LASSO regression visualization plot. by Wentao Yang (205781)

“…This study examines the eGDR-frailty link, develops a machine learning predictive model to address this gap, and explores diabetes mellitus (DM) as a mediator, providing new insights for clinical intervention.…”

SHAP dependence plots. by Wentao Yang (205781)

“…This study examines the eGDR-frailty link, develops a machine learning predictive model to address this gap, and explores diabetes mellitus (DM) as a mediator, providing new insights for clinical intervention.…”

Tertile stratified subgroup analysis. by Wentao Yang (205781)

“…This study examines the eGDR-frailty link, develops a machine learning predictive model to address this gap, and explores diabetes mellitus (DM) as a mediator, providing new insights for clinical intervention.…”

Arrangement of PHC facilities in a woreda NoCs. by Gizachew Tadele Tiruneh (8407686)

“…The <i>"Improve Primary Health Care Service Delivery (IPHCSD)"</i> project, implemented by JSI and Amref Health Africa since April 2022, seeks to address these gaps through a Networks of Care (NoCs) approach. This paper describes the lessons learned from implementing the NoCs approach to optimize primary health care in Ethiopia.…”

Fish hydrolysate supplementation prevents short-term and long-term spatial memory deficits and promotes the use of spatial strategies during spatial learning in D-gal mice. by Camille Mougin (19942355)

Image 1_Caffeine on the mind: EEG and cardiovascular signatures of cortical arousal revealed by wearable sensors and machine learning—a pilot study on a male group.jpeg by Shabbir Chowdhury (22246345)

“…Although systolic and diastolic BP showed a non-significant upward trend, HR decreased significantly after caffeine intake (77 ± 5.3 bpm to 72 ± 2.5 bpm, p = 0.027). …”

Image 2_A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.jpeg by Daisu Abe (20498225)

“…In this study, we developed a machine learning model to predict the occurrence of neurological deterioration after mild TBI using information obtained on admission.…”

Image 1_A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.jpeg by Daisu Abe (20498225)

“…In this study, we developed a machine learning model to predict the occurrence of neurological deterioration after mild TBI using information obtained on admission.…”

Table 2_A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.docx by Daisu Abe (20498225)

“…In this study, we developed a machine learning model to predict the occurrence of neurological deterioration after mild TBI using information obtained on admission.…”

Table 3_A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.docx by Daisu Abe (20498225)

“…In this study, we developed a machine learning model to predict the occurrence of neurological deterioration after mild TBI using information obtained on admission.…”

Table 1_A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.docx by Daisu Abe (20498225)

“…In this study, we developed a machine learning model to predict the occurrence of neurological deterioration after mild TBI using information obtained on admission.…”

A Comparison Study of Open Source LLMs for HPC Tickets Answering by Zhao Zhang (17060598)

Residual plot for the Teamwork and collaboration variable. by Thales Guardia de Barros (22216025)

Schema of the experimental procedure. by Marcel F. Hinss (14675457)

“…The results indicate that similarity between tasks substantially impacts performance with different effects on RT and accuracy. While learning effects may have negated the impact of mental fatigue across the 5 experimental blocks, a significant decrease in performance was observed within blocks. …”

The St Mary’s Hospital Sleep Questionnaire was used to estimate the sleep quality on the previous night. by Marcel F. Hinss (14675457)

Examples of different stimuli for all subtasks of the task. by Marcel F. Hinss (14675457)

Summary of demographics: Left: Distribution of age; Right: Distribution of handedness as assessed by the EDI [40]. by Marcel F. Hinss (14675457)

DDX5 and DDX17 decrease viral gene expression during KSHV lytic reactivation in BCBL1 cells and do not affect KSHV entry in HUVEC. by Praneet Kaur Sandhu (21156149)

Table 2_Unsupervised machine learning revealed a correlation between low-dose statins and favorable outcomes in ICH patients.docx by Chaohua Cui (1793155)

“…</p>Methods<p>We employed unsupervised machine learning techniques to analyze unidentified factors within a retrospective cohort related to the prognosis of ICH. …”

Table 1_Unsupervised machine learning revealed a correlation between low-dose statins and favorable outcomes in ICH patients.docx by Chaohua Cui (1793155)

“…</p>Methods<p>We employed unsupervised machine learning techniques to analyze unidentified factors within a retrospective cohort related to the prognosis of ICH. …”