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ACM Transactions on Computing for Healthcare is a peer-reviewed journal published by ACM, covering Machine Learning in Healthcare, Digital Mental Health Interventions, Context-Aware Activity Recognition Systems. Over its lifetime it has published 347 papers, which have received 5,707 citations.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. ACM Transactions on Computing for Healthcare 12, 3, Article 45 (2023).
Formats any DOI in ACM Transactions on Computing for Healthcare style. No sign-up. |
| Publishes research in | Machine Learning in Healthcare Digital Mental Health Interventions Context-Aware Activity Recognition Systems Artificial Intelligence in Healthcare and Education Non-Invasive Vital Sign Monitoring |
| ISSN | 2637-8051 |
| Citation impact (2-yr) | 2.68 |
| h-index | 28 |
| i10-index | 96 |
| Top institutions publishing here | University of Washington |
| Journal website | dl.acm.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. In this article, we challenge…
Recent advancements in computing systems and wireless communications have made healthcare systems more efficient than before. Modern healthcare devices can monitor and manage different health conditions of patients automatically without any manual intervention from medical professionals. Additionally, the use of implantable medical devices, body area networks, and Internet of Things technologies in healthcare systems improve…
Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our survey shows what…
The objective of this study was to understand the attitudes of professionals who work in mental health regarding the use of conversational user interfaces, or chatbots, to support people’s mental health and wellbeing. This study involves an online survey to measure the awareness and attitudes of mental healthcare professionals and experts. The findings from this…
Vital signs, such as respiration and heartbeat, are useful for health monitoring because such signals provide important clues of medical conditions. Effective solutions are needed to provide contact-free, easy deployment, low-cost, and long-term vital sign monitoring. In this article, we present PhaseBeat to exploit channel state information, in particular, phase difference data to monitor breathing…
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