Using GPT-4 to write a scientific review article: a pilot evaluation study.

GPT-4, as the most advanced version of OpenAI's large language models, has attracted widespread attention, rapidly becoming an indispensable AI tool across various areas. This includes its exploration by scientists for diverse applications. Our study focused on assessing GPT-4's capabilities in generating text, tables, and diagrams for biomedical review papers. We also assessed the consistency in text generation by GPT-4, along with potential plagiarism issues when employing this model for the composition of scientific review papers. Based on the results, we suggest the development of enhanced functionalities in ChatGPT, aiming to meet the needs of the scientific community more effectively. This includes enhancements in uploaded document processing for reference materials, a deeper grasp of intricate biomedical concepts, more precise and efficient information distillation for table generation, and a further refined model specifically tailored for scientific diagram creation.

Using Machine Learning to Predict Outcomes of Patients with Blunt Traumatic Aortic Injuries.

INTRODUCTION: The optimal management of blunt thoracic aortic injury (BTAI) remains controversial, with experienced centers offering therapy ranging from medical management to TEVAR. We investigated the utility of a machine learning (ML) algorithm to develop a prognostic model of risk factors on mortality in patients with BTAI. METHODS: The Aortic Trauma Foundation registry was utilized to examine demographics, injury characteristics, management and outcomes of patients with BTAI. A STREAMLINE (A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison) model as well as logistic regression (LR) analysis with imputation using chained equations was developed and compared. RESULTS: From a total of 1018 patients in the registry, 702 patients were included in the final analysis. Of the 258 (37%) patients who were medically managed, 44 (17%) died during admission, 14 (5.4%) of which were aortic related deaths. 444 (63%) patients underwent TEVAR and 343 of which underwent TEVAR within 24 hours of admission. Amongst TEVAR patients, 39 (8.8%) patients died and 7 (1.6%) had aortic related deaths. (Table 1) Comparison of the STREAMLINE and LR model showed no significant difference in ROC curves and high AUCs of 0.869 (95% CI, 0.813 - 0.925) and 0.840 (95% CI, 0.779 - 0.900) respectively in predicting in-hospital mortality. Unexpectedly, however, the variables prioritized in each model differed between models (Figure 1A-B). The top three variables identified from the LR model were similar to that from existing literature. The STREAMLINE model, however, prioritized location of the injury along the lesser curve, age and aortic injury grade (Figure 1A). CONCLUSIONS: Machine learning provides insight on prioritization of variables not typically identified in standard multivariable logistic regression. Further investigation and validation in other aortic injury cohorts are needed to delineate the utility of ML models. LEVEL OF EVIDENCE: Level IIIStudy TypeOriginal research (prognostic/epidemiological).

SynTwin: A graph-based approach for predicting clinical outcomes using digital twins derived from synthetic patients.

The concept of a digital twin came from the engineering, industrial, and manufacturing domains to create virtual objects or machines that could inform the design and development of real objects. This idea is appealing for precision medicine where digital twins of patients could help inform healthcare decisions. We have developed a methodology for generating and using digital twins for clinical outcome prediction. We introduce a new approach that combines synthetic data and network science to create digital twins (i.e. SynTwin) for precision medicine. First, our approach starts by estimating the distance between all subjects based on their available features. Second, the distances are used to construct a network with subjects as nodes and edges defining distance less than the percolation threshold. Third, communities or cliques of subjects are defined. Fourth, a large population of synthetic patients are generated using a synthetic data generation algorithm that models the correlation structure of the data to generate new patients. Fifth, digital twins are selected from the synthetic patient population that are within a given distance defining a subject community in the network. Finally, we compare and contrast community-based prediction of clinical endpoints using real subjects, digital twins, or both within and outside of the community. Key to this approach are the digital twins defined using patient similarity that represent hypothetical unobserved patients with patterns similar to nearby real patients as defined by network distance and community structure. We apply our SynTwin approach to predicting mortality in a population-based cancer registry (n=87,674) from the Surveillance, Epidemiology, and End Results (SEER) program from the National Cancer Institute (USA). Our results demonstrate that nearest network neighbor prediction of mortality in this study is significantly improved with digital twins (AUROC=0.864, 95% CI=0.857-0.872) over just using real data alone (AUROC=0.791, 95% CI=0.781-0.800). These results suggest a network-based digital twin strategy using synthetic patients may add value to precision medicine efforts.

DNAp: A Pipeline for DNA-seq Data Analysis.

Next-generation sequencing is empowering genetic disease research. However, it also brings significant challenges for efficient and effective sequencing data analysis. We built a pipeline, called DNAp, for analyzing whole exome sequencing (WES) and whole genome sequencing (WGS) data, to detect mutations from disease samples. The pipeline is containerized, convenient to use and can run under any system, since it is a fully automatic process in Docker container form. It is also open, and can be easily customized with user intervention points, such as for updating reference files and different software or versions. The pipeline has been tested with both human and mouse sequencing datasets, and it has generated mutations results, comparable to published results from these datasets, and reproducible across heterogeneous hardware platforms. The pipeline DNAp, funded by the US Food and Drug Administration (FDA), was developed for analyzing DNA sequencing data of FDA. Here we make DNAp an open source, with the software and documentation available to the public at http://bioinformatics.astate.edu/dna-pipeline/ .