Prediction of postpartum depression in women: development and validation of multiple machine learning models by Weijing Qi, Yongjian Wang, Yipeng Wang, Sha Huang, Cong Li, Haoyu Jin, Jinfan Zuo, Xuefei Cui, Ziqi Wei, Qing Guo, Jie Hu

“…Seven feature selection methods and six ML algorithms were employed to develop models, and their prediction performances were compared. …”
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Target repositioning using multi-layer networks and machine learning: The case of prostate cancer by Milan Picard, Marie-Pier Scott-Boyer, Antoine Bodein, Mickaël Leclercq, Julien Prunier, Olivier Périn, Arnaud Droit

“…Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.…”
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An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles by Prot Piyakawanich, Pattarapong Phasukkit

“…This research presents an innovative approach to enhance altitude estimation accuracy for UAVs weighing under 2 kg without cameras, utilizing advanced AI Deep Learning algorithms. The primary novelty of this study lies in its unique integration of unsupervised and supervised learning techniques. …”
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Residential Electricity Load Model Construction in District Scale by Jieyan XU, Wenyang XU, Yuan CHU, Yuan JIN, Xuyuan KANG, Zheng CHEN

“…To this end, this study proposes a method integrating auto-encoding and neighbor clustering algorithms for data outlier detection for data with high-dimensional characteristics at the spatial and temporal scales. …”
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Predicting PTSD development with early post-trauma assessments: a proof-of-concept for a concise tree-based classification method by Chia-Hao Shih, Elyssa Charlotte Feuer, Ben Kurzion, Kevin Xu, Hong Xie, Stephen R. Grider, Xin Wang

“…The performance of the CART model was benchmarked against two of the most powerful and widely used machine learning algorithms in the field, Random Forest (RF) and Gradient Boosting (GB) models.Results: The CART model, which incorporates just three critical questions from established assessments, predicted PTSD development with performance closely matched to that of the RF and GB models. …”
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Disinformation in the Digital Age: Climate Change, Media Dynamics, and Strategies for Resilience by Andrea Tomassi, Andrea Falegnami, Elpidio Romano

“…Our findings indicate that social media algorithms and user dynamics can amplify false scientific claims, as seen in case studies of viral misinformation campaigns on vaccines and climate change. …”
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Grid Search Based Hyperparameter-Tuned Deep Learning Model for Osteoporosis Diagnosis with Bi-Cubic Interpolation of X-Ray Images by Ruhul Amin, Md.Shamim Reza, Dewan Ahmed Muhtasim, Jungpil Shin, Md. Maniruzzaman, Md.Mahfujul Hasan

“…Even with the widespread use of deep learning (DL) and machine learning (ML) algorithms, the early diagnosis of osteoporosis patients can still be improved. …”
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Trends in the causes of stillbirths over 20 years in Southern Japan by Emi Kino, Yohei Maki, Naoshi Yamada, Yuki Kodama, Shinji Katsuragi, Hiroshi Sameshima, Tsuyomu Ikenoue

“…Investigations to prevent cord-accident stillbirths would be required to further reduce stillbirths in the study region. Establishment of algorithms that allow the identification of the causes of stillbirths would be crucial to reduce instances of stillbirths due to unidentified causes.…”
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Facial Beauty Prediction Combining Dual-Branch Feature Fusion With a Stacked Broad Learning System by Junying Gan, Hantian Chen, Wenchao Xu, Huicong Li, Zhenxin Zhuang, Zhen Chen

“…Facial beauty prediction (FBP) is a key computer vision task that uses algorithms to assess facial attractiveness. Current models rely on single feature extraction, such as using a single convolutional neural network to extract local feature, failing to capture other potentially more important information contained within facial data and limiting feature diversity. …”
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BlendNet: a blending-based convolutional neural network for effective deep learning of electrocardiogram signals by S. Premanand, Sathiya Narayanan

“…However, this traditional approach captures only a limited set of features of ECG and thereby limits the effectiveness of DL architectures in disease detection.MethodsThis work proposes “BlendNet,” a DL architecture that effectively extracts the features of an ECG signal using a blending approach termed “alpha blending.” …”
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Anatomically Guided Deep Learning System for Right Internal Jugular Line (RIJL) Segmentation and Tip Localization in Chest X-Ray by Siyuan Wei, Liza Shrestha, Gabriel Melendez-Corres, Matthew S. Brown

“…To reduce the workload of clinicians, deep learning-based automated detection algorithms have been developed to detect CVCs in CXRs. …”
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Downscaling air temperatures for high-resolution niche modeling in a valley of the Amazon lowland forests: A case study on the microclima R package. by M J Pohl, L Lehnert, B Thies, K Seeger, M B Berdugo, S R Gradstein, M Y Bader, J Bendix

“…Despite problems with the distinction between CAD and non-CAD events the microclima algorithms show difficulties in correctly modeling the diurnal course of the temperature data and the amplitudes of elevational temperature gradients. …”
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Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning by Shuxian Pan, Zibing Wang

“…Predictive models were constructed using three machine learning algorithms to analyze and statistically evaluate clinical characteristics, including immune cell data. …”
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Noninvasive diagnosis of interstitial fibrosis in chronic kidney disease: a systematic review and meta-analysis by Shanshan Wan, Shiping Wang, Xinyu He, Chao Song, Jiaping Wang

“…The sensitivity and specificity of US for detecting RF were 0.81 (95% CI: 0.76–0.86) and 0.79 (95% CI: 0.72–0.84). …”
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Stacking ensemble learning models diagnose pulmonary infections using host transcriptome data from metatranscriptomics by Tian Zhang, Ying Deng, Wentao Wang, Zhe Zhao, Yiling Wu, Haoqian Wang, Shutao Xia, Weifang Liao, Weijie Liao

“…Leveraging these characteristic genes, we constructed classification sub-models employing 13 types of machine learning algorithms, and we further integrated these sub-models into stacking-based ensemble models with Lasso regression, resulting in diagnostic models that required only a small set of gene expression inputs. …”
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Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic by Muhammed Ballı, Asli Ercan Dogan, Sevin Hun Senol, Hale Yapici Eser

“…Data from 924 university students seeking mental health services were analyzed using seven machine learning algorithms. Suicidal ideation was assessed through the 9th item of the Patient Health Questionnaire-9. …”
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A machine learning prediction model for Cardiac Amyloidosis using routine blood tests in patients with left ventricular hypertrophy by Yuling Pan, Qingkun Fan, Yu Liang, Yunfan Liu, Haihang You, Chunzi Liang

“…We divided the dataset into training and testing cohorts, applying ML algorithms such as logistic regression, random forest, and XGBoost for automated learning and prediction. …”
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Clinical applications of photon-counting CT in neuroimaging by LÜ Haiying, LU Yong, HE Naying

“…Photon-counting computed tomography (PCCT) is a next-generation CT imaging technology that markedly improves image quality while reducing radiation dose and image noise through single-photon detection and energy discrimination by photon detectors. …”
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Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis by Subo Zhang, Zhitao Zhu, Zhenfei Yu, Haifeng Sun, Yi Sun, Hai Huang, Lei Xu, Jinxin Wan

“…AI also enhanced image analysis efficiency (odds ratio 1.57, 95% CI 1.08-2.27; P=.02) and demonstrated high accuracy and sensitivity in detecting intracranial aneurysms, with low-dose CT using AI reconstruction showing noninferiority for liver lesion detection. …”
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Imaging-Based AI for Predicting Lymphovascular Space Invasion in Cervical Cancer: Systematic Review and Meta-Analysis by Lizhen She, Yunfeng Li, Hongyong Wang, Jun Zhang, Yuechen Zhao, Jie Cui, Ling Qiu

“… Abstract BackgroundThe role of artificial intelligence (AI) in enhancing the accuracy of lymphovascular space invasion (LVSI) detection in cervical cancer remains debated. …”
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