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Jaipuria J, Kaur I, Doja MN, Ahmad T, Singh A, Rawal SK, Talwar V, Sharma G. Comparative analysis of real-world data of frequent treatment sequences in metastatic prostate cancer. Curr Urol 2024; 18:104-109. [PMID: 39176299 PMCID: PMC11338004 DOI: 10.1097/cu9.0000000000000217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 05/08/2023] [Indexed: 08/24/2024] Open
Abstract
Background The incidence of prostate cancer is increasing worldwide. A significant proportion of patients develop metastatic disease and are initially prescribed androgen deprivation therapy (ADT). However, subsequent sequences of treatments in real-world settings that may improve overall survival remain an area of active investigation. Materials and methods Data were collected from 384 patients presenting with de novo metastatic prostate cancer from 2011 to 2015 at a tertiary cancer center. Patients were categorized into surviving (n = 232) and deceased (n = 152) groups at the end of 3 years. Modified sequence pattern mining techniques (Generalized Sequential Pattern Mining and Sequential Pattern Discovery using Equivalence Classes) were applied to determine the exact order of the most frequent sets of treatments in each group. Results Degarelix, as the initial form of ADT, was uniquely in the surviving group. The sequence of ADT followed by abiraterone and docetaxel was uniquely associated with a higher 3-year overall survival. Orchiectomy followed by fosfestrol was found to have a unique niche among surviving patients with a long duration of response to the initial ADT. Patients who received chemotherapy followed by radiotherapy and those who received radiotherapy followed by chemotherapy were found more frequently in the deceased group. Conclusions We identified unique treatment sequences among surviving and deceased patients at the end of 3 years. Degarelix should be the preferred form of ADT. Patients who received ADT followed by abiraterone and chemotherapy showed better results. Patients requiring palliative radiation and chemotherapy in any sequence were significantly more frequent in the deceased group, identifying the need to offer such patients the most efficacious agents and to target them in clinical trial design.
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Affiliation(s)
- Jiten Jaipuria
- Uro-oncology division, Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
- Amity Centre for Cancer Epidemiology and Cancer Research, Amity Institute of Biotechnology, Amity University, Noida, India
| | - Ishleen Kaur
- School of Engineering and Technology, Vivekananda Institute of Professional Studies–Technical Campus, New Delhi, India
- Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India
| | | | - Tanvir Ahmad
- Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India
| | - Amitabh Singh
- Uro-oncology division, Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| | - Sudhir Kumar Rawal
- Uro-oncology division, Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| | - Vineet Talwar
- Department of Medical Oncology, Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| | - Girish Sharma
- Amity Centre for Cancer Epidemiology and Cancer Research, Amity Institute of Biotechnology, Amity University, Noida, India
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Xu L, Guo C, Liu M. A weighted distance-based dynamic ensemble regression framework for gastric cancer survival time prediction. Artif Intell Med 2024; 147:102740. [PMID: 38184344 DOI: 10.1016/j.artmed.2023.102740] [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: 12/26/2022] [Revised: 10/28/2023] [Accepted: 11/28/2023] [Indexed: 01/08/2024]
Abstract
Accurate prediction of gastric cancer patient survival time is essential for clinical decision-making. However, unified static models lack specificity and flexibility in predictions owing to the varying survival outcomes among gastric cancer patients. We address these problems by using an ensemble learning approach and adaptively assigning greater weights to similar patients to make more targeted predictions when predicting an individual's survival time. We treat these problems as regression problems and introduce a weighted dynamic ensemble regression framework. To better identify similar patients, we devise a method to measure patient similarity, considering the diverse impacts of features. Subsequently, we use this measure to design both a weighted K-means clustering method and a fuzzy K-means sampling technique to group patients and train corresponding base regressors. To achieve more targeted predictions, we calculate the weight of each base regressor based on the similarity between the patient to be predicted and the patient clusters, culminating in the integration of the results. The model is validated on a dataset of 7791 patients, outperforming other models in terms of three evaluation metrics, namely, the root mean square error, mean absolute error, and the coefficient of determination. The weighted dynamic ensemble regression strategy can improve the baseline model by 1.75%, 2.12%, and 13.45% in terms of the three respective metrics while also mitigating the imbalanced survival time distribution issue. This enhanced performance has been statistically validated, even when tested on six public datasets with different sizes. By considering feature variations, patients with distinct survival profiles can be effectively differentiated, and the model predictive performance can be enhanced. The results generated by our proposed model can be invaluable in guiding decisions related to treatment plans and resource allocation. Furthermore, the model has the potential for broader applications in prognosis for other types of cancers or similar regression problems in various domains.
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Affiliation(s)
- Liangchen Xu
- Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, China.
| | - Chonghui Guo
- Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, China.
| | - Mucan Liu
- Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, China.
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Kaur I, Ahmad T. A cluster-based ensemble approach for congenital heart disease prediction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107922. [PMID: 37984098 DOI: 10.1016/j.cmpb.2023.107922] [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: 04/15/2023] [Revised: 10/24/2023] [Accepted: 11/06/2023] [Indexed: 11/22/2023]
Abstract
BACKGROUND One of the most prevalent birth disorders is congenital heart diseases (CHD). Although CHD risk factors have been the subject of numerous studies, their propensity to cause CHD has not been tested. Particularly few research has attempted to forecast CHD risk using population-based cross-sectional data, which is inherently imbalanced. OBJECTIVE The main goals of this study are to create a reliable data analysis model that can help with (i) a better understanding of congenital heart disease prediction in the presence of missing and unbalanced data and (ii) creating cohorts of expectant mothers with similar lifestyle characteristics. METHODS Clusters of patient cohorts are produced using the unsupervised data mining technique density-based spatial clustering of applications with noise (DBSCAN). For more accurate CHD prediction, a random forest model was trained using these clusters and their corresponding patterns. This study uses a dataset of 33,831 expectant mothers to make its prediction. Missing data were handled using the k-NN imputation approach, while extremely unbalanced data were balanced using SMOTE. These techniques are all data-driven and need little to no user or expert involvement. RESULTS AND CONCLUSION Using DBSCAN, three cohorts were found. The cluster information enhanced the random forest-based CHD prediction and revealed intricate factors that influence prediction accuracy. The proposed approach gave the highest results with 99 % accuracy and 0.91 AUC and performed better than the state-of-the-art methodologies. Hence, the suggested method using unsupervised learning can provide intricate information to the classifier and further enhance the performance of the classification.
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Affiliation(s)
- Ishleen Kaur
- Sri Guru Tegh Bahadur Khalsa College, University of Delhi, Delhi, India.
| | - Tanvir Ahmad
- Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India
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Caruana A, Bandara M, Musial K, Catchpoole D, Kennedy PJ. Machine learning for administrative health records: A systematic review of techniques and applications. Artif Intell Med 2023; 144:102642. [PMID: 37783537 DOI: 10.1016/j.artmed.2023.102642] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 08/21/2023] [Accepted: 08/25/2023] [Indexed: 10/04/2023]
Abstract
Machine learning provides many powerful and effective techniques for analysing heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a subset of EHR collected for administrative purposes, and the use of machine learning on AHRs is a growing subfield of EHR analytics. Existing reviews of EHR analytics emphasise that the data-modality of the EHR limits the breadth of suitable machine learning techniques, and pursuable healthcare applications. Despite emphasising the importance of data modality, the literature fails to analyse which techniques and applications are relevant to AHRs. AHRs contain uniquely well-structured, categorically encoded records which are distinct from other data-modalities captured by EHRs, and they can provide valuable information pertaining to how patients interact with the healthcare system. This paper systematically reviews AHR-based research, analysing 70 relevant studies and spanning multiple databases. We identify and analyse which machine learning techniques are applied to AHRs and which health informatics applications are pursued in AHR-based research. We also analyse how these techniques are applied in pursuit of each application, and identify the limitations of these approaches. We find that while AHR-based studies are disconnected from each other, the use of AHRs in health informatics research is substantial and accelerating. Our synthesis of these studies highlights the utility of AHRs for pursuing increasingly complex and diverse research objectives despite a number of pervading data- and technique-based limitations. Finally, through our findings, we propose a set of future research directions that can enhance the utility of AHR data and machine learning techniques for health informatics research.
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Affiliation(s)
- Adrian Caruana
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia.
| | - Madhushi Bandara
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia
| | - Katarzyna Musial
- Complex Adaptive Systems Lab, Data Science Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia
| | - Daniel Catchpoole
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia; Biospecimen Research Services, The Children's Cancer Research Unit, The Children's Hospital at Westmead, Australia
| | - Paul J Kennedy
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia; Joint Research Centre in AI for Health and Wellness, University of Technology Sydney, Australia, and Ontario Tech University, Canada
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Huang Y, Zhang R, Li H, Xia Y, Yu X, Liu S, Yang Y. A multi-label learning prediction model for heart failure in patients with atrial fibrillation based on expert knowledge of disease duration. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04487-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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Adeoye J, Akinshipo A, Koohi-Moghadam M, Thomson P, Su YX. Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review. Front Oncol 2022; 12:976168. [DOI: 10.3389/fonc.2022.976168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
BackgroundThe impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.MethodsPubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study.ResultsML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy.ConclusionOverall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designsSystematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.
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Kaur I, Doja M, Ahmad T. Data Mining and Machine Learning in Cancer Survival Research: An Overview and Future Recommendations. J Biomed Inform 2022; 128:104026. [DOI: 10.1016/j.jbi.2022.104026] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 02/07/2022] [Accepted: 02/09/2022] [Indexed: 12/29/2022]
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An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2021:6342226. [PMID: 34992648 PMCID: PMC8727098 DOI: 10.1155/2021/6342226] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 11/27/2021] [Indexed: 12/31/2022]
Abstract
Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients' survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.
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Li X, Gu L, Chen Y, Chong Y, Wang X, Guo P, He D. Systemic immune-inflammation index is a promising non-invasive biomarker for predicting the survival of urinary system cancers: a systematic review and meta-analysis. Ann Med 2021; 53:1827-1838. [PMID: 34647517 PMCID: PMC8519535 DOI: 10.1080/07853890.2021.1991591] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 10/05/2021] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE Systemic immune-inflammation index (SII) has been reported in numerous studies to effectively predict the survival outcomes of urinary system cancers; however no agreement has been reached. This meta-analysis aimed to explore the prognostic significance of pre-treatment SII in tumours of the urinary system. METHODS Relevant published articles were selected from Web of Science, PubMed, Embase, and the Cochrane Library up to 30 August 2020. The hazard ratios (HRs) with 95% confidence intervals (CIs) were computed to estimate the associations of pre-treatment SII with overall survival (OS), progression-free survival (PFS), cancer-specific survival (CSS) in urinary system cancers. RESULTS 13 papers were included in our meta-analysis. From the combined data, we found that a high pre-treatment SII indicated a markedly worse OS (HR = 1.98; 95% CI: 1.75-2.23; p < .001), PFS (HR: 2.08; 95% CI: 1.32-3.26; p = .002), and CSS (HR: 2.41, 95% CI: 1.73-3.35, p < .001). Additionally, patients with an elevated SII value might have undesirable pathological characteristics, including a large tumour size, a poor differentiation grade, and an advanced tumour stage (all p < .001). CONCLUSIONS Pre-treatment SII could be used as a non-invasive and promising biomarker to indicate the prognosis of urinary system cancer patients.KEY MESSAGES:This meta-analysis evaluates the predictive value of systemic immune-inflammation index (SII) for patients with urinary system cancer.A high pre-treatment SII indicates a poor prognosis.SII can serve as a promising non-invasive biomarker to help clinicians assess the prognosis and develop treatment strategies for urinary system cancer patients.
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Affiliation(s)
- Xing Li
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Lijiang Gu
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yuhang Chen
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yue Chong
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Xinyang Wang
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Key Laboratory for Tumour Precision Medicine of Shaanxi Province, Xi’an, China
- Oncology Research Lab, Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi’an, China
| | - Peng Guo
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Key Laboratory for Tumour Precision Medicine of Shaanxi Province, Xi’an, China
- Oncology Research Lab, Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi’an, China
| | - Dalin He
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Key Laboratory for Tumour Precision Medicine of Shaanxi Province, Xi’an, China
- Oncology Research Lab, Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi’an, China
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