AI-Assisted Interpretation of Complex Biochemical Panels in Critical Care Units
Downloads
Compiling patient biochemical tests into panels automates laboratory analysis especially in critical-care scenarios. Biochemical-panel analyses provide information on disease nature and severity. Consistent interpretation of clinical values supports accurate diagnoses, effective patient care, and data-driven decision-making at a broader level. Common tests include blood chemistry panels assessing the bloodstream according to clinical needs.
Biochemical assays focus on molecules, enzymes, or other analytes of clinical interest that react with reagents to generate measurable signals. The resultant signal correlates with relative blue dots separation, enabling sample identification. Amperometric detection uses samples containing redox-active species stimulating current flow upon reaction with an initial potential sent through electrodes. Consequently, DNA separation-migration occurs regularly, producing mirror-image guanine ratios and valuable band-design information.
Artificial intelligence contributes to well-informed and timely decisions based on large, diverse data. In emergency departments, where routine triage cannot keep pace with rapidly accumulating data, considerable attention has been devoted to developing computational systems facilitating diagnostic decision-making. Automating complex biochemical-panel interpretation constitutes a significant step toward open-source, self-sustained clinical decision support tools. AI methodologies encompass machine learning, natural-language processing (NLP), and data mining, all applicable to complex biochemical-panel analysis.
1. J. Cadamuro, "Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence—A Narrative Review," 2021. ncbi.nlm.nih.gov
2. L. Sun, A. Agarwal, A. Kornblith, B. Yu et al., "ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance," 2024. [PDF]
3. W. T. Wu, Y. J. Li, A. Z. Feng, L. Li, T. Huang, and A. D. Xu, "Data mining in clinical big data: the frequently used databases, steps, and methodological models," Military Medical Science Letters, vol. 2021, pp. 1-10, 2021. springer.com
4. K. Swanson, E. Wu, A. Zhang, A. A. Alizadeh et al., "From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment," Cell, 2023. cell.com
5. M. Beatriz Walter Costa, M. Wernsdorfer, A. Kehrer, M. Voigt et al., "The Clinical Decision Support System AMPEL for Laboratory Diagnostics: Implementation and Technical Evaluation," 2021. ncbi.nlm.nih.gov
6. I. Forsal, M. Bodelsson, A. Wieslander, A. Nilsson et al., "Analysis of acid–base disorders in an ICU cohort using a computer script," 2022. ncbi.nlm.nih.gov
7. M. Komorowski, "Artificial intelligence in intensive care: are we there yet?," 2019. [PDF]
8. E. Caires Silveira, S. Mattos Pretti, B. Almeida Santos, C. Fellipe Santos Corrêa et al., "Prediction of hospital mortality in intensive care unit patients from clinical and laboratory data: A machine learning approach," 2022. ncbi.nlm.nih.gov
9. G. Feretzakis, G. Karlis, E. Loupelis, D. Kalles et al., "Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department," 2022. ncbi.nlm.nih.gov
10. R. K. Taira, A. O. Garlid, and W. Speier, "Design considerations for a hierarchical semantic compositional framework for medical natural language understanding," 2022. [PDF]
11. E. Dolatabadi, B. Chen, S. A Buchan, A. Marchand Austin et al., "Natural Language Processing for Clinical Laboratory Data Repository Systems: Implementation and Evaluation for Respiratory Viruses," 2023. ncbi.nlm.nih.gov
12. M. Pulgar-Sánchez, K. Chamorro, M. Fors, F. X. Mora et al., "Biomarkers of severe COVID-19 pneumonia on admission using data-mining powered by common laboratory blood tests-datasets," 2021. ncbi.nlm.nih.gov
13. S. Evren Seker, Y. Unal, Z. Erdem, and H. Erdinc Kocer, "Ensembled Correlation Between Liver Analysis Outputs," 2014. [PDF]
14. A. Lal, Y. Pinevich, O. Gajic, V. Herasevich et al., "Artificial intelligence and computer simulation models in critical illness," 2020. ncbi.nlm.nih.gov
15. J. C. C. Kwong, G. C. Nickel, S. C. Y. Wang, and J. C. Kvedar, "Integrating artificial intelligence into healthcare systems: more than just the algorithm," 2024. ncbi.nlm.nih.gov
16. N. Prasad, A. Mandyam, C. Chivers, M. Draugelis et al., "Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach," 2022. ncbi.nlm.nih.gov
17. J. M. Spiegel, R. Ehrlich, A. Yassi, F. Riera et al., "Using Artificial Intelligence for High-Volume Identification of Silicosis and Tuberculosis: A Bio-Ethics Approach," 2021. ncbi.nlm.nih.gov
18. R. Pierce, S. Sterckx, and W. Van Biesen, "A riddle, wrapped in a mystery, inside an enigma: How semantic black boxes and opaque artificial intelligence confuse medical decision‐making," 2021. ncbi.nlm.nih.gov
19. H. Kondylakis, R. Catalan, S. Martinez Alabart, C. Barelle et al., "Documenting the de-identification process of clinical and imaging data for AI for health imaging projects," 2024. ncbi.nlm.nih.gov
20. Y. Yang, M. Lin, H. Zhao, Y. Peng et al., "A survey of recent methods for addressing AI fairness and bias in biomedicine," 2024. ncbi.nlm.nih.gov
21. E. Röösli, S. Bozkurt, and T. Hernandez-Boussard, "Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model," 2022. ncbi.nlm.nih.gov
22. R. Y. Cohen and V. P. Kovacheva, "A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform to Facilitate Healthcare AI Research," 2021. [PDF]
23. M. Radensky, D. Burson, R. Bhaiya, and D. S. Weld, "Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcare," 2022. [PDF]
24. N. Hallowell, S. Badger, A. Sauerbrei, C. Nellåker et al., "“I don’t think people are ready to trust these algorithms at face value”: trust and the use of machine learning algorithms in the diagnosis of rare disease," 2022. ncbi.nlm.nih.gov
25. M. P. McRae, K. S. Rajsri, T. M. Alcorn, and J. T. McDevitt, "Smart Diagnostics: Combining Artificial Intelligence and In Vitro Diagnostics," 2022. ncbi.nlm.nih.gov
26. M. R. Pinsky, A. Dubrawski, and G. Clermont, "Intelligent Clinical Decision Support," 2022. ncbi.nlm.nih.gov
27. F. Yu, A. Moehring, O. Banerjee, T. Salz et al., "Heterogeneity and predictors of the effects of AI assistance on radiologists," 2024. ncbi.nlm.nih.gov
28. WF Lems, AD Anastasilakis, CM Andreasen, et al., "Basic and Clinical Scientists Working Together—Do We Make the Best of Both Worlds?," Calcified Tissue, vol. 2025, Springer. springer.com
