LynxCare’s Post

Savana's Data and AI capabilities have demonstrated that Artificial Intelligence in Healthcare is a tangible reality today. By leveraging advanced analytics, Savana has achieved significant clinical outcomes, particularly in the analysis of cardiovascular events in Type-2 Diabetes patients. ➡ Article published in: https://lnkd.in/dG-MPTNB Journal of Clinical Medicine (JCM) MDPI #aiinhealthcare #advancedanalytics #clinicaldata #clinicaldatamanagement #nlp #clinicalnlp

About analysing diabetes + cardiovascular outcomes using AI.

Digital transformation in healthcare IS methodological (otherwise is only automation)

This is only the beginning, Artificial Intelligence in Biomedicine has been the focus of our company for the last 10 years, transforming real world data into real world evidence, that assists clinicians in the ongoing digital revolution of the healthcare sector, transforming clinical data insights into actionable knowledge at the point of care.

📃Scientific paper: A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics Abstract: In recent years, deep learning methods have achieved great success in various fields due to their strong performance in practical applications. In this paper, we present a light-weight neural network for Parkinson's disease diagnostics, in which a series of hand-drawn data are collected to distinguish Parkinson's disease patients from healthy control subjects. The proposed model consists of a convolution neural network (CNN) cascading to long-short-term memory (LSTM) to adapt the characteristics of collected time-series signals. To make full use of their advantages, a multilayered LSTM model is firstly used to enrich features which are then concatenated with raw data and fed into a shallow one-dimensional (1D) CNN model for efficient classification. Experimental results show that the proposed model achieves a high-quality diagnostic result over multiple evaluation metrics with much fewer parameters and operations, outperforming conventional methods such as support vector machine (SVM), random forest (RF), lightgbm (LGB) and CNN-based methods. Continued on ES/IODE ➡️ https://etcse.fr/PbOl ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

Searching for a suitable topic for your M.Phil thesis? Look no further! We offer a range of cutting-edge topics in the field of Machine Learning (ML) to ignite your research journey: Network Traffic Classification Apple Disease Detection Fall Detection Brain Tumor Classification Crop Classification Lung Cancer Detection Fake News Detection These topics offer exciting opportunities for exploration and innovation in the field of ML. Contact us today to explore these topics further and kickstart your M.Phil thesis journey! . . #MPhilThesis #MachineLearning #ResearchTopics #PhD #ResearchProposal #Techsparks #AI #NLP 📝🔍

📃Scientific paper: A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics Abstract: In recent years, deep learning methods have achieved great success in various fields due to their strong performance in practical applications. In this paper, we present a light-weight neural network for Parkinson's disease diagnostics, in which a series of hand-drawn data are collected to distinguish Parkinson's disease patients from healthy control subjects. The proposed model consists of a convolution neural network (CNN) cascading to long-short-term memory (LSTM) to adapt the characteristics of collected time-series signals. To make full use of their advantages, a multilayered LSTM model is firstly used to enrich features which are then concatenated with raw data and fed into a shallow one-dimensional (1D) CNN model for efficient classification. Experimental results show that the proposed model achieves a high-quality diagnostic result over multiple evaluation metrics with much fewer parameters and operations, outperforming conventional methods such as support vector machine (SVM), random forest (RF), lightgbm (LGB) and CNN-based methods. Continued on ES/IODE ➡️ https://etcse.fr/PbOl ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

📃Scientific paper: A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics Abstract: In recent years, deep learning methods have achieved great success in various fields due to their strong performance in practical applications. In this paper, we present a light-weight neural network for Parkinson's disease diagnostics, in which a series of hand-drawn data are collected to distinguish Parkinson's disease patients from healthy control subjects. The proposed model consists of a convolution neural network (CNN) cascading to long-short-term memory (LSTM) to adapt the characteristics of collected time-series signals. To make full use of their advantages, a multilayered LSTM model is firstly used to enrich features which are then concatenated with raw data and fed into a shallow one-dimensional (1D) CNN model for efficient classification. Experimental results show that the proposed model achieves a high-quality diagnostic result over multiple evaluation metrics with much fewer parameters and operations, outperforming conventional methods such as support vector machine (SVM), random forest (RF), lightgbm (LGB) and CNN-based methods. Continued on ES/IODE ➡️ https://etcse.fr/PbOl ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

📃Scientific paper: A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics Abstract: In recent years, deep learning methods have achieved great success in various fields due to their strong performance in practical applications. In this paper, we present a light-weight neural network for Parkinson's disease diagnostics, in which a series of hand-drawn data are collected to distinguish Parkinson's disease patients from healthy control subjects. The proposed model consists of a convolution neural network (CNN) cascading to long-short-term memory (LSTM) to adapt the characteristics of collected time-series signals. To make full use of their advantages, a multilayered LSTM model is firstly used to enrich features which are then concatenated with raw data and fed into a shallow one-dimensional (1D) CNN model for efficient classification. Experimental results show that the proposed model achieves a high-quality diagnostic result over multiple evaluation metrics with much fewer parameters and operations, outperforming conventional methods such as support vector machine (SVM), random forest (RF), lightgbm (LGB) and CNN-based methods. Continued on ES/IODE ➡️ https://etcse.fr/PbOl ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

📃Scientific paper: A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics Abstract: In recent years, deep learning methods have achieved great success in various fields due to their strong performance in practical applications. In this paper, we present a light-weight neural network for Parkinson's disease diagnostics, in which a series of hand-drawn data are collected to distinguish Parkinson's disease patients from healthy control subjects. The proposed model consists of a convolution neural network (CNN) cascading to long-short-term memory (LSTM) to adapt the characteristics of collected time-series signals. To make full use of their advantages, a multilayered LSTM model is firstly used to enrich features which are then concatenated with raw data and fed into a shallow one-dimensional (1D) CNN model for efficient classification. Experimental results show that the proposed model achieves a high-quality diagnostic result over multiple evaluation metrics with much fewer parameters and operations, outperforming conventional methods such as support vector machine (SVM), random forest (RF), lightgbm (LGB) and CNN-based methods. Continued on ES/IODE ➡️ https://etcse.fr/PbOl ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.