Unified Mobile App for Streamlining Verbal Autopsy and Cause of Death Assignment in India: Design and Development Study by Harleen Kaur, Stuti Tripathi, Manjeet Singh Chalga, Sudhir K Benara, Amit Dhiman, Shefali Gupta, Saritha Nair, Geetha Menon, B K Gulati, Sandeep Sharma, Saurabh Sharma

“…The app’s data collection and transmission efficiency were further improved based on feedback from user and subject area experts. …”
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Challenges and Opportunities of the Human-Centered Design Approach: Case Study Development of an Assistive Device for the Navigation of Persons With Visual Impairment by Mario Andres Chavarria, Luisa María Ortiz-Escobar, Bladimir Bacca-Cortes, Victor Romero-Cano, Isabella Villota, Jhon Kevin Muñoz Peña, José Roberto Londoño Sánchez, Oscar Campo, Silvan Suter, Jhon Jairo Cabrera-López, Maria Fernanda Sanchez Patiño, Eduardo Francisco Caicedo-Bravo, Michael Stein, Samia Hurst, Klaus Schönenberger, Minerva Rivas Velarde

“…The prototype met several user-defined expectations by prioritizing affordability; extended battery life; autonomy in internet-constrained environments; and improved ergonomics, concealability, aesthetics, and obstacle detection. …”
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A novel biochemical analysis for ApoE4 quantification in plasma and discrimination of homozygous and heterozygous APOE ε4 carriers by Andrés Rodríguez, Olga Calero, Sergio Veiga, Miriam Menacho-Román, Ignacio Arribas, Lluís Cano, Guillermo García-Ribas, Miguel Calero

“…Normalizing ApoE4 to total ApoE improved classification (sensitivity 94.3%, specificity 93.9%). …”
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Dynamic and interpretable deep learning model for predicting respiratory failure following cardiac surgery by Man Xu, Hao Liu, Anran Dai, Qilian Tan, Xinlong Zhang, Rui Ding, Chen Chen, Jianjun Zou, Yongjun Li, Yanna Si

“…In the validation cohort, the DNN model achieved superior performance, with an AUROC of 0.782 (95% CI 0.703–0.852) and an AUPRC of 0.496 based on preoperative variables, which improved to an AUROC of 0.855 (95% CI 0.796–0.906) and an AUPRC of 0.549 with the addition of intraoperative data. …”
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Development of a postoperative recurrence prediction model for stage Ⅰ non-small cell lung cancer patients using multimodal data based on machine learning by ZHANG Di, WU Yi, XU Yu

“…By integrating the 7 radiomic features and 4 clinical features using a feature-level fusion strategy, the combined model exhibited further improved predictive performance, with an AUC value of 0.953 (95% CI: 0.924~0.983; accuracy: 0.884, specificity: 0.860) and 0.852 (95% CI: 0.729~0.976; accuracy: 0.682, specificity: 0.629), respectively in the training set and the validation set. …”
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Data augmentation using SMOTE technique: Application for prediction of burst pressure of hydrocarbons pipeline using supervised machine learning models by Afzal Ahmed Soomro, Ainul Akmar Mokhtar, Masdi B. Muhammad, Mohamad Hanif Md Saad, Najeebullah Lashari, Muhammad Hussain, Abdul Sattar Palli

“…First, FEA was used to make a dataset. Then, a new way to improve machine learning (ML) model generalization for burst pressure prediction is suggested: combine publicly available datasets of different pipeline specifications. …”
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Explainable Artificial Intelligence to Predict the Water Status of Cotton (<i>Gossypium hirsutum</i> L., 1763) from Sentinel-2 Images in the Mediterranean Area by Simone Pietro Garofalo, Anna Francesca Modugno, Gabriele De Carolis, Nicola Sanitate, Mesele Negash Tesemma, Giuseppe Scarascia-Mugnozza, Yitagesu Tekle Tegegne, Pasquale Campi

“…This approach demonstrates the prospective for high-frequency, non-invasive monitoring of cotton’s water status, which could support smart irrigation strategies and improve water use efficiency in Mediterranean cotton production.…”
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A Low Power Memory-Integrated Hardware BNN-MLP Model on an FPGA for Current Signals in a Biosensor by Geon-Hoe Kim, Dong-Gyun Kim, Sung-Jae Lee, Jong-Han Kim, Da-Yeong An, Hyejin Kim, Young-Gun Pu, Heejeong Jasmine Lee, Jun-Eun Park, Kang-Yoon Lee

“…To reduce power consumption and area, a BNN-MLP model without an extraction layer was designed, and to improve accuracy, a dense layer was added to the final layer. …”
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A comprehensive analysis of the prognostic value, expression characteristics and immune correlation of MKI67 in cancers by Xiaolan Pan, Caibiao Wei, Jingyu Su, Min Fang, Qiumei Lin, Yuling Qin, Jie Gao, Jie Zhao, Huiliu Zhao, Fengfei Liu

“…These observed results contribute to the promise of MKI67 in a realistic clinical setting and improve the outcomes of cancer patients.…”
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Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study by Yimei Zhang, Yu Wang, Jingran Yang, Qinglan Li, Min Zhou, Jiafei Lu, Qiulan Hu, Fang Ma

“…Additionally, machine learning can identify novel risk factors and mechanisms overlooked by conventional methods, supporting early intervention and targeted prevention strategies to improve patient prognosis. Therefore, this study aims to develop and validate risk prediction models for ICUAW based on multiple machine learning algorithms. …”
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Construction of a Prediction Model for Sleep Quality in Embryo Repeated Implantation Failure Patients Undergoing Assisted Reproductive Technology Based on Machine Learning: A Singl... by Zhao Y, Xu C, Qin N, Bai L, Wang X, Wang K

“…Medical personnel should implement intervention strategies as early as possible based on relevant risk factors to improve the sleep quality of this population.Keywords: recurrent implantation failure, assisted reproductive technology, sleep quality, predictive model, risk factors…”
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Non-invasive detection of Parkinson’s disease based on speech analysis and interpretable machine learning by Huanqing Xu, Wei Xie, Mingzhen Pang, Ya Li, Luhua Jin, Fangliang Huang, Xian Shao

“…Early detection of PD through non-invasive methods, such as speech analysis, can improve treatment outcomes and quality of life for patients. …”
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From smoking cessation to physical activity: Can ontology-based methods for automated evidence synthesis generalise across behaviour change domains? [version 2; peer review: 2 appr... by Candice Moore, Emily Hayes, Oscar Castro, Ella Howes, Alison J Wright, Emma Norris, Susan Michie, Robert West

“…The Human Behaviour-Change Project (HBCP) aims to improve evidence synthesis in behavioural science by compiling intervention reports and annotating them with an ontology to train information extraction and prediction algorithms. …”
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The Impact of the Natural Grass-Growing Model on the Development of Korla Fragrant Pear Fruit, as Well as Its Influence on Post-Harvest Sugar Metabolism and the Expression of Key E... by Mingyang Yu, Lanfei Wang, Yan Chen, Weifan Fan, Hao Wang, Kailu Guo, Shutian Tao, Xin Gong, Jianping Bao

“…A classification model was constructed using machine learning algorithms (RF, KNN, SVM), and particle swarm optimization (PSO) was employed to identify key factors. …”
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Associations Between Intracranial Pressure Extremes and Continuous Metrics of Cerebrovascular Pressure Reactivity in Acute Traumatic Neural Injury: A Scoping Review by Kevin Y. Stein, Fiorella Amenta, Logan Froese, Alwyn Gomez, Amanjyot Singh Sainbhi, Nuray Vakitbilir, Younis Ibrahim, Abrar Islam, Tobias Bergmann, Izabella Marquez, Frederick A. Zeiler

“…However, further work is required to better understand this relationship and improve algorithmic derivation of such individualized ICP thresholds.…”
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AI-based thematic exploration to understand patients with myasthenia gravis by serological subtype by Louis Jackson, Caroline Brethenoux, Alyssa DeLuca, Jacqueline Pesa, Zia Choudhry, Patrick Furey, Rosario Alvarez, Laura Gonzalez, Alex Lorenzo, Raghav Govindarajan, Ashley E. L. Anderson

“…Advanced search techniques and AI-powered algorithms were used to extract/organize data by topics into a large, unstructured dataset. …”
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Enhancing Detection of Multi-Frequency-Modulated SSVEP Using Phase Difference Constrained Canonical Correlation Analysis by Chi Man Wong, Ze Wang, Boyu Wang, Agostinho Rosa, Tzyy-Ping Jung, Feng Wan

“…However, the existing calibration-free recognition algorithms based on the traditional canonical correlation analysis (CCA) cannot provide the merited performance. …”
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Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants by Hanieh Talebi, Seyed Alireza Dastgheib, Maryam Vafapour, Reza Bahrami, Mohammad Golshan-Tafti, Mahsa Danaei, Sepideh Azizi, Amirhossein Shahbazi, Melina Pourkazemi, Maryam Yeganegi, Amirmasoud Shiri, Ali Masoudi, Heewa Rashnavadi, Hossein Neamatzadeh

“…Overall, these findings emphasize the importance of combining biomarker analysis with advanced computational techniques to improve clinical decision-making and intervention strategies for managing BPD and nRDS in vulnerable preterm infants.…”
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Adherence to Clinical Quidelines on Preoperative Assessment and Correction of Cardiovascular Risk in Non-cardiac Surgery by M. D. Nigmatkulova, E. B. Kleymenova, L. P. Yashina, V. A. Otdelenov, S. A. Payushchik, O. D. Konova, D. A. Sychev

“…In 2014 the updated joint ESC/ESA guidelines on preoperative assessment and perioperative management of patients were published to improve patient safety in non-cardiac surgery. The increase in the adherence to clinical guidelines promotes the improving of the healthcare quality and safety improvement.Aim. …”
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Features of Statistical Accounting of Allergic Diseases in Children — Evidence from Moscow by Anita R. Denisova, Alexander B. Malahov, Alexandr N. Pampura, Elena A. Vishneva, Margarita A. Soloshenko, Nikoloz M. Gaboshvili, Leyla S. Namazova-Baranova

“…The differences in the incidence rates of allergic diseases revealed by a comparative analysis of data from various sources — UMIAS and FSO No. 12 — indicate the need to improve both the system of statistical registration of incidence and the development of modern algorithms for early diagnosis and dynamic monitoring of children with allergies.…”
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