Background: Creatinine is a type of metabolite of blood that is strongly correlated to glomerular filtration rate (GFR). As measuring GFR is difficult, creatinine value is used for indirectly determining GFR and then the stage of chronic kidney disease (CKD). Adding a creatinine test into routine health examination could detect CKD. As more items for comprehensive examination means higher cost, creatinine testing is not included in the routine health examination in many countries. An algorithm based on common test results, without creatinine test, to evaluate the risk of CKD will increase the chance of its early detection and treatment. Methods: In this study, we used open source data containing 1 million samples. These data contain 23 health-related features, including common diagnostic test results provided by National Health Insurance Sharing Service (NHISS). A low GFR indicates possible chronic kidney disease (CKD). As is commonly accepted in the medical community, a GFR of 60 mL/min is used as the threshold, below which is considered to have CKD. In this study, the first step aims to build a regression model to predict the value of creatinine from 23 features, and then combine the predicted value of creatinine with the original 23 features to evaluate the risk of CKD. We will show by simulation that by the proposed method we can achieve better prediction results compared to direct prediction from 23 features. The data is extremely unbalanced for predicting the target variable creatinine. We used undersampling method and proposed a new cost-sensitive mean-squared error (MSE) loss function to deal with the problem. Regrading model selection, this work used three machine learning models: a bagging tree model named Random Forest, a boosting tree model named XGBoost, and a neural network based model named ResNet. To improve the result of the creatinine predictor, we averaged results from eight predictors, a method known as ensemble learning. Finally, the predicted creatinine and the original 23 features is used to predict the risk of CKD. Results: We optimized results of R-Squared (R2) value to select the appropriate undersampling strategy and the regression model for the regression stage of creatinine prediction. Ensembled model achieved the best performance of R2 of 0.5590. The six factors from 23 are selected from the top of the list of how strongly they affect the creatinine value. They are sex, age, hemoglobin, the level of urine protein, waist circumference, and habit of smoking. Using the predicted value of creatinine, an area under Receiver Operating Characteristic curve (AUC) of 0.76 is achieved while classifying samples for CKD. Conclusions: Using commonly available health parameters, the proposed system can assess the risk of CKD for public health. High-risk subjects can be screened and advised to take a creatinine test for further confirmation. In this way, we can reduce the impact of CKD on public health and facilitate early detection for many, where a blanket test of creatinine is not available for all.

Hepatocellular carcinoma (HCC) is one of the most critical health problems in the world. For proper treatment, it is important to identify the grade of cancer morbidity from HCC biopsy image. The diagnostic work is not only time-consuming but also subjective. The same biopsy image may be diagnosed as of different grades by different doctors, due to lack of experience or difference in opinion. In this work, we proposed an automatic grading system with classification accuracy matching to an experienced doctor, to help augment the diagnosis process. First, we proposed a segmentation method to isolate all nucleus-like objects present in a biopsy image. Non-target objects (here the target is a single HCC nucleus) present in the biopsy image are isolated too in the segmentation process. To eliminate such non-target objects, we proposed clustering of segmented images and a novel method to filter out target objects. Next, we proposed a two track neural network, where input consists of 2 different images. It combines a single segmented nucleus and a random cropped texture patch of the biopsy image to which the nucleus belongs. At this classifier output, we grade the single nucleus. Finally, a majority voting method is used to identify the grade of the whole biopsy image. We achieved an accuracy of 99.03% for nucleus image grading and 99.67% accuracy for grading biopsy images.

Large Language Models (LLMs) have demonstrated efficacy in various domains, but deploying these models is economically challenging due to extensive parameter counts. Numerous efforts have been dedicated to reducing the parameter count of these models without compromising performance, employing a technique known as model pruning. Conventional pruning methods assess the significance of weights within individual layers and typically apply uniform sparsity levels across all layers, potentially neglecting the varying significance of each layer. To address this oversight, we first propose a dual-assessment driven pruning strategy that employs both intra-layer metric and global performance metric to comprehensively evaluate the impact of pruning. Then our method leverages an iterative optimization algorithm to find the optimal layer-wise sparsity distribution, thereby minimally impacting model performance. Extensive benchmark evaluations on state-of-the-art LLM architectures such as LLaMAv2 and OPT across a variety of NLP tasks demonstrate the effectiveness of our approach. When applied to the LLaMaV2-7B model with an overall pruning sparsity of 80%, our method achieves a 50% reduction in perplexity compared to the benchmark. The results indicate that our method significantly outperforms existing state-of-the-art methods in preserving performance after pruning.