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Dabla PK, Upreti K, Shrivastav D, Mehta V, Singh D. Discovering hidden patterns: Association rules for cardiovascular diseases in type 2 diabetes mellitus. World J Methodol 2024; 14:92608. [PMID: 38983667 PMCID: PMC11229869 DOI: 10.5662/wjm.v14.i2.92608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/15/2024] [Accepted: 04/09/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD), and studies are able to correlate their relationships with available biological and clinical evidence. The aim of the current study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features relevant to these diseases. ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient care. AIM To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern discovery. METHODS This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi, involving a total of 300 consented subjects categorized into three groups: CAD with diabetes, CAD without diabetes, and healthy controls, with 100 subjects in each group. The participants were enrolled from the Cardiology IPD & OPD for the sample collection. The study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original value. RESULTS The clinical dataset comprised 35 attributes from enrolled subjects. The analysis produced rules with a maximum branching factor of 4 and a rule length of 5, necessitating a 1% probability increase for enhancement. Prominent patterns emerged, highlighting strong links between health indicators and diabetes likelihood, particularly elevated HbA1C and random blood sugar levels. The ARM technique identified individuals with a random blood sugar level > 175 and HbA1C > 6.6 are likely in the "CAD-with-diabetes" group, offering valuable insights into health indicators and influencing factors on disease outcomes. CONCLUSION The application of this method holds promise for healthcare practitioners to offer valuable insights for enhancing patient treatment targeting specific subtypes of CAD with diabetes. Implying artificial intelligence techniques with medical data, we have shown the potential for personalized healthcare and the development of user-friendly applications aimed at improving cardiovascular health outcomes for this high-risk population to optimise the decision-making in patient care.
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Affiliation(s)
- Pradeep Kumar Dabla
- Department of Biochemistry, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research, Delhi 110002, India
| | - Kamal Upreti
- Department of Computer Science, CHRIST, Ghaziabad 201003, India
| | - Dharmsheel Shrivastav
- Department of Biochemistry, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research, Delhi 110002, India
| | - Vimal Mehta
- Department of Cardiology, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research, Delhi 110002, India
| | - Divakar Singh
- Barkatullah University Institute of Technology, Barkatullah University, Bhopal 462026, India
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Adam N, Wieder R. Temporal Association Rule Mining: Race-Based Patterns of Treatment-Adverse Events in Breast Cancer Patients Using SEER-Medicare Dataset. Biomedicines 2024; 12:1213. [PMID: 38927419 PMCID: PMC11200891 DOI: 10.3390/biomedicines12061213] [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: 03/18/2024] [Revised: 05/17/2024] [Accepted: 05/20/2024] [Indexed: 06/28/2024] Open
Abstract
PURPOSE Disparities in the screening, treatment, and survival of African American (AA) patients with breast cancer extend to adverse events experienced with systemic therapy. However, data are limited and difficult to obtain. We addressed this challenge by applying temporal association rule (TAR) mining using the SEER-Medicare dataset for differences in the association of specific adverse events (AEs) and treatments (TRs) for breast cancer between AA and White women. We considered two categories of cancer care providers and settings: practitioners providing care in the outpatient units of hospitals and institutions and private practitioners providing care in their offices. PATIENTS AN METHODS We considered women enrolled in the Medicare fee-for-service option at age 65 who qualified by age and not disability, who were diagnosed with breast cancer with attributed patient factors of age and race, marital status, comorbidities, prior malignancies, prior therapy, disease factors of stage, grade, and ER/PR and Her2 status and laterality. We included 141 HCPCS drug J codes for chemotherapy, biotherapy, and hormone therapy drugs, which we consolidated into 46 mechanistic categories and generated AE data. We consolidated AEs from ICD9 codes into 18 categories associated with breast cancer therapy. We applied TAR mining to determine associations between the 46 TR and 18 AE categories in the context of the patient categories outlined. We applied the spark.mllib implementation of the FPGrowth algorithm, a parallel version called PFP. We considered differences of at least one unit of lift as significant between groups. The model's results demonstrated a high overlap between the model's identified TR-AEs associated set and the actual set. RESULTS Our results demonstrate that specific TR/AE associations are highly dependent on race, stage, and venue of care administration. CONCLUSIONS Our data demonstrate the usefulness of this approach in identifying differences in the associations between TRs and AEs in different populations and serve as a reference for predicting the likelihood of AEs in different patient populations treated for breast cancer. Our novel approach using unsupervised learning enables the discovery of association rules while paying special attention to temporal information, resulting in greater predictive and descriptive power as a patient's health and life status change over time.
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Affiliation(s)
- Nabil Adam
- Phalcon, LLC., Manhasset, NY 11030, USA;
- Rutgers University, Newark Campus, Newark, NJ 07102, USA
| | - Robert Wieder
- Rutgers New Jersey Medical School, Newark, NJ 07103, USA
- Rutgers Cancer Institute of New Jersey, Newark, NJ 07103, USA
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Kohzadi Z, Nickfarjam AM, Arani LS, Kohzadi Z, Mahdian M. Extraction frequent patterns in trauma dataset based on automatic generation of minimum support and feature weighting. BMC Med Res Methodol 2024; 24:40. [PMID: 38365591 PMCID: PMC10870595 DOI: 10.1186/s12874-024-02154-0] [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: 11/03/2023] [Accepted: 01/17/2024] [Indexed: 02/18/2024] Open
Abstract
PURPOSE Data mining has been used to help discover Frequent patterns in health data. it is widely used to diagnose and prevent various diseases and to obtain the causes and factors affecting diseases. Therefore, the aim of the present study is to discover frequent patterns in the data of the Kashan Trauma Registry based on a new method. METHODS We utilized real data from the Kashan Trauma Registry. After pre-processing, frequent patterns and rules were extracted based on the classical Apriori algorithm and the new method. The new method based on the weight of variables and the harmonic mean was presented for the automatic calculation of minimum support with the Python. RESULTS The results showed that the minimum support generation based on the weighting features is done dynamically and level by level, while in the classic Apriori algorithm considering that only one value is considered for the minimum support manually by the user. Also, the performance of the new method was better compared to the classical Apriori method based on the amount of memory consumption, execution time, the number of frequent patterns found and the generated rules. CONCLUSIONS This study found that manually determining the minimal support increases execution time and memory usage, which is not cost-effective, especially when the user does not know the dataset's content. In trauma registries and massive healthcare datasets, its ability to uncover common item groups and association rules provides valuable insights. Also, based on the patterns produced in the trauma data, the care of the elderly by their families, education to the general public about encountering patients who have an accident and how to transport them to the hospital, education to motorcyclists to observe safety points in Recommended when using a motorcycle.
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Affiliation(s)
- Zahra Kohzadi
- Health Information Management Research Center, Kashan University of Medical Sciences, Kashan, Iran
- Department of Health Information Management and Technology, Allied Medical Sciences Faculty, Kashan University of Medical Sciences, Kashan, Iran
| | - Ali Mohammad Nickfarjam
- Health Information Management Research Center, Kashan University of Medical Sciences, Kashan, Iran.
- Department of Health Information Management and Technology, Allied Medical Sciences Faculty, Kashan University of Medical Sciences, Kashan, Iran.
| | - Leila Shokrizadeh Arani
- Health Information Management Research Center, Kashan University of Medical Sciences, Kashan, Iran
- Department of Health Information Management and Technology, Allied Medical Sciences Faculty, Kashan University of Medical Sciences, Kashan, Iran
| | - Zeinab Kohzadi
- Medical Informatics Department, School of Allied Medical Sciences Shahid, Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrdad Mahdian
- Trauma Research Center, Kashan University of Medical Sciences, Kashan, Iran
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Revilla-León M, Gómez-Polo M, Barmak AB, Inam W, Kan JYK, Kois JC, Akal O. Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review. J Prosthet Dent 2023; 130:816-824. [PMID: 35300850 DOI: 10.1016/j.prosdent.2022.01.026] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/16/2022] [Accepted: 01/19/2022] [Indexed: 11/23/2022]
Abstract
STATEMENT OF PROBLEM Artificial intelligence (AI) models have been developed for periodontal applications, including diagnosing gingivitis and periodontal disease, but their accuracy and maturity of the technology remain unclear. PURPOSE The purpose of this systematic review was to evaluate the performance of the AI models for detecting dental plaque and diagnosing gingivitis and periodontal disease. MATERIAL AND METHODS A review was performed in 4 databases: MEDLINE/PubMed, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies were classified into 4 groups: detecting dental plaque, diagnosis of gingivitis, diagnosis of periodontal disease from intraoral images, and diagnosis of alveolar bone loss from periapical, bitewing, and panoramic radiographs. Two investigators evaluated the studies independently by applying the Joanna Briggs Institute critical appraisal. A third examiner was consulted to resolve any lack of consensus. RESULTS Twenty-four articles were included: 2 studies developed AI models for detecting plaque, resulting in accuracy ranging from 73.6% to 99%; 7 studies assessed the ability to diagnose gingivitis from intraoral photographs reporting an accuracy between 74% and 78.20%; 1 study used fluorescent intraoral images to diagnose gingivitis reporting 67.7% to 73.72% accuracy; 3 studies assessed the ability to diagnose periodontal disease from intraoral photographs with an accuracy between 47% and 81%, and 11 studies evaluated the performance of AI models for detecting alveolar bone loss from radiographic images reporting an accuracy between 73.4% and 99%. CONCLUSIONS AI models for periodontology applications are still in development but might provide a powerful diagnostic tool.
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Affiliation(s)
- Marta Revilla-León
- Affiliate Assistant Professor Graduate Prosthodontics, Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Wash; Director of Research and Digital Dentistry, Kois Center, Seattle, Wash; Adjunct Professor Graduate Prosthodontics, Department of Prosthodontics, School of Dental Medicine, Tufts University, Boston, Mass
| | - Miguel Gómez-Polo
- Associate Professor, Department of Conservative Dentistry and Prosthodontics, School of Dentistry, Complutense University of Madrid, Madrid, Spain.
| | - Abdul B Barmak
- Assistant Professor Clinical Research and Biostatistics, Eastman Institute of Oral Health, University of Rochester Medical Center, Rochester, NY
| | | | - Joseph Y K Kan
- Professor, Advanced Education in Implant Dentistry, Loma Linda University School of Dentistry, Loma Linda, Calif
| | - John C Kois
- Founder and Director Kois Center, Seattle, Wash; Affiliate Professor, Graduate Prosthodontics, Department of Restorative Dentistry, University of Washington, Seattle, Wash; Private practice, Seattle, Wash
| | - Orhan Akal
- Machine Learning Scientist, Boston, Mass
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Overview of Transcriptomic Research on Type 2 Diabetes: Challenges and Perspectives. Genes (Basel) 2022; 13:genes13071176. [PMID: 35885959 PMCID: PMC9319211 DOI: 10.3390/genes13071176] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/24/2022] [Accepted: 06/28/2022] [Indexed: 02/04/2023] Open
Abstract
Type 2 diabetes (T2D) is a common chronic disease whose etiology is known to have a strong genetic component. Standard genetic approaches, although allowing for the detection of a number of gene variants associated with the disease as well as differentially expressed genes, cannot fully explain the hereditary factor in T2D. The explosive growth in the genomic sequencing technologies over the last decades provided an exceptional impetus for transcriptomic studies and new approaches to gene expression measurement, such as RNA-sequencing (RNA-seq) and single-cell technologies. The transcriptomic analysis has the potential to find new biomarkers to identify risk groups for developing T2D and its microvascular and macrovascular complications, which will significantly affect the strategies for early diagnosis, treatment, and preventing the development of complications. In this article, we focused on transcriptomic studies conducted using expression arrays, RNA-seq, and single-cell sequencing to highlight recent findings related to T2D and challenges associated with transcriptome experiments.
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Different Data Mining Approaches Based Medical Text Data. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:1285167. [PMID: 34912530 PMCID: PMC8668297 DOI: 10.1155/2021/1285167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/18/2021] [Indexed: 12/15/2022]
Abstract
The amount of medical text data is increasing dramatically. Medical text data record the progress of medicine and imply a large amount of medical knowledge. As a natural language, they are characterized by semistructured, high-dimensional, high data volume semantics and cannot participate in arithmetic operations. Therefore, how to extract useful knowledge or information from the total available data is very important task. Using various techniques of data mining can extract valuable knowledge or information from data. In the current study, we reviewed different approaches to apply for medical text data mining. The advantages and shortcomings for each technique compared to different processes of medical text data were analyzed. We also explored the applications of algorithms for providing insights to the users and enabling them to use the resources for the specific challenges in medical text data. Further, the main challenges in medical text data mining were discussed. Findings of this paper are benefit for helping the researchers to choose the reasonable techniques for mining medical text data and presenting the main challenges to them in medical text data mining.
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