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1.
Int J Mol Sci ; 25(5)2024 Feb 29.
Artigo em Inglês | MEDLINE | ID: mdl-38474094

RESUMO

The analysis of hematopoietic stem and progenitor cell populations (HSPCs) is fundamental in the understanding of normal hematopoiesis as well as in the management of malignant diseases, such as leukemias, and in their diagnosis and follow-up, particularly the measurement of treatment efficiency with the detection of measurable residual disease (MRD). In this study, I designed a 20-color flow cytometry panel tailored for the comprehensive analysis of HSPCs using a spectral cytometer. My investigation encompassed the examination of forty-six samples derived from both normal human bone marrows (BMs) and patients with acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) along with those subjected to chemotherapy and BM transplantation. By comparing my findings to those obtained through conventional flow cytometric analyses utilizing multiple tubes, I demonstrate that my innovative 20-color approach enables a more in-depth exploration of HSPC subpopulations and the detection of MRD with at least comparable sensitivity. Furthermore, leveraging advanced analytical tools such as t-SNE and FlowSOM learning algorithms, I conduct extensive cross-sample comparisons with two-dimensional gating approaches. My results underscore the efficacy of these two methods as powerful unsupervised alternatives for manual HSPC subpopulation analysis. I expect that in the future, complex multi-dimensional flow cytometric data analyses, such as those employed in this study, will be increasingly used in hematologic diagnostics.


Assuntos
Transplante de Células-Tronco Hematopoéticas , Leucemia Mieloide Aguda , Humanos , Citometria de Fluxo/métodos , Aprendizado de Máquina não Supervisionado , Leucemia Mieloide Aguda/tratamento farmacológico , Células-Tronco Hematopoéticas/patologia , Transplante de Células-Tronco Hematopoéticas/métodos , Neoplasia Residual/diagnóstico
2.
PLoS Comput Biol ; 20(3): e1011926, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-38442095

RESUMO

In many situations it is behaviorally relevant for an animal to respond to co-occurrences of perceptual, possibly polymodal features, while these features alone may have no importance. Thus, it is crucial for animals to learn such feature combinations in spite of the fact that they may occur with variable intensity and occurrence frequency. Here, we present a novel unsupervised learning mechanism that is largely independent of these contingencies and allows neurons in a network to achieve specificity for different feature combinations. This is achieved by a novel correlation-based (Hebbian) learning rule, which allows for linear weight growth and which is combined with a mechanism for gradually reducing the learning rate as soon as the neuron's response becomes feature combination specific. In a set of control experiments, we show that other existing advanced learning rules cannot satisfactorily form ordered multi-feature representations. In addition, we show that networks, which use this type of learning always stabilize and converge to subsets of neurons with different feature-combination specificity. Neurons with this property may, thus, serve as an initial stage for the processing of ecologically relevant real world situations for an animal.


Assuntos
Modelos Neurológicos , Aprendizado de Máquina não Supervisionado , Animais , Neurônios/fisiologia
3.
Front Public Health ; 12: 1329704, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38515596

RESUMO

Introduction: To analyze public perceptions of active aging in China on mainstream social media platforms to determine whether the "14th Five Year Plan for the Development of the Aging Career and Older Adult Care System" issued by the CPC in 2022 has fully addressed public needs. Methods: The original tweets posted on Weibo between January 1, 2020, and June 30, 2022, containing the words "aging" or "old age" were extracted. A bidirectional encoder representation from transformers (BERT)-based model was used to generate themes related to this perception. A qualitative thematic analysis and an independent review of the theme labels were conducted by the researchers. Results: The findings indicate that public perceptions revolved around four themes: (1) health prevention and protection, (2) convenient living environments, (3) cognitive health and social integration, and (4) protecting the rights and interests of the older adult. Discussion: Our study found that although the Plan aligns with most of these themes, it lacks clear planning for financial security and marital life.


Assuntos
COVID-19 , Mídias Sociais , Humanos , Idoso , COVID-19/psicologia , SARS-CoV-2 , Aprendizado de Máquina não Supervisionado , Opinião Pública
4.
An Acad Bras Cienc ; 96(1): e20230409, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38451625

RESUMO

This study utilizes Fourier transform infrared (FTIR) data from honey samples to cluster and categorize them based on their spectral characteristics. The aim is to group similar samples together, revealing patterns and aiding in classification. The process begins by determining the number of clusters using the elbow method, resulting in five distinct clusters. Principal Component Analysis (PCA) is then applied to reduce the dataset's dimensionality by capturing its significant variances. Hierarchical Cluster Analysis (HCA) further refines the sample clusters. 20% of the data, representing identified clusters, is randomly selected for testing, while the remainder serves as training data for a deep learning algorithm employing a multilayer perceptron (MLP). Following training, the test data are evaluated, revealing an impressive 96.15% accuracy. Accuracy measures the machine learning model's ability to predict class labels for new data accurately. This approach offers reliable honey sample clustering without necessitating extensive preprocessing. Moreover, its swiftness and cost-effectiveness enhance its practicality. Ultimately, by leveraging FTIR spectral data, this method successfully identifies similarities among honey samples, enabling efficient categorization and demonstrating promise in the field of spectral analysis in food science.


Assuntos
Mel , Aprendizado de Máquina não Supervisionado , Análise de Fourier , Espectroscopia de Infravermelho com Transformada de Fourier , Análise por Conglomerados
5.
Am J Sports Med ; 52(4): 881-891, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-38343270

RESUMO

BACKGROUND: Most clinical machine learning applications use a supervised learning approach using labeled variables. In contrast, unsupervised learning enables pattern detection without a prespecified outcome. PURPOSE/HYPOTHESIS: The purpose of this study was to apply unsupervised learning to the combined Danish and Norwegian knee ligament register (KLR) with the goal of detecting distinct subgroups. It was hypothesized that resulting groups would have differing rates of subsequent anterior cruciate ligament reconstruction (ACLR) revision. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: K-prototypes clustering was performed on the complete case KLR data. After performing the unsupervised learning analysis, the authors defined clinically relevant characteristics of each cluster using variable summaries, surgeons' domain knowledge, and Shapley Additive exPlanations analysis. RESULTS: Five clusters were identified. Cluster 1 (revision rate, 9.9%) patients were young (mean age, 22 years; SD, 6 years), received hamstring tendon (HT) autograft (91%), and had lower baseline Knee injury and Osteoarthritis Outcome Score (KOOS) Sport and Recreation (Sports) scores (mean, 25.0; SD, 15.6). Cluster 2 (revision rate, 6.9%) patients received HT autograft (89%) and had higher baseline KOOS Sports scores (mean, 67.2; SD, 16.5). Cluster 3 (revision rate, 4.7%) patients received bone-patellar tendon-bone (BPTB) or quadriceps tendon (QT) autograft (94%) and had higher baseline KOOS Sports scores (mean, 65.8; SD, 16.4). Cluster 4 (revision rate, 4.1%) patients received BPTB or QT autograft (88%) and had low baseline KOOS Sports scores (mean, 20.5; SD, 14.0). Cluster 5 (revision rate, 3.1%) patients were older (mean age, 42 years; SD, 7 years), received HT autograft (89%), and had low baseline KOOS Sports scores (mean, 23.4; SD, 17.6). CONCLUSION: Unsupervised learning identified 5 distinct KLR patient subgroups and each grouping was associated with a unique ACLR revision rate. Patients can be approximately classified into 1 of the 5 clusters based on only 3 variables: age, graft choice (HT, BPTB, or QT autograft), and preoperative KOOS Sports subscale score. If externally validated, the resulting groupings may enable quick risk stratification for future patients undergoing ACLR in the clinical setting. Patients in cluster 1 are considered high risk (9.9%), cluster 2 patients medium risk (6.9%), and patients in clusters 3 to 5 low risk (3.1%-4.7%) for revision ACLR.


Assuntos
Lesões do Ligamento Cruzado Anterior , Tendões dos Músculos Isquiotibiais , Ligamento Patelar , Humanos , Adulto Jovem , Adulto , Estudos de Coortes , Aprendizado de Máquina não Supervisionado , Lesões do Ligamento Cruzado Anterior/cirurgia , Autoenxertos , Ligamento Patelar/transplante , Tendões dos Músculos Isquiotibiais/transplante , Transplante Autólogo , Dinamarca
6.
Comput Biol Med ; 170: 108072, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-38301518

RESUMO

The scarcity of annotated data is a common issue in the realm of heartbeat classification based on deep learning. Transfer learning (TL) has emerged as an effective strategy for addressing this issue. However, current TL techniques in this realm overlook the probability distribution differences between the source domain (SD) and target domain (TD) databases. The motivation of this paper is to address the challenge of labeled data scarcity at the model level while exploring an effective method to eliminate domain discrepancy between SD and TD databases, especially when SD and TD are derived from inconsistent tasks. This study proposes a multi-module heartbeat classification algorithm. Initially, unsupervised feature extractors are designed to extract rich features from unlabeled SD and TD data. Subsequently, a novel adaptive transfer method is proposed to effectively eliminate domain discrepancy between features of SD for pre-training (PTF-SD) and features of TD for fine-tuning (FTF-TD). Finally, the adapted PTF-SD is employed to pre-train a designed classifier, and FTF-TD is used for classifier fine-tuning, with the objective of evaluating the algorithm's performance on the TD task. In our experiments, MNIST-DB serves as the SD database for handwritten digit image classification task, MIT-DB as the TD database for heartbeat classification task. The overall accuracy of classifying heartbeats into normal heartbeats, supraventricular ectopic beats (SVEBs), and ventricular ectopic beats (VEBs) reaches 96.7 %. Specifically, the sensitivity (Sen), positive predictive value (PPV), and F1 score for SVEBs are 0.802, 0.701, and 0.748, respectively. For VEBs, Sen, PPV, and F1 score are 0.976, 0.840, and 0.903, respectively. The results indicate that the proposed multi-module algorithm effectively addresses the challenge labeled data scarcity in heartbeat classification through unsupervised learning and adaptive feature transfer methods.


Assuntos
Aprendizado de Máquina não Supervisionado , Complexos Ventriculares Prematuros , Humanos , Frequência Cardíaca , Eletrocardiografia/métodos , Processamento de Sinais Assistido por Computador , Algoritmos
7.
Arch Gynecol Obstet ; 309(3): 1053-1063, 2024 03.
Artigo em Inglês | MEDLINE | ID: mdl-38310145

RESUMO

INTRODUCTION: This study used an unsupervised machine learning algorithm, sidClustering and random forests, to identify clusters of risk behaviors of Bacterial Vaginosis (BV), the most common cause of abnormal vaginal discharge linked to STI and HIV acquisition.  METHODS: Participants were 391 cisgender women in Miami, Florida, with a mean of 30.8 (SD = 7.81) years of age; 41.7% identified as Hispanic; 41.7% as Black and 44.8% as White. Participants completed measures of demographics, risk behaviors [sexual, medical, and reproductive history, substance use, and intravaginal practices (IVP)], and underwent collection of vaginal samples; 135 behavioral variables were analyzed. BV was diagnosed using Nugent criteria. RESULTS: We identified four clusters, and variables were ranked by importance in distinguishing clusters: Cluster 1: nulliparous women who engaged in IVPs to clean themselves and please sexual partners, and used substances frequently [n = 118 (30.2%)]; Cluster 2: primiparous women who engaged in IVPs using vaginal douches to clean themselves (n = 112 (28.6%)]; Cluster 3: primiparous women who did not use IVPs or substances [n = 87 (22.3%)]; and Cluster 4: nulliparous women who did not use IVPs but used substances [n = 74 (18.9%)]. Clusters were related to BV (p < 0.001). Cluster 2, the cluster of women who used vaginal douches as IVPs, had the highest prevalence of BV (52.7%). CONCLUSIONS: Machine learning methods may be particularly useful in identifying specific clusters of high-risk behaviors, in developing interventions intended to reduce BV and IVP, and ultimately in reducing the risk of HIV infection among women.


Assuntos
Infecções por HIV , Vaginose Bacteriana , Feminino , Humanos , Vaginose Bacteriana/diagnóstico , Vaginose Bacteriana/epidemiologia , Vaginose Bacteriana/microbiologia , Infecções por HIV/diagnóstico , Infecções por HIV/epidemiologia , Infecções por HIV/complicações , Aprendizado de Máquina não Supervisionado , Vagina/microbiologia , Comportamento Sexual
8.
Chemosphere ; 351: 141217, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-38246495

RESUMO

Groundwater is an essential resource in the Sundarban regions of India and Bangladesh, but its quality is deteriorating due to anthropogenic impacts. However, the integrated factors affecting groundwater chemistry, source distribution, and health risk are poorly understood along the Indo-Bangla coastal border. The goal of this study is to assess groundwater chemistry, associated driving factors, source contributions, and potential non-carcinogenic health risks (PN-CHR) using unsupervised machine learning models such as a self-organizing map (SOM), positive matrix factorization (PMF), ion ratios, and Monte Carlo simulation. For the Sundarban part of Bangladesh, the SOM clustering approach yielded six clusters, while it yielded five for the Indian Sundarbans. The SOM results showed high correlations among Ca2+, Mg2+, and K+, indicating a common origin. In the Bangladesh Sundarbans, mixed water predominated in all clusters except for cluster 3, whereas in the Indian Sundarbans, Cl--Na+ and mixed water dominated in clusters 1 and 2, and both water types dominated the remaining clusters. Coupling of SOM, PMF, and ionic ratios identified rock weathering as a driving factor for groundwater chemistry. Clusters 1 and 3 were found to be influenced by mineral dissolution and geogenic inputs (overall contribution of 47.7%), while agricultural and industrial effluents dominated clusters 4 and 5 (contribution of 52.7%) in the Bangladesh Sundarbans. Industrial effluents and agricultural activities were associated with clusters 3, 4, and 5 (contributions of 29.5% and 25.4%, respectively) and geogenic sources (contributions of 23 and 22.1% in clusters 1 and 2) in Indian Sundarbans. The probabilistic health risk assessment showed that NO3- poses a higher PN-CHR risk to human health than F- and As, and that potential risk to children is more evident in the Bangladesh Sundarban area than in the Indian Sundarbans. Local authorities must take urgent action to control NO3- emissions in the Indo-Bangla Sundarbans region.


Assuntos
Água Subterrânea , Poluentes Químicos da Água , Criança , Humanos , Monitoramento Ambiental/métodos , Aprendizado de Máquina não Supervisionado , Agricultura , Água , Poluentes Químicos da Água/análise , Qualidade da Água
9.
Gait Posture ; 109: 56-63, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-38277765

RESUMO

BACKGROUND: Ankle sprains are common and cause persistent ankle function reduction. To biomechanically evaluate the ankle function after ankle sprains, the ground reaction force (GRF) measurement during the single-legged landing had been used. However, previous studies focused on discrete features of vertical GRF (vGRF), which largely ignored vGRF waveform features that could better identify the ankle function. PURPOSE: To identify how the history of ankle sprain affect the vGRF waveform during the single-legged landing with unsupervised machine learning considering the time-series information of vGRF. METHODS: Eighty-seven currently healthy basketball athletes (12 athletes without ankle sprain, 49 athletes with bilateral, and 26 athletes with unilateral ankle sprain more than 6 months before the test day) performed single-legged landings from a 20 centimeters (cm) high box onto the force platform. Totally 518 trials vGRF data were collected from 87 athletes of 174 ankles, including 259 ankle sprain trials (from previous sprain ankles) and 259 non-ankle sprain trials (from without sprain ankles). The first 100 milliseconds (ms) vGRF waveforms after landing were extracted. Principal component analysis (PCA) was applied to the vGRF data, selecting 8 principal components (PCs) representing 96% of the information. Based on these 8 PCs, k-means method (k = 3) clustered the 518 trials into three clusters. Chi-square test assessed significant differences (p < 0.01) in the distribution of ankle sprain and non-ankle sprain trials among clusters. FINDINGS: The ankle sprain trials accounted for a significantly larger percentage (63.9%) in Cluster 3, which exhibited rapidly increased impulse vGRF waveforms with larger peaks in a short time. SIGNIFICANCE: PCA and k-means method for vGRF waveforms during single-legged landing identified that the history of previous ankle sprains caused a loss of ankle absorption ability lasting at least 6 months from an ankle sprain.


Assuntos
Traumatismos do Tornozelo , Entorses e Distensões , Humanos , Aprendizado de Máquina não Supervisionado , Traumatismos do Tornozelo/complicações , Extremidade Inferior , Tornozelo , Entorses e Distensões/complicações
10.
Biomech Model Mechanobiol ; 23(1): 349-372, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-38217746

RESUMO

From biological organs to soft robotics, highly deformable materials are essential components of natural and engineered systems. These highly deformable materials can have heterogeneous material properties, and can experience heterogeneous deformations with or without underlying material heterogeneity. Many recent works have established that computational modeling approaches are well suited for understanding and predicting the consequences of material heterogeneity and for interpreting observed heterogeneous strain fields. In particular, there has been significant work toward developing inverse analysis approaches that can convert observed kinematic quantities (e.g., displacement, strain) to material properties and mechanical state. Despite the success of these approaches, they are not necessarily generalizable and often rely on tight control and knowledge of boundary conditions. Here, we will build on the recent advances (and ubiquity) of machine learning approaches to explore alternative approaches to detect patterns in heterogeneous material properties and mechanical behavior. Specifically, we will explore unsupervised learning approaches to clustering and ensemble clustering to identify heterogeneous regions. Overall, we find that these approaches are effective, yet limited in their abilities. Through this initial exploration (where all data and code are published alongside this manuscript), we set the stage for future studies that more specifically adapt these methods to mechanical data.


Assuntos
Robótica , Aprendizado de Máquina não Supervisionado , Aprendizado de Máquina , Simulação por Computador , Fenômenos Biomecânicos , Robótica/métodos
11.
EBioMedicine ; 99: 104930, 2024 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-38168587

RESUMO

BACKGROUND: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction. METHODS: Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction. FINDINGS: Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36). INTERPRETATION: Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results. FUNDING: This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].


Assuntos
Doença da Artéria Coronariana , Infarto do Miocárdio , Humanos , Doença da Artéria Coronariana/diagnóstico por imagem , Infarto do Miocárdio/diagnóstico por imagem , Infarto do Miocárdio/etiologia , Perfusão , Prognóstico , Fatores de Risco , Aprendizado de Máquina não Supervisionado , Estudos Retrospectivos
12.
World Neurosurg ; 183: e953-e962, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-38253179

RESUMO

BACKGROUND: One of the most frequent phenomena in the follow-up of glioblastoma is pseudoprogression, present in up to half of cases. The clinical usefulness of discriminating this phenomenon through magnetic resonance imaging and nuclear medicine has not yet been standardized; in this study, we used machine learning on multiparametric magnetic resonance imaging to explore discriminators of this phenomenon. METHODS: For the study, 30 patients diagnosed with IDH wild-type glioblastoma operated on at both study centers in 2011-2020 were selected; 15 patients corresponded to early tumor progression and 15 patients to pseudoprogression. Using unsupervised learning, the number of clusters and tumor segmentation was recorded using gap-stat and k-means method, adjusting to voxel adjacency. In a second phase, a class prediction was carried out with a multinomial logistic regression supervised learning method; the outcome variables were the percentage of assignment, class overrepresentation, and degree of voxel adjacency. RESULTS: Unsupervised learning of the tumor in its diagnosis shows up to 14 well-differentiated tumor areas. In the supervised learning phase, there is a higher percentage of assigned classes (P < 0.01), less overrepresentation of classes (P < 0.01), and greater adjacency (55% vs. 33%) in cases of true tumor progression compared with pseudoprogression. CONCLUSIONS: True tumor progression preserves the multidimensional characteristics of the basal tumor at the voxel and region of interest level, resulting in a characteristic differential pattern when supervised learning is used.


Assuntos
Neoplasias Encefálicas , Glioblastoma , Imageamento por Ressonância Magnética Multiparamétrica , Humanos , Glioblastoma/diagnóstico por imagem , Glioblastoma/cirurgia , Glioblastoma/patologia , Aprendizado de Máquina não Supervisionado , Análise de Componente Principal , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/cirurgia , Neoplasias Encefálicas/patologia , Imageamento por Ressonância Magnética/métodos , Progressão da Doença
13.
Stud Health Technol Inform ; 310: 805-809, 2024 Jan 25.
Artigo em Inglês | MEDLINE | ID: mdl-38269920

RESUMO

Identifying potentially fraudulent or wasteful medical insurance claims can be difficult due to the large amounts of data and human effort involved. We applied unsupervised machine learning to construct interpretable models which rank variations in medical provider claiming behaviour in the domain of unilateral joint replacement surgery, using data from the Australian Medicare Benefits Schedule. For each of three surgical procedures reference models of claims for each procedure were constructed and compared analytically to models of individual provider claims. Providers were ranked using a score based on fees for typical claims made in addition to those in the reference model. Evaluation of the results indicated that the top-ranked providers were likely to be unusual in their claiming patterns, with typical claims from outlying providers adding up to 192% to the cost of a procedure. The method is efficient, generalizable to other procedures and, being interpretable, integrates well into existing workflows.


Assuntos
Artroplastia de Substituição , Programas Nacionais de Saúde , Idoso , Humanos , Austrália , Honorários e Preços , Aprendizado de Máquina não Supervisionado
14.
Sci Rep ; 14(1): 724, 2024 01 06.
Artigo em Inglês | MEDLINE | ID: mdl-38184749

RESUMO

A precise forecast of the need for blood transfusions (BT) in patients undergoing total hip arthroplasty (THA) is a crucial step toward the implementation of precision medicine. To achieve this goal, we utilized supervised machine learning (SML) techniques to establish a predictive model for BT requirements in THA patients. Additionally, we employed unsupervised machine learning (UML) approaches to identify clinical heterogeneity among these patients. In this study, we recruited 224 patients undergoing THA. To identify factors predictive of BT during the perioperative period of THA, we employed LASSO regression and the random forest (RF) algorithm as part of supervised machine learning (SML). Using logistic regression, we developed a predictive model for BT in THA patients. Furthermore, we utilized unsupervised machine learning (UML) techniques to cluster THA patients who required BT based on similar clinical features. The resulting clusters were subsequently visualized and validated. We constructed a predictive model for THA patients who required BT based on six predictive factors: Age, Body Mass Index (BMI), Hemoglobin (HGB), Platelet (PLT), Bleeding Volume, and Urine Volume. Before surgery, 1 h after surgery, 1 day after surgery, and 1 week after surgery, significant differences were observed in HGB and PLT levels between patients who received BT and those who did not. The predictive model achieved an AUC of 0.899. Employing UML, we identified two distinct clusters with significantly heterogeneous clinical characteristics. Age, BMI, PLT, HGB, bleeding volume, and urine volume were found to be independent predictors of BT requirement in THA patients. The predictive model incorporating these six predictors demonstrated excellent predictive performance. Furthermore, employing UML enabled us to classify a heterogeneous cohort of THA patients who received BT in a meaningful and interpretable manner.


Assuntos
Artroplastia de Quadril , Humanos , Período Perioperatório , Aprendizado de Máquina Supervisionado , Aprendizado de Máquina não Supervisionado , Transfusão de Sangue
15.
J Plast Reconstr Aesthet Surg ; 88: 330-339, 2024 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-38061257

RESUMO

BACKGROUND: Autologous breast reconstruction is composed of diverse techniques and results in a variety of outcome trajectories. We propose employing an unsupervised machine learning method to characterize such heterogeneous patterns in large-scale datasets. METHODS: A retrospective cohort study of autologous breast reconstruction patients was conducted through the National Surgical Quality Improvement Program database. Patient characteristics, intraoperative variables, and occurrences of acute postoperative complications were collected. The cohort was classified into patient subgroups via the K-means clustering algorithm, a similarity-based unsupervised learning approach. The characteristics of each cluster were compared for differences from the complementary sample (p < 2 ×10-4) and validated with a test set. RESULTS: A total of 14,274 female patients were included in the final study cohort. Clustering identified seven optimal subgroups, ordered by increasing rate of postoperative complication. Cluster 1 (2027 patients) featured breast reconstruction with free flaps (50%) and latissimus dorsi flaps (40%). In addition to its low rate of complications (14%, p < 2 ×10-4), its patient population was younger and with lower comorbidities when compared with the whole cohort. In the other extreme, cluster 7 (1112 patients) almost exclusively featured breast reconstruction with free flaps (94%) and possessed the highest rates of unplanned reoperations, readmissions, and dehiscence (p < 2 ×10-4). The reoperation profile of cluster 3 was also significantly different from the general cohort and featured lower proportions of vascular repair procedures (p < 8 ×10-4). CONCLUSIONS: This study presents a novel, generalizable application of an unsupervised learning model to organize patient subgroups with associations between comorbidities, modality of breast reconstruction, and postoperative outcomes.


Assuntos
Neoplasias da Mama , Retalhos de Tecido Biológico , Mamoplastia , Humanos , Feminino , Aprendizado de Máquina não Supervisionado , Estudos Retrospectivos , Mamoplastia/métodos , Complicações Pós-Operatórias/etiologia , Retalhos de Tecido Biológico/cirurgia , Neoplasias da Mama/complicações
16.
Res Q Exerc Sport ; 95(1): 47-53, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-36648412

RESUMO

In volleyball, the effect of different factors on serve performance has usually been analyzed with traditional statistical techniques such as logistic regression or discriminant analysis. Purpose: In this study, two of the main models used in unsupervised machine learning (cluster and principal component analysis) were applied to achieve these objectives: (a) to create groups of players considering their serve coefficient, age, height, and team ranking, and (b) to identify which variables related to the serve (type and performance), the players (role, age, and height), and the teams (ranking, match location, and quality of opposition) most explained the total variance of the data during an entire women's volleyball season. Method: A total of 20,936 serves were analyzed during the 132 matches played in the 2017-2018 season in the Liga Iberdrola (women Spanish first division). The variables were related to the serving action (type of serve and performance), the players' traits (player role, age, and height), and the teams' characteristics (final ranking, match location, quality of opposition, and tournament). Results: Cluster analysis showed five groups of players differing in age, serve coefficient, team ranking, and height. Principal component analysis showed how the first five components explained 72.12% of the total variance. From these components, serve coefficient, team ranking, match location, quality of opposition, and player role each contributed more than 10%. Conclusions: These findings can help coaches to improve talent selection and players' development according to competition demands.


Assuntos
Aprendizado de Máquina não Supervisionado , Voleibol , Humanos , Feminino , Análise por Conglomerados , Análise Discriminante , Estações do Ano
17.
J Am Med Inform Assoc ; 31(2): 406-415, 2024 Jan 18.
Artigo em Inglês | MEDLINE | ID: mdl-38070172

RESUMO

OBJECTIVE: Changes in cardiovascular health (CVH) during the life course are associated with future cardiovascular disease (CVD). Longitudinal clustering analysis using subgraph augmented non-negative matrix factorization (SANMF) could create phenotypic risk profiles of clustered CVH metrics. MATERIALS AND METHODS: Life's Essential 8 (LE8) variables, demographics, and CVD events were queried over 15 ears in 5060 CARDIA participants with 18 years of subsequent follow-up. LE8 subgraphs were mined and a SANMF algorithm was applied to cluster frequently occurring subgraphs. K-fold cross-validation and diagnostics were performed to determine cluster assignment. Cox proportional hazard models were fit for future CV event risk and logistic regression was performed for cluster phenotyping. RESULTS: The cohort (54.6% female, 48.7% White) produced 3 clusters of CVH metrics: Healthy & Late Obesity (HLO) (29.0%), Healthy & Intermediate Sleep (HIS) (43.2%), and Unhealthy (27.8%). HLO had 5 ideal LE8 metrics between ages 18 and 39 years, until BMI increased at 40. HIS had 7 ideal LE8 metrics, except sleep. Unhealthy had poor levels of sleep, smoking, and diet but ideal glucose. Race and employment were significantly different by cluster (P < .001) but not sex (P = .734). For 301 incident CV events, multivariable hazard ratios (HRs) for HIS and Unhealthy were 0.73 (0.53-1.00, P = .052) and 2.00 (1.50-2.68, P < .001), respectively versus HLO. A 15-year event survival was 97.0% (HIS), 96.3% (HLO), and 90.4% (Unhealthy, P < .001). DISCUSSION AND CONCLUSION: SANMF of LE8 metrics identified 3 unique clusters of CVH behavior patterns. Clustering of longitudinal LE8 variables via SANMF is a robust tool for phenotypic risk assessment for future adverse cardiovascular events.


Assuntos
Doenças Cardiovasculares , Indicadores de Qualidade em Assistência à Saúde , Humanos , Feminino , Estados Unidos , Masculino , Aprendizado de Máquina não Supervisionado , Doenças Cardiovasculares/epidemiologia , Dieta , Análise por Conglomerados , Fatores de Risco
18.
J Arthroplasty ; 39(3): 677-682, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-37770008

RESUMO

BACKGROUND: Patient-reported outcome measures (PROMs) are an important metric to assess total knee arthroplasty (TKA) patients. The purpose of this study was to use a machine learning (ML) algorithm to identify patient features that impact PROMs after TKA. METHODS: Data from 636 TKA patients enrolled in our patient database between 2018 and 2022, were retrospectively reviewed. Their mean age was 68 years (range, 39 to 92), 56.7% women, and mean body mass index of 31.17 (range, 16 to 58). Patient demographics and the Functional Comorbidity Index were collected alongside Patient-Reported Outcome Measures Information System Global Health v1.2 (PROMIS GH-P) physical component scores preoperatively, at 3 months, and 1 year after TKA. An unsupervised ML algorithm (spectral clustering) was used to identify patient features impacting PROMIS GH-P scores at the various time points. RESULTS: The algorithm identified 5 patient clusters that varied by demographics, comorbidities, and pain scores. Each cluster was associated with predictable trends in PROMIS GH-P scores across the time points. Notably, patients who had the worst preoperative PROMIS GH-P scores (cluster 5) had the most improvement after TKA, whereas patients who had higher global health rating preoperatively had more modest improvement (clusters 1, 2, and 3). Two out of Five patient clusters (cluster 4 and 5) showed improvement in PROMIS GH-P scores that met a minimally clinically important difference at 1-year postoperative. CONCLUSIONS: The unsupervised ML algorithm identified patient clusters that had predictable changes in PROMs after TKA. It is a positive step toward providing precision medical care for each of our arthroplasty patients.


Assuntos
Artroplastia do Joelho , Osteoartrite do Joelho , Humanos , Feminino , Idoso , Masculino , Articulação do Joelho/cirurgia , Estudos Retrospectivos , Aprendizado de Máquina não Supervisionado , Qualidade de Vida , Resultado do Tratamento , Medidas de Resultados Relatados pelo Paciente , Osteoartrite do Joelho/cirurgia
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