Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

Data Sheet 2_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.zip by Rui Zhang (13940)

Data Sheet 1_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.docx by Rui Zhang (13940)

Code and data - WST 895 thesis by Hoosain Ebrahim (14058588)

Definitions of relevant SSA parameters. by Yeping Shi (14279118)

Comparative performance evaluation of 3Mont with other feature selection algorithms over 10 iterations. by Miray Unlu Yazici (14792680)

Data Sheet 1_Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.pdf by Habtamu Setegn Ngusie (12178327)

Data Sheet 2_Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.pdf by Habtamu Setegn Ngusie (12178327)

DataSheet1_Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis.docx by Qiaozhi Hu (8775155)

“…Studies that developed ML models for predicting specific ADEs or ADEs associated with particular drugs were included using EHR data.…”

Data Sheet 1_Closing the loop: establishing an autonomous test-learn cycle to optimize induction of bacterial systems using a robotic platform.pdf by Jan Benedict Spannenkrebs (20595722)

“…To achieve this, each part must be characterised in a time-resolved manner under relevant conditions. Robotic platforms can be used to automate this task and provide sufficiently large and reproducible data sets including provenance. …”

External cohort raw data. by Lei Gao (6566)

“…</p><p>However, no predictive models for BSH have been developed using machine learning algorithms.</p><p>Objective</p><p>To evaluate the effectiveness of five machine learning algorithms in predicting thickening of the basal segment of the interventricular septum and to develop a simple, yet efficient, prediction model for BSH.…”

Internal cohort raw data. by Lei Gao (6566)

“…</p><p>However, no predictive models for BSH have been developed using machine learning algorithms.</p><p>Objective</p><p>To evaluate the effectiveness of five machine learning algorithms in predicting thickening of the basal segment of the interventricular septum and to develop a simple, yet efficient, prediction model for BSH.…”

The MRGs used for risk score. by Xuefen Lin (17785367)

“…As shown in the graphic summary, we retrieved gene expression and clinical data from open-access databases. To construct a predictive signature, we applied the Lasso Cox regression algorithm to MRGs. …”

Primer sequence used in this study. by Xuefen Lin (17785367)

“…As shown in the graphic summary, we retrieved gene expression and clinical data from open-access databases. To construct a predictive signature, we applied the Lasso Cox regression algorithm to MRGs. …”

The four areas of interest to the patients. by Anne Lübbeke (3918686)

“…</p><p>Methods</p><p>We created the information tool “Patients like me” in four steps. (1) The knowledge basis was the systematically collected detailed exposure and outcome information from the Geneva Arthroplasty Registry established 1996. (2) From the registry we randomly selected 275 patients about to undergo or having already undergone THA and asked them via interviews and a survey which benefits and harms associated with the operation and daily life with the prosthesis they perceived as most important. (3) The identified relevant data (39 predictor candidates, 15 outcomes) were evaluated using Conditional Inference Trees analysis to construct a classification algorithm for each of the 15 outcomes at three different time points/periods. …”

An example of a complication. by Anne Lübbeke (3918686)

“…</p><p>Methods</p><p>We created the information tool “Patients like me” in four steps. (1) The knowledge basis was the systematically collected detailed exposure and outcome information from the Geneva Arthroplasty Registry established 1996. (2) From the registry we randomly selected 275 patients about to undergo or having already undergone THA and asked them via interviews and a survey which benefits and harms associated with the operation and daily life with the prosthesis they perceived as most important. (3) The identified relevant data (39 predictor candidates, 15 outcomes) were evaluated using Conditional Inference Trees analysis to construct a classification algorithm for each of the 15 outcomes at three different time points/periods. …”

Baseline characteristics. by Anne Lübbeke (3918686)

“…</p><p>Methods</p><p>We created the information tool “Patients like me” in four steps. (1) The knowledge basis was the systematically collected detailed exposure and outcome information from the Geneva Arthroplasty Registry established 1996. (2) From the registry we randomly selected 275 patients about to undergo or having already undergone THA and asked them via interviews and a survey which benefits and harms associated with the operation and daily life with the prosthesis they perceived as most important. (3) The identified relevant data (39 predictor candidates, 15 outcomes) were evaluated using Conditional Inference Trees analysis to construct a classification algorithm for each of the 15 outcomes at three different time points/periods. …”

Building blocks of the project. by Anne Lübbeke (3918686)

“…</p><p>Methods</p><p>We created the information tool “Patients like me” in four steps. (1) The knowledge basis was the systematically collected detailed exposure and outcome information from the Geneva Arthroplasty Registry established 1996. (2) From the registry we randomly selected 275 patients about to undergo or having already undergone THA and asked them via interviews and a survey which benefits and harms associated with the operation and daily life with the prosthesis they perceived as most important. (3) The identified relevant data (39 predictor candidates, 15 outcomes) were evaluated using Conditional Inference Trees analysis to construct a classification algorithm for each of the 15 outcomes at three different time points/periods. …”

Fitted density estimates under the K⋅R model on a simulated dataset. by Koyel Majumdar (20416871)