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Sreenivasan S, Fang C, Flood EM, Markuzon N, Sze JYY. Insights Into the Patient Experience of Hormone Therapy for Early Breast Cancer Treatment Using Patient Forum Discussions and Natural Language Processing. JCO Clin Cancer Inform 2024; 8:e2400038. [PMID: 39102642 DOI: 10.1200/cci.24.00038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/01/2024] [Accepted: 06/12/2024] [Indexed: 08/07/2024] Open
Abstract
PURPOSE Understanding the real-world experience of patients with early breast cancer (eBC) is imperative for optimizing outcomes and evolving patient care. However, there is a lack of patient-level data, hindering clinical development. This social listening study was performed to understand patient insights into symptoms and impacts of hormone therapy (HT) for eBC using posts from patient forums on breastcancer.org to inform future clinical research. METHODS Natural language processing (NLP) and machine learning techniques were used to identify themes related to eBC from a sample of 500,000 posts. After relevant data selection, 362,074 eBC posts were retained for further analysis of symptoms and impacts related to HT, as well as insights into symptom severity, pain locations, and symptom management using exercise and yoga. RESULTS Overall, 32 symptoms and nine impacts had significant associations with ≥one HT. Hot flush (relative risk [RR], 6.70 [95% CI, 3.36 to 13.36]), arthralgia (RR, 6.67 [95% CI, 3.53 to 12.59]), weight increased (RR, 4.83 [95% CI, 3.20 to 7.28]), mood swings (RR, 7.36 [95% CI, 5.75 to 9.42]), insomnia (RR, 4.76 [95% CI, 3.14 to 7.22]), and depression (RR, 3.05 [95% CI, 1.71 to 5.44]) demonstrated the strongest associations. Severe headache, dizziness, back pain, and muscle spasms showed significant associations with ≥one HT despite their low overall prevalence in eBC posts. CONCLUSION The social listening approach allowed the identification of real-world insights from posts specific to eBC HT from a large-scale online breast cancer forum that captured experiences from a uniquely diverse group of patients. Using NLP has a potential to scale analysis of patient feedback and reveal actionable insights into patient experiences of treatment that can inform the development of future therapies and improve the care of patients with eBC.
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Griffith LE, Brini A, Muniz-Terrera G, St John PD, Stirland LE, Mayhew A, Oyarzún D, van den Heuvel E. A call for caution when using network methods to study multimorbidity: an illustration using data from the Canadian Longitudinal Study on Aging. J Clin Epidemiol 2024; 172:111435. [PMID: 38901709 DOI: 10.1016/j.jclinepi.2024.111435] [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: 11/30/2023] [Revised: 06/11/2024] [Accepted: 06/14/2024] [Indexed: 06/22/2024]
Abstract
OBJECTIVES To examine the impact of two key choices when conducting a network analysis (clustering methods and measure of association) on the number and type of multimorbidity clusters. STUDY DESIGN AND SETTING Using cross-sectional self-reported data on 24 diseases from 30,097 community-living adults aged 45-85 from the Canadian Longitudinal Study on Aging, we conducted network analyses using 5 clustering methods and 11 association measures commonly used in multimorbidity studies. We compared the similarity among clusters using the adjusted Rand index (ARI); an ARI of 0 is equivalent to the diseases being randomly assigned to clusters, and 1 indicates perfect agreement. We compared the network analysis results to disease clusters independently identified by two clinicians. RESULTS Results differed greatly across combinations of association measures and cluster algorithms. The number of clusters identified ranged from 1 to 24, with a low similarity of conditions within clusters. Compared to clinician-derived clusters, ARIs ranged from -0.02 to 0.24, indicating little similarity. CONCLUSION These analyses demonstrate the need for a systematic evaluation of the performance of network analysis methods on binary clustered data like diseases. Moreover, in individual older adults, diseases may not cluster predictably, highlighting the need for a personalized approach to their care.
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Affiliation(s)
- Lauren E Griffith
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada.
| | - Alberto Brini
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Philip D St John
- Section of Geriatric Medicine, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Lucy E Stirland
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, UK; Global Brain Health Institute, University of California, San Francisco, CA, USA
| | - Alexandra Mayhew
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada
| | - Diego Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh, UK; School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Edwin van den Heuvel
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
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Chang TH, Chen YD, Lu HHS, Wu JL, Mak K, Yu CS. Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning. Medicine (Baltimore) 2024; 103:e37112. [PMID: 38363886 PMCID: PMC10869094 DOI: 10.1097/md.0000000000037112] [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: 10/17/2023] [Accepted: 01/09/2024] [Indexed: 02/18/2024] Open
Abstract
Chronic kidney disease (CKD) is a major public health concern. But there are limited machine learning studies on non-cancer patients with advanced CKD, and the results of machine learning studies on cancer patients with CKD may not apply directly on non-cancer patients. We aimed to conduct a comprehensive investigation of risk factors for a 3-year risk of death among non-cancer advanced CKD patients with an estimated glomerular filtration rate < 60.0 mL/min/1.73m2 by several machine learning algorithms. In this retrospective cohort study, we collected data from in-hospital and emergency care patients from 2 hospitals in Taiwan from 2009 to 2019, including their international classification of disease at admission and laboratory data from the hospital's electronic medical records (EMRs). Several machine learning algorithms were used to analyze the potential impact and degree of influence of each factor on mortality and survival. Data from 2 hospitals in northern Taiwan were collected with 6565 enrolled patients. After data cleaning, 26 risk factors and approximately 3887 advanced CKD patients from Shuang Ho Hospital were used as the training set. The validation set contained 2299 patients from Taipei Medical University Hospital. Predictive variables, such as albumin, PT-INR, and age, were the top 3 significant risk factors with paramount influence on mortality prediction. In the receiver operating characteristic curve, the random forest had the highest values for accuracy above 0.80. MLP, and Adaboost had better performance on sensitivity and F1-score compared to other methods. Additionally, SVM with linear kernel function had the highest specificity of 0.9983, while its sensitivity and F1-score were poor. Logistic regression had the best performance, with an area under the curve of 0.8527. Evaluating Taiwanese advanced CKD patients' EMRs could provide physicians with a good approximation of the patients' 3-year risk of death by machine learning algorithms.
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Affiliation(s)
- Tzu-Hao Chang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yu-Da Chen
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan
- School of Health Care Administration, College of Management, Taipei Medical University, Taipei, Taiwan
- Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Henry Horng-Shing Lu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Institute of Data Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Jenny L. Wu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | | | - Cheng-Sheng Yu
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
- Fintech RD Center, Nan Shan Life Insurance Co., Ltd
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Dhafari TB, Pate A, Azadbakht N, Bailey R, Rafferty J, Jalali-Najafabadi F, Martin GP, Hassaine A, Akbari A, Lyons J, Watkins A, Lyons RA, Peek N. A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods. J Clin Epidemiol 2024; 165:111214. [PMID: 37952700 DOI: 10.1016/j.jclinepi.2023.11.004] [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: 05/17/2023] [Revised: 10/14/2023] [Accepted: 11/05/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns. STUDY DESIGN AND SETTING We systematically searched PubMed and Web of Science for studies published between July 2017 and July 2023 that used analytical methods to discover multimorbidity patterns. RESULTS Out of 31,617 studies returned by the searches, 172 were included. Of these, 111 studies (64%) conducted validation, the number of studies with validation increased from 53.13% (17 out of 32 studies) to 71.25% (57 out of 80 studies) in 2017-2019 to 2022-2023, respectively. Five types of validation were identified: assessing the association of multimorbidity patterns with clinical outcomes (n = 79), stability across subsamples (n = 26), clinical plausibility (n = 22), stability across methods (n = 7) and exploring common determinants (n = 2). Some studies used multiple types of validation. CONCLUSION The number of studies conducting a validation of multimorbidity patterns is clearly increasing. The most popular validation approach is assessing the association of multimorbidity patterns with clinical outcomes. Methodological guidance on the validation of multimorbidity patterns is needed.
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Affiliation(s)
- Thamer Ba Dhafari
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Alexander Pate
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Narges Azadbakht
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Rowena Bailey
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - James Rafferty
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Farideh Jalali-Najafabadi
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, M13 9PL Manchester, UK
| | - Glen P Martin
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Abdelaali Hassaine
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Ashley Akbari
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Jane Lyons
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Alan Watkins
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Ronan A Lyons
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Niels Peek
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK; NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
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Esnault C, Rollot M, Guilmin P, Zucker JD. Qluster: An easy-to-implement generic workflow for robust clustering of health data. Front Artif Intell 2023; 5:1055294. [PMID: 36814808 PMCID: PMC9939832 DOI: 10.3389/frai.2022.1055294] [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: 09/27/2022] [Accepted: 12/22/2022] [Indexed: 02/08/2023] Open
Abstract
The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors.
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Affiliation(s)
- Cyril Esnault
- Quinten, Paris, France,*Correspondence: Cyril Esnault ✉
| | | | | | - Jean-Daniel Zucker
- Sorbonne University, IRD, UMMISCO, Bondy, France,Sorbonne University, INSERM, NUTRIOMICS, Paris, France
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Wikström K, Linna M, Reissell E, Laatikainen T. Multimorbidity transitions and the associated healthcare cost among the Finnish adult population during a two-year follow-up. JOURNAL OF MULTIMORBIDITY AND COMORBIDITY 2023; 13:26335565231202325. [PMID: 37711666 PMCID: PMC10498690 DOI: 10.1177/26335565231202325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
Background Ageing of the population increases the prevalence and coexistence of many chronic diseases; a condition called multimorbidity. In Finland, information on the significance of multimorbidity and its relation to the sustainability of healthcare is scarce. Aim To assess the prevalence of multimorbidity, the transitions between patient groups with and without multiple diseases and the associated healthcare cost in Finland in 2017-2019. Methods A register-based cohort study covering all adults (n = 3,326,467) who used Finnish primary or specialised healthcare services in 2017. At baseline, patients were classified as 'non-multimorbid', 'multimorbid' or 'multimorbid at risk' based on the recordings of a diagnosis of interest. The costs were calculated using the care-related patient grouping and national standard rates. Transition plots were drawn to observe the transition of patients and costs between groups during the two-year follow-up. Results At baseline, 62% of patients were non-multimorbid, 23% multimorbid and 15% multimorbid at risk. In two years, the proportion of multimorbid patients increased, especially those at risk. Within the multimorbid at-risk group, total healthcare costs were greatest (€5,027 million), accounting for 62% of the total healthcare cost of the overall patient cohort in 2019. Musculoskeletal diseases, cardiometabolic diseases and tumours were the most common and expensive chronic diseases contributing to the onset of multimorbidity. Conclusion Multimorbidity is causing a heavy burden on Finnish healthcare. The estimates of its effect on healthcare usage and costs should be used to guide healthcare planning.
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Affiliation(s)
- Katja Wikström
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Miika Linna
- Department of Health and Social Management, University of Eastern Finland, Kuopio, Finland
- Institute of Healthcare Engineering, Management and Architecture, Aalto University, Helsinki, Finland
| | - Eeva Reissell
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Tiina Laatikainen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Joint Municipal Authority for North Karelia Social and Health Services, Joensuu, Finland
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