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Haghayegh F, Norouziazad A, Haghani E, Feygin AA, Rahimi RH, Ghavamabadi HA, Sadighbayan D, Madhoun F, Papagelis M, Felfeli T, Salahandish R. Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2400595. [PMID: 38958517 DOI: 10.1002/advs.202400595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/19/2024] [Indexed: 07/04/2024]
Abstract
Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.
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Affiliation(s)
- Fatemeh Haghayegh
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Alireza Norouziazad
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Elnaz Haghani
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Ariel Avraham Feygin
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Reza Hamed Rahimi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Hamidreza Akbari Ghavamabadi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Deniz Sadighbayan
- Department of Biology, Faculty of Science, York University, Toronto, ON, M3J 1P3, Canada
| | - Faress Madhoun
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Manos Papagelis
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Tina Felfeli
- Department of Ophthalmology and Vision Sciences, University of Toronto, Ontario, M5T 3A9, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Ontario, M5T 3M6, Canada
| | - Razieh Salahandish
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
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Çubukçu HC, Topcu Dİ, Yenice S. Machine learning-based clinical decision support using laboratory data. Clin Chem Lab Med 2024; 62:793-823. [PMID: 38015744 DOI: 10.1515/cclm-2023-1037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 11/17/2023] [Indexed: 11/30/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.
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Affiliation(s)
- Hikmet Can Çubukçu
- General Directorate of Health Services, Rare Diseases Department, Turkish Ministry of Health, Ankara, Türkiye
- Hacettepe University Institute of Informatics, Ankara, Türkiye
| | - Deniz İlhan Topcu
- Health Sciences University İzmir Tepecik Education and Research Hospital, Medical Biochemistry, İzmir, Türkiye
| | - Sedef Yenice
- Florence Nightingale Hospital, Istanbul, Türkiye
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Morita Y, Sakai E, Isago H, Ono Y, Yatomi Y, Kurano M. Alterations in urinary ceramides, sphingoid bases, and their phosphates among patients with kidney disease. FRONTIERS IN NEPHROLOGY 2024; 4:1343181. [PMID: 38504855 PMCID: PMC10949895 DOI: 10.3389/fneph.2024.1343181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/05/2024] [Indexed: 03/21/2024]
Abstract
Background To avoid an invasive renal biopsy, noninvasive laboratory testing for the differential diagnosis of kidney diseases is a desirable goal. As sphingolipids are demonstrated to be involved in the pathogenesis of various kidney diseases, we investigated the possible usefulness of the simultaneous measurement of urinary sphingolipids for differentiating kidney diseases. Materials and methods Residual urine specimens were collected from patients who had been clinically diagnosed with chronic glomerulonephritis (CGN), diabetic mellitus (DM), systemic lupus erythematosus (SLE), and arterial hypertension (AH). The urinary sphingolipids-CERs C16:0, C18:0, C18:1, C20:0, C22:0, and C24:0; sphingosine [Sph]; dihydrosphingosine; sphingosine 1-phosphate [S1P]; and dihydroS1P [dhS1P]-were measured by liquid chromatography-tandem mass spectrometry. Based on the results, machine learning models were constructed to differentiate the various kidney diseases. Results The urinary S1P was higher in patients with DM than in other participants (P < 0.05), whereas dhS1P was lower in the CGN and AH groups compared with control participants (P < 0.05). Sph and dhSph were higher in patients with CGN, AH, and SLE than in those with control participants (P < 0.05). The urinary CERs were significantly higher in patients with CGN, AH, and SLE than in those with control participants (P < 0.05). As a results of constructing a machine learning model discriminating kidney diseases, the resulting diagnostic accuracy and precision were improved from 94.03% and 66.96% to 96.10% and 78.26% respectively, when the urinary CERs, Sph, dhSph, S1P, dhS1P, and their ratios were added to the models. Conclusion The urinary CERs, sphingoid bases, and their phosphates show alterations among kidney diseases, suggesting their potential involvement in the development of kidney injury.
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Affiliation(s)
- Yoshifumi Morita
- Department of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan
| | - Eri Sakai
- Department of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan
| | - Hideaki Isago
- Department of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yoshikazu Ono
- Department of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan
| | - Yutaka Yatomi
- Department of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Makoto Kurano
- Department of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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Starolis MW, Zaydman MA, Liesman RM. Working with the Electronic Health Record and Laboratory Information System to Maximize Ordering and Reporting of Molecular Microbiology Results. Clin Lab Med 2024; 44:95-107. [PMID: 38280801 DOI: 10.1016/j.cll.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2024]
Abstract
Molecular microbiology assays have a higher cost of testing compared to traditional methods and need to be utilized appropriately. Results from these assays may also require interpretation and appropriate follow-up. Electronic tools available in the electronic health record and laboratory information system can be deployed both preanalytically and postanalytically to influence ordering behaviors and positively impact diagnostic stewardship. Next generation technologies, such as machine learning and artificial intelligence, have the potential to expand upon the capabilities currently available and warrant additional study and development but also require regulation around their use in health care.
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Affiliation(s)
- Meghan W Starolis
- Molecular Infectious Disease, Quest Diagnostics, 14225 Newbrook Drive, Chantilly, VA 20151, USA.
| | - Mark A Zaydman
- Department of Pathology & Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St Louis, MO 63110, USA
| | - Rachael M Liesman
- Clinical Microbiology and Molecular Diagnostics Pathology, Department of Pathology, Medical College of Wisconsin, 9200 West Wisconsin, Milwaukee, WI 53226, USA
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Turčić A, Štajduhar A, Vogrinc Ž, Zaninović L, Rogić D. Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction. Clin Chem Lab Med 2024; 62:436-441. [PMID: 37782817 DOI: 10.1515/cclm-2023-1013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/21/2023] [Indexed: 10/04/2023]
Abstract
OBJECTIVES To create a supervised machine learning algorithm aimed at predicting an optimal cerebrospinal fluid (CSF) dilution when determining virus specific antibody indices to reduce the need for repeated tests. METHODS The CatBoost model was trained, optimized, and tested on a dataset with five input variables: albumin quotient, immunoglobulin G (IgG) in CSF, IgG quotient (QIgG), intrathecal synthesis (ITS) and limes quotient (LIM IgG). Albumin and IgG concentrations in CSF and serum were performed by immunonephelometry on Atellica NEPH 630 (Siemens Healthineers, Erlangen, Germany) and ITS and LIM IgG were calculated according to Reiber. Concentrations of IgG antibodies to measles, rubella, varicella zoster and herpes simplex 1/2 viruses were analysed in CSF and serum by ELISA (Euroimmun, Lübeck, Germany). Optimal CSF dilution was defined for each virus and used as a classification variable while the standard operating procedure was set to start at 2×-dilution of CSF. RESULTS The dataset included 571 samples with the imbalanced distribution of the optimal CSF dilutions: 2× dilution n=440, 3× dilution n=109, 4× dilution n=22. The optimized CatBoost model achieved an area under the curve (AUC) score of 0.971, and a test accuracy of 0.900. The model falsely classified 14 (9.9 %) samples of the testing set but reduced the need for repeated testing compared to the standard protocol by 42 %. The output of the CatBoost model is mostly dependant on the QIgG, ITS and CSF IgG variables. CONCLUSIONS An accurate algorithm was achieved for predicting the optimal CSF dilution, which reduces the number of test repeats.
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Affiliation(s)
- Ana Turčić
- Department of Laboratory Diagnostics, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Andrija Štajduhar
- Andrija Štampar School of Public Health, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Željka Vogrinc
- Department of Laboratory Diagnostics, University Hospital Centre Zagreb, Zagreb, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Ljiljana Zaninović
- Department of Laboratory Diagnostics, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Dunja Rogić
- Department of Laboratory Diagnostics, University Hospital Centre Zagreb, Zagreb, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
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Panner Selvam MK, Moharana AK, Baskaran S, Finelli R, Hudnall MC, Sikka SC. Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:279. [PMID: 38399566 PMCID: PMC10890589 DOI: 10.3390/medicina60020279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/25/2024]
Abstract
Background and Objectives: Infertility rates and the number of couples undergoing reproductive care have both increased substantially during the last few decades. Semen analysis is a crucial step in both the diagnosis and the treatment of male infertility. The accuracy of semen analysis results remains quite poor despite years of practice and advancements. Artificial intelligence (AI) algorithms, which can analyze and synthesize large amounts of data, can address the unique challenges involved in semen analysis due to the high objectivity of current methodologies. This review addresses recent AI advancements in semen analysis. Materials and Methods: A systematic literature search was performed in the PubMed database. Non-English articles and studies not related to humans were excluded. We extracted data related to AI algorithms or models used to evaluate semen parameters from the original studies, excluding abstracts, case reports, and meeting reports. Results: Of the 306 articles identified, 225 articles were rejected in the preliminary screening. The evaluation of the full texts of the remaining 81 publications resulted in the exclusion of another 48 articles, with a final inclusion of 33 original articles in this review. Conclusions: AI and machine learning are becoming increasingly popular in biomedical applications. The examination and selection of sperm by andrologists and embryologists may benefit greatly from using these algorithms. Furthermore, when bigger and more reliable datasets become accessible for training, these algorithms may improve over time.
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Affiliation(s)
- Manesh Kumar Panner Selvam
- Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA; (A.K.M.); (S.B.); (S.C.S.)
| | - Ajaya Kumar Moharana
- Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA; (A.K.M.); (S.B.); (S.C.S.)
- Redox Biology & Proteomics Laboratory, Department of Zoology, School of Life Sciences, Ravenshaw University, Cuttack 753003, Odisha, India
| | - Saradha Baskaran
- Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA; (A.K.M.); (S.B.); (S.C.S.)
| | | | | | - Suresh C. Sikka
- Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA; (A.K.M.); (S.B.); (S.C.S.)
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Devis L, Catry E, Honore PM, Mansour A, Lippi G, Mullier F, Closset M. Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review. Ann Intensive Care 2024; 14:9. [PMID: 38224401 PMCID: PMC10789714 DOI: 10.1186/s13613-024-01244-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/03/2024] [Indexed: 01/16/2024] Open
Abstract
Healthcare expenses are increasing, as is the utilization of laboratory resources. Despite this, between 20% and 40% of requested tests are deemed inappropriate. Improper use of laboratory resources leads to unwanted consequences such as hospital-acquired anemia, infections, increased costs, staff workload and patient stress and discomfort. The most unfavorable consequences result from unnecessary follow-up tests and treatments (overuse) and missed or delayed diagnoses (underuse). In this context, several interventions have been carried out to improve the appropriateness of laboratory testing. To date, there have been few published assessments of interventions specific to the intensive care unit. We reviewed the literature for interventions implemented in the ICU to improve the appropriateness of laboratory testing. We searched literature from 2008 to 2023 in PubMed, Embase, Scopus, and Google Scholar databases between April and June 2023. Five intervention categories were identified: education and guidance (E&G), audit and feedback, gatekeeping, computerized physician order entry (including reshaping of ordering panels), and multifaceted interventions (MFI). We included a sixth category exploring the potential role of artificial intelligence and machine learning (AI/ML)-based assisting tools in such interventions. E&G-based interventions and MFI are the most frequently used approaches. MFI is the most effective type of intervention, and shows the strongest persistence of effect over time. AI/ML-based tools may offer valuable assistance to the improvement of appropriate laboratory testing in the near future. Patient safety outcomes are not impaired by interventions to reduce inappropriate testing. The literature focuses mainly on reducing overuse of laboratory tests, with only one intervention mentioning underuse. We highlight an overall poor quality of methodological design and reporting and argue for standardization of intervention methods. Collaboration between clinicians and laboratory staff is key to improve appropriate laboratory utilization. This article offers practical guidance for optimizing the effectiveness of an intervention protocol designed to limit inappropriate use of laboratory resources.
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Affiliation(s)
- Luigi Devis
- Department of Laboratory Medicine, Biochemistry, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
| | - Emilie Catry
- Department of Laboratory Medicine, Biochemistry, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
- Institute for Experimental and Clinical Research (IREC), Pôle Mont Godinne (MONT), UCLouvain, Yvoir, Belgium
| | - Patrick M Honore
- Department of Intensive Care, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
| | - Alexandre Mansour
- Department of Anesthesia and Critical Care, Pontchaillou University Hospital of Rennes, Rennes, France
- IRSET-INSERM-1085, Univ Rennes, Rennes, France
| | - Giuseppe Lippi
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
| | - François Mullier
- Department of Laboratory Medicine, Hematology, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
- Namur Thrombosis and Hemostasis Center (NTHC), Namur Research Institute for Life Sciences (NARILIS), Namur, Belgium
- Institute for Experimental and Clinical Research (IREC), Pôle Mont Godinne (MONT), UCLouvain, Yvoir, Belgium
| | - Mélanie Closset
- Department of Laboratory Medicine, Biochemistry, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium.
- Institute for Experimental and Clinical Research (IREC), Pôle Mont Godinne (MONT), UCLouvain, Yvoir, Belgium.
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Agnello L, Vidali M, Padoan A, Lucis R, Mancini A, Guerranti R, Plebani M, Ciaccio M, Carobene A. Machine learning algorithms in sepsis. Clin Chim Acta 2024; 553:117738. [PMID: 38158005 DOI: 10.1016/j.cca.2023.117738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/20/2023] [Accepted: 12/20/2023] [Indexed: 01/03/2024]
Abstract
Sepsis remains a significant global health challenge due to its high mortality and morbidity, compounded by the difficulty of early detection given its variable clinical manifestations. The integration of machine learning (ML) into laboratory medicine for timely sepsis identification and outcome forecasting is an emerging field of interest. This comprehensive review assesses the current body of research on ML applications for sepsis within the realm of laboratory diagnostics, detailing both their strengths and shortcomings. An extensive literature search was performed by two independent investigators across PubMed and Scopus databases, employing the keywords "Sepsis," "Machine Learning," and "Laboratory" without publication date limitations, culminating in January 2023. Each selected study was meticulously evaluated for various aspects, including its design, intent (diagnostic or prognostic), clinical environment, demographics, sepsis criteria, data gathering period, and the scope and nature of features, in addition to the ML methodologies and their validation procedures. Out of 135 articles reviewed, 39 fulfilled the criteria for inclusion. Among these, the majority (30 studies) were focused on devising ML algorithms for diagnosis, fewer (8 studies) on prognosis, and one study addressed both aspects. The dissemination of these studies across an array of journals reflects the interdisciplinary engagement in the development of ML algorithms for sepsis. This analysis highlights the promising role of ML in the early diagnosis of sepsis while drawing attention to the need for uniformity in validating models and defining features, crucial steps for ensuring the reliability and practicality of ML in clinical setting.
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Affiliation(s)
- Luisa Agnello
- Institute of Clinical Biochemistry, Clinical Molecular Medicine and Clinical Laboratory Medicine, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Matteo Vidali
- Clinical Pathology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy
| | - Andrea Padoan
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University-Hospital of Padova, Padova, Italy; QI.LAB.MED., Spin-off of the University of Padova, Padova, Italy
| | - Riccardo Lucis
- Department of Medicine (DAME), University of Udine, 33100, Udine, Italy; Microbiology and Virology Unit, Department of Laboratory Medicine, Azienda Sanitaria Friuli Occidentale (ASFO), Santa Maria degli Angeli Hospital, 33170, Pordenone, Italy
| | - Alessio Mancini
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy; Operative Unit of Clinical Pathology, AST2 Ancona, Senigallia, Italy
| | - Roberto Guerranti
- Department of Medical Biotechnologies, University of Siena, Siena, Italy; Clinical Pathology Unit, Innovation, Experimentation and Clinical and Translational Research Department, University Hospital of Siena, Siena, Italy
| | - Mario Plebani
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University-Hospital of Padova, Padova, Italy; QI.LAB.MED., Spin-off of the University of Padova, Padova, Italy; Clinical Biochemistry and Clinical Molecular Biology, School of Medicine, University of Padova, Padova, Italy
| | - Marcello Ciaccio
- Institute of Clinical Biochemistry, Clinical Molecular Medicine and Clinical Laboratory Medicine, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy; Department of Laboratory Medicine, University Hospital "P. Giaccone", Palermo, Italy.
| | - Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
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Dias AC, Jácomo RH, Nery LFA, Naves LA. Effect size and inferential statistical techniques coupled with machine learning for assessing the association between prolactin concentration and metabolic homeostasis. Clin Chim Acta 2024; 552:117688. [PMID: 38049046 DOI: 10.1016/j.cca.2023.117688] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/29/2023] [Accepted: 12/01/2023] [Indexed: 12/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Recent guidelines classify low prolactin levels as low as <7 ng/mL and high levels as >25 ng/mL, while the "Homeostatically Functionally Increased Transient Prolactinemia" (HomeoFIT-PRL) range (25-100 ng/mL) suggests that a temporary increase in prolactin could be metabolically beneficial if no related health issues are present. The aim of this study was to investigate the association between mean prolactin concentrations and disturbances in glycidic and lipidic metabolism and to identify the gray zone associated with prolactin inflection points that correlate with these metabolic changes. METHODS This cross-sectional study involved 65,795 adults who underwent HOMA-IR, glucose, insulin, total cholesterol, HDL-c, LDL-c, and triglyceride tests. Data was categorized into 106 partitions based on prolactin results. Employing an approach referred to in this study as "Hierarchical Multicriteria Analysis of Differences Between Groups - Statistical and Effect Size Approach" (HiMADiG-SESA) comparing the mean concentrations of metabolic tests across prolactin ranges. A machine learning model was utilized to determine inflection points and their corresponding confidence intervals (CIs). These CIs helped establish gray zones in mean prolactin results related to metabolic changes. RESULTS Statistically and clinically, metabolic test means differed for prolactin <7 ng/mL, except insulin. In the HomeoFIT-PRL range, means were lower except for HDL-c. The gray zones of the mean prolactin results associated with changes in glycidic and lipidic metabolism were 9.58-12.87 ng/mL and 13.81-18.73 ng/mL, respectively. CONCLUSION A strong correlation was identified between mean prolactin concentrations and the results of metabolism tests below the gray zones associated with inflection points, indicating the potential role of prolactin in the appearance of metabolic disorders. Mean prolactin results can provide deeper insight into metabolic balance.
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Affiliation(s)
- Alan Carvalho Dias
- Sabin Medicina Diagnóstica, Brasilia, Federal District, Brazil; Post-Graduation in Health Sciences, University of Brasilia, Brasilia, Federal District, Brazil.
| | | | | | - Luciana Ansaneli Naves
- Sabin Medicina Diagnóstica, Brasilia, Federal District, Brazil; Post-Graduation in Health Sciences, University of Brasilia, Brasilia, Federal District, Brazil; Faculty of Medicine, University of Brasilia, Brasilia, Federal District, Brazil.
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Duan M, Zhang Y, Liu Y, Mao B, Li G, Han D, Zhang X. Machine learning aided non-invasive diagnosis of coronary heart disease based on tongue features fusion. Technol Health Care 2024; 32:441-457. [PMID: 37840506 DOI: 10.3233/thc-230590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
BACKGROUND Coronary heart disease (CHD) is the first cause of death globally. Hypertension is considered to be the most important independent risk factor for CHD. Early and accurate diagnosis of CHD in patients with hypertension can plays a significant role in reducing the risk and harm of hypertension combined with CHD. OBJECTIVE To propose a non-invasive method for early diagnosis of coronary heart disease according to tongue image features with the help of machine learning techniques. METHODS We collected standard tongue images and extract features by Diagnosis Analysis System (TDAS) and ResNet-50. On the basis of these tongue features, a common machine learning method is used to customize the non-invasive CHD diagnosis algorithm based on tongue image. RESULTS Based on feature fusion, our algorithm has good performance. The results showed that the XGBoost model with fused features had the best performance with accuracy of 0.869, the AUC of 0.957, the AUPR of 0.961, the precision of 0.926, the recall of 0.806, and the F1-score of 0.862. CONCLUSION We provide a feasible, convenient, and non-invasive method for the diagnosis and large-scale screening of CHD. Tongue image information is a possible effective marker for the diagnosis of CHD.
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Affiliation(s)
- Mengyao Duan
- School of Life Science, Beijing University of Chinese Medicine, Beijing, China
- School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China
| | - Yiming Zhang
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Yixing Liu
- School of Management, Beijing University of Chinese Medicine, Beijing, China
| | - Boyan Mao
- School of Life Science, Beijing University of Chinese Medicine, Beijing, China
| | - Gaoyang Li
- Institute of Fluid Science, Tohoku University, Sendai, Japan
| | - Dongran Han
- School of Life Science, Beijing University of Chinese Medicine, Beijing, China
| | - Xiaoqing Zhang
- School of Life Science, Beijing University of Chinese Medicine, Beijing, China
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11
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Narayanan S, Ramakrishnan R, Durairaj E, Das A. Artificial Intelligence Revolutionizing the Field of Medical Education. Cureus 2023; 15:e49604. [PMID: 38161821 PMCID: PMC10755136 DOI: 10.7759/cureus.49604] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2023] [Indexed: 01/03/2024] Open
Abstract
Medical education has ventured into a new arena of computer-assisted teaching powered by artificial intelligence (AI). In medical institutions, AI can serve as an intelligent tool facilitating the decision-making process effectively. AI can enhance teaching by assisting in developing new strategies for educators. Similarly, students also benefit from intelligent systems playing the role of competent teachers. Thus, AI-integrated medical education paves new opportunities for advanced teaching and learning experiences and improved outcomes. On the other hand, optical mark recognition and automated scoring are ways AI can also transform into a real-time assessor and evaluator in medical education. This review summarizes the AI tools and their application in medical teaching or learning, assessment, and administrative support. This article can aid medical institutes in planning and implementing AI according to the needs of the educators.
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Affiliation(s)
- Suresh Narayanan
- Department of Anatomy, All India Institute of Medical Sciences, Madurai, Madurai, IND
| | | | - Elantamilan Durairaj
- Department of Microbiology, All India Institute of Medical Sciences, Madurai, Madurai, IND
| | - Arghya Das
- Department of Microbiology, All India Institute of Medical Sciences, Madurai, Madurai, IND
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12
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Klamrowski MM, Klein R, McCudden C, Green JR, Ramsay T, Rashidi B, White CA, Oliver MJ, Akbari A, Hundemer GL. Short Timeframe Prediction of Kidney Failure among Patients with Advanced Chronic Kidney Disease. Clin Chem 2023; 69:1163-1173. [PMID: 37522430 DOI: 10.1093/clinchem/hvad112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 07/03/2023] [Indexed: 08/01/2023]
Abstract
BACKGROUND Development of a short timeframe (6-12 months) kidney failure risk prediction model may serve to improve transitions from advanced chronic kidney disease (CKD) to kidney failure and reduce rates of unplanned dialysis. The optimal model for short timeframe kidney failure risk prediction remains unknown. METHODS This retrospective study included 1757 consecutive patients with advanced CKD (mean age 66 years, estimated glomerular filtration rate 18 mL/min/1.73 m2). We compared the performance of Cox regression models using (a) baseline variables alone, (b) time-varying variables and machine learning models, (c) random survival forest, (d) random forest classifier in the prediction of kidney failure over 6/12/24 months. Performance metrics included area under the receiver operating characteristic curve (AUC-ROC) and maximum precision at 70% recall (PrRe70). Top-performing models were applied to 2 independent external cohorts. RESULTS Compared to the baseline Cox model, the machine learning and time-varying Cox models demonstrated higher 6-month performance [Cox baseline: AUC-ROC 0.85 (95% CI 0.84-0.86), PrRe70 0.53 (95% CI 0.51-0.55); Cox time-varying: AUC-ROC 0.88 (95% CI 0.87-0.89), PrRe70 0.62 (95% CI 0.60-0.64); random survival forest: AUC-ROC 0.87 (95% CI 0.86-0.88), PrRe70 0.61 (95% CI 0.57-0.64); random forest classifier AUC-ROC 0.88 (95% CI 0.87-0.89), PrRe70 0.62 (95% CI 0.59-0.65)]. These trends persisted, but were less pronounced, at 12 months. The random forest classifier was the highest performing model at 6 and 12 months. At 24 months, all models performed similarly. Model performance did not significantly degrade upon external validation. CONCLUSIONS When predicting kidney failure over short timeframes among patients with advanced CKD, machine learning incorporating time-updated data provides enhanced performance compared with traditional Cox models.
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Affiliation(s)
- Martin M Klamrowski
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
| | - Ran Klein
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
- Division of Nuclear Medicine, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Christopher McCudden
- Eastern Ontario Regional Laboratory Association, Ottawa, ON, Canada
- Division of Biochemistry, Department of Pathology and Laboratory Medicine, University of Ottawa, Ottawa, ON, Canada
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
| | - Tim Ramsay
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Babak Rashidi
- Division of General Internal Medicine, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Christine A White
- Division of Nephrology, Department of Medicine, Queen's University, Kingston, ON, Canada
| | - Matthew J Oliver
- Division of Nephrology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Ayub Akbari
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
- Division of Nephrology, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Gregory L Hundemer
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
- Division of Nephrology, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
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Mitra P, Gupta S, Sharma P. Artificial Intelligence in Clinical Chemistry: Dawn of a New Era? Indian J Clin Biochem 2023; 38:405-406. [PMID: 37746531 PMCID: PMC10516838 DOI: 10.1007/s12291-023-01150-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Affiliation(s)
- Prasenjit Mitra
- Department of Biochemistry, Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | - Shruti Gupta
- Department of Biochemistry, Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | - Praveen Sharma
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, India
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McFadden BR, Reynolds M, Inglis TJJ. Developing machine learning systems worthy of trust for infection science: a requirement for future implementation into clinical practice. Front Digit Health 2023; 5:1260602. [PMID: 37829595 PMCID: PMC10565494 DOI: 10.3389/fdgth.2023.1260602] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/15/2023] [Indexed: 10/14/2023] Open
Abstract
Infection science is a discipline of healthcare which includes clinical microbiology, public health microbiology, mechanisms of microbial disease, and antimicrobial countermeasures. The importance of infection science has become more apparent in recent years during the SARS-CoV-2 (COVID-19) pandemic and subsequent highlighting of critical operational domains within infection science including the hospital, clinical laboratory, and public health environments to prevent, manage, and treat infectious diseases. However, as the global community transitions beyond the pandemic, the importance of infection science remains, with emerging infectious diseases, bloodstream infections, sepsis, and antimicrobial resistance becoming increasingly significant contributions to the burden of global disease. Machine learning (ML) is frequently applied in healthcare and medical domains, with growing interest in the application of ML techniques to problems in infection science. This has the potential to address several key aspects including improving patient outcomes, optimising workflows in the clinical laboratory, and supporting the management of public health. However, despite promising results, the implementation of ML into clinical practice and workflows is limited. Enabling the migration of ML models from the research to real world environment requires the development of trustworthy ML systems that support the requirements of users, stakeholders, and regulatory agencies. This paper will provide readers with a brief introduction to infection science, outline the principles of trustworthy ML systems, provide examples of the application of these principles in infection science, and propose future directions for moving towards the development of trustworthy ML systems in infection science.
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Affiliation(s)
- Benjamin R. McFadden
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Mark Reynolds
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Timothy J. J. Inglis
- Western Australian Country Health Service, Perth, WA, Australia
- School of Medicine, University of Western Australia, Perth, WA, Australia
- Department of Microbiology, Pathwest Laboratory Medicine, Perth, WA, Australia
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Zhang F, Yang J, Wang Y, Cai M, Ouyang J, Li J. TT@MHA: A Machine Learning-based Webpage Tool for Discriminating Thalassemia Trait from Microcytic Hypochromic Anemia Patients. Clin Chim Acta 2023; 545:117368. [PMID: 37127232 DOI: 10.1016/j.cca.2023.117368] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/27/2023] [Accepted: 04/23/2023] [Indexed: 05/03/2023]
Abstract
BACKGROUND Iron deficiency anemia (IDA) and thalassemia trait (TT) are the most common causes of microcytic hypochromic anemia (MHA) and are endemic in lower resource settings and rural areas with poor medical infrastructure. Accurate discrimination between IDA and TT is an essential issue for MHA patients. Although various discriminant formulas have been reported, distinguishing between IDA and TT is still a challenging problem due to the diversity of anemic populations. METHODS We retrospectively collected laboratory data from 798 MHA patients. High proportions of α-TT (43.33%) and TT concomitant with IDA (TT&IDA) patients (14.04%) were found among TT patients. Five machine learning (ML) approaches, including Liner SVC (L-SVC), support vector machine learning (SVM), Extreme gradient boosting (XGB), Logistic Regression (LR), and Random Forest (RF), were applied to develop a discriminant model. Performance was assessed and compared with six existing discriminant formulas. RESULTS The RF model was chosen as the discriminant algorithm, namely TT@MHA. TT@MHA was tested in an interlaboratory cohort with a sensitivity, specificity, accuracy, and AUC of 91.91%, 91.00%, 91.53%, and 0.942, respectively. A webpage tool of TT@MHA (https://dxonline.deepwise.com/prediction/index.html?baseUrl=%2Fapi%2F&id=26408&topicName=undefined&from=share&platformType=wisdom) was developed to facilitate the healthcare providers in rural areas. CONCLUSION The ML-based TT@MHA algorithm, with high sensitivity and specificity, could help discriminate TT patients from MHA patients, especially in populations with high proportions of α-TT patients and TT&IDA patients. Moreover, a user-friendly webpage tool for TT@MHA could facilitate healthcare providers in rural areas where advanced technologies are not accessible.
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Affiliation(s)
- Fan Zhang
- Department of Medical Laboratory, First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Jing Yang
- Department of Medical Laboratory, Second Affiliated Hospital of Guangzhou Medical University, 250 Changgang Zhong Road, 510260, China
| | - Yang Wang
- Department of Medical Laboratory, First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Manyi Cai
- BGI Genomics Co.,Ltd, National Gene Bank of Guanyinshan Park, Jinsha Road, Dapeng Street, Dapeng New District, Shenzhen, Guangdong Province, 518120, China
| | - Juan Ouyang
- Department of Medical Laboratory, First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China.
| | - JunXun Li
- Department of Medical Laboratory, First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China.
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Gomes KB, Pereira RG, Braga AA, Guimarães HC, Resende EDPF, Teixeira AL, Barbosa MT, Junior WM, Carvalho MDG, Caramelli P. Machine Learning-Based Routine Laboratory Tests Predict One-Year Cognitive and Functional Decline in a Population Aged 75+ Years. Brain Sci 2023; 13:brainsci13040690. [PMID: 37190655 DOI: 10.3390/brainsci13040690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 03/30/2023] [Accepted: 04/15/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Cognitive and functional decline are common problems in older adults, especially in those 75+ years old. Currently, there is no specific plasma biomarker able to predict this decline in healthy old-age people. Machine learning (ML) is a subarea of artificial intelligence (AI), which can be used to predict outcomes Aim: This study aimed to evaluate routine laboratory variables able to predict cognitive and functional impairment, using ML algorithms, in a cohort aged 75+ years, in a one-year follow-up study. METHOD One hundred and thirty-two older adults aged 75+ years were selected through a community-health public program or from long-term-care institutions. Their functional and cognitive performances were evaluated at baseline and one year later using a functional activities questionnaire, Mini-Mental State Examination, and the Brief Cognitive Screening Battery. Routine laboratory tests were performed at baseline. ML algorithms-random forest, support vector machine (SVM), and XGBoost-were applied in order to describe the best model able to predict cognitive and functional decline using routine tests as features. RESULTS The random forest model showed better accuracy than other algorithms and included triglycerides, glucose, hematocrit, red cell distribution width (RDW), albumin, hemoglobin, globulin, high-density lipoprotein cholesterol (HDL-c), thyroid-stimulating hormone (TSH), creatinine, lymphocyte, erythrocyte, platelet/leucocyte (PLR), and neutrophil/leucocyte (NLR) ratios, and alanine transaminase (ALT), leukocyte, low-density lipoprotein cholesterol (LDL-c), cortisol, gamma-glutamyl transferase (GGT), and eosinophil as features to predict cognitive decline (accuracy = 0.79). For functional decline, the most important features were platelet, PLR and NLR, hemoglobin, globulin, cortisol, RDW, glucose, basophil, B12 vitamin, creatinine, GGT, ALT, aspartate transferase (AST), eosinophil, hematocrit, erythrocyte, triglycerides, HDL-c, and monocyte (accuracy = 0.92). CONCLUSIONS Routine laboratory variables could be applied to predict cognitive and functional decline in oldest-old populations using ML algorithms.
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Affiliation(s)
- Karina Braga Gomes
- Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
| | - Ramon Gonçalves Pereira
- Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
| | - Alexandre Alberto Braga
- Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
| | | | | | | | - Maira Tonidandel Barbosa
- Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
| | - Wagner Meira Junior
- Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
| | | | - Paulo Caramelli
- Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
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Rabbani N, Ma SP, Li RC, Winget M, Weber S, Boosi S, Pham TD, Svec D, Shieh L, Chen JH. Targeting repetitive laboratory testing with electronic health records-embedded predictive decision support: A pre-implementation study. Clin Biochem 2023; 113:70-77. [PMID: 36623759 PMCID: PMC9936847 DOI: 10.1016/j.clinbiochem.2023.01.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 12/07/2022] [Accepted: 01/05/2023] [Indexed: 01/09/2023]
Abstract
INTRODUCTION Unnecessary laboratory testing contributes to patient morbidity and healthcare waste. Despite prior attempts at curbing such overutilization, there remains opportunity for improvement using novel data-driven approaches. This study presents the development and early evaluation of a clinical decision support tool that uses a predictive model to help providers reduce low-yield, repetitive laboratory testing in hospitalized patients. METHODS We developed an EHR-embedded SMART on FHIR application that utilizes a laboratory test result prediction model based on historical laboratory data. A combination of semi-structured physician interviews, usability testing, and quantitative analysis on retrospective laboratory data were used to inform the tool's development and evaluate its acceptability and potential clinical impact. KEY RESULTS Physicians identified culture and lack of awareness of repeat orders as key drivers for overuse of inpatient blood testing. Users expressed an openness to a lab prediction model and 13/15 physicians believed the tool would alter their ordering practices. The application received a median System Usability Scale score of 75, corresponding to the 75th percentile of software tools. On average, physicians desired a prediction certainty of 85% before discontinuing a routine recurring laboratory order and a higher certainty of 90% before being alerted. Simulation on historical lab data indicates that filtering based on accepted thresholds could have reduced ∼22% of repeat chemistry panels. CONCLUSIONS The use of a predictive algorithm as a means to calculate the utility of a diagnostic test is a promising paradigm for curbing laboratory test overutilization. An EHR-embedded clinical decision support tool employing such a model is a novel and acceptable intervention with the potential to reduce low-yield, repetitive laboratory testing.
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Affiliation(s)
- Naveed Rabbani
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA.
| | - Stephen P Ma
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ron C Li
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Marcy Winget
- Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Susan Weber
- Technology and Digital Solutions, Stanford University School of Medicine, Stanford, CA, USA
| | - Srinivasan Boosi
- Technology and Digital Solutions, Stanford University School of Medicine, Stanford, CA, USA
| | - Tho D Pham
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - David Svec
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Lisa Shieh
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Jonathan H Chen
- Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA; Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA; Division of Hospital Medicine, Stanford University School of Medicine, Stanford, CA, USA; Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA
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18
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Zhang T, Pang A, Lyu J, Ren H, Song J, Zhu F, Liu J, Cui Y, Ling C, Tian Y. Application of Nonlinear Models Combined with Conventional Laboratory Indicators for the Diagnosis and Differential Diagnosis of Ovarian Cancer. J Clin Med 2023; 12:jcm12030844. [PMID: 36769493 PMCID: PMC9917843 DOI: 10.3390/jcm12030844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/11/2023] [Accepted: 01/17/2023] [Indexed: 01/24/2023] Open
Abstract
Existing biomarkers for ovarian cancer lack sensitivity and specificity. We compared the diagnostic efficacy of nonlinear machine learning and linear statistical models for diagnosing ovarian cancer using a combination of conventional laboratory indicators. We divided 901 retrospective samples into an ovarian cancer group and a control group, comprising non-ovarian malignant gynecological tumor (NOMGT), benign gynecological disease (BGD), and healthy control subgroups. Cases were randomly assigned to training and internal validation sets. Two linear (logistic regression (LR) and Fisher's linear discriminant (FLD)) and three nonlinear models (support vector machine (SVM), random forest (RF), and artificial neural network (ANN)) were constructed using 22 conventional laboratory indicators and three demographic characteristics. Model performance was compared. In an independent prospectively recruited validation set, the order of diagnostic efficiency was RF, SVM, ANN, FLD, LR, and carbohydrate antigen 125 (CA125)-only (AUC, accuracy: 0.989, 95.6%; 0.985, 94.4%; 0.974, 93.4%; 0.915, 82.1%; 0.859, 80.1%; and 0.732, 73.0%, respectively). RF maintained satisfactory classification performance for identifying different ovarian cancer stages and for discriminating it from NOMGT-, BGD-, or CA125-positive control. Nonlinear models outperformed linear models, indicating that nonlinear machine learning models can efficiently use conventional laboratory indicators for ovarian cancer diagnosis.
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Affiliation(s)
- Tongshuo Zhang
- Department of Laboratory Medicine and Pathology, Jiangsu Provincial Corps Hospital of Chinese People’s Armed Police Force (PAP), Yangzhou 225003, China
| | - Aibo Pang
- Center for Birth Defects Prevention and Control Technology Research, Chinese PLA General Hospital, Beijing 100853, China
| | - Jungang Lyu
- Third Department of Internal Medicine, Beijing Corps Hospital of PAP, Beijing 100027, China
| | - Hefei Ren
- Department of Laboratory Medicine, The Second Affiliated Hospital, Naval Medical University, Shanghai 200003, China
| | - Jiangnan Song
- Department of Obstetrics and Gynecology, The First Medical Centre, Chinese PLA General Hospital, Beijing 100853, China
| | - Feng Zhu
- Department of Laboratory Medicine and Pathology, Jiangsu Provincial Corps Hospital of Chinese People’s Armed Police Force (PAP), Yangzhou 225003, China
| | - Jinlong Liu
- Department of Obstetrics and Gynecology, The 79th Group Army Hospital of PLA, Liaoyang 111000, China
| | - Yuntao Cui
- Department of Laboratory Medicine, Characteristic Medical Center of PAP, Tianjin 300162, China
| | - Cunbao Ling
- Center for Birth Defects Prevention and Control Technology Research, Chinese PLA General Hospital, Beijing 100853, China
| | - Yaping Tian
- Center for Birth Defects Prevention and Control Technology Research, Chinese PLA General Hospital, Beijing 100853, China
- Correspondence:
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Wen X, Leng P, Wang J, Yang G, Zu R, Jia X, Zhang K, Mengesha BA, Huang J, Wang D, Luo H. Clinlabomics: leveraging clinical laboratory data by data mining strategies. BMC Bioinformatics 2022; 23:387. [PMID: 36153474 PMCID: PMC9509545 DOI: 10.1186/s12859-022-04926-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 09/13/2022] [Indexed: 11/29/2022] Open
Abstract
The recent global focus on big data in medicine has been associated with the rise of artificial intelligence (AI) in diagnosis and decision-making following recent advances in computer technology. Up to now, AI has been applied to various aspects of medicine, including disease diagnosis, surveillance, treatment, predicting future risk, targeted interventions and understanding of the disease. There have been plenty of successful examples in medicine of using big data, such as radiology and pathology, ophthalmology cardiology and surgery. Combining medicine and AI has become a powerful tool to change health care, and even to change the nature of disease screening in clinical diagnosis. As all we know, clinical laboratories produce large amounts of testing data every day and the clinical laboratory data combined with AI may establish a new diagnosis and treatment has attracted wide attention. At present, a new concept of radiomics has been created for imaging data combined with AI, but a new definition of clinical laboratory data combined with AI has lacked so that many studies in this field cannot be accurately classified. Therefore, we propose a new concept of clinical laboratory omics (Clinlabomics) by combining clinical laboratory medicine and AI. Clinlabomics can use high-throughput methods to extract large amounts of feature data from blood, body fluids, secretions, excreta, and cast clinical laboratory test data. Then using the data statistics, machine learning, and other methods to read more undiscovered information. In this review, we have summarized the application of clinical laboratory data combined with AI in medical fields. Undeniable, the application of Clinlabomics is a method that can assist many fields of medicine but still requires further validation in a multi-center environment and laboratory.
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Artificial intelligence: A step forward in the clinical laboratory, a decision maker hub. Clin Biochem 2022; 105-106:23-24. [DOI: 10.1016/j.clinbiochem.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/11/2022] [Accepted: 05/19/2022] [Indexed: 11/16/2022]
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