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Hematologic Tests

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Avoiding Overfitting in Deep Neural Networks for Clinical Opinions Generation from General Blood Test Results.

Studies in health technology and informatics
We have used deep neural networks (DNNs) to generate clinical opinions from general blood test results. DNNs have overfitting problem in general. We believe the complex structure of DNN and insufficient data to be the major reasons of overfitting in ...

Risk Prediction for Portal Vein Thrombosis in Acute Pancreatitis Using Radial Basis Function.

Annals of vascular surgery
BACKGROUND: Acute pancreatitis (AP) can induce portosplenomesenteric vein thrombosis (PVT), which may generate higher morbidity and mortality. However current diagnostic modalities for PVT are still controversial. In recent decades, artificial neural...

Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images.

Journal of medical systems
Peripheral Blood Smear analysis plays a vital role in diagnosis of many diseases such as leukemia, anemia, malaria, lymphoma and infections. Unusual variations in color, shape and size of blood cells indicate abnormal condition. We used a total of 11...

Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors.

Journal of medical systems
Machine learning and data mining approaches are being successfully applied to different fields of life sciences for the past 20 years. Medicine is one of the most suitable application domains for these techniques since they help model diagnostic info...

Classification of acute lymphoblastic leukemia using deep learning.

Microscopy research and technique
Acute Leukemia is a life-threatening disease common both in children and adults that can lead to death if left untreated. Acute Lymphoblastic Leukemia (ALL) spreads out in children's bodies rapidly and takes the life within a few weeks. To diagnose A...

Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study.

The Journal of antimicrobial chemotherapy
BACKGROUND: Infection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood par...

Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears.

IEEE journal of biomedical and health informatics
OBJECTIVE: This work investigates the possibility of automated malaria parasite detection in thick blood smears with smartphones.

ATBdiscrimination: An in Silico Tool for Identification of Active Tuberculosis Disease Based on Routine Blood Test and T-SPOT.TB Detection Results.

Journal of chemical information and modeling
Tuberculosis remains one of the deadliest infectious diseases worldwide. Only 5-15% of people infected with develop active TB disease (ATB), while others remain latently infected (LTBI) during their lifetime, which has a completely different clinica...

Diagnosing brain tumours by routine blood tests using machine learning.

Scientific reports
Routine blood test results are assumed to contain much more information than is usually recognised even by the most experienced clinicians. Using routine blood tests from 15,176 neurological patients we built a machine learning predictive model for t...