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BMJ Open Jul 2023To measure differences at various deciles in days alive and out of hospital to 90 days (DAOH) and explore its utility for identifying outliers of performance among...
OBJECTIVES
To measure differences at various deciles in days alive and out of hospital to 90 days (DAOH) and explore its utility for identifying outliers of performance among district health boards (DHBs).
METHODS
Days in hospital and mortality within 90 days of surgery were extracted by linking data from the New Zealand National Minimum Data Set and the births and deaths registry between 1 January 2011 and 31 December 2021 for all adults in New Zealand undergoing acute laparotomy (AL-a relatively high-risk group), elective total hip replacement (THR-a medium risk group) or lower segment caesarean section (LSCS-a low-risk group). DAOH was calculated without censoring to zero in cases of mortality. For each DHB, direct risk standardisation was used to adjust for potential confounders and presented in deciles according to baseline patient risk. The Mann-Whitney U test assessed overall DAOH differences between DHBs, and comparisons are presented between selected deciles of DAOH for each operation.
RESULTS
We obtained national data for 35 175, 52 032 and 117 695 patients undergoing AL, THR and LSCS procedures, respectively. We have demonstrated that calculating DAOH without censoring zero allows for differences between procedures and DHBs to be identified. Risk-adjusted national mean DAOH Scores were 64.0 days, 79.0 days and 82.0 days at the 0.1 decile and 75.0 days, 82.0 days and 84.0 days at the 0.2 decile for AL, THR and LSCS, respectively, matching to their expected risk profiles. Differences between procedures and DHBs were most marked at lower deciles of the DAOH distribution, and outlier DHBs were detectable. Corresponding 90-day mortality rates were 5.45%, 0.78% and 0.01%.
CONCLUSION
In New Zealand after direct risk adjustment, differences in DAOH between three types of surgical procedure reflected their respective risk levels and associated mortality rates. Outlier DHBs were identified for each procedure. Thus, our approach to analysing DAOH appears to have considerable face validity and potential utility for contributing to the measurement of perioperative outcomes in an audit or quality improvement setting.
Topics: Pregnancy; Adult; Humans; Female; Cross-Sectional Studies; New Zealand; Cesarean Section; Hospitals; Treatment Outcome
PubMed: 37491100
DOI: 10.1136/bmjopen-2022-063787 -
Sensors (Basel, Switzerland) Dec 2023Human-to-human communication via the computer is mainly carried out using a keyboard or microphone. In the field of virtual reality (VR), where the most immersive...
Human-to-human communication via the computer is mainly carried out using a keyboard or microphone. In the field of virtual reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g., silent commands during task-force training) or simply not possible (e.g., if the user has hearing loss). Data gloves help to increase immersion within VR, as they correspond to our natural interaction. At the same time, they offer the possibility of accurately capturing hand shapes, such as those used in non-verbal communication (e.g., thumbs up, okay gesture, …) and in sign language. In this paper, we present a hand-shape recognition system using data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an and a approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial , i.e., we created new artificial data from the recorded and filtered data to augment the training data set. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. The voting meta-classifier (VL2) proved to be the most accurate, albeit slowest, classifier. A good alternative is random forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. was proven to be an effective approach, especially in improving the classification time. Overall, we have shown that our hand-shape recognition system using data gloves is suitable for communication within VR.
Topics: Humans; Hand; Recognition, Psychology; Gestures; Virtual Reality; Sign Language
PubMed: 38139692
DOI: 10.3390/s23249847 -
International Journal of Medical... 2024In observational studies, gastroesophageal reflux disease (GERD) is linked to atrial fibrillation (AF). It is uncertain whether the relationship is due to GERD-induced... (Meta-Analysis)
Meta-Analysis
In observational studies, gastroesophageal reflux disease (GERD) is linked to atrial fibrillation (AF). It is uncertain whether the relationship is due to GERD-induced AF or GERD caused by AF, or confusion with factors related to GERD and AF such as obesity and sleep-disordered breathing. We applied bidirectional Mendelian randomization (MR), in which genetic variations are used as instrumental variables to resolve confounding and reverse causation issues, to determine the causal effect between GERD and AF. Using summary data from the GERD and AF genome-wide association study (GWAS), a bidirectional MR was performed to estimate the causative impact of GERD on AF risk and AF on GERD risk. The GWAS of GERD meta-analysis comprised 78707 cases and 288734 controls. GWAS summary data for AF, including 45766 AF patients and 191924 controls, were used to genetically predicted AF. The inverse variance weighted (IVW) method was the major MR approach used. MR-PRESSO was implemented to detect heterogeneity and correct the effect of outliers. Weighted median and MR-Egger regression were applied to test heterogeneity and pleiotropy. The genetic instruments of GERD related to increasing the risk of AF, with an OR of 1.339 (95% CI: 1.242-1.444, < 0.001). However, after removing the outlier 8 SNPs, genetically predicted AF was not associated with an elevated risk of GERD ( = 0.351). Our result suggested that GERD had a causal effect on AF. However, no evidence was identified that AF elevated the risk of GERD.
Topics: Humans; Gastroesophageal Reflux; Mendelian Randomization Analysis; Atrial Fibrillation; Genome-Wide Association Study; Polymorphism, Single Nucleotide; Genetic Predisposition to Disease; Risk Factors
PubMed: 38818473
DOI: 10.7150/ijms.95518 -
Medicine Nov 2023To evaluate the causal relationship between genetically determined telomere length (TL) and atherosclerosis (AS). We performed a 2-sample Mendelian randomization (MR)...
To evaluate the causal relationship between genetically determined telomere length (TL) and atherosclerosis (AS). We performed a 2-sample Mendelian randomization (MR) study to assess the potential causal relationship between TL and AS (coronary AS, cerebral AS, peripheral atherosclerosis (PAD), and AS, excluding cerebral, coronary, and PAD). The TL phenotype contained 472,174 participants, and the 4 subtypes of AS had 361,194, 218,792, 168,832, and 213,140 participants, all of European ancestries. The single nucleotide polymorphisms (SNPs) of TL strongly associated with the 4 atherosclerotic subtypes included in this study were 101, 92, 91, and 92, respectively. The odds ratios (ORs) and 95% confidence interval (CI) between TL and coronary AS calculated using inverse variance weighted (IVW) were 0.993 (0.988, 0.997), and the results were statistically significant (P < .05). The results between TL and cerebral AS, PAD, and AS (excluding cerebral, coronary, and PAD) were not statistically significant (P > .05). "Egger-intercept test" showed that there was no horizontal pleiotropy (P > .05); "leave-one-out analysis" sensitivity analysis showed that the results were stable and there were no instrumental variables with strong effects on the results; "MR- pleiotropy residual sum and outlier (PRESSO) test" showed 1 outlier for coronary AS and no outliers for the remaining subgroups. The results of the 2-sample MR analysis showed a causal association between TL and coronary AS but not with cerebral AS, PAD, and AS (excluding cerebral, coronary, and PAD). This may elucidate the observation that various vascular regions can be affected by AS but highlights the propensity of coronary arteries to be more susceptible to AS development.
Topics: Humans; Mendelian Randomization Analysis; Atherosclerosis; Coronary Artery Disease; Heart; Intracranial Arteriosclerosis; Telomere; Genome-Wide Association Study
PubMed: 37986353
DOI: 10.1097/MD.0000000000035875 -
Frontiers in Genetics 2023Glioblastoma (GBM) is the most prevalent malignant brain tumor, significantly impacting the physical and mental wellbeing of patients. Several studies have demonstrated...
Glioblastoma (GBM) is the most prevalent malignant brain tumor, significantly impacting the physical and mental wellbeing of patients. Several studies have demonstrated a close association between gut microbiota and the development of GBM. In this investigation, Mendelian randomization (MR) was employed to rigorously evaluate the potential causal relationship between gut microbiota and GBM. We utilized summary statistics derived from genome-wide association studies (GWAS) encompassing 211 gut microbiota and GBM. The causal association between gut microbiota and GBM was scrutinized using Inverse Variance Weighted (IVW), MR-Egger, and Weighted Median (WM) methods. Cochrane's Q statistic was employed to conduct a heterogeneity test. MR-Pleiotropic Residuals and Outliers (MR-PRESSO) were applied to identify and eliminate SNPs with horizontal pleiotropic outliers. Additionally, Reverse MR was employed to assess the causal relationship between GBM and pertinent gut microbiota. The MR study estimates suggest that the nine gut microbiota remain stable, considering heterogeneity and sensitivity methods. Among these, the and were associated with an increased risk of GBM, whereas , , , , , , and were associated with a reduced risk of GBM. Following Benjamini and Hochberg (BH) correction, (OR = 0.04, 95% CI: 0.01-0.19, FDR = 0.003) was identified as playing a protective role against GBM. This groundbreaking study is the first to demonstrate that is significantly associated with a reduced risk of GBM. The modulation of for the treatment of GBM holds considerable potential clinical significance.
PubMed: 38239850
DOI: 10.3389/fgene.2023.1308263 -
Frontiers in Aging Neuroscience 2023Visuospatial memory impairment is a common symptom of Alzheimer's disease; however, conventional visuospatial memory tests are insufficient to fully reflect visuospatial...
BACKGROUND
Visuospatial memory impairment is a common symptom of Alzheimer's disease; however, conventional visuospatial memory tests are insufficient to fully reflect visuospatial memory impairment in daily life.
METHODS
To address patients' difficulties in locating and recalling misplaced objects, we introduced a novel visuospatial memory test, the Hidden Objects Test (HOT), conducted in a virtual environment. We categorized HOT scores into prospective memory, item free-recall, place free-recall, item recognition, and place-item matching scores. To validate the VR memory test, we compared HOT scores among individuals with Alzheimer's disease (AD), amnestic mild cognitive impairment (aMCI), and normal controls (NC), and also compared these scores with those of conventional neuropsychological tests. We tracked the participants' movement paths in the virtual environment and assessed basic features, such as total distance, duration, and speed. Additionally, we performed walking trajectory pattern mining such as outlier and stay-point detection.
RESULTS
We designed and implemented the HOT to simulate a house's living room and assess participants' ability to locate hidden objects. Our preliminary results showed that the total HOT score differed among 17 patients with AD, 14 with aMCI, and 15 NC ( < 0.001). The total HOT score correlated positively with conventional memory test scores ( < 0.001). Walking trajectories showed that patients with AD and aMCI wandered rather than going straight to the hidden objects. In terms of basic features, the total duration was significantly greater in AD than in NC ( = 0.008). In terms of trajectory pattern mining, the number of outliers, which were over 95% of the estimated trajectory, was significantly higher in AD than in NC ( = 0.002). The number of stay points, an index in which participants stayed in the same position for more than 2 s, was significantly higher in patients with AD and aMCI compared with NC (AD vs. NC: = 0.003, aMCI vs. NC: = 0.019).
CONCLUSION
The HOT simulating real life showed potential as an ecologically valid test for assessing visuospatial memory function in daily life. Walking trajectory analysis suggested that patients with AD and aMCI wandered rather than going straight toward the hidden objects.
PubMed: 38076533
DOI: 10.3389/fnagi.2023.1236084 -
ArXiv Apr 2024Recent advancements in machine learning have led to novel imaging systems and algorithms that address ill-posed problems. Assessing their trustworthiness and...
Recent advancements in machine learning have led to novel imaging systems and algorithms that address ill-posed problems. Assessing their trustworthiness and understanding how to deploy them safely at test time remains an important and open problem. We propose a method that leverages conformal prediction to retrieve upper/lower bounds and statistical inliers/outliers of reconstructions based on the prediction intervals of downstream metrics. We apply our method to sparse-view CT for downstream radiotherapy planning and show 1) that metric-guided bounds have valid coverage for downstream metrics while conventional pixel-wise bounds do not and 2) anatomical differences of upper/lower bounds between metric-guided and pixel-wise methods. Our work paves the way for more meaningful reconstruction bounds. Code available at https://github.com/matthewyccheung/conformal-metric.
PubMed: 38711427
DOI: No ID Found -
ESMO Open Oct 2023
PubMed: 37769399
DOI: 10.1016/j.esmoop.2023.101833 -
Mathematical Biosciences and... Jul 2023Trajectory outlier detection can identify abnormal phenomena from a large number of trajectory data, which is helpful to discover or predict potential traffic risks. In...
Trajectory outlier detection can identify abnormal phenomena from a large number of trajectory data, which is helpful to discover or predict potential traffic risks. In this work, we proposed a trajectory outlier detection model based on variational auto-encoder. First, the model encodes the trajectory data as parameters of distribution functions based on the statistical characteristics of urban traffic. Then, an auto-encoder network is built and trained. The training goal of the auto-encoder network is to maximize the generation probability of original trajectories when decoding. Once the model training is completed, we can detect the trajectory outlier by the difference between a trajectory and the trajectory generated by the model. The advantage of the proposed model is that it only needs to compute the difference between the original trajectory and the trajectory generated by the model when detecting the trajectory outlier, which greatly reduces the amount of calculation and makes the model very suitable for real-time detection scenarios. In addition, the distance threshold between the abnormal trajectory and the normal trajectory can be set by referring to the proportion of the abnormal trajectory in the training data set, which eliminates the difficulty of setting the threshold manually and makes the model more convenient to be applied in different actual scenes. In terms of effect, the proposed model has achieved more than 95% in accuracy, which is better than the two typical density-based and classification-based detection methods, and also better than the methods based on machine learning in recent years. In terms of efficiency, the model has good convergence in the training phase and the training time increases slowly with the data scale, which is better than or as the same as the comparison methods.
PubMed: 37679172
DOI: 10.3934/mbe.2023675 -
Scientific Reports Oct 2023This study conducted a comprehensive analysis of multiple supervised machine learning models, regressors and classifiers, to accurately predict diamond prices. Diamond...
This study conducted a comprehensive analysis of multiple supervised machine learning models, regressors and classifiers, to accurately predict diamond prices. Diamond pricing is a complex task due to the non-linear relationships between key features such as carat, cut, clarity, table, and depth. The analysis aimed to develop an accurate predictive model by utilizing both regression and classification approaches. To preprocess the data, the study employed various techniques. The work addressed outliers, standardized the predictors, performed median imputation of missing values, and resolved multicollinearity issues. Equal-width binning on the cut variable was performed to handle class imbalance. Correlation-based feature selection was utilized to eliminate highly correlated variables, ensuring that only relevant features were included in the models. Outliers were handled using the inter-quartile range method, and numerical features were normalized through standardization. Missing values in numerical features were imputed using the median, preserving the integrity of the dataset. Among the models evaluated, the RF regressor exhibited exceptional performance. It achieved the lowest root mean squared error (RMSE) of 523.50, indicating superior accuracy compared to the other models. The RF regressor also obtained a high R-squared ([Formula: see text]) score of 0.985, suggesting it explained a significant portion of the variance in diamond prices. Furthermore, the area under the curve with RF classifier for the test set was 1.00 [Formula: see text], indicating perfect classification performance. These results solidify the RF's position as the best-performing model in terms of accuracy and predictive power, both in regression and classification. The MLP regressor showed promising results with an RMSE of 563.74 and an [Formula: see text] score of 0.980, demonstrating its ability to capture the complex relationships in the data. Although it achieved slightly higher errors than the RF regressor, further analysis is needed to determine its suitability and potential advantages compared to the RF regressor. The XGBoost Regressor achieved an RMSE of 612.88 and an [Formula: see text] score of 0.972, indicating its effectiveness in predicting diamond prices but with slightly higher errors compared to the RF regressor. The Boosted Decision Tree Regressor had an RMSE of 711.31 and an [Formula: see text] score of 0.968, demonstrating its ability to capture some of the underlying patterns but with higher errors than the RF and XGBoost models. In contrast, the KNN regressor yielded a higher RMSE of 1346.65 and a lower [Formula: see text] score of 0.887, indicating its inferior performance in accurately predicting diamond prices compared to the other models. Similarly, the Linear Regression model performed similarly to the KNN regressor, with an RMSE of 1395.41 and an [Formula: see text] score of 0.876. The Support Vector Regression model showed the highest RMSE of 3044.49 and the lowest [Formula: see text] score of 0.421, indicating its limited effectiveness in capturing the complex relationships in the data. Overall, the study demonstrates that the RF outperforms the other models in terms of accuracy and predictive power, as evidenced by its lowest RMSE, highest [Formula: see text] score, and perfect classification performance. This highlights its suitability for accurately predicting diamond prices. The study not only provides an effective tool for the diamond industry but also emphasizes the importance of considering both regression and classification approaches in developing accurate predictive models. The findings contribute valuable insights for pricing strategies, market trends, and decision-making processes in the diamond industry and related fields.
PubMed: 37828360
DOI: 10.1038/s41598-023-44326-w