Latest AI and machine learning research in hematology for healthcare professionals.
A major challenge in biology is predicting how cells transition between states over time and how perturbations disrupt these transitions. Understanding such dynamics is critical for identifying interventions that reverse pathological programs or reprogram cells toward desired states. Although recent computational approaches can predict single-cell perturbation responses in silico, they cannot pred...
In this work, we investigate uncertainty-aware neural network models for blood glucose prediction and adverse glycemic event identification in Type 1 diabetes. We consider three families of sequence models based on LSTM, GRU, and Transformer architectures, with uncertainty quantification enabled by either Monte Carlo dropout or through evidential output layers compatible with Deep Evidential Regre...
Summary Background Once the treatment starts, early prediction of treatment benefit and its correlation with overall survival (OS) remains challenging...
The visual world offers a critical axis for advancing foundation models beyond language. Despite growing interest in this direction, the design space ...
Platelets expire within five days. Blood banks face uncertain daily demand and must balance ordering decisions between costly wastage from overstockin...
White blood cell (WBC) classification is fundamental for hematology applications such as infection assessment, leukemia screening, and treatment monit...
Background Prognosis and therapeutic management in Parkinson's disease is a challenging task by its highly heterogeneous disease progression and sympt...
Medical oncology education faces a dual crisis: knowledge velocity that outpaces static curricula and large language model (LLM) risks hallucination a...
Objective: Electronic Health Record (EHR)-based trial emulation can support translation of randomized clinical trial (RCT) evidence into practice, yet...
We present p-Brain, an end-to-end neuroimaging analysis framework for reproducible, automated quantitative DCE-MRI analysis at scale. From standard ac...
Background: Large language models (LLMs) show promise for clinical decision support, yet most validation studies evaluate single models, leaving quest...
Recent advances in machine learning (ML)-based protein design methods have enabled the rapid in silico generation of large libraries of miniprotein bi...
Aging of hematopoietic stem and progenitor cells (HSPCs) impairs regenerative capacity and predisposes to hematological diseases. Here, we constructed...
Fanconi anemia (FA) is a rare genetic disorder of impaired DNA repair characterized by progressive bone marrow failure, congenital malformations, and ...
Identifying type 2 diabetes mellitus can be challenging, particularly for primary care physicians. Clinical decision support systems incorporating art...
Pregnancy care often involves simultaneous obstetric and other medical conditions, but their co-occurrence patterns are rarely modeled explicitly in a...
Perivascular adipose tissue (PVAT), an intriguing layer of fat surrounding blood vessels, regulates vascular tone and mediates vascular dysfunction th...
Background and Purpose Embolic stroke of undetermined source (ESUS) emains a major diagnostic challenge in vascular neurology, as a substantial propor...
Background and Objective: Blood pressure treatment response is variable in individual patients, and the choice of medical therapy is often dependent o...
Cuffless blood pressure screening based on easily acquired photoplethysmography (PPG) signals offers a practical pathway toward scalable cardiovascula...