Deep learning applications for IoT in health care: A systematic review
Bibliographic record
Abstract
In machine learning, deep learning is the most popular topic having a wide range of applications such as computer vision, natural language processing, speech recognition, visual object detection, disease prediction, drug discovery, bioinformatics, biomedicine, etc. Of these applications, health care and medical science-related applications are dramatically on the rise. The tremendous big data growth, the Internet of Things (IoT), connected devices, and high-performance computers utilizing GPUs and TPUs are the main reasons why deep learning is so popular. Based on their specific tasks, medical IoT, digital images, electronic health record (EHR) data, genomic data, and central medical databases are the primary data sources for deep learning systems. Several potential issues such as privacy, QoS optimization, and deployment indicate the pivotal part of deep learning. In this paper, deep learning for IoT applications in health care systems is reviewed based on the Systematic Literature Review (SLR). This paper investigates the related researches, selected from among 44 published research papers, conducted within a period of ten years – 2010 to 2020. Firstly, theoretical concepts and ideas of deep learning and technical taxonomy are proposed. Afterwards, major deep learning applications for IoT in health care and medical sciences are presented through analyzing the related works. Later, the main idea, advantages, disadvantages, and limitations of each study are discussed, preceding suggestions for further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".