A compressed large language model embedding dataset of ICD 10 CM descriptions.

This paper presents novel datasets providing numerical representations of ICD-10-CM codes by generating description embeddings using a large language model followed by a dimension reduction via autoencoder. The embeddings serve as informative input features for machine learning models by capturing relationships among categories and preserving inherent context information. The model generating the data was validated in two ways. First, the dimension reduction was validated using an autoencoder, and secondly, a supervised model was created to estimate the ICD-10-CM hierarchical categories. Results show that the dimension of the data can be reduced to as few as 10 dimensions while maintaining the ability to reproduce the original embeddings, with the fidelity decreasing as the reduced-dimension representation decreases. Multiple compression levels are provided, allowing users to choose as per their requirements, download and use without any other setup. The readily available datasets of ICD-10-CM codes are anticipated to be highly valuable for researchers in biomedical informatics, enabling more advanced analyses in the field. This approach has the potential to significantly improve the utility of ICD-10-CM codes in the biomedical domain.

A Compressed Language Model Embedding Dataset of ICD 10 CM Descriptions.

This paper presents novel datasets providing numerical representations of ICD-10-CM codes by generating description embeddings using a large language model followed by a dimension reduction via autoencoder. The embeddings serve as informative input features for machine learning models by capturing relationships among categories and preserving inherent context information. The model generating the data was validated in two ways. First, the dimension reduction was validated using an autoencoder, and secondly, a supervised model was created to estimate the ICD-10-CM hierarchical categories. Results show that the dimension of the data can be reduced to as few as 10 dimensions while maintaining the ability to reproduce the original embeddings, with the fidelity decreasing as the reduced-dimension representation decreases. Multiple compression levels are provided, allowing users to choose as per their requirements. The readily available datasets of ICD-10-CM codes are anticipated to be highly valuable for researchers in biomedical informatics, enabling more advanced analyses in the field. This approach has the potential to significantly improve the utility of ICD-10-CM codes in the biomedical domain.

The coordination of allocation: Logistics of kidney organ allocation to highly sensitized patients.

Since implementation, the new UNOS OPTN kidney allocation system (KAS) has drastically expanded the pool of available kidneys to candidates that may have previously waited extended periods for an organ offer. This is particularly true for highly sensitized patients. The changes to the KAS have had ramifications throughout the transplant process, including for organ procurement organizations (OPO) and local transplant hospital call centers. Here, we will examine the impact of the new KAS on the organ donation process and highlight the necessary interactions between the OPO and transplant centers to best match donor kidneys and highly sensitized recipients.

Autism spectrum disorder, Klinefelter syndrome, and chromosome 3p21.31 duplication: a case report.

Autism spectrum disorders are heterogeneous in nature with idiopathic and genetic origins. We present a 7-year-old boy with a long history of multiple behavioral concerns, poor school performance, repetitive/compulsive tendencies, poor social skills, and language delays. A multidisciplinary evaluation concluded that the patient met full criteria for autism. A genetic evaluation demonstrated Klinefelter syndrome 47, XXY karyotype with concurrent duplication of 3p21.31 by microarray analysis. Maternal genetic analysis demonstrated the same 3p21.31 duplication. The potential implication with regard to autism spectrum disorders has not been previously discussed in the literature.