Data Science and Artificial Intelligence in Laboratory Medicine

A Special Issue of Diagnostics (ISSN 2075-4418) belonging to the section "Clinical Laboratory Medicine".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 355

Editor


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Guest Editor
Laboratory Medicine Department, Laboratori Clínic Metropolitana Nord, Germans Trias i Pujol University Hospital, Barcelona, Spain
Interests: microRNA; laboratory quality control; spectrometry; allergic diseases; pharmacogenomics; data analysis; clinical biochemistry; applied artificial intelligence; laboratory medicine; big data; biological rhythms; physiology regulation
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Special Issue Information

Dear Colleagues,

The application of data science in laboratory medicine allows us to obtain information that may have been hidden when working with small data volumes (a traditional approach). Furthermore, artificial intelligence allows us to automate and search for patterns in clinical databases. The development of algorithms that incorporate both data science and artificial intelligence tools will allow us to exploit the information contained in laboratory databases to obtain high-value information. I welcome researchers in relevant fields to contribute their excellent studies to this Special Issue. The potential topics include, but are not limited to, research related to laboratory medicine, ranging from genomic or proteomic studies, molecular diagnostics, clinical chemistry, immunology, hematology, and microbiology to data science and artificial intelligence algorithms. I believe that this broad collection of research will highlight our current understanding and utility of this field.

Dr. Fernando Marques-Garcia
Guest Editor

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Keywords

  • artificial intelligence
  • data science
  • algorithm
  • laboratory medicine

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Published Papers (1 paper)

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Research

27 pages, 671 KB  
Article
Unsupervised Descriptive Phenotyping of Cardiometabolic Laboratory Patterns from a Routine Five-Analyte Panel: Stability, Transportability and a Rule-Based Laboratory Interpretation Layer
by Yerlan Suleimenov, Ayat Assemov, Bakhytzhan Seksenbayev, Akezhan Koishybay, Albina Omarova and Baurzhan Nurlan
Diagnostics 2026, 16(18), 3025; https://doi.org/10.3390/diagnostics16183025 (registering DOI) - 18 Sep 2026
Abstract
(1) Background: Reference-interval flags read a lipid–glucose panel one analyte at a time. We asked whether a routine five-analyte panel supports reproducible, transportable, auditable descriptive grouping of laboratory profiles. (2) Methods: Retrospective analysis of 21,832 laboratory-order records (adults; glucose, total cholesterol, triglycerides, [...] Read more.
(1) Background: Reference-interval flags read a lipid–glucose panel one analyte at a time. We asked whether a routine five-analyte panel supports reproducible, transportable, auditable descriptive grouping of laboratory profiles. (2) Methods: Retrospective analysis of 21,832 laboratory-order records (adults; glucose, total cholesterol, triglycerides, HDL-C, LDL-C; collection dates not exported) from purchasers of a screening panel in Kazakhstan: log transform, robust scaling, PCA, K-means; eight cluster-number criteria; bootstrap recovery with and without pipeline refitting; frozen-classifier transfer to an identifier-disjoint cohort (n=11,382) with de novo re-derivation; a fully specified reference-interval rule layer. No clinical endpoints were available. (3) Results: Four descriptive laboratory-pattern clusters (lower glucose and lipids; higher LDL-C/TC; higher triglycerides, lower HDL-C; marked hyperglycaemia) had bootstrap Jaccard 0.965–0.981 with and without pipeline refitting and were re-derived independently in the validation cohort (ARI 0.82); a five-cluster configuration was not (0.33). Prevalence-shift point estimates were within 5 percentage points (post hoc exploratory benchmark; the C1 interval slightly crossed it). Rule-based reconciliation changed the delivered category of 8.2% of records from their K-means label; flags alone reproduced 77% of reports but 30% of the triglyceride–HDL-C pattern. (4) Conclusions: The panel supports a stable descriptive partition; prognostic and clinical validity remain to be shown. Full article
(This article belongs to the Special Issue Data Science and Artificial Intelligence in Laboratory Medicine)
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