Predict or draw blood: An integrated method to reduce lab tests

Yu, Lishan; Zhang, Qiuchen; Bernstam, Elmer V.; Jiang, Xiaoqian

doi:10.1016/j.jbi.2020.103394

Cited by 14 publications

(13 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chicco and Jurman [35], 2020 [37], 2020 [38], 2020 [39], 2020 [40], 2020 [41], 2020 [42], 2020 [43], 2020 [45], 2020 [46], 2020 [47], 2020 [49], 2021 [52], 2021 Propose a novel DL ap method to jointly predict future laboratory test events to be omitted Predict or draw blood: An integrated method to reduce lab tests Yu et al [36], 2020…”

Section: Resultsmentioning

confidence: 99%

Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review

Cardozo¹,

Tirloni²,

Moro³

et al. 2022

JMIR Bioinform Biotech

View full text Add to dashboard Cite

Background In recent decades, the use of artificial intelligence has been widely explored in health care. Similarly, the amount of data generated in the most varied medical processes has practically doubled every year, requiring new methods of analysis and treatment of these data. Mainly aimed at aiding in the diagnosis and prevention of diseases, this precision medicine has shown great potential in different medical disciplines. Laboratory tests, for example, almost always present their results separately as individual values. However, physicians need to analyze a set of results to propose a supposed diagnosis, which leads us to think that sets of laboratory tests may contain more information than those presented separately for each result. In this way, the processes of medical laboratories can be strongly affected by these techniques. Objective In this sense, we sought to identify scientific research that used laboratory tests and machine learning techniques to predict hidden information and diagnose diseases. Methods The methodology adopted used the population, intervention, comparison, and outcomes principle, searching the main engineering and health sciences databases. The search terms were defined based on the list of terms used in the Medical Subject Heading database. Data from this study were presented descriptively and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; 2020) statement flow diagram and the National Institutes of Health tool for quality assessment of articles. During the analysis, the inclusion and exclusion criteria were independently applied by 2 authors, with a third author being consulted in cases of disagreement. Results Following the defined requirements, 40 studies presenting good quality in the analysis process were selected and evaluated. We found that, in recent years, there has been a significant increase in the number of works that have used this methodology, mainly because of COVID-19. In general, the studies used machine learning classification models to predict new information, and the most used parameters were data from routine laboratory tests such as the complete blood count. Conclusions Finally, we conclude that laboratory tests, together with machine learning techniques, can predict new tests, thus helping the search for new diagnoses. This process has proved to be advantageous and innovative for medical laboratories. It is making it possible to discover hidden information and propose additional tests, reducing the number of false negatives and helping in the early discovery of unknown diseases.

show abstract

Section: Resultsmentioning

confidence: 99%

Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review

Cardozo¹,

Tirloni²,

Moro³

et al. 2022

JMIR Bioinform Biotech

View full text Add to dashboard Cite

show abstract

“…Yasin et al, developed calculations, able to derive potassium levels from the patient's electrocardiogram (ECG) with an error rate of only 9% [50]. Yu et al, aimed to reduce unnecessary repeated lab tests, based on observed previous testing values, while maintaining the maximum precision in patient diagnostic testing [51]. They developed a deep neural network (DNN) model, able to predict which repeated tests could be omitted and estimate its result, leading to a calculated 15% reduction in blood draws with an accuracy of 95%.…”

Section: Predicting Test Resultsmentioning

confidence: 99%

Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence—A Narrative Review

Cadamuro

2021

Diagnostics

View full text Add to dashboard Cite

Laboratory medicine has evolved from a mainly manual profession, providing few selected test results to a highly automated and standardized medical discipline, generating millions of test results per year. As the next inevitable evolutional step, artificial intelligence (AI) algorithms will need to assist us in structuring and making sense of the masses of diagnostic data collected today. Such systems will be able to connect clinical and diagnostic data and to provide valuable suggestions in diagnosis, prognosis or therapeutic options. They will merge the often so separated worlds of the laboratory and the clinics. When used correctly, it will be a tool, capable of freeing the physicians time so that he/she can refocus on the patient. In this narrative review I therefore aim to provide an overview of what AI is, what applications currently are available in healthcare and in laboratory medicine in particular. I will discuss the challenges and pitfalls of applying AI algorithms and I will elaborate on the question if healthcare workers will be replaced by such systems in the near future.

show abstract

“…A stimulating field of investigation is represented by the ability of ML models to predict laboratory test values without performing them. Yu et al [ 49 ] developed a neural network model with the aim of reducing the number of tests performed, losing only a small percentage of accuracy. Using data from 12 lab tests obtained from the MIMIC-III dataset, they trained their models, which consisted of two modules.…”

Section: Resultsmentioning

confidence: 99%

“…Among the best models, those in the Ensemble family (e.g., XGB, GBT) were chosen both for their medium–high performance [ 21 , 30 , 32 , 33 , 44 , 51 , 58 , 61 ] and their training speed. Models in the DL family [ 27 , 35 , 37 , 49 , 52 , 53 , 60 ], especially RNN and ANN, have been increasingly chosen in recent years. The advantage of these systems is their potential in terms of performance, although the resources (time and the amount of data) required for training are reported to be higher for DL models than traditional ML models.…”

Section: Discussionmentioning

confidence: 99%

Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine

et al. 2021

View full text Add to dashboard Cite

This article presents a systematic literature review that expands and updates a previous review on the application of machine learning to laboratory medicine. We used Scopus and PubMed to collect, select and analyse the papers published from 2017 to the present in order to highlight the main studies that have applied machine learning techniques to haematochemical parameters and to review their diagnostic and prognostic performance. In doing so, we aim to address the question we asked three years ago about the potential of these techniques in laboratory medicine and the need to leverage a tool that was still under-utilised at that time.

show abstract

Predict or draw blood: An integrated method to reduce lab tests

Cited by 14 publications

References 12 publications

Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review

Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review

Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence—A Narrative Review

Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine

Contact Info

Product

Resources

About