AIMC Topic: Hematologic Tests
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Development and External Validation of a Machine Learning Tool to Rule Out COVID-19 Among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study.
Journal of medical Internet research
Dec 2, 2020
BACKGROUND: Conventional diagnosis of COVID-19 with reverse transcription polymerase chain reaction (RT-PCR) testing (hereafter, PCR) is associated with prolonged time to diagnosis and significant costs to run the test. The SARS-CoV-2 virus might lea...
Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests.
Clinical chemistry and laboratory medicine
Oct 21, 2020
OBJECTIVES: The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15-20%, and expen...
Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study.
Journal of medical systems
Jul 1, 2020
The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwide with more than 2 million confirmed cases (probably a much higher number of infected), and almost ...
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
Oct 25, 2019
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
Oct 9, 2019
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...
Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears.
IEEE journal of biomedical and health informatics
Sep 23, 2019
OBJECTIVE: This work investigates the possibility of automated malaria parasite detection in thick blood smears with smartphones.
Artificial intelligence and machine learning in haematology.
British journal of haematology
Feb 6, 2019
Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors.
Journal of medical systems
Oct 27, 2018
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
Oct 23, 2018
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...
Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images.
Journal of medical systems
May 2, 2018
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...
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