Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

Feature engineering. by Youli Jiang (15249070)

Conceptual diagram of readiness for interprofessional learning. by Thales Guardia de Barros (22216025)

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Process mining, combined with decision tree analysis using machine learning, can reveal insights into various process inefficiencies, such as rework. by Mohammad Bakhtiari (11838264)

Word cloud with key elements of an Active Learning Classroom. by Mairena Sánchez-López (684092)

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Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Table 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…SHapley Additive exPlanations interpretability analysis revealed SUV39H1 as the dominant risk contributor, while GLUL exhibited a protective effect. …”

Table 4_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…SHapley Additive exPlanations interpretability analysis revealed SUV39H1 as the dominant risk contributor, while GLUL exhibited a protective effect. …”

Table 5_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…SHapley Additive exPlanations interpretability analysis revealed SUV39H1 as the dominant risk contributor, while GLUL exhibited a protective effect. …”

Table 2_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…SHapley Additive exPlanations interpretability analysis revealed SUV39H1 as the dominant risk contributor, while GLUL exhibited a protective effect. …”

Table 3_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…SHapley Additive exPlanations interpretability analysis revealed SUV39H1 as the dominant risk contributor, while GLUL exhibited a protective effect. …”

Data Sheet 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.docx by Jingjing Chen (293564)

“…SHapley Additive exPlanations interpretability analysis revealed SUV39H1 as the dominant risk contributor, while GLUL exhibited a protective effect. …”

Interaction plot with 95% confidence intervals generated from the GAM regression model 1 in Table 2 examining initial ED SBP and management group interactions. by Randeep S. Jawa (20489181)

GPT-4 results in decreased error rate for all tasks with increasing complexity when compared to GPT-3.5-turbo. by Jacqueline A. Jansen (14894276)

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

“…</p>Conclusion<p>Unsupervised machine learning revealed a correlation between low-dose statins and patient prognosis. …”

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

“…</p>Conclusion<p>Unsupervised machine learning revealed a correlation between low-dose statins and patient prognosis. …”

Aims, outcomes and main results of the included studies. by Mairena Sánchez-López (684092)

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Characteristics of the studies. by Mairena Sánchez-López (684092)

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