Clinical laboratory results are ubiquitous in any diagnosis making. Predicting abnormal values of not prescribed tests based on the results of performed tests looks intriguing, as it would be possible to make early diagnosis available to everyone. The special place is taken by the Common Blood Count (CBC) test, as it is the most widely used clinical procedure. Combining routine biochemical panels with CBC presents a set of test-value pairs that varies from patient to patient, or, in common settings, a table with missing values. Here we formulate a tabular modeling problem as a set translation problem where the source set comprises pairs of GPT-like label column embedding and its corresponding value while the target set consists of the same type embeddings only. The proposed approach can effectively deal with missing values without implicitly estimating them and bridges the world of LLM with the tabular domain. Applying this method to clinical laboratory data, we achieve an improvement up to 8% AUC for joint predictions of high uric acid, glucose, cholesterol, and low ferritin levels.
Hemoglobin is the iron containing protein in red blood cells which carries oxygen from lungs to rest of the body tissues. Accurate measurement of hemoglobin is essential for diagnosing anemia, a condition characterized by a deficiency of red blood cells. This measurement is particularly vital before initiating blood transfusions for thalassemia patients. Non-invasive estimation of hemoglobin levels can be achieved through photoplethysmography (PPG)-based methods. PPG is an optical method to measure blood volume changes in successive heart beats. PPG signals can be obtained from fingertip videos using a light source and a photodetector. SmartphonePPG utilizes a smartphone’s flashlight as a light source and its camera as a photodetector to acquire PPG signals, offering an affordable and portable point-of-care tool. Despite the ubiquity of smartphones, signals from their cameras often contain noise, making feature selection from PPG characteristics challenging. While PPG-based methods are invaluable, the lack of real-world datasets poses a significant challenge in maximizing the benefits of PPG technology. In this paper, we introduce a dataset comprising 1-minute fingertip video recordings from 150 anemic patients, obtained using a smartphone’s camera. The dataset, publicly accessible for research purposes (https://forms.gle/LB4qn81ZMqEuy3V27), covers an age range of 6 months to 32 years, with diverse hemoglobin values (4.3 gm/dL - 12.4 gm/dL). Utilizing this dataset, we propose a deep learning-based technique employing the ResNet-18 architecture to estimate hemoglobin levels. This approach eliminates the need for manual feature extraction and selection from PPG signals, overcoming a limitation in existing smartphone PPG-based hemoglobin estimation systems. Our model achieves a hemoglobin level estimation with an RMSE of 0.81-1.39 when compared with the gold standard laboratory method, Complete Blood Count (CBC) test reports. In contrast, HemaApp, a state-of-the-art research utilizing a machine learning-based classifier (SVM), yields an RMSE of 1.7 on our dataset. The accuracy and simplicity of our model position it as a promising alternative to existing noninvasive hemoglobin level estimation methods.
For more than two decades, advances in personalised medicine and precision healthcare have largely been based on genomics and other omics data. These strategies aim to tailor interventions to individual patient profiles, promising greater treatment efficacy and more efficient allocation of healthcare resources. Here, we show that widely collected common haematologic markers can reliably predict and discriminate individual chronological age and health status from even noisy sources. Our analysis includes synthetic and real retrospective patient data, including medically relevant and extreme cases, and draws on more than 100\,000 complete blood count records over 13 years from the United States Centers for Disease Control and Prevention's National Health and Nutrition Examination Survey (CDC NHANES). We combine fully explainable risk assessment scores with machine and deep learning techniques to focus on clinically significant patterns and characteristics without functioning purely as a ''black-box model allowing interpretation and control. We validated the results with the UK Biobank, a larger cohort independent of the CDC NHANES and with very different collection techniques, the former a survey and the second a longitudinal study. Unlike current biological ageing indicators, this approach may offer rapid, and scalable implementations of personalised, precision and predictive approaches to healthcare and medicine without or before requiring other specialised, uncommon or costly tests.
A complete blood cell count is an important test in medical diagnosis to evaluate overall health condition. Traditionally blood cells are counted manually using haemocytometer along with other laboratory equipment’s and chemical compounds, which is a time-consuming and tedious task. In this work, the authors present a machine learning approach for automatic identification and counting of three types of blood cells using ‘you only look once’ (YOLO) object detection and classification algorithm. YOLO framework has been trained with a modified configuration BCCD Dataset of blood smear images to automatically identify and count red blood cells, white blood cells, and platelets. Moreover, this study with other convolutional neural network architectures considering architecture complexity, reported accuracy, and running time with this framework and compare the accuracy of the models for blood cells detection. They also tested the trained model on smear images from a different dataset and found that the learned models are generalized. Overall the computer-aided system of detection and counting enables us to count blood cells from smear images in less than a second, which is useful for practical applications.