1
|
Wändell P, Carlsson AC, Wierzbicka M, Sigurdsson K, Ärnlöv J, Eriksson J, Wachtler C, Ruge T. A machine learning tool for identifying patients with newly diagnosed diabetes in primary care. Prim Care Diabetes 2024; 18:501-505. [PMID: 38944562 DOI: 10.1016/j.pcd.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/01/2024]
Abstract
BACKGROUND AND AIM It is crucial to identify a diabetes diagnosis early. Create a predictive model utilizing machine learning (ML) to identify new cases of diabetes in primary health care (PHC). METHODS A case-control study utilizing data on PHC visits for sex-, age, and PHC-matched controls. Stochastic gradient boosting was used to construct a model for predicting cases of diabetes based on diagnostic codes from PHC consultations during the year before index (diagnosis) date and number of consultations. Variable importance was estimated using the normalized relative influence (NRI) score. Risks of having diabetes were calculated using odds ratios of marginal effects (ORME). Four groups by age and sex were studied, age-groups 35-64 years and ≥ 65 years in men and women, respectively. RESULTS The most important predictive factors were hypertension with NRI 21.4-29.7 %, and obesity 4.8-15.2 %. The NRI for other top ten diagnoses and administrative codes generally ranged 1.0-4.2 %. CONCLUSIONS Our data confirm the known risk patterns for predicting a new diagnosis of diabetes, and the need to test blood glucose frequently. To assess the full potential of ML for risk prediction purposes in clinical practice, future studies could include clinical data on life-style patterns, laboratory tests and prescribed medication.
Collapse
Affiliation(s)
- Per Wändell
- Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden
| | - Axel C Carlsson
- Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden; Academic Primary Health Care Centre, Region Stockholm, Stockholm, Sweden.
| | - Marcelina Wierzbicka
- Department of Emergency and Internal Medicine, Skånes University Hospital, Malmö, Sweden; Department of Clinical Sciences Malmö, Lund University & Department of Internal Medicine, Skåne, Sweden
| | - Karolina Sigurdsson
- Department of Emergency and Internal Medicine, Skånes University Hospital, Malmö, Sweden; Department of Clinical Sciences Malmö, Lund University & Department of Internal Medicine, Skåne, Sweden
| | - Johan Ärnlöv
- Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden; School of Health and Social Studies, Dalarna University, Falun, Sweden
| | - Julia Eriksson
- Division of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Caroline Wachtler
- Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden
| | - Toralph Ruge
- Department of Emergency and Internal Medicine, Skånes University Hospital, Malmö, Sweden; Department of Clinical Sciences Malmö, Lund University & Department of Internal Medicine, Skåne, Sweden
| |
Collapse
|
2
|
Zhang G, Sun S, Wang Y, Zhao Y, Sun L. Unveiling Immune-related feature genes for Alzheimer's disease based on machine learning. Front Immunol 2024; 15:1333666. [PMID: 38915415 PMCID: PMC11194375 DOI: 10.3389/fimmu.2024.1333666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 05/23/2024] [Indexed: 06/26/2024] Open
Abstract
The identification of diagnostic and therapeutic biomarkers for Alzheimer's Disease (AD) remains a crucial area of research. In this study, utilizing the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, we identified RHBDF2 and TNFRSF10B as feature genes associated with AD pathogenesis. Analyzing data from the GSE33000 dataset, we revealed significant upregulation of RHBDF2 and TNFRSF10B in AD patients, with correlations to age and gender. Interestingly, their expression profile in AD differs notably from that of other neurodegenerative conditions. Functional analysis unveiled their involvement in immune response and various signaling pathways implicated in AD pathogenesis. Furthermore, our study demonstrated the potential of RHBDF2 and TNFRSF10B as diagnostic biomarkers, exhibiting high discrimination power in distinguishing AD from control samples. External validation across multiple datasets confirmed the robustness of the diagnostic model. Moreover, utilizing molecular docking analysis, we identified dinaciclib and tanespimycin as promising small molecule drugs targeting RHBDF2 and TNFRSF10B for potential AD treatment. Our findings highlight the diagnostic and therapeutic potential of RHBDF2 and TNFRSF10B in AD management, shedding light on novel strategies for precision medicine in AD.
Collapse
Affiliation(s)
- Guimei Zhang
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Jilin University, Changchun, China
- Cognitive Center, Department of Neurology, The First Hospital of Jilin University, Jilin University, Changchun, China
| | - Shuo Sun
- Department of Urology, The Affiliated Hospital of Changchun University of Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Yingying Wang
- The Second Department of Pediatrics, The First Hospital of Jilin University, Jilin University, Changchun, China
| | - Yang Zhao
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Jilin University, Changchun, China
- Cognitive Center, Department of Neurology, The First Hospital of Jilin University, Jilin University, Changchun, China
| | - Li Sun
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Jilin University, Changchun, China
- Cognitive Center, Department of Neurology, The First Hospital of Jilin University, Jilin University, Changchun, China
| |
Collapse
|
3
|
Kim Y, Lim M, Kim SY, Kim TU, Lee SJ, Bok SK, Park S, Han Y, Jung HY, Hyun JK. Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients. J Clin Med 2024; 13:990. [PMID: 38398304 PMCID: PMC10889422 DOI: 10.3390/jcm13040990] [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: 12/19/2023] [Revised: 01/19/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
(1) Background: Pressure ulcers (PUs) substantially impact the quality of life of spinal cord injury (SCI) patients and require prompt intervention. This study used machine learning (ML) techniques to develop advanced predictive models for the occurrence of PUs in patients with SCI. (2) Methods: By analyzing the medical records of 539 patients with SCI, we observed a 35% incidence of PUs during hospitalization. Our analysis included 139 variables, including baseline characteristics, neurological status (International Standards for Neurological Classification of Spinal Cord Injury [ISNCSCI]), functional ability (Korean version of the Modified Barthel Index [K-MBI] and Functional Independence Measure [FIM]), and laboratory data. We used a variety of ML methods-a graph neural network (GNN), a deep neural network (DNN), a linear support vector machine (SVM_linear), a support vector machine with radial basis function kernel (SVM_RBF), K-nearest neighbors (KNN), a random forest (RF), and logistic regression (LR)-focusing on an integrative analysis of laboratory, neurological, and functional data. (3) Results: The SVM_linear algorithm using these composite data showed superior predictive ability (area under the receiver operating characteristic curve (AUC) = 0.904, accuracy = 0.944), as demonstrated by a 5-fold cross-validation. The critical discriminators of PU development were identified based on limb functional status and laboratory markers of inflammation. External validation highlighted the challenges of model generalization and provided a direction for future research. (4) Conclusions: Our study highlights the importance of a comprehensive, multidimensional data approach for the effective prediction of PUs in patients with SCI, especially in the acute and subacute phases. The proposed ML models show potential for the early detection and prevention of PUs, thus contributing substantially to improving patient care in clinical settings.
Collapse
Affiliation(s)
- Yuna Kim
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Myungeun Lim
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Seo Young Kim
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Tae Uk Kim
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Seong Jae Lee
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
| | - Soo-Kyung Bok
- Department of Rehabilitation Medicine, College of Medicine, Chungnam National University, Daejeon 35015, Republic of Korea;
| | - Soojun Park
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Youngwoong Han
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Ho-Youl Jung
- Digital Biomedical Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea; (M.L.); (S.P.); (Y.H.)
| | - Jung Keun Hyun
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea; (Y.K.); (S.Y.K.); (T.U.K.); (S.J.L.)
- Department of Nanobiomedical Science and BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan 31116, Republic of Korea
- Institute of Tissue Regeneration Engineering, Dankook University, Cheonan 31116, Republic of Korea
| |
Collapse
|