| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Di Jin, Elena Sergeeva, Wei-Hung Weng, Geeticka Chauhan, Peter Szolovit. Explainable deep learning in healthcare: A methodological survey from an attribution view. WIREs mechanisms of disease. 2022-01-17. PMID:35037736. |
therefore, there is an emerging need for interpretable dl, which allows end users to evaluate the model decision making to know whether to accept or reject predictions and recommendations before an action is taken. |
2022-01-17 |
2023-08-13 |
Not clear |
| Małgorzata Wasilewska, Hanna Bogucka, Adrian Klik. Federated Learning for 5G Radio Spectrum Sensing. Sensors (Basel, Switzerland). vol 22. issue 1. 2022-01-11. PMID:35009739. |
deep learning (dl) has proven to be a good choice as an intelligent ss algorithm that considers radio environmental factors in the decision-making process. |
2022-01-11 |
2023-08-13 |
Not clear |
| Malliga Subramanian, Kogilavani Shanmuga Vadivel, Wesam Atef Hatamleh, Abeer Ali Alnuaim, Mohamed Abdelhady, Sathishkumar V . The role of contemporary digital tools and technologies in Covid-19 crisis: An exploratory analysis. Expert systems. 2021-12-13. PMID:34898797. |
to avoid, detect, monitor, regulate, track, and manage diseases, predict outbreaks and conduct data analysis and decision-making processes, a variety of digital technologies are used, ranging from artificial intelligence (ai)-powered machine learning (ml) or deep learning (dl) focused applications to blockchain technology and big data analytics enabled by cloud computing and the internet of things (iot). |
2021-12-13 |
2023-08-13 |
Not clear |
| Lin Lu, Laurent Dercle, Binsheng Zhao, Lawrence H Schwart. Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging. Nature communications. vol 12. issue 1. 2021-12-02. PMID:34789774. |
our study suggests that dl network could provide a noninvasive mean for quantitative and comprehensive characterization of tumor morphological change, which may potentially benefit personalized early on-treatment decision making. |
2021-12-02 |
2023-08-13 |
Not clear |
| David Chushig-Muzo, Cristina Soguero-Ruiz, Pablo de Miguel-Bohoyo, Inmaculada Mora-Jiméne. Interpreting clinical latent representations using autoencoders and probabilistic models. Artificial intelligence in medicine. vol 122. 2021-11-26. PMID:34823836. |
electronic health records (ehrs) are a valuable data source that, in conjunction with deep learning (dl) methods, have provided important outcomes in different domains, contributing to supporting decision-making. |
2021-11-26 |
2023-08-13 |
Not clear |
| Justin Grandinett. Examining embedded apparatuses of AI in Facebook and TikTok. AI & society. 2021-09-21. PMID:34539095. |
in popular discussions, the nuances of ai are often abridged as "the algorithm", as the specific arrangements of machine learning (ml), deep learning (dl) and automated decision-making on social media platforms are typically shrouded in proprietary secrecy punctuated by press releases and transparency initiatives. |
2021-09-21 |
2023-08-13 |
Not clear |
| Nidal Nasser, Qazi Emad-Ul-Haq, Muhammad Imran, Asmaa Ali, Imran Razzak, Abdulaziz Al-Helal. A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing. Neural computing & applications. 2021-09-16. PMID:34522068. |
this architecture uses smart connectivity sensors and deep learning (dl) for intelligent decision-making from the perspective of the smart city. |
2021-09-16 |
2023-08-13 |
Not clear |
| Adnan A Khan, Hamza Ibad, Kaleem Sohail Ahmed, Zahra Hoodbhoy, Shahzad M Shami. Deep learning applications in neuro-oncology. Surgical neurology international. vol 12. 2021-09-14. PMID:34513198. |
one of the medical tenets is judgment, a facet of medical decision making in dl that is often missing because of its inherent nature as a "black box." |
2021-09-14 |
2023-08-13 |
human |
| Amelie Echle, Niklas Timon Rindtorff, Titus Josef Brinker, Tom Luedde, Alexander Thomas Pearson, Jakob Nikolas Kathe. Deep learning in cancer pathology: a new generation of clinical biomarkers. British journal of cancer. vol 124. issue 4. 2021-07-14. PMID:33204028. |
exceeding such basic approaches, dl has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. |
2021-07-14 |
2023-08-13 |
Not clear |
| Amelie Echle, Niklas Timon Rindtorff, Titus Josef Brinker, Tom Luedde, Alexander Thomas Pearson, Jakob Nikolas Kathe. Deep learning in cancer pathology: a new generation of clinical biomarkers. British journal of cancer. vol 124. issue 4. 2021-07-14. PMID:33204028. |
predictions made by such dl systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings. |
2021-07-14 |
2023-08-13 |
Not clear |
| Danju Huang, Han Bai, Li Wang, Yu Hou, Lan Li, Yaoxiong Xia, Zhirui Yan, Wenrui Chen, Li Chang, Wenhui L. The Application and Development of Deep Learning in Radiotherapy: A Systematic Review. Technology in cancer research & treatment. vol 20. 2021-07-06. PMID:34142614. |
the rise of deep learning (dl) algorithms, such as convolutional neural networks (cnn), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. |
2021-07-06 |
2023-08-13 |
Not clear |
| Miguel Jiménez Pérez, Rocío González Grand. Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review. World journal of gastroenterology. vol 26. issue 37. 2021-05-14. PMID:33088156. |
the vast amount of data available lend themselves to study and analysis by ai in its various branches, such as deep-learning (dl) and machine learning (ml), which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation. |
2021-05-14 |
2023-08-13 |
human |
| Sivaramakrishnan Rajaraman, Sudhir Sornapudi, Philip O Alderson, Les R Folio, Sameer K Antan. Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs. PloS one. vol 15. issue 11. 2020-11-23. PMID:33180877. |
however, their use in medical computer vision tasks faces several limitations, viz., (i) adapting to visual characteristics that are unlike natural images; (ii) modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; (iii) challenges in explaining dl black-box behavior to support clinical decision-making; and (iv) inter-reader variability in the ground truth (gt) annotations affecting learning and evaluation. |
2020-11-23 |
2023-08-13 |
Not clear |
| Chayakrit Krittanawong, Kipp W Johnson, Robert S Rosenson, Zhen Wang, Mehmet Aydar, Usman Baber, James K Min, W H Wilson Tang, Jonathan L Halperin, Sanjiv M Naraya. Deep learning for cardiovascular medicine: a practical primer. European heart journal. vol 40. issue 25. 2020-09-29. PMID:30815669. |
deep learning (dl) is a branch of machine learning (ml) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. |
2020-09-29 |
2023-08-13 |
Not clear |
| Chayakrit Krittanawong, Kipp W Johnson, Robert S Rosenson, Zhen Wang, Mehmet Aydar, Usman Baber, James K Min, W H Wilson Tang, Jonathan L Halperin, Sanjiv M Naraya. Deep learning for cardiovascular medicine: a practical primer. European heart journal. vol 40. issue 25. 2020-09-29. PMID:30815669. |
strengths of dl include its ability to automate medical image interpretation, enhance clinical decision-making, identify novel phenotypes, and select better treatment pathways in complex diseases. |
2020-09-29 |
2023-08-13 |
Not clear |
| Nickson M Karie, Victor R Kebande, H S Vente. Diverging deep learning cognitive computing techniques into cyber forensics. Forensic science international. Synergy. vol 1. 2020-09-28. PMID:32411955. |
dl uses some machine learning techniques to solve problems through the use of neural networks that simulate human decision-making. |
2020-09-28 |
2023-08-13 |
human |
| Chiwoo Cho, Wooyeol Choi, Taewoon Ki. Leveraging Uncertainties in Softmax Decision-Making Models for Low-Power IoT Devices. Sensors (Basel, Switzerland). vol 20. issue 16. 2020-09-18. PMID:32824357. |
in this paper, we propose a light-weight framework to enhance the performance of softmax decision-making models for dl. |
2020-09-18 |
2023-08-13 |
Not clear |
| Kai Wang, Qinyang Shou, Samantha J Ma, David Liebeskind, Xin J Qiao, Jeffrey Saver, Noriko Salamon, Hosung Kim, Yannan Yu, Yuan Xie, Greg Zaharchuk, Fabien Scalzo, Danny J J Wan. Deep Learning Detection of Penumbral Tissue on Arterial Spin Labeling in Stroke. Stroke. vol 51. issue 2. 2020-06-29. PMID:31884904. |
conclusions- pcasl perfusion magnetic resonance imaging in conjunction with the dl algorithm provides a promising approach for assisting decision-making for endovascular treatment in patients with acute ischemic stroke. |
2020-06-29 |
2023-08-13 |
Not clear |
| Dongbin Zhao, Derong Liu, F L Lewis, Jose C Principe, Stefanoi Squarti. Editorial Special Issue on Deep Reinforcement Learning and Adaptive Dynamic Programming. IEEE transactions on neural networks and learning systems. 2019-11-20. PMID:29993895. |
deep rl is able to output control signal directly based on input images, which incorporates both the advantages of the perception of deep learning (dl) and the decision making of rl or adaptive dynamic programming (adp). |
2019-11-20 |
2023-08-13 |
human |
| Jiexiong Tang, Chenwei Deng, Guang-Bin Huan. Extreme Learning Machine for Multilayer Perceptron. IEEE transactions on neural networks and learning systems. vol 27. issue 4. 2016-07-20. PMID:25966483. |
by doing so, it achieves more compact and meaningful feature representations than the original elm; 2) by exploiting the advantages of elm random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (dl), the hidden layers of the proposed framework are trained in a forward manner. |
2016-07-20 |
2023-08-13 |
Not clear |