Ignite Creation Date:
2024-10-26 @ 3:39 PM
Last Modification Date:
2024-10-26 @ 3:39 PM
Study NCT ID:
NCT06584890
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-09-03
Brief Title:
Capabilities ofArtificial Intelligence Models in Externation Decision of Patient Who Followed in Intensive Care Unit
Sponsor:
None
Organization:
None
Study Overview
Official Title:
The Evaluation of the Effectiveness of General Artificial Intelligence Models in Extubation Decision-Making in the Intensive Care Unit
Status:
RECRUITING
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
ICU
Brief Summary:
This clinical study aims to evaluate the effectiveness of General Artificial Intelligence AI models specifically ChatGPT and Gemini in assisting with the decision-making process for discharging patients from the Intensive Care Unit ICU to a general ward or home The timing of ICU discharge is a critical decision that significantly impacts patient outcomes and the efficient use of ICU resources This study seeks to determine whether AI models can accurately and efficiently predict the optimal time for patient discharge supporting clinicians in making informed decisions
The primary hypothesis is that AI models can improve the accuracy and speed of discharge decisions compared to traditional methods The study will assess the agreement between the AI model predictions and the decisions made by ICU specialists Additionally the study will compare the performance of ChatGPT and Gemini AI models to identify which model offers the most reliable and timely discharge decisions
By exploring the potential of AI in clinical decision-making this research could contribute to the development of innovative tools for ICU management ultimately enhancing patient care and optimizing ICU operations The findings could lead to the integration of AI models into clinical decision support systems facilitating more accurate and efficient patient management in the ICU
The primary hypothesis is that AI models can improve the accuracy and speed of discharge decisions compared to traditional methods The study will assess the agreement between the AI model predictions and the decisions made by ICU specialists Additionally the study will compare the performance of ChatGPT and Gemini AI models to identify which model offers the most reliable and timely discharge decisions
By exploring the potential of AI in clinical decision-making this research could contribute to the development of innovative tools for ICU management ultimately enhancing patient care and optimizing ICU operations The findings could lead to the integration of AI models into clinical decision support systems facilitating more accurate and efficient patient management in the ICU
Detailed Description:
This study is designed to assess the effectiveness of General Artificial Intelligence AI models specifically ChatGPT and Gemini in facilitating discharge decisions from the Intensive Care Unit ICU The focus is on evaluating these AI models39 performance in predicting the appropriate timing for transitioning patients from the ICU to a general ward or discharge to home The study will leverage machine learning techniques including Random Forest and Decision Tree algorithms to analyze patient data and generate predictions
Study Design and Methodology
This prospective study will include all patients admitted to the ICU during this time frame The study will gather comprehensive clinical and demographic data including indications for ICU admission comorbid conditions abnormal laboratory and imaging findings physical examination results vital signs and daily treatments The data will be anonymized to protect patient privacy with only clinical information used for AI model training and evaluation
The AI models will be trained on historical hospital data applying machine learning algorithms to predict the need for continued ICU care or the suitability for discharge These predictions will be compared daily with the clinical decisions made by ICU specialists The study will utilize various statistical methods to assess the models39 accuracy and alignment with clinical decisions including Pearson Chi-Square tests Kappa statistics McNemar tests and ROC Receiver Operating Characteristic analysis
AI Model Training and Evaluation
The AI models ChatGPT and Gemini will undergo training using anonymized patient data with a focus on optimizing their predictive accuracy for ICU discharge decisions The training process will involve analyzing a wide range of clinical variables including demographic data age gender comorbidities vital signs laboratory results and imaging findings The models will be evaluated based on their ability to predict ICU discharge needs accurately with the results validated against actual clinical decisions made by ICU specialists
Machine learning techniques such as Random Forest and Decision Tree algorithms will be employed to develop the predictive models These techniques are chosen for their robustness in handling complex clinical data and their ability to provide insights into the factors most predictive of ICU discharge readiness
Statistical Analysis
The study will apply several statistical methods to evaluate the AI models39 performance Descriptive statistics will be used to summarize the demographic and clinical characteristics of the study population Pearson Chi-Square tests will assess the association between AI model predictions and actual discharge decisions while Kappa statistics will measure the agreement between AI predictions and ICU specialist decisions The McNemar test will be used to evaluate changes in predictions over time and ROC analysis will be conducted to assess the overall performance of the AI models with a focus on sensitivity and specificity
Expected Outcomes and Significance
This study aims to determine whether AI models can enhance the accuracy and efficiency of ICU discharge decisions The findings could have significant implications for clinical practice potentially leading to the integration of AI-driven decision support systems in ICU management By improving the timing and accuracy of discharge decisions AI models could help optimize ICU resource utilization reduce patient length of stay and improve overall patient outcomes
The study will also explore the comparative performance of ChatGPT and Gemini providing insights into which AI model is better suited for integration into clinical workflows The results could pave the way for the development of more advanced AI-driven tools tailored to the specific needs of ICU settings contributing to the ongoing evolution of healthcare through innovative technology
Study Design and Methodology
This prospective study will include all patients admitted to the ICU during this time frame The study will gather comprehensive clinical and demographic data including indications for ICU admission comorbid conditions abnormal laboratory and imaging findings physical examination results vital signs and daily treatments The data will be anonymized to protect patient privacy with only clinical information used for AI model training and evaluation
The AI models will be trained on historical hospital data applying machine learning algorithms to predict the need for continued ICU care or the suitability for discharge These predictions will be compared daily with the clinical decisions made by ICU specialists The study will utilize various statistical methods to assess the models39 accuracy and alignment with clinical decisions including Pearson Chi-Square tests Kappa statistics McNemar tests and ROC Receiver Operating Characteristic analysis
AI Model Training and Evaluation
The AI models ChatGPT and Gemini will undergo training using anonymized patient data with a focus on optimizing their predictive accuracy for ICU discharge decisions The training process will involve analyzing a wide range of clinical variables including demographic data age gender comorbidities vital signs laboratory results and imaging findings The models will be evaluated based on their ability to predict ICU discharge needs accurately with the results validated against actual clinical decisions made by ICU specialists
Machine learning techniques such as Random Forest and Decision Tree algorithms will be employed to develop the predictive models These techniques are chosen for their robustness in handling complex clinical data and their ability to provide insights into the factors most predictive of ICU discharge readiness
Statistical Analysis
The study will apply several statistical methods to evaluate the AI models39 performance Descriptive statistics will be used to summarize the demographic and clinical characteristics of the study population Pearson Chi-Square tests will assess the association between AI model predictions and actual discharge decisions while Kappa statistics will measure the agreement between AI predictions and ICU specialist decisions The McNemar test will be used to evaluate changes in predictions over time and ROC analysis will be conducted to assess the overall performance of the AI models with a focus on sensitivity and specificity
Expected Outcomes and Significance
This study aims to determine whether AI models can enhance the accuracy and efficiency of ICU discharge decisions The findings could have significant implications for clinical practice potentially leading to the integration of AI-driven decision support systems in ICU management By improving the timing and accuracy of discharge decisions AI models could help optimize ICU resource utilization reduce patient length of stay and improve overall patient outcomes
The study will also explore the comparative performance of ChatGPT and Gemini providing insights into which AI model is better suited for integration into clinical workflows The results could pave the way for the development of more advanced AI-driven tools tailored to the specific needs of ICU settings contributing to the ongoing evolution of healthcare through innovative technology
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None