Clustering streaming data has gained importance in recent years due to an expanding opportunity to discover knowledge in widely available data streams. As streams are potentially evolving and unbounded sequence of data objects, clustering algorithms capable of performing fast and incremental processing of data points are necessary. This paper presents a method of clustering high-dimensional data streams using approximate methods called streamingRPHash. streamingRPHash combines random projections with locality-sensitivity hashing to construct a high-performance clustering method. streamingRPHash is amenable to distributed processing frameworks such as Map-Reduce, and also has the benefits of constrained overall complexity growth. This paper describes streamingRPHash algorithm and its various configurations. The clustering performance of streamingRPHash is compared to several alternatives. Experimental results show that streamingRPHash has comparable clustering accuracy and substantially lower runtime and memory usage.

The advent of transformer technology and large language models (LLMs) has further broadened the already extensive application field of artificial intelligence (AI). A large portion of medical records is stored in text format, such as clinical trial texts. Part of these texts is information regarding eligibility criteria. We aimed to harness the immense capabilities of an LLM by fine-tuning an open-source LLM (Llama-2) to develop a classifier from the clinical trial data. We were interested in investigating whether a fine-tuned LLM could better decide the eligibility criteria from the clinical trial text and compare the results with a more traditional method. Such an investigation can help us determine the extent to which we can rely on text-based applications developed from large language models and possibly open new avenues of application in the medical domain. Our results are comparable to the best-performing methods for this task. Since we used state-of-the-art technology, this research has the potential to open new avenues in the field of LLM application in the healthcare sector.