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Public Health Public Health

Artificial Intelligence for COVID-19: A Rapid Review.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Coronavirus Disease 2019 (COVID-19) was first discovered in December 2019 and has since evolved into a pandemic.

OBJECTIVE : To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the healthcare system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19.

METHODS : We performed an extensive search of the PubMed and Embase databases for COVID-19-related English-language studies published between 1/12/2019 and 31/3/2020. We supplemented the database search with reference list checks. Thematic analysis and narrative review of AI applications for COVID-19 documented was conducted.

RESULTS : 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision-making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of COVID-19 patients.

CONCLUSIONS : In view of the continuing increase in infected cases, and given that multiple waves of infections may occur, there is need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by large patient numbers.

CLINICALTRIAL :

Chen Jiayang, See Kay Choong

2020-Sep-15

General General

Advanced Energy Kernel-Based Feature Extraction Scheme for Improved EMG-PR-Based Prosthesis Control Against Force Variation.

In IEEE transactions on cybernetics

The EMG signal is a widely focused, clinically viable, and reliable source for controlling bionics and prosthesis devices with the aid of machine-learning algorithms. The decisive step in the EMG pattern recognition (EMG-PR)-based control scheme is to extract the features with minimum neural information loss. This article proposes a novel feature extraction method based on advanced energy kernel-based features (AEKFs). The proposed method is evaluated on a scientific dataset which contains six types of upper limb motion with three different force variations. Furthermore, the EMG signal is acquired for eight upper limb gestures for the testing algorithm on the DSP processor. The efficiency of the proposed feature set has been investigated using classification accuracy (CA), Davies-Bouldin (DB) index-based separability measurement, and time complexity as performance metrics. Moreover, the proposed AEKF features, along with the LDA classifier, have been implemented on the DSP processor (ARM cortex M4) for real-time viability. Offline metrics comparison with the existing approaches prove that AEKF features exhibit lower time complexity along with a higher CA of 97.33%. The algorithm is tested on the DSP processor and CA is reported ≈ 92%. MATLAB 2015a has been deployed in Intel Core i7, 3.40-GHz RAM for all offline analyses.

Pancholi Sidharth, Joshi Amit M

2020-Sep-18

General General

Wearables and Deep Learning Classify Fall Risk from Gait in Multiple Sclerosis.

In IEEE journal of biomedical and health informatics

Falls are a significant problem for persons with multiple sclerosis (PwMS). Yet fall prevention interventions are not often prescribed until after a fall has been reported to a healthcare provider. While still nascent, objective fall risk assessments could help in prescribing preventative interventions. To this end, retrospective fall status classification commonly serves as an intermediate step in developing prospective fall risk assessments. Previous research has identified measures of gait biomechanics that differ between PwMS who have fallen and those who have not, but these biomechanical indices have not yet been leveraged to detect PwMS who have fallen. Moreover, they require the use of laboratory-based measurement technologies, which prevent clinical deployment. Here we demonstrate that a bidirectional long short-term (BiLSTM) memory deep neural network was able to identify PwMS who have recently fallen with good performance (AUC of 0.88) based on accelerometer data recorded from two wearable sensors during a one-minute walking task. These results provide substantial improvements over machine learning models trained on spatiotemporal gait parameters (21% improvement in AUC), statistical features from the wearable sensor data (16%), and patient-reported (19%) and neurologist-administered (24%) measures in this sample. The success and simplicity (two wearable sensors, only one-minute of walking) of this approach indicates the promise of inexpensive wearable sensors for capturing fall risk in PwMS.

Meyer Brett M, Tulipani Lindsey J, Gurchiek Reed D, Allen Dakota A, Adamowicz Lukas, Larie Dale, Solomon Andrew J, Cheney Nick, McGinnis Ryan

2020-Sep-18

Public Health Public Health

Big Data, Decision Models, and Public Health.

In International journal of environmental research and public health ; h5-index 73.0

Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.

Chan Chien-Lung, Chang Chi-Chang

2020-Sep-15

big data, decision models, public health

Public Health Public Health

Artificial Intelligence for COVID-19: A Rapid Review.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Coronavirus Disease 2019 (COVID-19) was first discovered in December 2019 and has since evolved into a pandemic.

OBJECTIVE : To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the healthcare system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19.

METHODS : We performed an extensive search of the PubMed and Embase databases for COVID-19-related English-language studies published between 1/12/2019 and 31/3/2020. We supplemented the database search with reference list checks. Thematic analysis and narrative review of AI applications for COVID-19 documented was conducted.

RESULTS : 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision-making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of COVID-19 patients.

CONCLUSIONS : In view of the continuing increase in infected cases, and given that multiple waves of infections may occur, there is need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by large patient numbers.

CLINICALTRIAL :

Chen Jiayang, See Kay Choong

2020-Sep-15

General General

EgoCom: A Multi-person Multi-modal Egocentric Communications Dataset.

In IEEE transactions on pattern analysis and machine intelligence ; h5-index 127.0

Multi-modal datasets in artificial intelligence (AI) often capture a third-person perspective, but our embodied human intelligence evolved with sensory input from the egocentric, first-person perspective. Towards embodied AI, we introduce the Egocentric Communications (EgoCom) dataset to advance the state-of-the-art in conversational AI, natural language, audio speech analysis, computer vision, and machine learning. EgoCom is a first-of-its-kind natural conversations dataset containing multi-modal human communication data captured simultaneously from the participants' egocentric perspectives. EgoCom includes 38.5 hours of synchronized embodied stereo audio, egocentric video with 240,000 ground-truth, time-stamped word-level transcriptions and speaker labels from 34 diverse speakers. We study baseline performance on two novel applications that benefit from embodied data: (1) predicting turn-taking in conversations and (2) multi-speaker transcription. For (1), we investigate Bayesian baselines to predict turn-taking within 5% of human performance. For (2), we use simultaneous egocentric capture to combine Google speech-to-text outputs, improving global transcription by 79% relative to a single perspective. Both applications exploit EgoCom's synchronous multi-perspective data to augment performance of embodied AI tasks.

Northcutt Curtis, Zha Shengxin, Lovegrove Steven, Newcombe Richard

2020-Sep-18