Table_4_Operation analysis of the tele-critical care service demonstrates value delivery, service adaptation over time, and distress among tele-providers.docx حسب Krzysztof Laudanski (4252075)

Image_6_Operation analysis of the tele-critical care service demonstrates value delivery, service adaptation over time, and distress among tele-providers.PNG حسب Krzysztof Laudanski (4252075)

Image_1_Operation analysis of the tele-critical care service demonstrates value delivery, service adaptation over time, and distress among tele-providers.JPEG حسب Krzysztof Laudanski (4252075)

Image_3_Operation analysis of the tele-critical care service demonstrates value delivery, service adaptation over time, and distress among tele-providers.PNG حسب Krzysztof Laudanski (4252075)

Image_4_Operation analysis of the tele-critical care service demonstrates value delivery, service adaptation over time, and distress among tele-providers.PNG حسب Krzysztof Laudanski (4252075)

Table_1_Operation analysis of the tele-critical care service demonstrates value delivery, service adaptation over time, and distress among tele-providers.docx حسب Krzysztof Laudanski (4252075)

Image_5_Operation analysis of the tele-critical care service demonstrates value delivery, service adaptation over time, and distress among tele-providers.PNG حسب Krzysztof Laudanski (4252075)

Table_3_Operation analysis of the tele-critical care service demonstrates value delivery, service adaptation over time, and distress among tele-providers.docx حسب Krzysztof Laudanski (4252075)

Table_2_Operation analysis of the tele-critical care service demonstrates value delivery, service adaptation over time, and distress among tele-providers.docx حسب Krzysztof Laudanski (4252075)

Image_2_Operation analysis of the tele-critical care service demonstrates value delivery, service adaptation over time, and distress among tele-providers.PNG حسب Krzysztof Laudanski (4252075)

Table_1_Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings.DOCX حسب Hasan Zulfiqar (12117255)

Image_3_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF حسب Joshua A. Krachman (11660266)

"…The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. …"

Image_5_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF حسب Joshua A. Krachman (11660266)

"…The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. …"

Table_3_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx حسب Joshua A. Krachman (11660266)

"…The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. …"

Table_1_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx حسب Joshua A. Krachman (11660266)

"…The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. …"

Image_1_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF حسب Joshua A. Krachman (11660266)

"…The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. …"

Image_2_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF حسب Joshua A. Krachman (11660266)

"…The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. …"

Image_6_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF حسب Joshua A. Krachman (11660266)

"…The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. …"

Table_2_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx حسب Joshua A. Krachman (11660266)

"…The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. …"

Image_4_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF حسب Joshua A. Krachman (11660266)

"…The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. …"