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MedRxiv : the Preprint Server For... Jun 2024The NKF-ASN Task Force recommends accurate kidney function estimation avoiding biases through racial adjustments. We explored the use of multiple kidney function...
RATIONALE AND OBJECTIVE
The NKF-ASN Task Force recommends accurate kidney function estimation avoiding biases through racial adjustments. We explored the use of multiple kidney function biomarkers and hence estimated glomerular filtration rate (eGFR) equations to improve kidney function calculations in an ethnically diverse patient population.
STUDY DESIGN
Prospective community cohort study.
SETTING AND PARTICIPANTS
Rural New Mexico clinic with patients > 18 yo.
METHODS
Markers of kidney function, IDMS-Creatinine (SCr), chemiluminescence Beta-2 Microglobulin (B2M), Nephelometry-calibrated ELISA Cystatin C (CysC), inflammation, glucose tolerance, demographics, BUN/UACR from the baseline visit of the COMPASS cohort, were analyzed by Kernel-based Virtual Machine learning methods.
RESULTS
Among 205 participants, the mean age was 50.1, 62% were female, 54.1% Hispanic American and 30.2% Native American. Average kidney function biomarkers were: SCr 0.9 mg/dl, B2M 1.8 mg/L, and CysC 0.7 mg/dl. The highest agreement was observed between SCr and B2M-based eGFR equations [mean difference in eGFRs: (4.48 ml/min/1.73m ], and the lowest agreement between B2M and CysC-based eGFR equations (-24.75 ml/min/1.73m ). There was no pattern of association between the differences in eGFR measures and gender. In the continuous analyses, the absolute eGFR value (p<2 × 10 ) and serum albumin (p =6.4 × 10 ) predicted the difference between B2M- and SCr-based e-GFR. The absolute eGFR value (p<2 × 10 ) and age (p =7.6 x 10 ) predicted the difference between CysC- and SCr-based e-GFR.
LIMITATIONS
Relatively small sample size, elevated inflammatory state in majority of study participants and no inulin excretion rate measurements.
CONCLUSION
B2M should be strongly considered as a kidney function biomarker fulfilling the criteria for the NKF-ASN. B2M's eGFR equation does not need adjustment for gender or race and showed the highest agreement with SCr-based eGFR equations.
PubMed: 38946981
DOI: 10.1101/2024.06.10.24308724 -
Research Square Jun 2024Objective The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance...
Objective The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance T1-weighted imaging. Explore the predictive performance of an autoencoder model based on reconstruction regularization for fluid intelligence in adolescents. Methods We collected actual fluid intelligence scores and T1-weighted MRIs of 11,534 adolescents who completed baseline tasks from ABCD Data Release 3.0. A total of 148 ROIs were selected and 604 features were proposed by FreeSurfer segmentation. The training and testing sets were divided in a ratio of 7:3. To predict fluid intelligence scores, we used AE, MLP and classic machine learning models, and compared their performance on the test set. In addition, we explored their performance across gender subpopulations. Moreover, we evaluated the importance of features using the SHapley Additive Explain method. Results: The proposed model achieves optimal performance on the test set for predicting actual fluid intelligence scores (PCC = 0.209 ± 0.02, MSE = 105.212 ± 2.53). Results show that autoencoders with refactoring regularization are significantly more effective than MLPs and classical machine learning models. In addition, all models performed better on female adolescents than on male adolescents. Further analysis of relevant characteristics in different populations revealed that this may be related to gender differences in underlying fluid intelligence mechanisms. Conclusions We construct a weak but stable correlation between brain structural features and raw fluid intelligence using autoencoders. Future research may need to explore ensemble regression strategies utilizing multiple machine learning algorithms on multimodal data in order to improve the predictive performance of fluid intelligence based on neuroimaging features.
PubMed: 38946976
DOI: 10.21203/rs.3.rs-4482953/v1 -
Research Square Jun 2024Spastic cerebral palsy, the most common pediatric-onset disabling condition with an estimated prevalence of 0.2% in children, is a complex condition characterized by...
Spastic cerebral palsy, the most common pediatric-onset disabling condition with an estimated prevalence of 0.2% in children, is a complex condition characterized by stiff movement, muscle contractures, and abnormal gait that can diminish quality of life. Spastic CP accounts for approximately 83% of all CP cases and frequently co-occurs with other complex conditions, like epilepsy. An estimated 42% of spastic CP cases have co-occurring epilepsy. Unfortunately, CP is often difficult to diagnose. Although most children with CP are born with it or acquire it immediately after birth, many are not identified until after 19 months of age with CP diagnosis often not confirmed until 5 years of age. New bioinformatic approaches to identify CP earlier are needed. Recent studies indicate that altered DNA methylation patterns associated with CP may have diagnostic value. The potential confounding effects of co-occurrent epilepsy on these patterns are not known. We evaluated machine learning classification of CP patients with or without co-occurring epilepsy. Whole blood samples were collected from 30 study participants diagnosed with epilepsy (n=4), spastic CP (n=10), both (n=8), or neither (n=8). A novel Support-Vector-Machine learning algorithm was developed to identify methylation loci that have ability to classify CP from controls in the presence or absence of epilepsy. This algorithm was also employed to measure classification ability of identified methylation loci. After preprocessing of data, isolation of important methylation loci was performed in a binary comparison between CP and controls, as well as in a 4-way scheme, encapsulating epilepsy diagnoses. The classification ability was similarly assessed. CP Classification performance wasevaluated with and without inclusion of epilepsy as a feature. Median F1 scoreswere 0.67 in 4-class comparison, and 1.0 in the binary classification, outperforming Linear-Discriminant-Analysis (0.57 and 0.86, respectively). This novel algorithm was able to classify study participants with spastic CPand/or epilepsy from controls with significant performance. The algorithm shows promise for rapid identification in methylation data of diagnostic methylation loci. In this model, Support Vector Machines outperformed Linear Discriminant Analysis in classification. In the evaluation of epigenetics-based diagnostics for CP, epilepsy may not be a significant confounding factor.
PubMed: 38946953
DOI: 10.21203/rs.3.rs-4560364/v1 -
Frontiers in Big Data 2024Hyperdimensional Computing (HDC) is a brain-inspired and lightweight machine learning method. It has received significant attention in the literature as a candidate to...
INTRODUCTION
Hyperdimensional Computing (HDC) is a brain-inspired and lightweight machine learning method. It has received significant attention in the literature as a candidate to be applied in the wearable Internet of Things, near-sensor artificial intelligence applications, and on-device processing. HDC is computationally less complex than traditional deep learning algorithms and typically achieves moderate to good classification performance. A key aspect that determines the performance of HDC is encoding the input data to the hyperdimensional (HD) space.
METHODS
This article proposes a novel lightweight approach relying only on native HD arithmetic vector operations to encode binarized images that preserves the similarity of patterns at nearby locations by using point of interest selection and .
RESULTS
The method reaches an accuracy of 97.92% on the test set for the MNIST data set and 84.62% for the Fashion-MNIST data set.
DISCUSSION
These results outperform other studies using native HDC with different encoding approaches and are on par with more complex hybrid HDC models and lightweight binarized neural networks. The proposed encoding approach also demonstrates higher robustness to noise and blur compared to the baseline encoding.
PubMed: 38946939
DOI: 10.3389/fdata.2024.1371518 -
World Journal of Gastroenterology Jun 2024Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models...
BACKGROUND
Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data.
AIM
To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.
METHODS
Data of patients treated for colorectal cancer ( = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group ( = 60) and a control group ( = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (7:3 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model.
RESULTS
More patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation ( < 0.05): Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.
CONCLUSION
This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.
Topics: Humans; Male; Colorectal Neoplasms; Female; Machine Learning; Middle Aged; Reoperation; Retrospective Studies; Risk Factors; Risk Assessment; Aged; Postoperative Complications; Nomograms; ROC Curve; China; Adult
PubMed: 38946868
DOI: 10.3748/wjg.v30.i23.2991 -
Biomedical Engineering Letters Jul 2024Schizophrenia (SZ) is a severe, chronic mental disorder without specific treatment. Due to the increasing prevalence of SZ in societies and the similarity of the...
Schizophrenia (SZ) is a severe, chronic mental disorder without specific treatment. Due to the increasing prevalence of SZ in societies and the similarity of the characteristics of this disease with other mental illnesses such as bipolar disorder, most people are not aware of having it in their daily lives. Therefore, early detection of this disease will allow the sufferer to seek treatment or at least control it. Previous SZ detection studies through machine learning methods, require the extraction and selection of features before the classification process. This study attempts to develop a novel, end-to-end approach based on a 15-layers convolutional neural network (CNN) and a 16-layers CNN- long short-term memory (LSTM) to help psychiatrists automatically diagnose SZ from electroencephalogram (EEG) signals. The deep model uses CNN layers to learn the temporal properties of the signals, while LSTM layers provide the sequence learning mechanism. Also, data augmentation method based on generative adversarial networks is employed over the training set to increase the diversity of the data. Results on a large EEG dataset show the high diagnostic potential of both proposed methods, achieving remarkable accuracy of 98% and 99%. This study shows that the proposed framework is able to accurately discriminate SZ from healthy subject and is potentially useful for developing diagnostic tools for SZ disorder.
PubMed: 38946814
DOI: 10.1007/s13534-024-00360-9 -
Health Science Reports Jul 2024Ovarian cancer is a common type of cancer and a leading cause of death in women. Therefore, accurate and fast prediction of ovarian tumors is crucial. One of the...
PURPOSE
Ovarian cancer is a common type of cancer and a leading cause of death in women. Therefore, accurate and fast prediction of ovarian tumors is crucial. One of the appropriate and precise methods for predicting and diagnosing this cancer is to build a model based on artificial intelligence methods. These methods provide a tool for predicting ovarian cancer according to the characteristics and conditions of each person.
METHOD
In this study, a data set included records related to 171 cases of benign ovarian tumors, and 178 records related to cases of ovarian cancer were analyzed. The data set contains the records of blood test results and tumor markers of the patients. After data preprocessing, including removing outliers and replacing missing values, the weight of the effective factors was determined using information gain indices and the Gini index. In the next step, predictive models were created using random forest (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN) models. The performance of these models was evaluated using the 10-fold cross-validation method using the indicators of specificity, sensitivity, accuracy, and the area under the receiver operating characteristic curve. Finally, by comparing the performance of the models, the best predictive model of ovarian cancer was selected.
RESULTS
The most important predictive factors were HE4, CA125, and NEU. The RF model was identified as the best predictive model, with an accuracy of more than 86%. The predictive accuracy of DT, SVM, and ANN models was estimated as 82.91%, 85.25%, and 79.35%, respectively. Various artificial intelligence (AI) tools can be used with high accuracy and sensitivity in predicting ovarian cancer.
CONCLUSION
Therefore, the use of these tools can help specialists and patients with early, easier, and less expensive diagnosis of ovarian cancer. Future studies can leverage AI to integrate image data with serum biomarkers, thereby facilitating the creation of novel models and advancing the diagnosis and treatment of ovarian cancer.
PubMed: 38946777
DOI: 10.1002/hsr2.2203 -
RSC Advances Jun 2024In recent years, smartphones have been integrated into rapid colorimetric sensors for heavy metal ions, but challenges persist in accuracy and efficiency. Our study...
In recent years, smartphones have been integrated into rapid colorimetric sensors for heavy metal ions, but challenges persist in accuracy and efficiency. Our study introduces a novel approach to utilize biogenic gold nanoparticle (AuNP) sensors in conjunction with designing a lightbox with a color reference and machine learning for detection of Fe ions in water. AuNPs were synthesized using the aqueous extract of leaf as reductants and stabilizing agents. Physicochemical analyses revealed diverse AuNP shapes and sizes with an average size of 19.8 nm, with a crystalline structure confirmed SAED and XRD techniques. AuNPs exhibited high sensitivity and selectivity in detection of Fe ions through UV-vis spectroscopy and smartphones, relying on nanoparticle aggregation. To enhance image quality, we developed a lightbox and implemented a reference color value for standardization, significantly improving performance of machine learning algorithms. Our method achieved approximately 6.7% higher evaluation metrics ( = 0.8780) compared to non-normalized approaches ( = 0.8207). This work presented a promising tool for quantitative Fe ion analysis in water.
PubMed: 38946772
DOI: 10.1039/d4ra03265a -
Telemedicine Journal and E-health : the... Jul 2024Mobile health (mHealth) has an emerging potential for remote assessment of traumatic dental injuries (TDI) and support of emergency care. This study aimed to determine...
Mobile health (mHealth) has an emerging potential for remote assessment of traumatic dental injuries (TDI) and support of emergency care. This study aimed to determine the diagnostic accuracy of TDI detection from smartphone-acquired photographs. The upper and lower anterior teeth of 153 individuals aged ≥ 6 years were photographed using a smartphone camera app. The photos of 148 eligible participants were reviewed independently by a dental specialist, two general dentists, and two dental therapists, using predetermined TDI classification and criteria. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and inter-rater reliability were estimated to evaluate the diagnostic performance of the photographic method relative to the reference standard established by the dental specialist. Of the 1,870 teeth screened, one-third showed TDI; and one-seventh of the participants had primary or mixed dentitions. Compared between the specialist's reference standard and four dental professionals' reviews, the diagnostic sensitivity and specificity for TDI versus non-TDI were 59-95% and 47-93%, respectively, with better performance for urgent types of TDI (78-89% and 99-100%, separately). The diagnostic consistency was also better for the primary/mixed dentitions than the permanent dentition. This study suggested a valid mHealth practice for remote assessment of TDI. A better diagnostic performance in the detection of urgent types of TDI and examination of the primary/mixed dentition was also reported. Future directions include professional development activities involving dental photography and photographic assessment, incorporation of a machine learning technology to aid photographic reviews, and randomized controlled trials in multiple clinical settings.
PubMed: 38946689
DOI: 10.1089/tmj.2024.0012 -
Journal of the American Medical... Jul 2024Acute hepatic porphyria (AHP) is a group of rare but treatable conditions associated with diagnostic delays of 15 years on average. The advent of electronic health...
BACKGROUND
Acute hepatic porphyria (AHP) is a group of rare but treatable conditions associated with diagnostic delays of 15 years on average. The advent of electronic health records (EHR) data and machine learning (ML) may improve the timely recognition of rare diseases like AHP. However, prediction models can be difficult to train given the limited case numbers, unstructured EHR data, and selection biases intrinsic to healthcare delivery. We sought to train and characterize models for identifying patients with AHP.
METHODS
This diagnostic study used structured and notes-based EHR data from 2 centers at the University of California, UCSF (2012-2022) and UCLA (2019-2022). The data were split into 2 cohorts (referral and diagnosis) and used to develop models that predict (1) who will be referred for testing of acute porphyria, among those who presented with abdominal pain (a cardinal symptom of AHP), and (2) who will test positive, among those referred. The referral cohort consisted of 747 patients referred for testing and 99 849 contemporaneous patients who were not. The diagnosis cohort consisted of 72 confirmed AHP cases and 347 patients who tested negative. The case cohort was 81% female and 6-75 years old at the time of diagnosis. Candidate models used a range of architectures. Feature selection was semi-automated and incorporated publicly available data from knowledge graphs. Our primary outcome was the F-score on an outcome-stratified test set.
RESULTS
The best center-specific referral models achieved an F-score of 86%-91%. The best diagnosis model achieved an F-score of 92%. To further test our model, we contacted 372 current patients who lack an AHP diagnosis but were predicted by our models as potentially having it (≥10% probability of referral, ≥50% of testing positive). However, we were only able to recruit 10 of these patients for biochemical testing, all of whom were negative. Nonetheless, post hoc evaluations suggested that these models could identify 71% of cases earlier than their diagnosis date, saving 1.2 years.
CONCLUSIONS
ML can reduce diagnostic delays in AHP and other rare diseases. Robust recruitment strategies and multicenter coordination will be needed to validate these models before they can be deployed.
PubMed: 38946554
DOI: 10.1093/jamia/ocae141