Hematology

Evaluating biomedical feature fusion on machine learning’s predictability and interpretability of COVID-19 severity types

Accurately differentiating severe from non-severe COVID-19 clinical types is critical for the healthcare system to optimize workflow. Current techniques lack the ability to accurately predict COVID-19 patients’ clinical type, especially as SARS-CoV-2 continues to mutate. We explore predictability and interpretability of multiple state-of-the-art machine learning (ML) techniques trained and tested ...

Spine age estimation using deep learning in lateral spine radiographs and DXA VFA to predict incident fracture and mortality

Spine age estimated from lateral spine radiographs and DXA vertebral fracture assessments (VFAs) could be associated with fracture and mortality risk. In the VERTE-X cohort (n=10,341, age 40 or older; derivation set) and KURE cohort (n=3,517; age 65 or older; external test set), predicted age difference was defined as estimated spine age minus chronological age. The primary outcome was incident fr...

Octopi 2.0: Point-of-care Multi-disease Detection and Diagnosis via Edge AI Imaging Platform

Access to quantitative, robust, and affordable diagnostic tools is essential to address the global burden of infectious diseases. While manual microsc...

Predicting agranulocytosis in patients treated with clozapine – development and validation of a machine learning algorithm based on 5,550 patients

To prevent clozapine-induced agranulocytosis (CIA), patients’ white blood cell counts are closely monitored, with treatment stopped if the absolute ne...

Real-World Type 2 Diabetes Second-Line Treatment Allocation Among Patients

This study aimed to evaluate the impact of socioeconomic disparities on the allocation of second-line treatments among patients with type 2 diabetes (...

NTDscope: A multi-contrast portable microscope for disease diagnosis

Accurate diagnostics are essential for disease control and elimination efforts. However, access to diagnostics for neglected tropical diseases (NTDs) ...

Transformer-based artificial intelligence on single-cell clinical data for homeostatic mechanism inference and rational biomarker discovery

Artificial intelligence (AI) applied to single-cell data has the potential to transform our understanding of biological systems by revealing patterns ...

Investigating CAR-T Treatment Access for Multiple Myeloma Patients Using Real-World Evidence

Multiple myeloma (MM) is the second most common hematologic malignancy in the U.S., with Black patients being diagnosed at twice the rate of White pat...

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...

Hierarchical representation learning of preeclampsia interactome connecting endometrial maturation, placentation, chorioamnionitis, and HELLP syndrome

Existing proposed pathogenesis for preeclampsia (PE) was only applied for early-onset PE (EOPE). Our previous work identified the transcriptome to dec...

A multivariate cell-based assay for blood-based diagnostics enhances lung cancer risk stratification

The indicator cell assay platform (iCAP) is a tool for blood-based diagnostics that addresses the low signal-to-noise ratio of blood biomarkers by usi...

Disentangling Blood-Based Markers of Multiple Sclerosis Through Machine Learning: An Evaluation Study

In the search for markers to aid early diagnosis, sustainable monitoring, and accurate prognosis of Multiple Sclerosis (MS), researchers have turned t...

Deep learning predicts onset acceleration of 38 age-associated diseases from blood and body composition biomarkers in the UK Biobank

A major challenge in multimorbid aging is understanding how diseases co-occur and identifying high-risk groups for accelerated disease development, bu...

Plasma Cell-Free RNA Captures Immune Dynamics and Predicts GVHD after Hematopoietic Stem Cell Transplantation

Despite long-standing success of hematopoietic stem cell transplantation (HSCT) in the treatment of blood cancers and severe immune disorders, monitor...

Combining Mass Spectrometry with Machine Learning to Identify Novel Protein Signatures: The Example of Multisystem Inflammatory Syndrome in Children

We demonstrate an approach that integrates biomarker analysis with machine learning to identify protein signatures, using the example of SARS-CoV-2-in...

Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome: a prospective cohort study

Sepsis therapy is still limited to treatment of the underlying infection and supportive measures. To date, various sepsis subtypes were proposed, but ...

Machine learning-Based Classification of Papillary Thyroid Carcinoma Versus Multinodular Goiter Using Preoperative Laboratory and Cytology Data

Thyroid nodules are frequently encountered in clinical practice, with their detection increasing due to advancements in imaging modalities. While most...

Extracting Carotid Stenosis Severity from Clinical Notes Using Natural Language Processing: Development, Validation, and Application in a Nationwide Veteran Cohort

Carotid stenosis, which is atherosclerotic narrowing of the extracranial carotid arteries, is an important risk factor for ischemic stroke. The preval...

DISCO: A Delta-NIHSS-Based Machine Learning Model for Predicting Recurrence, Disability, and Mortality Following Acute Ischemic Stroke

An accurate, robust, clinically accessible, and explainable predictive model for post-stroke composite outcomes could identify high-risk patients for ...

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-...