nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
1 |
Aid of a machine learning algorithm can improve clinician predictions of patient quality of life during breast cancer treatments
|
Nuutinen, Mikko |
|
|
13 |
2 |
p. 229-244 |
artikel |
2 |
A robotic arm for safe and accurate control of biomedical equipment during COVID-19
|
Iadanza, Ernesto |
|
|
13 |
2 |
p. 285-300 |
artikel |
3 |
Artificial Intelligence Applied to clinical trials: opportunities and challenges
|
Askin, Scott |
|
|
13 |
2 |
p. 203-213 |
artikel |
4 |
A survey of deep learning for MRI brain tumor segmentation methods: Trends, challenges, and future directions
|
Krishnapriya, Srigiri |
|
|
13 |
2 |
p. 181-201 |
artikel |
5 |
A systematic scoping review of digital health technologies during COVID-19: a new normal in primary health care delivery
|
Ndayishimiye, Costase |
|
|
13 |
2 |
p. 273-284 |
artikel |
6 |
Evolution – removing paper and digitising the hospital
|
Baniulyte, G. |
|
|
13 |
2 |
p. 263-271 |
artikel |
7 |
Factors contributing to poor healthcare data quality: qualitative study from Southern Ethiopia
|
Endriyas, Misganu |
|
|
13 |
2 |
p. 245-251 |
artikel |
8 |
Internet of Things (IoT) based automated sanitizer dispenser and COVID-19 statistics reporter in a post-pandemic world
|
G., Ashok |
|
|
13 |
2 |
p. 327-341 |
artikel |
9 |
Real-world treatment response in Japanese patients with cancer using unstructured data from electronic health records
|
Araki, Kenji |
|
|
13 |
2 |
p. 253-262 |
artikel |
10 |
Survival study on deep learning techniques for IoT enabled smart healthcare system
|
Munnangi, Ashok Kumar |
|
|
13 |
2 |
p. 215-228 |
artikel |
11 |
The applications and the effectiveness of mHealth interventions to manage lung cancer patients: a systematic review
|
Amiri, Parastoo |
|
|
13 |
2 |
p. 171-180 |
artikel |
12 |
The first year of the Covid-19 pandemic through the lens of r/Coronavirus subreddit: an exploratory study
|
Tan, Zachary |
|
|
13 |
2 |
p. 301-326 |
artikel |