Spine J
Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Surgical Sciences, Uppsala University, Uppsala, Sweden; Capio Spine Center Stockholm, Löwenströmska Hospital, Upplands-Väsby, Sweden; Department of Medical Sciences, Örebro University, Örebro, Sweden. Electronic address:
Published: November 2024
Background: Dysphonia is one of the more common complications following anterior cervical discectomy and fusion (ACDF). ACDF is the gold standard for treating degenerative cervical spine disorders, and identifying high-risk patients is therefore crucial.
Purpose: This study aimed to evaluate different machine learning models to predict persistent dysphonia after ACDF.
Study Design: A retrospective review of the nationwide Swedish spine registry (Swespine).
Patient Sample: All adults in the Swespine registry who underwent elective ACDF between 2006 and 2020.
Outcome Measures: The primary outcome was self-reported dysphonia lasting at least 1 month after surgery. Predictive performance was assessed using discrimination and calibration metrics.
Methods: Patients with missing dysphonia data at the 1-year follow-up were excluded. Data preprocessing involved one-hot encoding categorical variables, scaling continuous variables, and imputing missing values. Four machine learning models (logistic regression, random forest (RF), gradient boosting, K-nearest neighbor) were employed. The models were trained and tested using an 80:20 data split and 5-fold cross-validation, with performance metrics guiding the selection of the best model for predicting persistent dysphonia.
Results: In total, 2,708 were included in the study. Twelve key predictors were identified. Four machine learning models were tested, with the RF model achieving the best performance (AUC=0.794). The most significant predictors across models included preoperative NDI, EQ5D, preoperative neurology, number of operated levels, and use of a fusion cage. The RF model, chosen for its superior performance, showed high sensitivity and consistent accuracy, but a low specificity and positive predictive value.
Conclusions: In this study, machine learning models were employed to identify predictors of persistent dysphonia following ACDF. Among the models tested, the RF classifier demonstrated superior performance, with an AUC value of 0.790. The RF model identified NDI, EQ5D, and number of fused vertebrae as key variables. These findings underscore the potential of machine learning models in identifying patients at increased risk for dysphonia persisting for more than 1 month after surgery.
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http://dx.doi.org/10.1016/j.spinee.2024.10.010 | DOI Listing |
Adv Sci (Weinh)
January 2025
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA.
Natural olfactory systems possess remarkable sensitivity and precision beyond what is currently achievable by engineered gas sensors. Unlike their artificial counterparts, noses are capable of distinguishing scents associated with mixtures of volatile molecules in complex, typically fluctuating environments and can adapt to changes. This perspective examines the multifaceted biological principles that provide olfactory systems their discriminatory prowess, and how these ideas can be ported to the design of electronic noses for substantial improvements in performance across metrics such as sensitivity and ability to speciate chemical mixtures.
View Article and Find Full Text PDFSpine (Phila Pa 1976)
January 2025
Department of Orthopedics, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China.
Study Design: A retrospective review of a prospective adult spinal deformity data.
Objective: To identify distinct patient clinical profiles and recovery trajectories in patients with adult spinal deformity (ASD).
Summary Of Background Data: Patients with ASD exhibit a diverse array of symptoms and significant heterogeneity in clinical presentations, posing challenges to precise clinical decision-making.
Background: The cotton jassid, Amrasca biguttula, a dangerous and polyphagous pest, has recently invaded the Middle East, Africa and South America, raising concerns about the future of cotton and other food crops including okra, eggplant and potato. However, its potential distribution remains largely unknown, posing a challenge in developing effective phytosanitary strategies. We used an ensemble model of six machine-learning algorithms including random forest, maxent, support vector machines, classification and regression tree, generalized linear model and boosted regression trees to forecast the potential distribution of A.
View Article and Find Full Text PDFAnal Methods
January 2025
College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
The efficacy and safety of drugs are closely related to the geographical origin and quality of the raw materials. This study focuses on using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms to construct content prediction models and origin identification models to predict the components and origin of Radix Paeoniae Rubra (RPR). These models are quick, non-destructive, and accurate for assessing both component content and origin.
View Article and Find Full Text PDFClin Chem
January 2025
Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Background: The accurate and prompt diagnosis of infections is essential for improving patient outcomes and preventing bacterial drug resistance. Host gene expression profiling as an approach to infection diagnosis holds great potential in assisting early and accurate diagnosis of infection.
Methods: To improve the precision of infection diagnosis, we developed InfectDiagno, a rank-based ensemble machine learning algorithm for infection diagnosis via host gene expression patterns.
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