 years following it. We introduce (i) MedMet, a network metric to quantify the contribution of each medical condition, and (ii) growth and decay function for temporal trajectory analysis of medical conditions. Our experiments demonstrate that certain health conditions are more prevalent among females than males. Thus, our findings underscore the pressing need to examine differences between men and women, which could be important for healthcare utilization after a dementia diagnosis.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"4065-4072"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10883293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139974920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing Transfer Learning for Dementia Prediction: Leveraging Sex-Different Mild Cognitive Impairment Prognosis.","authors":"Ziming Liu, Muskan Garg, Sunyang Fu, Surjodeep Sarkar, Maria Vassilaki, Ronald C Petersen, Jennifer St Sauver, Sunghwan Sohn","doi":"10.1109/bibm58861.2023.10385516","DOIUrl":"10.1109/bibm58861.2023.10385516","url":null,"abstract":"<p><p>This paper presents a machine learning-based prediction for dementia, leveraging transfer learning to reuse the knowledge learned from prediction of mild cognitive impairment, a precursor of dementia. We also examine the impacts of temporal aspects of longitudinal data and sex differences. The methodology encompasses key components such as setting the duration window, comparing different modeling strategies, conducting comprehensive evaluations, and examining the sex-specific impacts of simulated scenarios. The findings reveal that cognitive deficits in females, once detected at the mild cognitive impairment stage, tend to deteriorate over time, while males exhibit more diverse decline across various characteristics without highlighting specific ones. However, the underlying reasons for these sex differences remain unknown and warrant further investigation.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"2097-2100"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10883588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139974919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Assessment of Pneumocystosis with MIMIC Data.","authors":"Huanfei Wang, Qian Zhu, Jian Pei","doi":"10.1109/BIBM58861.2023.10385603","DOIUrl":"10.1109/BIBM58861.2023.10385603","url":null,"abstract":"<p><p>Pneumocystosis remains a life-threatening disease with a high mortality rate. It's critical to understand its clinical course and risk factors for better disease management. In this retrospective analysis, we aimed to elucidate the prognostic determinants of in-hospital mortality among patients diagnosed with pneumocystosis. Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database, encompassing all recorded cases of pneumocystosis. The dataset included patient admission records, comprehensive laboratory results, and medication administration data, which were meticulously analyzed to identify relevant features. Employing logistic regression and random forest, we discerned that the administration of micafungin sodium and vasopressin have significant impacts as risk factors on the survival rate of pneumocystosis patients.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"2751-2753"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12451645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ASD-GResTM: Deep Learning Framework for ASD classification using Gramian Angular Field.","authors":"Fahad Almuqhim, Fahad Saeed","doi":"10.1109/bibm58861.2023.10385743","DOIUrl":"10.1109/bibm58861.2023.10385743","url":null,"abstract":"<p><p>Autism Spectrum Disorder (ASD) is a heterogeneous disorder in children, and the current clinical diagnosis is accomplished using behavioral, cognitive, developmental, and language metrics. These clinical metrics can be imperfect measures as they are subject to high test-retest variability, and are influenced by assessment factors such as environment, social structure, or comorbid disorders. Advances in neuroimaging coupled with machine-learning provides an opportunity to develop methods that are more quantifiable, and reliable than existing clinical techniques. In this paper, we design and develop a deep-learning model that operates on functional magnetic resonance imaging (fMRI) data, and can classify between ASD and neurotypical brains. We introduce a novel strategy to transform time-series data extracted from fMRI signals into Gramian Angular Field (GAF) while locking in the temporal and spatial patterns in the data. Our motivation is to design and develop a novel framework that could encode the time-series, acquired from fMRI data, into images that can be used by deep-learning architectures that have been successful in computer vision. In our proposed framework called <i>ASD-GResTM</i>, we used a Convolutional Neural Network (CNN) to extract useful features from GAF images. We then used a Long Short-Term Memory (LSTM) layer to learn the activities between the regions. Finally, the output representations of the last LSTM layer are applied to a single-layer perceptron (SPL) to get the final classification. Our extensive experimentation demonstrates high accuracy across 4 centers, and outperforms state-of-the-art models on two centers with an increase in the accuracy of 17.58% and 6.7%, respectively as compared to the state of the art. Our model achieved the maximum accuracy of 81.78% with high degree of sensitivity and specificity. All training, validation, and testing was accomplished using openly available ABIDE-I benchmarking dataset.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"2837-2843"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11254319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A generalizable physiological model for detection of Delayed Cerebral Ischemia using Federated Learning.","authors":"Ahmed Elhussein, Murad Megjhani, Daniel Nametz, Miriam Weiss, Jude Savarraj, Soon Bin Kwon, David J Roh, Sachin Agarwal, E Sander Connolly, Angela Velazquez, Jan Claassen, Huimahn A Choi, Gerrit A Schubert, Soojin Park, Gamze Gürsoy","doi":"10.1109/bibm58861.2023.10385383","DOIUrl":"10.1109/bibm58861.2023.10385383","url":null,"abstract":"<p><p>Delayed cerebral ischemia (DCI) is a complication seen in patients with subarachnoid hemorrhage stroke. It is a major predictor of poor outcomes and is detected late. Machine learning models are shown to be useful for early detection, however training such models suffers from small sample sizes due to rarity of the condition. Here we propose a Federated Learning approach to train a DCI classifier across three institutions to overcome challenges of sharing data across hospitals. We developed a framework for federated feature selection and built a federated ensemble classifier. We compared the performance of FL model to that obtained by training separate models at each site. FL significantly improved performance at only two sites. We found that this was due to feature distribution differences across sites. FL improves performance in sites with similar feature distributions, however, FL can worsen performance in sites with heterogeneous distributions. The results highlight both the benefit of FL and the need to assess dataset distribution similarity before conducting FL.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"1886-1889"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10883332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139934591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Approach Integrating Prior Knowledge for Identifying miRNA-mRNA Interactions in Hepatocellular Carcinoma.","authors":"Yichen Guo, Marie Denis, Rency S Varghese, Sidharth S Jain, Mahlet G Tadesse, Habtom W Ressom","doi":"10.1109/bibm58861.2023.10385314","DOIUrl":"10.1109/bibm58861.2023.10385314","url":null,"abstract":"<p><p>Oncogenesis, a complex and multifaceted process, is profoundly modulated by miRNA's regulatory role in gene expression. Over the years, a substantial body of knowledge concerning miRNA and mRNA has been accumulated, drawing from both rigorous biological experiments and intricate statistical analyses. In the realm of statistical modeling, the integration of such information as \"prior knowledge\" often amplifies the model's ability to pinpoint molecular targets of significance. This study seeks to leverage prior knowledge of miRNA-mRNA regulatory interactions to map the dynamic landscape of interactions in the specific context of hepatocellular carcinoma (HCC). To address this, we introduce an evolved iteration of a Bayesian two-step integrative method previously established in the literature. This augmented approach includes improved computing efficiency when dealing with high dimensional data and a novel mechanistic submodel, which operates autonomously, devoid of prior knowledge. Employing this method, we identified two discrete gene lists: one informed by prior knowledge and the other independently inferred. This bifurcated strategy provides a comprehensive perspective on gene interactions. Our methodological advancement allows for a nuanced analysis of gene networks, distinguishing between direct and indirect gene relationships and considering miRNA influences with two available sub-mechanistic submodels. We introduce an approach to validate our findings using a biological interaction network, emphasizing the quality and relevance of identified gene-gene relationships. Metrics like the Matthews Correlation Coefficient (MCC) and the true discovery rate (TDR) further attest to the robustness of our findings. In summation, aside from improving the existing sub-mechanistic model that requires prior knowledge, this paper presents an innovative prior knowledge-free sub-mechanistic model as an alternative. It champions the use of biological networks for validation, underscoring the significance of methodological advancements in genomics research.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"3768-3774"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12090934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mining NIH BTRIS Data for Drug Repurposing: A Case Study of Glioblastoma.","authors":"Shixue Sun, Yitao Tian, Qian Zhu","doi":"10.1109/bibm58861.2023.10385957","DOIUrl":"10.1109/bibm58861.2023.10385957","url":null,"abstract":"<p><p>The purpose of drug repurposing is to identify alternative uses of FDA approved drugs, which significantly accelerates the drug development process. Meanwhile, clinical data illustrate the patterns and clinical outcomes of drug use, so they have been increasingly applied to support drug development, particularly for drug repurposing. The NIH Biomedical Translational Research Information System (BTRIS) is a resource which compiles deidentified patient data from clinical research done across NIH Institutes and Centers. In this study, we analyzed clinical data available from BTRIS to identify drug repurposing candidates, i.e., identifying drugs that were correlated with an increased survival rate for glioblastoma (GBM) patients. Specifically, we extracted all the administered drugs on GBM patients and fitted them to elastic-net penalized Cox proportional hazards (CPH) models, a regression model for investigating the association between the survival rate of patients and covariates (administered drugs in this study). We were able to identify several potential drug candidates for GBM to be further evaluated with other data types and by performing biological experiments.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"2748-2750"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12434630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}