Comparison of Baseline Data between the Two Groups of Patients. by Xiaoyan Yang (259084)

Subjects:

Types of machine learning algorithms. by Md. Merajul Islam (12646837)

“…<div><p>Background and objectives</p><p>Child undernutrition is a leading global health concern, especially in low and middle-income developing countries, including Bangladesh. Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.…”

The details of the results related to the evaluated algorithms. by Mohammadreza Namdar (22439057)

Advancing Continuous Glucose Monitoring (CGM) for Inpatient Clinical Decision Support: Individual Algorithmic Mean Absolute Relative Difference (MARD) by Jill von Conta (22379489)

Research framework. by Shifeng Wu (16612230)

Subjects:

Computational cost of anchor-based algorithms. by Olarewaju Mubashiru Lawal (14710251)

Decision-making model for location selection of tourism and leisure facilities. by Shifeng Wu (16612230)

Subjects:

Mortality of MM patients based on the MYLORD algorithm (2014–2020). by Cyrille Touzeau (14982494)

Image 5_A machine learning model based on emergency clinical data predicting 3-day in-hospital mortality for stroke and trauma patients.jpeg by Xu Chen (432759)

“…This approach, which incorporates emergency data, holds promise for assisting decision-making in clinical practice, thereby improving patient outcomes.…”

Image 1_A machine learning model based on emergency clinical data predicting 3-day in-hospital mortality for stroke and trauma patients.tif by Xu Chen (432759)

“…This approach, which incorporates emergency data, holds promise for assisting decision-making in clinical practice, thereby improving patient outcomes.…”

Image 2_A machine learning model based on emergency clinical data predicting 3-day in-hospital mortality for stroke and trauma patients.tif by Xu Chen (432759)

“…This approach, which incorporates emergency data, holds promise for assisting decision-making in clinical practice, thereby improving patient outcomes.…”

Image 4_A machine learning model based on emergency clinical data predicting 3-day in-hospital mortality for stroke and trauma patients.tif by Xu Chen (432759)

“…This approach, which incorporates emergency data, holds promise for assisting decision-making in clinical practice, thereby improving patient outcomes.…”

Image 3_A machine learning model based on emergency clinical data predicting 3-day in-hospital mortality for stroke and trauma patients.tif by Xu Chen (432759)

“…This approach, which incorporates emergency data, holds promise for assisting decision-making in clinical practice, thereby improving patient outcomes.…”

Table 1_A machine learning model based on emergency clinical data predicting 3-day in-hospital mortality for stroke and trauma patients.docx by Xu Chen (432759)

“…This approach, which incorporates emergency data, holds promise for assisting decision-making in clinical practice, thereby improving patient outcomes.…”

Morpheus: A fragment-based algorithm to predict metamorphic behaviour in proteins across proteomes by Vijay Subramanian (20718933)

Algorithm and its parameter settings. by Lin Li (28817)

Subjects:

Performance of algorithms outside the training species. by Sven B. Gould (12237287)

Data_Sheet_1_Development and pilot evaluation of an evidence-based algorithm for MASLD (formerly NAFLD) management in primary care in Europe.docx by Marilena Anastasaki (4453894)

“…</p>Methods<p>An international multidisciplinary panel of experts oversaw pathway development, which was designed as a two-step algorithm with defined and sequenced tasks. …”

Accuracy verification of big data diagnosis and early warning algorithm. by Hao-yang Li (17815508)

The results of bug reports classification by the MNB algorithm. by Mohammadreza Namdar (22439057)