A Brief Review of Domain Adaptation

Farahani, Abolfazl; Voghoei, Sahar; Rasheed, Khaled; Arabnia, Hamid R.

doi:10.48550/arxiv.2010.03978

Cited by 22 publications

(20 citation statements)

References 47 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We consider our cohort and analysis to pave the way for clinical validation studies to deploy models that could be ex-ante externally validated using our cohort. In this context, we hypothesise that the use of domain adaptation techniques [7] will further increase model transferability between different sites, thus potentially facilitating deployments in practice via pre-trained models.…”

Section: Discussionmentioning

confidence: 99%

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning

Moor,

Bennet,

Plecko

et al. 2021

Preprint

View full text Add to dashboard Cite

Despite decades of clinical research, sepsis remains a global public health crisis with high mortality, and morbidity. Currently, when sepsis is detected and the underlying pathogen is identified, organ damage may have already progressed to irreversible stages. Effective sepsis management is therefore highly time-sensitive. By systematically analysing trends in the plethora of clinical data available in the intensive care unit (ICU), an early prediction of sepsis could lead to earlier pathogen identification, resistance testing, and effective antibiotic and supportive treatment, and thereby become a life-saving measure. Here, we developed and validated a machine learning (ML) system for the prediction of sepsis in the ICU. Our analysis represents the largest multi-national, multi-centre in-ICU study for sepsis prediction using ML to date. Our dataset contains 156, 309 unique ICU admissions, which represent a refined and harmonised subset of five large ICU databases originating from three countries. Using the international consensus definition Sepsis-3, we derived hourly-resolved sepsis label annotations, amounting to 26, 734 (17.1%) septic stays. We compared our approach, a deep self-attention model, to several clinical baselines as well as ML baselines and performed an extensive internal and external validation within and across databases. On average, our model was able to predict sepsis with an AUROC of 0.847 ± 0.050 (internal out-of sample validation) and 0.761 ± 0.052 (external validation). For a harmonised prevalence of 17%, at 80% recall our model detects septic patients with 39% precision 3.7 h in advance.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning

Moor,

Bennet,

Plecko

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the future, the research will expand to this area to incorporate the sign words and sentences. Domain adaptation [48] will be also a future goal as real-time applications include the population which belongs to the different distributions than the training and validation data. In addition, the real-time application is done using the webcam as an input device but to make it more user oriented a smart phone implementation of this research will be a future goal.…”

Section: Recommendationsmentioning

confidence: 99%

Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

Podder

Chowdhury

Tahir

et al. 2022

Sensors

View full text Add to dashboard Cite

A real-time Bangla Sign Language interpreter can enable more than 200 k hearing and speech-impaired people to the mainstream workforce in Bangladesh. Bangla Sign Language (BdSL) recognition and detection is a challenging topic in computer vision and deep learning research because sign language recognition accuracy may vary on the skin tone, hand orientation, and background. This research has used deep machine learning models for accurate and reliable BdSL Alphabets and Numerals using two well-suited and robust datasets. The dataset prepared in this study comprises of the largest image database for BdSL Alphabets and Numerals in order to reduce inter-class similarity while dealing with diverse image data, which comprises various backgrounds and skin tones. The papers compared classification with and without background images to determine the best working model for BdSL Alphabets and Numerals interpretation. The CNN model trained with the images that had a background was found to be more effective than without background. The hand detection portion in the segmentation approach must be more accurate in the hand detection process to boost the overall accuracy in the sign recognition. It was found that ResNet18 performed best with 99.99% accuracy, precision, F1 score, sensitivity, and 100% specificity, which outperforms the works in the literature for BdSL Alphabets and Numerals recognition. This dataset is made publicly available for researchers to support and encourage further research on Bangla Sign Language Interpretation so that the hearing and speech-impaired individuals can benefit from this research.

show abstract

“…Such limitations stem partly from the data distribution covered: the narrower the data distribution, the more limited the use cases. This problem does not pertain to healthcare and generalization is a field of research on its own (see [18,46,68,69] for reviews).…”

Section: Challenge Tests and Confounding Factorsmentioning

confidence: 99%

Healthsheet: Development of a Transparency Artifact for Health Datasets

Rostamzadeh¹,

Mincu²,

Roy³

et al. 2022

Preprint

View full text Add to dashboard Cite

Machine learning (ML) approaches have demonstrated promising results in a wide range of healthcare applications. Data plays a crucial role in developing ML-based healthcare systems that directly affect people's lives. Many of the ethical issues surrounding the use of ML in healthcare stem from structural inequalities underlying the way we collect, use, and handle data. Developing guidelines to improve documentation practices regarding the creation, use, and maintenance of ML healthcare datasets is therefore of critical importance. In this work, we introduce Healthsheet, a contextualized adaptation of the original datasheet questionnaire [22] for healthspecific applications. Through a series of semi-structured interviews, we adapt the datasheets for healthcare data documentation. As part of the Healthsheet development process and to understand the obstacles researchers face in creating datasheets, we worked with three publicly-available healthcare datasets as our case studies, each with different types of structured data: Electronic health Records (EHR), clinical trial study data, and smartphone-based performance outcome measures. Our findings from the interviewee study and case studies show 1) that datasheets should be contextualized for healthcare, 2) that despite incentives to adopt accountability practices such as datasheets, there is a lack of consistency in the broader use of these practices 3) how the ML for health community views datasheets and particularly Healthsheets as diagnostic tool to surface the limitations and strength of datasets and 4) the relative importance of different fields in the datasheet to healthcare concerns.

show abstract

A Brief Review of Domain Adaptation

Cited by 22 publications

References 47 publications

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning

Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

Healthsheet: Development of a Transparency Artifact for Health Datasets

Contact Info

Product

Resources

About