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Hematologic Tests

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Class Imbalance Impact on the Prediction of Complications during Home Hospitalization: A Comparative Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Home hospitalization (HH) is presented as a healthcare alternative capable of providing high standards of care when patients no longer need hospital facilities. Although HH seems to lower healthcare costs by shortening hospital stays and improving pa...

[Artificial intelligence (AI) and hematological diseases: establishment of a peripheral blood convolutional neural network (CNN)-based digital morphology analysis system].

[Rinsho ketsueki] The Japanese journal of clinical hematology
Morphological analysis of the blood smear is an essential element of diagnosing a disease hematologically and has been performed by conventional manual light microscopy for several decades. Although this method is the gold standard, it is labor-inten...

Predictive models of hypertensive disorders in pregnancy based on support vector machine algorithm.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: The risk factors of hypertensive disorders in pregnancy (HDP) could be summarized into three categories: clinical epidemiological factors, hemodynamic factors and biochemical factors.

Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study.

Journal of medical systems
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 ...

Detection of Falsely Elevated Point-of-Care Potassium Results Due to Hemolysis Using Predictive Analytics.

American journal of clinical pathology
OBJECTIVES: Preanalytical factors, such as hemolysis, affect many components of a test panel. Machine learning can be used to recognize these patterns, alerting clinicians and laboratories to potentially erroneous results. In particular, machine lear...

Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests.

Clinical chemistry and laboratory medicine
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...

Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning.

Clinical chemistry
BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PC...

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

The Lancet. Digital health
BACKGROUND: The early clinical course of COVID-19 can be difficult to distinguish from other illnesses driving presentation to hospital. However, viral-specific PCR testing has limited sensitivity and results can take up to 72 h for operational reaso...

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
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...

Prediction of Recurrence in Patients with Stage III Colon Cancer Using Conventional Clinicopathological Factors and Peripheral Blood Test Data: A New Analysis with Artificial Intelligence.

Oncology
BACKGROUND: Survival rate may be predicted by tumor-node-metastasis staging systems in colon cancer. In clinical practice, about 20 to 30 clinicopathological factors and blood test data have been used. Various predictive factors for recurrence have b...