Unleashing the power of federated learning in fragmented digital healthcare systems: a visionary perspective

Digital healthcare landscape, including infrastructure, governance, interoperability, and user adoption, are continuously evolving, some taking more centralised approach, while others with higher degree of fragmentation. Attitude towards centralised healthcare systems in affluent countries are primarily influenced by historical development, infrastructure investments, and regulatory frameworks, which offers advantages with respect to standardised practises, centralised decision making, and economies of scale. In contrast, complexities due to diverse stakeholders, interoperability challenges, privacy and security concerns often pose challenges in achieving a completely centralised healthcare system even in high income countries such as the United Kingdom or in federal systems such as the United States. Moreover, decentralised healthcare systems are more prevalent in resource-poor countries. This paper presents our viewpoint and perspectives on the potential of federated learning in decentralised healthcare systems, especially in countries with infrastructure constraints and discusses its advantages, privacy and security concerns, and challenges. As data-hungry artificial intelligence-enabled systems are gradually changing the healthcare ecosystem, federated learning presents an opportunity for distributing the machine learning training process across multiple decentralised edge devices with reduced data transfer. Therefore, the decentralised digital healthcare system can leverage the collaborative model training while protecting highly sensitive and personal health information. However, challenges related to data heterogeneity, communication latency, and model aggregation need to be addressed for successful implementation of such systems. Adapting the federated learning framework to the specific needs and constraints of low and middle-income countries is crucial to unlock its potential in improving healthcare outcomes

Arts and Science of Digital Mental Health Support

Globally, over 600 million people are affected by mental health-related issues, according to the recent statistics. Similar to the global scenario, these issues are the single source of disease burden in the UK as well. On the bright side, the boom in the mobile application or `app' market and the paradigm shift in the global disease burden has promoted a growing interest for mental health-related apps. This paper critically explores the mobile application for mental health support to understand what apps are available in the market and their merits and capabilities to meet the need of users' demand. This paper also provides a recommendation for future directions

Classification Method for Thai Elderly People Based on Controllability of Sugar Consumption

Nowadays, the number of Thai elders is rapidly increasing among world elderly population, how to keep their health is a major concern. Cardiovascular Diseases (CVDs) which are severe diseases for Thai have higher mortality than cancers, and elderly people have a higher possibility to predispose CVDs. Hence, the risk factors for CVDs should be addressed. Obesity, as one of the risk factors of CVDs, seriously affects Thai elders' wellbeing; excessive sugar consumption is a way leading to overweight and obesity. The amount of consumed sugar by Thai is much higher than the standard sugar consumption, and it also could cause many other diseases. Therefore, this paper proposes a classification method for the elderly group who have the potential to control their blood sugar in order to prevent them from sugar overconsumption. This paper explored machine learning algorithms to find an appropriate classification method for elderly data. Artificial neuron network and K-nearest neighbors are applied for classifying elderly groups. Glycated hemoglobin (HbA1c) and fasting plasma glucose (FPG) are the noninvasive measurements of evaluating blood sugar, based on the two measurements, the 242 data from 121 elderly people are divided into two groups which are controllable group and uncontrollable group. The result indicates that the artificial neuron network is more suitable for the dataset with 70.59% accuracy as compared to the accuracy of K-nearest neighbors

Enhancing City Sustainability through Smart Technologies: A Framework for Automatic Pre-Emptive Action to Promote Safety and Security Using Lighting and ICT-Based Surveillance

The scope of the present paper is to promote social, cultural and environmental sustainability in cities by establishing a conceptual framework and the relationship amongst safety in urban public space (UPS), lighting and Information and Communication Technology (ICT)-based surveillance. This framework uses available technologies and tools, as these can be found in urban equipment such as lighting posts, to enhance security and safety in UPS, ensuring protection against attempted criminal activity. Through detailed literary research, publications on security and safety concerning crime and lighting can be divided into two periods, the first one pre-1994, and the second one from 2004–2008. Since then, a significant reduction in the number of publications dealing with lighting and crime is observed, while at the same time, the urban nightscape has been reshaped with the immersion of light-emitting diode (LED) technologies. Especially in the last decade, where most municipalities in the EU28 (European Union of all the member states from the accession of Croatia in 2013 to the withdrawal of the United Kingdom in 2020) are refurbishing their road lighting with LED technology and the consideration of smart networks and surveillance is under development, the use of lighting to deter possible attempted felonies in UPS is not addressed. To capitalize on the potential of lighting as a deterrent, this paper proposes a framework that uses existing technology, namely, dimmable LED light sources, presence sensors, security cameras, as well as emerging techniques such as artificial intelligence (AI)-enabled image recognition algorithms and big data analytics and presents a possible system that could be developed as a stand-alone product to alert possible dangerous situations, deter criminal activity and promote the perception of safety thus linking lighting and ICT-based surveillance towards safety and security in UPS

Pathological test type and chemical detection using deep neural networks:a case study using ELISA and LFA assays

Purpose: The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals have created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology and so on. Design/methodology/approach: The concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training deep learning (DL) models on thousands of images of these tests using transfer learning, this paper (1) classifies the type of the assay and (2) classifies the colourimetric results. Findings: This paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time. Originality/value: To the best of the authors’ knowledge, this is the first attempt to provide colourimetric assay type classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities, it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.</p

A comparative study of supervised machine learning approaches to predict patient triage outcomes in hospital emergency departments

Background The inconsistency in triage evaluation in emergency departments (EDs) and the limitations in practice within the standard triage tools among triage nurses have led researchers to seek more accurate and robust triage evaluation that provides better patient prioritization based on their medical conditions. This study aspires to establish the best methodological practices for applying machine learning (ML) techniques to build an automated triage model for more accurate evaluation. Methods A comparative study of selected supervised ML models was conducted to determine the best-performing approach to evaluate patient triage outcomes in hospital emergency departments. A retrospective dataset of 2688 patients who visited the ED between April 1, 2020 and June 9, 2020 was collected. Data included patient demographics (age and gender), Vital signs (body temperature, respiratory rate, heart rate, blood pressure and oxygen saturation), chief complaints, and chronic illness. Nine supervised ML techniques were investigated in this study. Models were trained based on patient disposition outcomes and then validated to evaluate their performance. Findings ML models show high capabilities in predicting patient disposition outcomes in ED settings. Four models (KNN, GBDT, XGBoost, and RF) performed better than the rest. RF was selected as the optimal model as it demonstrated a slight advantage over the other models with 89.1% micro accuracy, 89.0% precision, 89.1% recall, and 89.0% F1-score, exhibiting outstanding performance in differentiation between patients with critical outcomes (e.g., Mortality and ICU admission) from those patients with less critical outcomes (e.g., discharged and hospitalized) in ED settings. Conclusion Machine learning techniques demonstrate high promise in improving predictive abilities in emergency medicine and providing robust decision-making tools that can enhance the patient triage process, assist triage personnel in their decision and thus reduce the effects of ED overcrowding and enhance patient outcomes

Adversarial De-confounding in Individualised Treatment Effects Estimation

Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a fundamental problem of individualised treatment effects (ITE) estimation. This paper proposes disentangled representations with adversarial training to selectively balance the confounders in the binary treatment setting for the ITE estimation. The adversarial training of treatment policy selectively encourages treatment-agnostic balanced representations for the confounders and helps to estimate the ITE in the observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets, with varying degrees of confounding, prove that our proposed approach improves the state-of-the-art methods in achieving lower error in the ITE estimation.Comment: accepted to AISTATS 202

Clustering and Classification of a Qualitative Colorimetric Test

In this paper, we present machine learning based detection methods for a qualitative colorimetric test. Such an automatic system on mobile platform can emancipate the test result from the color perception of individuals and its subjectivity of interpretation, which can help millions of populations to access colorimetric test results for healthcare, allergen detection, forensic analysis, environmental monitoring and agricultural decision on point-of-care platforms. The case of plasmonic enzyme-linked immunosorbent assay (ELISA) based tuberculosis disease is utilized as a model experiment. Both supervised and unsupervised machine learning techniques are employed for the binary classification based on color moments. Using 10-fold cross validation, the ensemble bagged tree and k-nearest neighbors algorithm achieved 96.1% and 97.6% accuracy, respectively. The use of multi-layer perceptron with Bayesian regularization backpropagation provided 99.2% accuracy. Such high accuracy system can be trained off-line and deployed to mobile devices to produce an automatic colourimetric diagnostic decision anytime anywhere

Assay Type Detection Using Advanced Machine Learning Algorithms

The colourimetric analysis has been used in diversified fields for years. This paper provides a unique overview of colourimetric tests from the perspective of computer vision by describing different aspects of a colourimetric test in the context of image processing, followed by an investigation into the development of a colorimetric assay type detection system using advanced machine learning algorithms. To the best of our knowledge, this is the first attempt to define colourimetric assay types from the eyes of a machine and perform any colorimetric test using deep learning. This investigation utilizes the state-of-the-art pre-trained models of Convolutional Neural Network (CNN) to perform the assay type detection of an enzyme-linked immunosorbent assay (ELISA) and lateral flow assay (LFA). The ELISA dataset contains images of both positive and negative samples, prepared for the plasmonic ELISA based TB-antigen specific antibody detection. The LFA dataset contains images of the universal pH indicator paper of eight pH levels. It is noted that the pre-trained models offered 100% accurate visual recognition for the assay type detection. Such detection can assist novice users to initiate a colorimetric test using his/her personal digital devices. The assay type detection can also aid in calibrating an image-based colorimetric classification