| Nome |
# |
|
Monitoring and detecting faults in wastewater treatment plants using deep learning, file ddb241a5-6450-ba8a-e053-3a05fe0afd55
|
1.256
|
|
Comm2Sense: Detecting proximity through smartphones, file ddb241a5-7d47-ba8a-e053-3a05fe0afd55
|
272
|
|
Machine learning predicts mortality in septic patients using only routinely available ABG variables: a multi-centre evaluation, file ddb241a5-7d93-ba8a-e053-3a05fe0afd55
|
106
|
|
Prediction of stress levels in the workplace using surrounding stress, file 170e451f-31f0-4abc-9229-3a98e98c5c9a
|
90
|
|
Stress Modelling and Prediction in Presence of Scarce Data, file ddb241a5-3b31-ba8a-e053-3a05fe0afd55
|
66
|
|
Detecting dressing failures using temporal–relational visual grammars, file ddb241a5-5b93-ba8a-e053-3a05fe0afd55
|
33
|
|
Multi-Modal Mobile Sensing of Social Interactions, file ddb241a5-788e-ba8a-e053-3a05fe0afd55
|
32
|
|
Mobile Health Systems for Bipolar Disorder, file ddb241a5-7fdf-ba8a-e053-3a05fe0afd55
|
31
|
|
Tell me your apps and I will tell you your mood, file ddb241a5-7f02-ba8a-e053-3a05fe0afd55
|
30
|
|
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review, file ddb241a5-6275-ba8a-e053-3a05fe0afd55
|
25
|
|
Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients, file ddb241a5-785b-ba8a-e053-3a05fe0afd55
|
25
|
|
Automatic Sensing of Speech Activity and Correlation with Mood Changes, file ddb241a5-7e71-ba8a-e053-3a05fe0afd55
|
24
|
|
Indoor Positioning Using FM Radio, file ddb241a5-8252-ba8a-e053-3a05fe0afd55
|
24
|
|
Using Intermediate Models and Knowledge Learning to Improve Stress Prediction, file ddb241a5-7e6d-ba8a-e053-3a05fe0afd55
|
23
|
|
Blood Lactate Concentration Prediction in Critical Care, file ddb241a5-68ec-ba8a-e053-3a05fe0afd55
|
21
|
|
Speech activity detection using accelerometer, file ddb241a5-7d45-ba8a-e053-3a05fe0afd55
|
19
|
|
Benchmarking machine learning models on multi-centre eICU critical care dataset, file ddb241a5-6903-ba8a-e053-3a05fe0afd55
|
18
|
|
Sex-specific outcomes and management in critically ill septic patients, file ddb241a5-6e4d-ba8a-e053-3a05fe0afd55
|
18
|
|
Disease-course adapting machine learning prognostication models in critically ill elderly COVID-19 patients: a multi-centre cohort study with external validation, file ddb241a5-85aa-ba8a-e053-3a05fe0afd55
|
16
|
|
Tuning to your position: FM radio based indoor localization with spontaneous recalibration, file ddb241a5-7884-ba8a-e053-3a05fe0afd55
|
15
|
|
Monitoring dressing activity failures through RFID and video, file ddb241a5-7fb0-ba8a-e053-3a05fe0afd55
|
14
|
|
Investigation of indoor localization with ambient FM radio stations, file ddb241a5-7efd-ba8a-e053-3a05fe0afd55
|
12
|
|
FINDR: Low-Cost Indoor Positioning Using FM Radio, file ddb241a5-78c3-ba8a-e053-3a05fe0afd55
|
10
|
|
Correlation Between Self-Reported Mood States and Objectively Measured Social Interactions at Work: A Pilot Study, file ddb241a5-7fe1-ba8a-e053-3a05fe0afd55
|
10
|
|
AID-ME: Automatic identification of dressing failures through monitoring of patients and activity Evaluation, file ddb241a5-79b6-ba8a-e053-3a05fe0afd55
|
9
|
|
Indoor Localization Using Audio Features of FM Radio Signals, file ddb241a5-7e6f-ba8a-e053-3a05fe0afd55
|
9
|
|
Smart Phone Sensing to Examine Effects of Social Interactions and Non-sedentary Work Time on Mood Changes, file ddb241a5-8255-ba8a-e053-3a05fe0afd55
|
9
|
|
Participants’ Experience and Adherence in Repeated Measurement Studies Among Office-Based Workers, file ddb241a5-82f5-ba8a-e053-3a05fe0afd55
|
9
|
|
Machine Learning Models Cannot Replace Screening Colonoscopy for the Prediction of Advanced Colorectal Adenoma, file ddb241a5-873e-ba8a-e053-3a05fe0afd55
|
8
|
|
Delirium prediction in the ICU: designing a screening tool for preventive interventions, file 3fd7accb-3f49-4fcd-96b7-0cb5d5a5d8c3
|
7
|
|
Policy Paper on Healthy Ageing, file ddb241a5-824d-ba8a-e053-3a05fe0afd55
|
7
|
|
Management of intoxicated patients – a descriptive outcome analysis of 4,267 ICU patients, file ddb241a5-8579-ba8a-e053-3a05fe0afd55
|
7
|
|
Red Cell Distribution Width is independently associated with Mortality in Sepsis, file ddb241a5-87d5-ba8a-e053-3a05fe0afd55
|
7
|
|
ICU-Mortality in Old and Very Old Patients Suffering From Sepsis and Septic Shock, file ddb241a5-7788-ba8a-e053-3a05fe0afd55
|
6
|
|
Underweight but not overweight is associated with excess mortality in septic ICU patients, file ddb241a5-80a1-ba8a-e053-3a05fe0afd55
|
6
|
|
Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation, file ddb241a5-75f3-ba8a-e053-3a05fe0afd55
|
5
|
|
The evolution of personalized healthcare and the pivotal role of European regions in its implementation, file ddb241a5-8107-ba8a-e053-3a05fe0afd55
|
5
|
|
Failure of Lactate Clearance Predicts the Outcome of Critically Ill Septic Patients, file ddb241a5-8163-ba8a-e053-3a05fe0afd55
|
5
|
|
COVID-19 machine learning model predicts outcomes in older patients from various European countries, between pandemic waves, and in a cohort of Asian, African, and American patients, file 64f4f38c-6b11-4cc9-8789-e3b325ea4708
|
4
|
|
Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation, file ddb241a5-86fe-ba8a-e053-3a05fe0afd55
|
4
|
|
Virtual uniforms: using sound frequencies for grouping individuals, file ddb241a5-7db9-ba8a-e053-3a05fe0afd55
|
3
|
|
Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data, file 1c60e9dc-fed8-483e-8a95-5ce80bf154fd
|
2
|
|
Prediction of blood lactate values in critically ill patients: a retrospective multi-center cohort study, file ddb241a5-7db1-ba8a-e053-3a05fe0afd55
|
2
|
|
The impact of ethnic background on ICU care and outcome in sepsis and septic shock - A retrospective multicenter analysis on 17,949 patients, file 181c9a84-b25e-493b-8fe6-634303e95bd8
|
1
|
|
Monitoring activity of patients with bipolar disorder using smart phones, file ddb241a5-7886-ba8a-e053-3a05fe0afd55
|
1
|
|
Mental health and the impact of ubiquitous technologies, file ddb241a5-7898-ba8a-e053-3a05fe0afd55
|
1
|
|
Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation, file ddb241a5-78c7-ba8a-e053-3a05fe0afd55
|
1
|
|
Investigating Presence of Ethnoracial Bias in Clinical Data using Machine Learning, file ddb241a5-7989-ba8a-e053-3a05fe0afd55
|
1
|
|
FM radio for indoor localization with spontaneous recalibration, file ddb241a5-7eba-ba8a-e053-3a05fe0afd55
|
1
|
|
Human activity recognition in pervasive health-care: Supporting efficient remote collaboration, file ddb241a5-7f64-ba8a-e053-3a05fe0afd55
|
1
|
|
Analysis of Social Interactions Through Mobile Phones, file ddb241a5-7fae-ba8a-e053-3a05fe0afd55
|
1
|
| Totale |
2.312 |