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Laferrière-Langlois P, Imrie F, Geraldo MA, Wingert T, Lahrichi N, van der Schaar M, Cannesson M. Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach. Anesth Analg 2024; 139:174-185. [PMID: 38051671 PMCID: PMC11150330 DOI: 10.1213/ane.0000000000006753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
BACKGROUND Classification of perioperative risk is important for patient care, resource allocation, and guiding shared decision-making. Using discriminative features from the electronic health record (EHR), machine-learning algorithms can create digital phenotypes among heterogenous populations, representing distinct patient subpopulations grouped by shared characteristics, from which we can personalize care, anticipate clinical care trajectories, and explore therapies. We hypothesized that digital phenotypes in preoperative settings are associated with postoperative adverse events including in-hospital and 30-day mortality, 30-day surgical redo, intensive care unit (ICU) admission, and hospital length of stay (LOS). METHODS We identified all laminectomies, colectomies, and thoracic surgeries performed over a 9-year period from a large hospital system. Seventy-seven readily extractable preoperative features were first selected from clinical consensus, including demographics, medical history, and lab results. Three surgery-specific datasets were built and split into derivation and validation cohorts using chronological occurrence. Consensus k -means clustering was performed independently on each derivation cohort, from which phenotypes' characteristics were explored. Cluster assignments were used to train a random forest model to assign patient phenotypes in validation cohorts. We reconducted descriptive analyses on validation cohorts to confirm the similarity of patient characteristics with derivation cohorts, and quantified the association of each phenotype with postoperative adverse events by using the area under receiver operating characteristic curve (AUROC). We compared our approach to American Society of Anesthesiologists (ASA) alone and investigated a combination of our phenotypes with the ASA score. RESULTS A total of 7251 patients met inclusion criteria, of which 2770 were held out in a validation dataset based on chronological occurrence. Using segmentation metrics and clinical consensus, 3 distinct phenotypes were created for each surgery. The main features used for segmentation included urgency of the procedure, preoperative LOS, age, and comorbidities. The most relevant characteristics varied for each of the 3 surgeries. Low-risk phenotype alpha was the most common (2039 of 2770, 74%), while high-risk phenotype gamma was the rarest (302 of 2770, 11%). Adverse outcomes progressively increased from phenotypes alpha to gamma, including 30-day mortality (0.3%, 2.1%, and 6.0%, respectively), in-hospital mortality (0.2%, 2.3%, and 7.3%), and prolonged hospital LOS (3.4%, 22.1%, and 25.8%). When combined with the ASA score, digital phenotypes achieved higher AUROC than the ASA score alone (hospital mortality: 0.91 vs 0.84; prolonged hospitalization: 0.80 vs 0.71). CONCLUSIONS For 3 frequently performed surgeries, we identified 3 digital phenotypes. The typical profiles of each phenotype were described and could be used to anticipate adverse postoperative events.
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Affiliation(s)
- Pascal Laferrière-Langlois
- Department of Anesthesiology and Perioperative Medicine, UCLA David Geffen School of Medicine, Los Angeles, USA
- Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
- Maisonneuve-Rosemont Hospital Research Center, Montréal, Québec, Canada
- Department of Anesthesiology and Pain Medicine, Maisonneuve-Rosemont Hospital, CIUSSS de l’Est de L’Ile de Montréal, Montréal, Québec, Canada
| | - Fergus Imrie
- Department of Electrical and Computer Engineering, UCLA, Los Angeles, USA
| | - Marc-Andre Geraldo
- Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
- Maisonneuve-Rosemont Hospital Research Center, Montréal, Québec, Canada
| | - Theodora Wingert
- Department of Anesthesiology and Perioperative Medicine, UCLA David Geffen School of Medicine, Los Angeles, USA
| | - Nadia Lahrichi
- Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
- The Alan Turing Institute, London, UK
| | - Maxime Cannesson
- Department of Anesthesiology and Perioperative Medicine, UCLA David Geffen School of Medicine, Los Angeles, USA
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Yajima S, Nakanishi Y, Ogasawara RA, Imasato N, Hirose K, Katsumura S, Kataoka M, Masuda H. Comparing Preoperative Screening Tools for Elective Urologic Cancer Surgery: Insights from a Cluster Analysis. Gerontology 2024:1-14. [PMID: 38583416 DOI: 10.1159/000538733] [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: 08/28/2023] [Accepted: 03/31/2024] [Indexed: 04/09/2024] Open
Abstract
INTRODUCTION The aim of this study was to evaluate the features and benefits of different geriatric screening tools for enhancing the perioperative care of patients who undergo elective cancer surgery using cluster analysis. METHODS This study was a retrospective, observational analysis of 1,019 consecutive patients who had elective major cancer surgery in the urology department of our hospital from October 2019 to January 2023. Before the surgery, a trained nurse screened the patients using six tools: Eastern Clinical Oncology Group performance status (ECOG-PS), flemish version of the triage risk screening tool (fTRST), geriatric-8 (G8), instrumental activities of daily living, patient health questionnaire-2 (PHQ-2), and simple questionnaire to rapidly diagnose sarcopenia (SARC-F). The study grouped the patients into four clusters based on their scores on these tools and compared their outcomes after the surgery. The outcomes included overall survival, ambulation failure, delirium, and severe complications. The study also examined how each screening tool was associated with the outcomes. RESULTS Based on their clinical data and screening results, we classified the patients into four groups: Healthy (73%), Depressive (11%), Intermediate (11%), and Unhealthy (5%). The Unhealthy group had the worst outcomes in overall survival (OS), ambulation failure, and delirium, followed by the Intermediate group. In addition, fTRST and SARC-F emerged as significant predictors of OS; ECOG-PS, fTRST, G8, and SARC-F of ambulation failure; ECOG-PS, fTRST, and G8 of delirium; and G8 of severe complications. CONCLUSION Various geriatric screening tools were found to have the potential to forecast diverse postoperative outcomes.
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Affiliation(s)
- Shugo Yajima
- Department of Urology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Yasukazu Nakanishi
- Department of Urology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Ryo Andy Ogasawara
- Department of Urology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Naoki Imasato
- Department of Urology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Kohei Hirose
- Department of Urology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Sao Katsumura
- Department of Urology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Madoka Kataoka
- Department of Urology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Hitoshi Masuda
- Department of Urology, National Cancer Center Hospital East, Kashiwa, Japan
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He L, Li ST, Qin MX, Yan Y, La YY, Cao X, Cai YT, Wang YX, Liu J, Wu DH, Feng Q. Unsupervised clustering analysis of comprehensive health status and its influencing factors on women of childbearing age: a cross-sectional study from a province in central China. BMC Public Health 2023; 23:2206. [PMID: 37946124 PMCID: PMC10634171 DOI: 10.1186/s12889-023-17096-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/29/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Most previous studies on women of childbearing age have focused on reproductive health and fertility intentions, and evidence regarding the comprehensive health status of women of childbearing age is limited. This study aimed to comprehensively examine the health status of women of childbearing age through a multi-method and multi-indicator evaluation, analyze the factors that influence their overall health, and provide sound recommendations for the improvement and promotion of healthy behaviors. METHODS Data on women of childbearing age living in Shanxi Province were collected between September 2021 and January 2022 through online and offline surveys. The k-means algorithm was used to assess health-related patterns in women, and multivariate nonconditional logistic regression was used to assess the influencing factors of women's overall health. RESULTS In total, 1,258 of 2,925 (43%) participants were classified as having a good health status in all five domains of the three health dimensions: quality of life, mental health, and illness. Multivariate logistic regression showed that education level, gynecological examination status, health status of family members, access to medical treatment, age, cooking preferences, diet, social support, hand washing habits, attitude toward breast cancer prevention, and awareness of reproductive health were significantly associated with different health patterns. CONCLUSIONS The comprehensive health status of women of childbearing age in Shanxi Province is generally good; however, a large proportion of women with deficiencies in some dimensions remains. Since lifestyle greatly impacts women's health, health education on lifestyle and health-related issues should be strengthened.
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Affiliation(s)
- Lu He
- Department of Social Medicine, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China.
- Ministry of Education, Key Laboratory of Coal Environmental Pathopoiesis and Control at Shanxi Medial University, Taiyuan, Shanxi, 030001, People's Republic of China.
| | - Si-Tian Li
- Department of Social Medicine, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China
| | - Meng-Xia Qin
- Department of Social Medicine, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China
| | - Yan Yan
- Department of Contingency Management, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, 030001, People's Republic of China
| | - Yuan-Yuan La
- School of Social Development and Public Policy, Beijing Normal University, Beijing, 100000, People's Republic of China
| | - Xi Cao
- Department of Social Medicine, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China
| | - Yu-Tong Cai
- Department of Social Medicine, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China
| | - Yu-Xiao Wang
- Department of Health Economics, School of Management, Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China
| | - Jie Liu
- Department of Social Medicine, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China
| | - Da-Hong Wu
- Department of Social Medicine, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China
| | - Qilong Feng
- Department of Physiology, Key Laboratory of Cellular Physiology, Ministry of Education, Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China.
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Chang X, Hallais S, Danas K, Roux S. PeakForce AFM Analysis Enhanced with Model Reduction Techniques. SENSORS (BASEL, SWITZERLAND) 2023; 23:4730. [PMID: 37430644 DOI: 10.3390/s23104730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 04/28/2023] [Accepted: 05/11/2023] [Indexed: 07/12/2023]
Abstract
PeakForce quantitative nanomechanical AFM mode (PF-QNM) is a popular AFM technique designed to measure multiple mechanical features (e.g., adhesion, apparent modulus, etc.) simultaneously at the exact same spatial coordinates with a robust scanning frequency. This paper proposes compressing the initial high-dimensional dataset obtained from the PeakForce AFM mode into a subset of much lower dimensionality by a sequence of proper orthogonal decomposition (POD) reduction and subsequent machine learning on the low-dimensionality data. A substantial reduction in user dependency and subjectivity of the extracted results is obtained. The underlying parameters, or "state variables", governing the mechanical response can be easily extracted from the latter using various machine learning techniques. Two samples are investigated to illustrate the proposed procedure (i) a polystyrene film with low-density polyethylene nano-pods and (ii) a PDMS film with carbon-iron particles. The heterogeneity of material, as well as the sharp variation in topography, make the segmentation challenging. Nonetheless, the underlying parameters describing the mechanical response naturally offer a compact representation allowing for a more straightforward interpretation of the high-dimensional force-indentation data in terms of the nature (and proportion) of phases, interfaces, or topography. Finally, those techniques come with a low processing time cost and do not require a prior mechanical model.
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Affiliation(s)
- Xuyang Chang
- Université Paris-Saclay/CentraleSupélec/ENS Paris-Saclay/C.N.R.S., LMPS-Laboratoire de Mécanique Paris-Saclay, 91190 Gif-sur-Yvette, France
- LMS, C.N.R.S., École Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Simon Hallais
- LMS, C.N.R.S., École Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Kostas Danas
- LMS, C.N.R.S., École Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Stéphane Roux
- Université Paris-Saclay/CentraleSupélec/ENS Paris-Saclay/C.N.R.S., LMPS-Laboratoire de Mécanique Paris-Saclay, 91190 Gif-sur-Yvette, France
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Polisetty TS, Jain S, Pang M, Karnuta JM, Vigdorchik JM, Nawabi DH, Wyles CC, Ramkumar PN. Concerns surrounding application of artificial intelligence in hip and knee arthroplasty. Bone Joint J 2022; 104-B:1292-1303. [DOI: 10.1302/0301-620x.104b12.bjj-2022-0922.r1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article: Bone Joint J 2022;104-B(12):1292–1303.
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Affiliation(s)
- Teja S. Polisetty
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Samagra Jain
- Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Michael Pang
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jaret M. Karnuta
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Danyal H. Nawabi
- Sports Medicine Institute, Hospital for Special Surgery, New York, New York, USA
| | - Cody C. Wyles
- Department of Orthopaedic Surgery, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Prem N. Ramkumar
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Sports Medicine Institute, Hospital for Special Surgery, New York, New York, USA
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Alsoof D, McDonald CL, Kuris EO, Daniels AH. Machine Learning for the Orthopaedic Surgeon: Uses and Limitations. J Bone Joint Surg Am 2022; 104:1586-1594. [PMID: 35383655 DOI: 10.2106/jbjs.21.01305] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
➤ Machine learning is a subset of artificial intelligence in which computer algorithms are trained to make classifications and predictions based on patterns in data. The utilization of these techniques is rapidly expanding in the field of orthopaedic research. ➤ There are several domains in which machine learning has application to orthopaedics, including radiographic diagnosis, gait analysis, implant identification, and patient outcome prediction. ➤ Several limitations prevent the widespread use of machine learning in the daily clinical environment. However, future work can overcome these issues and enable machine learning tools to be a useful adjunct for orthopaedic surgeons in their clinical decision-making.
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Affiliation(s)
- Daniel Alsoof
- Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Providence, Rhode Island
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Non-Linear Association between Folate/Vitamin B12 Status and Cognitive Function in Older Adults. Nutrients 2022; 14:nu14122443. [PMID: 35745173 PMCID: PMC9227588 DOI: 10.3390/nu14122443] [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: 05/10/2022] [Revised: 05/31/2022] [Accepted: 06/09/2022] [Indexed: 12/10/2022] Open
Abstract
Although folate and vitamin B12 status have long been implicated in cognitive function, there is no consensus on the threshold of folate and vitamin B12 for assessing their impacts on cognition. The goal of this study was to detail the association between folate and vitamin B12 with cognitive performance. We analyzed cross-sectional data of older adults (≥60 y; n = 2204) from the NHANES (National Health and Nutrition Examination Surveys) cohort from 2011–2014. The restricted cubic spline model was used for describing the associations between serum total folate, RBC folate, 5-methyltetrahydrofolate, and vitamin B12 and the Consortium to Establish a Registry for Alzheimer’s Disease Word Learning (CERAD-WL) and Delayed Recall (CERAD-DR) tests, the Animal Fluency (AF) test, and the Digit Symbol Substitution Test (DSST), respectively. Older adults with a different folate and vitamin B12 status were clustered by artificial intelligence unsupervised learning. The statistically significant non-linear relationships between the markers of folate or vitamin B12 status and cognitive function were found after adjustments for potential confounders. Inverse U-shaped associations between folate/vitamin B12 status and cognitive function were observed, and the estimated breakpoint was described. No statistically significant interaction between vitamin B12 and folate status on cognitive function was observed in the current models. In addition, based on the biochemical examination of these four markers, older adults could be assigned into three clusters representing relatively low, medium, and high folate/vitamin B12 status with significantly different scores on the CERAD-DR and DSST. Low or high folate and vitamin B12 status affected selective domains of cognition, and was associated with suboptimal cognitive test outcomes.
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Polce EM, Kunze KN, Dooley MS, Piuzzi NS, Boettner F, Sculco PK. Efficacy and Applications of Artificial Intelligence and Machine Learning Analyses in Total Joint Arthroplasty: A Call for Improved Reporting. J Bone Joint Surg Am 2022; 104:821-832. [PMID: 35045061 DOI: 10.2106/jbjs.21.00717] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND There has been a considerable increase in total joint arthroplasty (TJA) research using machine learning (ML). Therefore, the purposes of this study were to synthesize the applications and efficacies of ML reported in the TJA literature, and to assess the methodological quality of these studies. METHODS PubMed, OVID/MEDLINE, and Cochrane libraries were queried in January 2021 for articles regarding the use of ML in TJA. Study demographics, topic, primary and secondary outcomes, ML model development and testing, and model presentation and validation were recorded. The TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines were used to assess the methodological quality. RESULTS Fifty-five studies were identified: 31 investigated clinical outcomes and resource utilization; 11, activity and motion surveillance; 10, imaging detection; and 3, natural language processing. For studies reporting the area under the receiver operating characteristic curve (AUC), the median AUC (and range) was 0.80 (0.60 to 0.97) among 26 clinical outcome studies, 0.99 (0.83 to 1.00) among 6 imaging-based studies, and 0.88 (0.76 to 0.98) among 3 activity and motion surveillance studies. Twelve studies compared ML to logistic regression, with 9 (75%) reporting that ML was superior. The average number of TRIPOD guidelines met was 11.5 (range: 5 to 18), with 38 (69%) meeting greater than half of the criteria. Presentation and explanation of the full model for individual predictions and assessments of model calibration were poorly reported (<30%). CONCLUSIONS The performance of ML models was good to excellent when applied to a wide variety of clinically relevant outcomes in TJA. However, reporting of certain key methodological and model presentation criteria was inadequate. Despite the recent surge in TJA literature utilizing ML, the lack of consistent adherence to reporting guidelines needs to be addressed to bridge the gap between model development and clinical implementation.
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Affiliation(s)
- Evan M Polce
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Matthew S Dooley
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Nicolas S Piuzzi
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio
| | - Friedrich Boettner
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Peter K Sculco
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
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Research on the Correlation between Multisource Big Data Virtual Assisted Preschool Education and the Development of Children’s Innovative Ability. Occup Ther Int 2022; 2022:3880201. [PMID: 35572165 PMCID: PMC9068341 DOI: 10.1155/2022/3880201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/02/2022] [Accepted: 04/06/2022] [Indexed: 11/17/2022] Open
Abstract
Objective. Innovation ability is an important part of children’s core literacy and the core goal of science curriculum. As one of the important contents of scientific literacy, innovation ability is the key ability of young children to make informed decisions when facing scientific problems in social and personal life. In order to adapt to the future life, it is very important for children to have the ability to innovate. This research will provide a reference for the cultivation of children’s innovative ability. Method. In the process of database design, certain design principles need to be followed. In this paper, the system and user experience are greatly optimized by reasonably constructing table structure, allocating storage space, and establishing indexes. In this system, the MySQL database is used to store system data, such as user registration information, subscription information, and system-provided services, and the data uploaded by users that needs to be processed is stored in Hive. Although the GFP algorithm can solve the problem of load balancing, when the largest conditional pattern base of a frequent item is projected to other nodes, a large amount of data transmission will occur, resulting in increased communication between nodes. In order to solve this problem, the FP-growth parallel algorithm based on traffic optimization gives priority to assigning each frequent item to the node that needs the least traffic when grouping it. Results/Discussion. Experiments show that the TFP algorithm not only satisfies the load balance of nodes but also ensures a small amount of communication between nodes, which is more efficient than the traditional FP-growth parallel algorithm. The survey results of the influencing factors of children’s innovation ability match the theoretical hypothesis, and different influencing factors have different effects on each dimension of children’s innovation ability. Through the basic fit index of the model, the evaluation of the external quality of the model and the test of the internal quality of the model, it is shown that the survey results of the influencing factors of children’s innovation ability match the theoretical hypothesis. The three influencing factors of family participation and investment, teacher teaching, and peer collaboration and communication have a positive role in promoting children’s innovation ability.
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Liu F, Yang D, Liu Y, Zhang Q, Chen S, Li W, Ren J, Tian X, Wang X. Use of latent profile analysis and k-means clustering to identify student anxiety profiles. BMC Psychiatry 2022; 22:12. [PMID: 34986837 PMCID: PMC8728926 DOI: 10.1186/s12888-021-03648-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 12/07/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Anxiety disorders are often the first presentation of psychopathology in youth and are considered the most common psychiatric disorders in children and adolescents. This study aimed to identify distinct student anxiety profiles to develop targeted interventions. METHODS A cross-sectional study was conducted with 9738 students in Yingshan County. Background characteristics were collected and Mental Health Test (MHT) were completed. Latent profile analysis (LPA) was applied to define student anxiety profiles, and then the analysis was repeated using k-means clustering. RESULTS LPA yielded 3 profiles: the low-risk, mild-risk and high-risk groups, which comprised 29.5, 38.1 and 32.4% of the sample, respectively. Repeating the analysis using k-means clustering resulted in similar groupings. Most students in a particular k-means cluster were primarily in a single LPA-derived student profile. The multinomial ordinal logistic regression results showed that the high-risk group was more likely to be female, junior, and introverted, to live in a town, to have lower or average academic performance, to have heavy or average academic pressure, and to be in schools that have never or occasionally have organized mental health education activities. CONCLUSIONS The findings suggest that students with anxiety symptoms may be categorized into distinct profiles that are amenable to varying strategies for coordinated interventions.
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Affiliation(s)
- Fang Liu
- grid.412449.e0000 0000 9678 1884School of Public Health, China Medical University, No.77 Puhe Road, Shenyang North New District, Shenyang, 110122 Liaoning China
| | - Dan Yang
- Nanchong Physical and Mental Hospital (Nanchong Sixth People’s Hospital), No.99 Jincheng Street, Yingshan County, Nanchong, 637000 Sichuan China
| | - Yueguang Liu
- Nanchong Physical and Mental Hospital (Nanchong Sixth People’s Hospital), No.99 Jincheng Street, Yingshan County, Nanchong, 637000 Sichuan China
| | - Qin Zhang
- grid.412449.e0000 0000 9678 1884School of Public Health, China Medical University, No.77 Puhe Road, Shenyang North New District, Shenyang, 110122 Liaoning China
| | - Shiyu Chen
- Nanchong Physical and Mental Hospital (Nanchong Sixth People’s Hospital), No.99 Jincheng Street, Yingshan County, Nanchong, 637000 Sichuan China
| | - Wanxia Li
- Nanchong Physical and Mental Hospital (Nanchong Sixth People’s Hospital), No.99 Jincheng Street, Yingshan County, Nanchong, 637000 Sichuan China
| | - Jidong Ren
- Nanchong Physical and Mental Hospital (Nanchong Sixth People’s Hospital), No.99 Jincheng Street, Yingshan County, Nanchong, 637000 Sichuan China
| | - Xiaobin Tian
- Nanchong Physical and Mental Hospital (Nanchong Sixth People's Hospital), No.99 Jincheng Street, Yingshan County, Nanchong, 637000, Sichuan, China. .,Department of Preventive Medicine, North Sichuan Medical College, No.234 Fujiang Road, Nanchong, 637000, Sichuan, China.
| | - Xin Wang
- School of Health Management, China Medical University, No.77 Puhe Road, Shenyang North New District, Shenyang, 110122, Liaoning, China.
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Kunze KN, Orr M, Krebs V, Bhandari M, Piuzzi NS. Potential benefits, unintended consequences, and future roles of artificial intelligence in orthopaedic surgery research : a call to emphasize data quality and indications. Bone Jt Open 2022; 3:93-97. [PMID: 35084227 PMCID: PMC9047073 DOI: 10.1302/2633-1462.31.bjo-2021-0123.r1] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
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Affiliation(s)
- Kyle N Kunze
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York, USA
| | - Melissa Orr
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA
| | - Viktor Krebs
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA
| | - Mohit Bhandari
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.,Department of Surgery, Division of Orthopaedic Surgery, McMaster University, Cleveland, Ohio, USA
| | - Nicolas S Piuzzi
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA
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