Sökning: "Sepsis Prediction"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden Sepsis Prediction.

1. 1. Predicting True Sepsis and Culture-positive Sepsis in Intensive Care Unit with Machine Learning Techniques

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Zeyuan Wu; [2024]
   Nyckelord :Machine Learning; Diagnosis of Sepsis; XGBoost; Logistic Regression; Mathematics and Statistics;

   Sammanfattning : Sepsis, a serious medical condition often leading to patients requiring intensive care, has prompted numerous scientists to employ mathematical techniques to aid in its diagnosis. This thesis uses logistic regression and a machine learning technique, XGBoost, to predict true sepsis (as opposed to sepsis mimics) and culture-positive sepsis (among true sepsis) in critical care using blood test results, physiological measurements and other patient characteristics. LÄS MER

3. 2. ASSESSING PREDICTION CONDITIONS ANDSEQUENTIAL CLASSIFICATION IN ICU SEPSISPREDICTION

   Master-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Petter Lind; [2023]
   Nyckelord :Sepsis Prediction; Sequential Prediction; Conditional Predictions; XGBoost;

   Sammanfattning : Patients admitted to intensive care units (ICUs) often have a higher risk of sepsis due to weakened immune systems. Early sepsis diagnosis is crucial for timely treatment, emphasizing the need to improve the predictive capabilities of sepsis prediction models. LÄS MER

4. 3. MODELING INPUT VARIABLE AGE IN SEPSIS PREDICTION USING TREE-BASED MODELS

   Master-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Oscar Wastesson; [2023]
   Nyckelord :Machine learning; decision trees; sepsis; classification;

   Sammanfattning : Last observation carried forward (LOCF) is a common imputation method, regularly used for clinical data. It is based on the principle that the most recent observation that is known is carried forward to replace missing values. LÄS MER

5. 4. Explainable AI techniques for sepsis diagnosis : Evaluating LIME and SHAP through a user study

   Master-uppsats, Högskolan i Skövde/Institutionen för informationsteknologi

   Författare :Christian Norrie; [2021]
   Nyckelord :Explainable AI; local interpretable model-agnostic explanations; shapley additive explanations; sepsis;

   Sammanfattning : Articial intelligence has had a large impact on many industries and transformed some domains quite radically. There is tremendous potential in applying AI to the eld of medical diagnostics. A major issue with applying these techniques to some domains is an inability for AI models to provide an explanation or justication for their predictions. LÄS MER

7. 5. Sepsis : Genotypic analysis of clinical Klebsiella spp. using next-generation sequencing

   Master-uppsats, Högskolan i Skövde/Institutionen för biovetenskap

   Författare :Patricia Saxenborn; [2018]
   Nyckelord :Sepsis; Klebsiella; systems biology; next-generation sequencing; NGS;

   Sammanfattning : Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response system and can occur when the immune system over- or under- reacts to an infection. Klebsiella spp. LÄS MER