OBJECTIVE: Extracting social determinants of health (SDoHs) from medical notes depends heavily on labor-intensive annotations, which are typically task-specific, hampering reusability and limiting sharing. Here, we introduce SDoH-GPT, a novel framework leveraging few-shot learning large language models (LLMs) to automate the extraction of SDoH from unstructured text, aiming to improve both efficiency and generalizability. MATERIALS AND METHODS: SDoH-GPT is a framework including the few-shot learning LLM methods to extract the SDoH from medical notes and the XGBoost classifiers which continue to classify SDoH using the annotations generated by the few-shot learning LLM methods as training datasets. The unique combination of the few-shot learning LLM methods with XGBoost utilizes the strength of LLMs as great few shot learners and the efficiency of XGBoost when the training dataset is sufficient. Therefore, SDoH-GPT can extract SDoH without relying on extensive medical annotations or costly human intervention. RESULTS: Our approach achieved tenfold and twentyfold reductions in time and cost, respectively, and superior consistency with human annotators measured by Cohen's kappa of up to 0.92. The innovative combination of LLM and XGBoost can ensure high accuracy and computational efficiency while consistently maintaining 0.90+ AUROC scores. DISCUSSION: This study has verified SDoH-GPT on three datasets and highlights the potential of leveraging LLM and XGBoost to revolutionize medical note classification, demonstrating its capability to achieve highly accurate classifications with significantly reduced time and cost. CONCLUSION: The key contribution of this study is the integration of LLM with XGBoost, which enables cost-effective and high quality annotations of SDoH. This research sets the stage for SDoH can be more accessible, scalable, and impactful in driving future healthcare solutions.

In recent years, many discrete neural dynamics models are presented based on continuous models to solve the multilinear tensor equation. However, these existing discrete models all depend on numerical algorithms, such as Euler difference formula and Taylor-type difference formula, which may suffer from the problem of fixed selections with limited feasible parameters. In this article, a few-shot-learning-like neural dynamics (FLLND) model is constructed to find the solution to the time-dependent multilinear tensor equation (TMTE), which opens a new road in constructing the discrete computing model from its continuous counterpart. Specifically, to keep the consistency and better generalization of the constructed model, a few-shot-learning-like method is leveraged to learn parameters from a small dataset. Then, theoretical analyses are conducted to demonstrate the convergence and robustness of the constructed FLLND model in solving the TMTE problem. Finally, several TMTE examples are provided to illustrate the effectiveness and practicality of the FLLND model.

This study compares AI-powered social media analysis with computer-assisted human coding by analyzing online posts and comments from the subreddit "r/EmergencyRoom". It identifies several limitations of AI-powered text analysis methods and offers recommendations for their improvement.

The existing discrete-time neural dynamics methods for solving the multilinear tensor equation (MTE) with$\mathcal {M}$-tensor are all derived from the continuous-time one and depend on the Euler difference formula, which cannot be applied to essentially discrete problems and have low solution accuracy. Moreover, these methods all focus on static problems rather than time-varying ones, and thus may have unsatisfactory performance in applications with time-varying parameters. Additionally, most of these methods fail to handle the MTE with$\mathcal {M}$-tensor under noisy conditions. To remedy these issues, a high-order robust discrete-time neural dynamics (HRDND) method with a directly discrete approach is proposed for solving the time-varying MTE (TMTE) with$\mathcal {M}$-tensor in this article. Theoretical analyses on convergence and robustness are provided to prove that the proposed HRDND method is feasible and effective. Finally, simulative experiments on four time-varying numerical examples and an application derived from the Bellman equation solved by the proposed HRDND method and other four methods are given, whose results illustrate the superiority of the proposed HRDND method.

The COVID-19 pandemic has heightened the necessity for pervasive data and system interoperability to manage healthcare information and knowledge. There is an urgent need to better understand the role of interoperability in improving the societal responses to the pandemic. This paper explores data and system interoperability, a very specific area that could contribute to fighting COVID-19. Specifically, the authors propose a unified health information system framework to connect data, systems, and devices to increase interoperability and manage healthcare information and knowledge. A blockchain-based solution is also provided as a recommendation for improving the data and system interoperability in healthcare.

Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated a high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In DSCN algorithm, various scoring schemes were evaluated. The 'diffusion-path' method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon and VIPER, in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN's computational speed is also at least ten times fast than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), DSCNi showed high correlation between target combinations predicted and real synergistic combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations.

SUMMARY: Cancer Gene and Pathway Explorer (CGPE) is developed to guide biological and clinical researchers, especially those with limited informatics and programming skills, performing preliminary cancer-related biomedical research using transcriptional data and publications. CGPE enables three user-friendly online analytical and visualization modules without requiring any local deployment. The GenePub HotIndex applies natural language processing, statistics and association discovery to provide analytical results on gene-specific PubMed publications, including gene-specific research trends, cancer types correlations, top-related genes and the WordCloud of publication profiles. The OnlineGSEA enables Gene Set Enrichment Analysis (GSEA) and results visualizations through an easy-to-follow interface for public or in-house transcriptional datasets, integrating the GSEA algorithm and preprocessed public TCGA and GEO datasets. The preprocessed datasets ensure gene sets analysis with appropriate pathway alternation and gene signatures. The CellLine Search presents evidence-based guidance for cell line selections with combined information on cell line dependency, gene expressions and pathway activity maps, which are valuable knowledge to have before conducting gene-related experiments. In a nutshell, the CGPE webserver provides a user-friendly, visual, intuitive and informative bioinformatics tool that allows biomedical researchers to perform efficient analyses and preliminary studies on in-house and publicly available bioinformatics data. AVAILABILITY AND IMPLEMENTATION: The webserver is freely available online at https://cgpe.soic.iupui.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The coronavirus disease 2019 (COVID-19) outbreak caused by SARS-CoV-2 virus began in Wuhan, China, and has spread quickly throughout the world. The development of vaccines for SARS-CoV-2 is difficult due to many obstacles, such as the lack of knowledge of important proteins, genes, and mutations of the viral genome. In this study, we selected and utilized 852 strains of COVID-19 from major countries in the National Center for Biotechnology Information (NCBI) global virus bank. The information of these strains was processed by using Nextstrain software, a program that provided a visual phylogenetic tree, transmission map, and diversity panel that explains entropy and number of mutations for each codon in the genome. The general data about the spread and evolution of COVID-19 supported the current knowledge that it began in China and spread throughout the country in an interrelated manner instead of a clear “patient zero” manner. A recent study reported that codon 614 on COVID-19 spike protein (S614) was an important codon for viral spread, specifically, a mutation from aspartic acid to glycine facilitated the spread of the virus. Therefore, we chose to geographically track this mutation during the spread of COVID-19 to investigate where it emerged and whether it can affect the spread COVID-19. Our results showed that the glycine mutation first emerged in France. Also, the transmission rates in France versus China, where the mutation was not prevalent, did reflect the hypothesized change in viral behavior.

The Department of BioHealth Informatics at the School of Informatics and Computing, Indiana University Purdue University Indianapolis (IUPUI) has successfully built strong collaborations and partnerships with local communities. The partners include but not limited to other higher education institutes, biomedical research institutes, healthcare organizations, pharmaceutical companies, government agencies, and other biomedical technology industries. The department drafted a 5-year strategic plan to foster teamwork and practice with community partners, which benefit both faculty and students. The department also recruited members for the BHI community-based advisory boards with diverse backgrounds and different decision making. The advisory board strengthens our programs to meet the industrial needs, promote faculty interactions with local communities, and increase student employment opportunities. Through the weekly BHI seminar series, including colloquium speakers, Ph.D. student work-in-progress reports, and faculty proposal development, BHI faculty have been successful in building new collaborative projects and secure joint grants. Another significant initiative is the establishment of the BioHealth Informatics Research Center (BHIRC), which systematically promotes collaborations among faculty members, students, and external collaborators. Students have enriched research and learning experience from hands-on experience, application-oriented projects, and internships with local companies. The practices and strategic planning can be extended to other informatics disciplines easily.

Summary In the field of bioinformatics and DNA computing, simulated hybridization experiments can replace real molecular hybridization experiments to some extent, avoiding some disadvantages of the actual experimental design. However, the core techniques, which are employed by the popular DNA simulation software, are limited to the exponential computational complexity of the combinatorial problems. As a result, it is impossible to decide whether a specific hybridization among complex DNA molecules is effective or not within acceptable time. To address this common problem, we hereby introduce a new method based on the machine learning technique. First, a sample set is employed to train the boosted tree algorithm, which resulted in a corresponding machine learning model. Second, this model is applied to predict the classification results of molecular hybridization for a given group of DNA molecular coding. The experiment results showed that the new method had an average accuracy level of 94.2% and an average efficiency level 90 839 times higher than that of the existing representative approaches. Especially for the case study in this paper, the efficiency of the new method is 235 000, 250 000, and 990 000 times higher than that of the three existing methods, respectively. These experimental results indicate that our new approach can quickly and accurately determine the biological effectiveness of molecular hybridization for a given DNA design.

Background: Information reconciliation is a common yet complex and often time-consuming task performed by healthcare providers. While electronic health record systems can receive "outside information" about a patient in electronic documents, rarely does the computer automate reconciling information about a patient across all documents. Materials and Methods: Using a mixed methods design, we evaluated an information system designed to reconcile information across multiple electronic documents containing health records for a patient received from a health information exchange (HIE) network. Nine healthcare providers participated in scenario-based sessions in which they manually consolidated information across multiple documents. Accuracy of consolidation was measured along with the time spent completing 3 different reconciliation scenarios with and without support from the information system. Participants also attended an interview about their experience. Perceived workload was evaluated quantitatively using the NASA-TLX tool. Qualitative analysis focused on providers' impression of the system and the challenges faced when reconciling information in practice. Results: While 5 providers made mistakes when trying to manually reconcile information across multiple documents, no participants made a mistake when the system supported their work. Overall perceived workload decreased significantly for scenarios supported by the system (37.2% in referrals, 18.4% in medications, and 31.5% in problems scenarios, P < 0.001). Information reconciliation time was reduced significantly when the system supported provider tasks (58.8% in referrals, 38.1% in medications, and 65.1% in problem scenarios). Conclusion: Automating retrieval and reconciliation of information across multiple electronic documents shows promise for reducing healthcare providers' task complexity and workload.

For graduate programs, usually, the curricula are developed based on the generic learning outcomes of the matching undergraduate programs. However, most graduate programs have diversified students from various backgrounds with undergraduate degrees from an assortment of majors. Even for graduate students coming from the same undergraduate majors, they might come from different countries with differing undergraduate learning outcomes. It is even more challenging for graduate programs where there are no corresponding undergraduate programs. The graduate program curriculum should be customized based on the backgrounds of special student groups. This paper will describe the redesign of our professional Bioinformatics MS program for incoming domestic and international students with diverse backgrounds. The MS students in Bioinformatics program may have a previous degree in biotechnology, computer science, biology, computer engineering, electrical engineering, biomedical engineering, or other science and engineering fields. Thus, it is of great significance to categorize the program specific competencies and student learning outcomes from their previous study. It will help to customize the student specific plan of study in the Bioinformatics MS program so that they can complete the graduate study with the job-ready skills. For example, detailed studies have been carried out for the Bioinformatics related programs in India, where a substantial population of our Bioinformatics MS students comes from. In India, Bioinformatics is a growing subject and has emerged as an independent program from biomedical engineering and biotechnology. The Bioinformatics in India is taught at in all different degree levels: Bachelors, Masters, PhD, and certificates. Graduate students from India who are familiar with the Indian education system have helped to compare and contrast the various Bioinformatics related degree programs, including both Bioinformatics and Biotechnology degree programs of BS, MS, B. Tech. (Bachelor of Technology), M.Tech. (Master of Technology), M.Phil (Master of Philosophy), PhD, and integrated (such as BS+MS or MS+PhD) programs. The corresponding program competencies and the student learning outcomes are tabulated and compared. The job market in India and the USA are also analyzed. The information has been used to redesign our MS Bioinformatics program, including the alternative prerequisites, the different sequences of courses, and the diverse plans of study for students with various backgrounds. For example, some existing 3-credit introductory courses (such as Programming in Bioinformatics) are re-designed into various 1-credit common course modules (such as Programming in R, or Python, or Perl). Students are provided with more flexibility to select specific course modules based on their backgrounds and future career interests. New course modules have also been developed based on the job market needs, such as big data analysis for Bioinformatics. These student-oriented and career-ready customization of graduate programs will better serve our expanded student groups and provide a better workforce for the job market.

The rapid advancement in biological data acquisition technologies has led to massive biological datasets, which requires the development and application of computational methods to analyze and interpret the information. Bioinformatics is the confluence of biology, computer science, and information technology. The Bioinformatics programs are offered by more than 100 universities in the United States, and much more worldwide. Different degree (including BS, MS, and PhD), and certificate programs in Bioinformatics have been performed. The current bioinformatics programs in the US have been studied, regarding their curriculum, program competencies, sizes of the faculty, and student enrollments. The job market is also explored for bioinformatics professional training and career planning. The bioinformatics skill requirements are analyzed. Systematical analysis is carried out by integrating the core competences and curriculum improvements in bioinformatics. The potential employers for bioinformatics professionals are analyzed according to the properties of the companies, such as the sizes, the focus areas, the locations, the skill requirements, and other information. The results provide guidance for bioinformatics curriculum development, such as the minimized courses to cover the basic required skill sets for a bioinformatics student to be a successful bioinformatician. In addition, the analytical results are applied to the redesign of the curriculum in our bioinformatics program which offers MS, PhD, and PhD Minor. In summary, the systematic study of the existing bioinformatics programs in the US and the current market needs for professionals in bioinformatics provide great insight for education in bioinformatics. It helps the curriculum development and reexamination. It also provides the students with the required knowledge for their future career.

Respiratory motion induces discrepancy between the expected tumor positions used in treatment planning and the actual positions during treatment delivery. Such motion degrades greatly the effectiveness of the radiation treatment. To address this challenge, we have proposed an online treatment failure detection approach with image guidance. Tumor motion is tracked in real-time during treatment delivery and compared to the baseline motion used in treatment planning. Tracking errors are recovered online with subdivided subsequence correlation. A stop-n-wait dose delivery procedure is applied to minimize treatment errors. Two approaches have been developed to address baseline shift in tumor motion. The performances are evaluated using three different metrics: the misplacement of the tumor, the treatment efficacy, and the intervention frequency. The results showed that the new approaches will reduce treatment errors, improve dose delivery efficiency, and reduce treatment interventions. This study has the potential to be employed in clinical practice thus improving radiation outcome.

Subsequence matching in time series databases is a useful technique, with applications in pattern matching, prediction, and rule discovery. Internal structure within the time series data can be used to improve these tasks, and provide important insight into the problem domain. This paper introduces our research effort in using the internal structure of a time series directly in the matching process. This idea is applied to the problem domain of respiratory motion data in cancer radiation treatment. We propose a comprehensive solution for analysis, clustering, and online prediction of respiratory motion using subsequence similarity matching. In this system, a motion signal is captured in real time as a data stream, and is analyzed immediately for treatment and also saved in a database for future study. A piecewise linear representation of the signal is generated from a finite state model, and is used as a query for subsequence matching. To ensure that the query subsequence is representative, we introduce the concept of subsequence stability, which can be used to dynamically adjust the query subsequence length. To satisfy the special needs of similarity matching over breathing patterns, a new subsequence similarity measure is introduced. This new measure uses a weighted L1 distance function to capture the relative importance of each source stream, amplitude, frequency, and proximity in time. From the subsequence similarity measure, stream and patient similarity can be defined, which are then used for offline and online applications. The matching results are analyzed and applied for motion prediction and correlation discovery. While our system has been customized for use in radiation therapy, our approach to time series modeling is general enough for application domains with structured time series data.

Subsequence similarity matching in time series databases is an important research area for many applications. This paper presents a new approximate approach for automatic online subsequence similarity matching over massive data streams. With a simultaneous on-line segmentation and pruning algorithm over the incoming stream, the resulting piecewise linear representation of the data stream features high sensitivity and accuracy. The similarity definition is based on a permutation followed by a metric distance function, which provides the similarity search with flexibility, sensitivity and scalability. Also, the metric-based indexing methods can be applied for speed-up. To reduce the system burden, the event-driven similarity search is performed only when there is a potential event. The query sequence is the most recent subsequence of piecewise data representation of the incoming stream which is automatically generated by the system. The retrieved results can be analyzed in different ways according to the requirements of specific applications. This paper discusses an application for future data movement prediction based on statistical information. Experiments on real stock data are performed. The correctness of trend predictions is used to evaluate the performance of subsequence similarity matching.

The CenSSIS image database is a scientific database that enables effective data management and collaboration to accelerate fundamental research. This paper describes the design and use of a state-of-the-art relational image database management system, accessible through a standard Web-browser interface. The application utilizes a robust security architecture and is designed for efficient data submission. Our database query engine provides complex query capabilities to facilitate fast and efficient data retrieval. The system offers a highly extensible metadata schema, with the option of storing data within a hierarchical format.