Machine learning algorithms to predict feeding practices during diarrheal disease and its determinants among under-five children in East Africa by Tirualem Zeleke Yehuala, Nebebe Demis Baykemagn, Bewuketu Terefe

“…We employed four ML algorithms, such as Random Forest (RF), Decision Tree (DT), XGB (Extreme Gradient Boosting), and Logistic Regression (LR). …”
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Impact of burnout on turnover, medical errors, medical leave and a cross-sectional study of contributing factors among Chilean physicians by Rodrigo Guiloff, Francisco Villalón López, Adrian P Mundt, Alejandro Hirmas, Rita M Rivera

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Assessment of Meat Quality and Shelf Life from Broilers Fed with Different Sources and Concentrations of Methionine by Antonia Albrecht, Martin Hebel, Céline Heinemann, Ulrike Herbert, Dennis Miskel, Behnam Saremi, Judith Kreyenschmidt

“…White striping was positively correlated to fillet weight as well as color values and significantly affected the Purchase Decision, the sensory investigation, and thus the shelf life of the samples.…”
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Classification of intracranial tumors based on optical-spectral analysis by I. D. Romanishkin, T. A. Savelieva, A. Ospanov, K. G. Linkov, S. V. Shugai, S. A. Goryajnov, G. V. Pavlova, I. N. Pronin, V. B. Loschenov

“…In case the number of parameters exceeds a couple of dozens, it is necessary to use machine learning algorithms  to build a intraoperative decision support system for the surgeon. This paper presents research in this direction. …”
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Digital Phenotyping for Detecting Depression Severity in a Large Payor-Provider System: Retrospective Study of Speech and Language Model Performance by Bradley Karlin, Doug Henry, Ryan Anderson, Salvatore Cieri, Michael Aratow, Elizabeth Shriberg, Michelle Hoy

“…The use of speech as a digital biomarker of depression represents a considerable opportunity for transforming and accelerating depression identification and treatment; however, research to date has primarily consisted of small-sample feasibility or pilot studies incorporating highly controlled applications and settings. …”
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Assessing the health workforce in Afghanistan: a situational analysis into the country’s capacity for Universal health coverage by Narges Neyazi, Nima Yaghmaei, Mirwais Ahmadzai, Elisabeth Kleipool, Nadine Naumann, Myrte Wassenaar, Muhammad Haider Omar, Fethiye Gülin Gedik, Sandra Alba, Marjolein Dieleman, Abdul Ghani Ibrahimi, Alaa AbouZeid

“…A fit-for-purpose health workforce is crucial for achieving UHC, and requires decision-making by policy-makers driven by sufficient evidence. …”
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Effect of feedback-integrated reflection, on deep learning of undergraduate medical students in a clinical setting by Zainab Maqsood, Madiha Sajjad, Raheela Yasmin

“…Feedback, in turn, provides external insights that complement reflection, helping learners recognize their strengths and weaknesses, adjust their learning strategies, and enhance clinical reasoning and decision-making skills. However, reflection alone may not produce the desirable effects unless coupled with feedback. …”
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Diagnostic and prognostic value of systemic immune-inflammation index for heart failure: a systematic review and meta-analysis by Jiajun Yu, Tian Zuo, Sihan Peng, Danping Xu

“…Furthermore, subgroup analyses revealed the association between SII and mortality in patients with HF was not influenced by sample size, age, country, study design, or ejection fraction. …”
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Weight bias in mental health settings: a scoping review by Samantha R. Philip, Erin C. Standen, Jordan Schueler, Sherecce A. Fields, Sean M. Phelan, Sean M. Phelan

“…Studies using experimental designs demonstrated that providers’ clinical judgment and decision-making were impacted by client body size, generally showing that higher-weight clients were perceived to have lower global functioning, greater pathology, and more negative attributes than lower-weight clients. …”
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Machine learning-based prediction model for arteriovenous fistula thrombosis risk: a retrospective cohort study from 2017 to 2024 by Peng Shu, Ling Huang, Xia Wang, Zhuping Wen, Yiqi Luo, Fang Xu

“…The dataset was partitioned into a training set and a test set, comprising 70% and 30% of the samples, respectively. Six distinct machine learning models—namely, Random Forest, Extreme Gradient Boosting, Decision Tree, Logistic Regression, K-Nearest Neighbors, and Naive Bayes—were constructed to predict the risk of thrombosis in arteriovenous fistulas. …”
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Research on Fracture Energy Prediction and Size Effect of Concrete Based on Deep Learning with SHAP Interpretability Method by Huiming Wang, Weiqi Zhang, Jie Lin, Shengpin Guo

“…Nevertheless, this approach is hindered by protracted sample production cycles and test loading conditions that contribute to elevated expenses. …”
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Clinical significance of the combined systemic immune-inflammatory index and prognostic nutritional index in predicting the prognosis of patients with extensive-stage small-cell lu... by Bingbing Wang, Jingdan Zhang, Yingnan Shi, Yan Wang

“…A nomogram was constructed based on these independent factors. With 1000 repeated samples, the bootstrap method was used to validate the nomogram model internally. …”
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Comparative Analysis of Cervical Range of Motion Assessment in Mechanical Neck Pain Using Conventional Techniques versus Artificial Intelligence Approaches: A Cross-sectional Obser... by Sonia Pawaria, Shail Sachdeva, Rahul Bajaj, Priyanka Siwach

“…Future research ought to investigate how AIbased evaluations affect clinical outcomes and decision-making over the long run.…”
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Construction and Validation of a PCA-Based Prediction Model for Preterm Infant Stunting Risk: A Retrospective Study by Kun Dai, Rong Yu, Yushi Meng, Xiaomeng Yang, Yixin Jiang, Jing Luo, Kui Fang, Suqing Wang, Zhihui Rong

“…The calibration curves, consistency index, and decision curve analysis all showed that the model was significantly better than a single indicator in predicting the risk of stunting in preterm infants. …”
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Cost-utility analysis of a multispecialty interprofessional team dementia care model in Ontario, Canada by Andrew P Costa, Linda Lee, Samir K Sinha, Tejal Patel, Loretta M Hillier, William W L Wong, Sasha Walker, Catherine Lee

“…The results of this economic evaluation can inform decision-making and improvements to health system design, resource allocation and care experience for persons living with dementia. …”
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Protocol for the derivation and external validation of a 30-day mortality risk prediction model for older patients having emergency general surgery (PAUSE score—Probability of mort... by Carl van Walraven, Daniel I McIsaac, Manoj Lalu, Simon Feng, Husein Moloo, Reilly Musselman

“…Older age is associated with doubling of risk when undergoing emergency general surgery (EGS) procedures and often coexists with medical complexity and considerations of end-of-life care, creating prognostic and decisional uncertainty. Combined with the time-sensitive nature of EGS, it is challenging to gauge perioperative risk and ensure that clinical decisions are aligned with the patient values. …”
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Bridging intentions and outcomes: a program theory approach to residency accreditation standards by Dana Tenne-Fishbain, Yehuda L. Danon, Rachel Nissanholz-Gannot

“…Interviews were conducted in three rounds (2021–2024), with participants selected to represent diverse perspectives including decision-makers, department heads, scientific societies, residents, and site visit teams. …”
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Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health by Yiyang Liu, Aokun Chen, Hwayoung Cho, Khairul A. Siddiqi, Robert L. Cook, Mattia Prosperi

“…Future work could involve stakeholders in implementing the model into HIV PrEP decision support and exploring causal pathways to guide risk-reduction interventions.…”
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A proposed stochastic growth model for monitoring the population dynamics in Ghana by Richel O. Attafuah, Eric Ocran, Enoch Sakyi-Yeboah, Edward Acheampong, Louis Asiedu

“…Population size modelling offers crucial insights into societal, economic, environmental, and public health dynamics, aiding in informed decision-making and sustainable development efforts. …”
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Estimation of the air conditioning energy consumption of a classroom using machine learning in a tropical climate by Liliana Ortega-Diaz, Julian Jaramillo-Ibarra, German Osma-Pinto

“…Predicting energy consumption is critical to making informed decisions and identifying factors influencing power consumption. …”
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