A text style transfer system for reducing the physician–patient expertise gap:An analysis with automatic and human evaluations

Physicians and patients often come from different backgrounds and have varying levels of education, which can result in communication difficulties in the healthcare process. To address this expertise gap, we present a “Text Style Transfer” system. Our system uses Semantic Textual Similarity techniques based on Sentence Transformers models to create pseudo-parallel datasets from a large, non-parallel corpus of lay and expert texts. This approach allowed us to train a denoising autoencoder model (BART), overcoming the limitations of previous systems. Our extensive analysis, which includes both automatic metrics and human evaluations from both lay (patients) and expert (physicians) individuals, shows that our system outperforms state-of-the-art models and is comparable to human-provided gold references in some cases.</p

A text style transfer system for reducing the physician–patient expertise gap:An analysis with automatic and human evaluations

Physicians and patients often come from different backgrounds and have varying levels of education, which can result in communication difficulties in the healthcare process. To address this expertise gap, we present a “Text Style Transfer” system. Our system uses Semantic Textual Similarity techniques based on Sentence Transformers models to create pseudo-parallel datasets from a large, non-parallel corpus of lay and expert texts. This approach allowed us to train a denoising autoencoder model (BART), overcoming the limitations of previous systems. Our extensive analysis, which includes both automatic metrics and human evaluations from both lay (patients) and expert (physicians) individuals, shows that our system outperforms state-of-the-art models and is comparable to human-provided gold references in some cases.</p

A text style transfer system for reducing the physician–patient expertise gap:An analysis with automatic and human evaluations

Physicians and patients often come from different backgrounds and have varying levels of education, which can result in communication difficulties in the healthcare process. To address this expertise gap, we present a “Text Style Transfer” system. Our system uses Semantic Textual Similarity techniques based on Sentence Transformers models to create pseudo-parallel datasets from a large, non-parallel corpus of lay and expert texts. This approach allowed us to train a denoising autoencoder model (BART), overcoming the limitations of previous systems. Our extensive analysis, which includes both automatic metrics and human evaluations from both lay (patients) and expert (physicians) individuals, shows that our system outperforms state-of-the-art models and is comparable to human-provided gold references in some cases.</p

A text style transfer system for reducing the physician–patient expertise gap:An analysis with automatic and human evaluations

Physicians and patients often come from different backgrounds and have varying levels of education, which can result in communication difficulties in the healthcare process. To address this expertise gap, we present a “Text Style Transfer” system. Our system uses Semantic Textual Similarity techniques based on Sentence Transformers models to create pseudo-parallel datasets from a large, non-parallel corpus of lay and expert texts. This approach allowed us to train a denoising autoencoder model (BART), overcoming the limitations of previous systems. Our extensive analysis, which includes both automatic metrics and human evaluations from both lay (patients) and expert (physicians) individuals, shows that our system outperforms state-of-the-art models and is comparable to human-provided gold references in some cases.</p

A text style transfer system for reducing the physician–patient expertise gap:An analysis with automatic and human evaluations

Physicians and patients often come from different backgrounds and have varying levels of education, which can result in communication difficulties in the healthcare process. To address this expertise gap, we present a “Text Style Transfer” system. Our system uses Semantic Textual Similarity techniques based on Sentence Transformers models to create pseudo-parallel datasets from a large, non-parallel corpus of lay and expert texts. This approach allowed us to train a denoising autoencoder model (BART), overcoming the limitations of previous systems. Our extensive analysis, which includes both automatic metrics and human evaluations from both lay (patients) and expert (physicians) individuals, shows that our system outperforms state-of-the-art models and is comparable to human-provided gold references in some cases.</p

A text style transfer system for reducing the physician–patient expertise gap:An analysis with automatic and human evaluations

Physicians and patients often come from different backgrounds and have varying levels of education, which can result in communication difficulties in the healthcare process. To address this expertise gap, we present a “Text Style Transfer” system. Our system uses Semantic Textual Similarity techniques based on Sentence Transformers models to create pseudo-parallel datasets from a large, non-parallel corpus of lay and expert texts. This approach allowed us to train a denoising autoencoder model (BART), overcoming the limitations of previous systems. Our extensive analysis, which includes both automatic metrics and human evaluations from both lay (patients) and expert (physicians) individuals, shows that our system outperforms state-of-the-art models and is comparable to human-provided gold references in some cases.</p

Endoscopic sinus surgery in individuals with facial pain due to chronic maxillary sinusitis ? a functional controlled study

Objective: To measure the intra-sinus pressure and the maxillary sinus functional efficiency (MSFE) in individuals with chronic facial pain after conservative or conventional endoscopic maxillary surgery, as well as in controls. Method: Sinus manometry was performed 5 times during inhalation. Results: The resemblance of pressure values comparing those treated with minimally invasive surgery and controls was remarkable, while traditional surgery significantly decreased intrasinusal pressures. The MSFE was 100% in the three tested times for controls, close to that in those submitted to minimally invasive surgery (98.3%, 98.8%, and 98.0%) and significantly impaired after conventional surgery (48.8%, 52.1%, 48.5 %, p<0.01). All patients submitted to minimally invasive surgery remained pain-free after three months of surgery, relative to 46.7% of the submitted to conventional surgery (p<0.05). Conclusion: Minimally invasive sinus surgery is associated with functionality of the chambers that resemble what is found in normal individuals

A computer-aided diagnosis system for HEp-2 fluorescence intensity classification

Background and objective: The indirect immunofluorescence (IIF) on HEp-2 cells is the recommended technique for the detection of antinuclear antibodies. However, it is burdened by some limitations, as it is time consuming and subjective, and it requires trained personnel. In other fields the adoption of deep neural networks has provided an effective high-level abstraction of the raw data, resulting in the ability to automatically generate optimized high-level features. Methods: To alleviate IIF limitations, this paper presents a computer-aided diagnosis (CAD) system classifying HEp-2 fluorescence intensity: it represents each image using an Invariant Scattering Convolutional Network (Scatnet), which is locally translation invariant and stable to deformations, a characteristic useful in case of HEp-2 samples. To cope with the inter-observer discrepancies found in the dataset, we also introduce a method for gold standard computation that assigns a label and a reliability score to each HEp-2 sample on the basis of annotations provided by expert physicians. Features by Scatnet and gold standard information are then used to train a Support Vector Machine. Results: The proposed CAD is tested on a new dataset of 1771 images annotated by three independent medical centers. The performances achieved by our CAD in recognizing positive, weak positive and negative samples are also compared against those obtained by other two approaches presented so far in the literature. The same system trained on this new dataset is then tested on two public datasets, namely MIVIA and I3Asel. Conclusions: The results confirm the effectiveness of our proposal, also revealing that it achieves the same performance as medical experts

ECG databases for biometric systems: A systematic review

Computer-based biometric systems (CBBSs) individual recognition are expert and intelligent systems that are gaining increasing interest in many areas, such as securing financial systems, telecommunications and healthcare applications. The electrocardiogram (ECG) has been used as biometric feature for its low circumvention, large acceptability and uniqueness, thus being at the basis of several CBBSs. As ECG databases collected for clinical applications are not adequate for biometric applications, we have assisted to the development of other repositories of ECG, each one different from the others and highlighting certain issues of ECG-based biometric recognition. Through a systematic framework presented here, we quantitative analyse, evaluate and compare the acquisition hardware and the acquisition protocols of ECG databases available in literature and suited to develop CBBSs. Although the most recent ones, namely CYBHI and UofTDB, result the best for the acquisition hardware and the acquisition protocols, respectively, our survey shows that none is exhaustive for developing a robust and general enough CBBSs. The analysis also highlights the current lack of standardization in this field and the difficulty of performing an effective benchmarking activity. Since a publicly available database is essential for the research community in ECG-based CBBS to correctly assess the performance of existing algorithms or even commercial expert systems, we also discuss here the main features that an “optimal” repository for the intelligent application at hand