Smartphone-based Eye Tracking System using Edge Intelligence and Model Optimisation

A significant limitation of current smartphone-based eye-tracking algorithms is their low accuracy when applied to video-type visual stimuli, as they are typically trained on static images. Also, the increasing demand for real-time interactive applications like games, VR, and AR on smartphones requires overcoming the limitations posed by resource constraints such as limited computational power, battery life, and network bandwidth. Therefore, we developed two new smartphone eye-tracking techniques for video-type visuals by combining Convolutional Neural Networks (CNN) with two different Recurrent Neural Networks (RNN), namely Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Our CNN+LSTM and CNN+GRU models achieved an average Root Mean Square Error of 0.955cm and 1.091cm, respectively. To address the computational constraints of smartphones, we developed an edge intelligence architecture to enhance the performance of smartphone-based eye tracking. We applied various optimisation methods like quantisation and pruning to deep learning models for better energy, CPU, and memory usage on edge devices, focusing on real-time processing. Using model quantisation, the model inference time in the CNN+LSTM and CNN+GRU models was reduced by 21.72% and 19.50%, respectively, on edge devices.

A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries

Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. The current scenario of assigning codes is a manual process which is very expensive, time-consuming and error prone. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) and Deep Learning (DL) methods and techniques to resolve the problem of manual coding of clinical narratives and to assist human coders to assign clinical codes more accurately and efficiently. This systematic literature review provides a comprehensive overview of automated clinical coding systems that utilises appropriate NLP, ML and DL methods and techniques to assign ICD codes to discharge summaries. We have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines and conducted a comprehensive search of publications from January, 2010 to December 2020 in four academic databases- PubMed, ScienceDirect, Association for Computing Machinery(ACM) Digital Library, and the Association for Computational Linguistics(ACL) Anthology. We reviewed 7,556 publications; 38 met the inclusion criteria. This review identified: datasets having discharge summaries; NLP techniques along with some other data extraction processes, different feature extraction and embedding techniques. To measure the performance of classification methods, different evaluation metrics are used. Lastly, future research directions are provided to scholars who are interested in automated ICD code assignment. Efforts are still required to improve ICD code prediction accuracy, availability of large-scale de-identified clinical corpora with the latest version of the classification system. This can be a platform to guide and share knowledge with the less experienced coders and researchers.