Masquerader Detection Using OCLEP: One-Class Classification Using Length Statistics of Emerging Patterns

We introduce a new method for masquerader detection that only uses a user’s own data for training, called Oneclass Classification using Length statistics of Emerging Patterns (OCLEP). Emerging patterns (EPs) are patterns whose support increases from one dataset/class to another with a big ratio, and have been very useful in earlier studies. OCLEP classifies a case T as self or masquerader by using the average length of EPs obtained by contrasting T against sets of samples of a user’s normal data. It is based on the observation that one needs long EPs to differentiate instances from a common class, but needs short EPs to differentiate instances from different classes. OCLEP has two novel features: for training it uses EPs mined from just the self class; for classification it uses the length statistics instead of the EPs themselves. Experiments show that OCLEP can achieve very good accuracy while keeping the false positive rate low, it achieves slightly better area-under-ROC-curve than SVM, and it can achieve good results when other approaches can not. OCLEP requires little effort in choosing parameters; the SVM requires significant tuning and it is hard to reach the theoretical optimal result. These features imply that OCLEP is a good complementary component for a robust masquerader detection system, even though its average performance in false positive rate is not as good as SVM’s

Image-Guided Hypofractionated Radiosurgery of Large and Complex Brain Lesions

Hypofractionated radiosurgery either through frame or image guidance has emerged as the most important area of research and development for intracranial and extracranial radiosurgery. In this chapter, we focused on discussions of three state-of-the-art platforms: Frame- and Image-Guided Gamma Knife, Robotic X-Band Cykerknife, and Flattening-Filter-Free intensity-modulated S-band medical linear accelerators. Practical principles with detailed workflow and clinical implementations are presented in a systematic approach. With rapid evolvement of both hardware and software in the realm of delivering hypofractionated radiosurgery, this chapter aims to offer a reader physical clarity on judging and balancing of achieving high-precision and high-quality treatments with practical examples and guidelines on intracranial applications

Nanoparticles for post-infarct ventricular remodeling

YesIn recent years, tremendous progress has been made in the treatment of acute myocardial infarction (AMI), but pathological ventricular remodeling often causes survivors to suffer from fatal heart failure. Currently, there is no effective therapy to attenuate ventricular remodeling. Recently, nanoparticles-based drug delivery system is widely applied in biomedicine especially in cancer and liver fibrosis, owing to its excellent physical, chemical, and biological properties. Therefore, using nanoparticles as delivery vehicles of small molecules, polypeptides, etc to improve post-infarct ventricular remodeling are expected. In this review, we summarized the updated researches in this fast-growing area and suggested further works needed

Medical Data Augmentation via ChatGPT: A Case Study on Medication Identification and Medication Event Classification

The identification of key factors such as medications, diseases, and relationships within electronic health records and clinical notes has a wide range of applications in the clinical field. In the N2C2 2022 competitions, various tasks were presented to promote the identification of key factors in electronic health records (EHRs) using the Contextualized Medication Event Dataset (CMED). Pretrained large language models (LLMs) demonstrated exceptional performance in these tasks. This study aims to explore the utilization of LLMs, specifically ChatGPT, for data augmentation to overcome the limited availability of annotated data for identifying the key factors in EHRs. Additionally, different pre-trained BERT models, initially trained on extensive datasets like Wikipedia and MIMIC, were employed to develop models for identifying these key variables in EHRs through fine-tuning on augmented datasets. The experimental results of two EHR analysis tasks, namely medication identification and medication event classification, indicate that data augmentation based on ChatGPT proves beneficial in improving performance for both medication identification and medication event classification