Myeloma

Screening for anemia using multi-modal machine learning models on smartphones: protocol for a comparative accuracy study in rural India

Anemia, or low blood hemoglobin (Hb), affects one third of the world population, and is particularly prevalent in women and children in lower resource settings. However, screening for anemia is limited by the availability of accurate, easy to use, lower cost and non-invasive methods. We aim to generate research to support potential development of a point of care test to detect anemia using smartph...

Predictors of Anemia in Ethiopia: A Systematic-Review of Machine Learning Approaches

Anemia remains a critical public health issue globally which is disproportionately affecting population in low- and middle income countries, with sub-Saharan Africa particularly Ethiopia experiencing high prevalence rates, Despite ongoing interventions, understanding the multifactorial causes of anemia and enhancing predictive Modelling through modern analytic approaches remains limited. This syst...

Stacking Ensemble Learning-based Models Enabling Accurate Diagnosis of Cardiac Amyloidosis using SPECT/CT:an International and Multicentre Study

Cardiac amyloidosis (CA), a life-threatening infiltrative cardiomyopathy, can be non-invasively diagnosed using [99mTc]Tc-bisphosphonate SPECT/CT. How...

Development, implementation, and validation of an open-source Federated Learning platform to accelerate innovation and boost personalized medicine in rare and ultra-rare haematological diseases: an initiative by GenoMed4All Consortium

Rare haematological diseases (RHD) pose significant clinical challenges due to their heterogeneity, limited patient populations, and fragmented datase...

Application of Machine Learning (ML) to Predict Under-Five Anemia using the 2018 Zambia Demographic and Health Survey (ZDHS)

Accurate prediction of the risk of anemia in under five children using ML can help reduce the burden of anemia in Zambia. This study applied ML models...

Incidence, Outcomes and Risk Factors of Cardiac Arrest Among Surgical Patients in the UK Biobank: A Population-Based Cohort Study

Perioperative cardiac arrest (CA) is a devastating surgical complication, yet its epidemiology and risk factors across diverse surgical populations ar...

Does LLM Assistance Improve Healthcare Delivery? An Evaluation Using On-site Physicians and Laboratory Tests∗

We deployed large language model (LLM) decision support for health workers at two outpatient clinics in Nigeria. For each patient, health workers draf...

Nationwide Spatiotemporal Dynamics and Machine Learning Prediction of Anemia Among Women in Lesotho, 2023–2024

Despite substantial efforts, anemia continues to pose a significant public health challenge, disproportionately affecting women of reproductive age. I...

Improving Sickle Cell Disease Classification: A Fusion of Conventional Classifiers, Segmented Images, and Convolutional Neural Networks

Sickle cell anemia, which is characterized by abnormal erythrocyte morphology, can be detected using microscopic images. Computational techniques in...

Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning

Clinical diagnostic guidelines outline the key questions to answer to reach a diagnosis. Inspired by guidelines, we aim to develop a model that lear...

Early identification of patients at risk for iron-deficiency anemia using deep learning techniques.

OBJECTIVES: Iron-deficiency anemia (IDA) is a common health problem worldwide, and up to 10% of adult patients with incidental IDA may have gastrointe...

Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.

PURPOSE: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to ...

Artificial intelligence-based application in multiple myeloma.

Dynamic risk prediction model for multiple myeloma: Through deep learning, the model is able to adapt to future data, such as emerging treatment modalities and combinations.

A prediction model for reactivation of Langerhans cell histiocytosis based on machine-learning algorithms.

Langerhans cell histiocytosis (LCH) is a rare inflammatory myeloid neoplasm characterized by the clonal proliferation of myeloid progenitor cells. The...

Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies.

UNLABELLED: Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphologic...

Automated abstraction of clinical parameters of multiple myeloma from real-world clinical notes using large language models

Real-world evidence (RWE) is increasingly recognized as a valuable type of oncology research but extracting fit-for-purpose real-world data (RWD) from...

Skin Changes in Suspected Lyme Disease.

Dear Editor, Ticks carry many diseases, bacteria, and viruses and represent a very important healthcare issue both in Croatia and globally. Although m...

Interleukin-6 and interleukin-8 levels in children with aplastic anemia and its correlation with disease severity and response to immunosuppressive therapy.

BACKGROUND: Aplastic anemia (AA) is an uncommon condition characterized by pancytopenia and hypocellular bone marrow. Interleukin (IL)-6 and IL-8 have...

Clinical insights into hematologic malignancies and comparative analysis of molecular signatures of acute myeloid leukemia in different ethnicities using an artificial intelligence offering.

Next generation sequencing generates copious amounts of genomics data, causing manual interpretation to be laborious and non-scalable while remaining ...