NAND flash memory employs high code rate low-density parity-check (LDPC) codes to reduce the amount of redundant data that must be added. When the code rate is high, although the redundancy space is small, the error correction capability is inferior to medium or low code rate LDPC. RBER varies among the storage layers for 3D triple-level cell (TLC) NAND flash memory, which increases the frequency of read retry operations. Repeatedly initiating read retry seriously increases the decoding latency and decreases the performance of the 3D TLC NAND flash memory. To alleviate this problem, this article proposes Intra-Layer Variation aware LDPC coding, called LVLDPC. The proposed LVLDPC scheme establishes the correlation between inter-layer interference and raw bit error rate (RBER) based on a neural network model. By analyzing and predicting RBER through the neural network model, we are able to categorize RBER into distinct levels. Then, we then select LDPC codes with appropriate error correction capabilities to decode data with varying levels of RBER. Through this scheme, we don’t need to start read retry when RBER <1.56×10-2. The iteration number is reduced by 67% in total. This scheme only causes 1.15% space overhead, which is negligible. For the stage with high RBER, the number of iterations of LVLDPC is still large, and the extended LVLDPC scheme (eLVLDPC) is further proposed to reduce the use of high code rate and reduce the number of iterations by 19.1%, expanding the correctable RBER threshold to 2.68×10-2.

OBJECTIVES: The application of natural language processing (NLP) in the clinical domain is important due to the rich unstructured information in clinical documents, which often remains inaccessible in structured data. When applying NLP methods to a certain domain, the role of benchmark datasets is crucial as benchmark datasets not only guide the selection of best-performing models but also enable the assessment of the reliability of the generated outputs. Despite the recent availability of language models capable of longer context, benchmark datasets targeting long clinical document classification tasks are absent. MATERIALS AND METHODS: To address this issue, we propose Long Clinical Document (LCD) benchmark, a benchmark for the task of predicting 30-day out-of-hospital mortality using discharge notes of Medical Information Mart for Intensive Care IV and statewide death data. We evaluated this benchmark dataset using baseline models, from bag-of-words and convolutional neural network to instruction-tuned large language models. Additionally, we provide a comprehensive analysis of the model outputs, including manual review and visualization of model weights, to offer insights into their predictive capabilities and limitations. RESULTS: Baseline models showed 28.9% for best-performing supervised models and 32.2% for GPT-4 in F1 metrics. Notes in our dataset have a median word count of 1687. DISCUSSION: Our analysis of the model outputs showed that our dataset is challenging for both models and human experts, but the models can find meaningful signals from the text. CONCLUSION: We expect our LCD benchmark to be a resource for the development of advanced supervised models, or prompting methods, tailored for clinical text.