AIMC Topic: Clinical Laboratory Techniques

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Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology
Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more tr...

Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests.

JAMA network open
IMPORTANCE: Laboratory testing is an important target for high-value care initiatives, constituting the highest volume of medical procedures. Prior studies have found that up to half of all inpatient laboratory tests may be medically unnecessary, but...

Streamlining Quality Review of Mass Spectrometry Data in the Clinical Laboratory by Use of Machine Learning.

Archives of pathology & laboratory medicine
CONTEXT.—: Turnaround time and productivity of clinical mass spectrometric (MS) testing are hampered by time-consuming manual review of the analytical quality of MS data before release of patient results.

Artificial Neural Network for Total Laboratory Automation to Improve the Management of Sample Dilution.

SLAS technology
Diluting a sample to obtain a measure within the analytical range is a common task in clinical laboratories. However, for urgent samples, it can cause delays in test reporting, which can put patients' safety at risk. The aim of this work is to show a...

An unsupervised learning method to identify reference intervals from a clinical database.

Journal of biomedical informatics
Reference intervals are critical for the interpretation of laboratory results. The development of reference intervals using traditional methods is time consuming and costly. An alternative approach, known as an a posteriori method, requires an expert...

Improving diagnosis in health care: laboratory medicine.

Diagnosis (Berlin, Germany)
Accurate and timely diagnosis remains one of the most complex and challenging processes in medicine. Diagnostic errors pose a significant burden on patients and healthcare systems, with laboratory-related errors playing a substantial role, especially...

Evaluating large language models as clinical laboratory test recommenders in primary and emergency care: a crucial step in clinical decision making.

Clinical chemistry and laboratory medicine
OBJECTIVES: Large language models (LLMs), such as OpenAI's GPT-4o, have demonstrated considerable promise in transforming clinical decision support systems. In this study, we focused on a single but crucial task of clinical decision-making: laborator...

Multi-Dimensional Laboratory Test Score as a Proxy for Health.

Studies in health technology and informatics
The standard of care for a physician to review laboratory tests results is to weigh each individual laboratory test result and compare it to against a standard reference range. Such a method of scanning can lead to missing high-level information. Dif...

The emergence of new trends in clinical laboratory diagnosis.

Saudi medical journal
Diagnostic processes typically rely on traditional and laborious methods, that are prone to human error, resulting in frequent misdiagnosis of diseases. Computational approaches are being increasingly used for more precise diagnosis of the clinical p...