Publications by authors named "Matthew Chin Heng Chua"

9 Publications

  • Page 1 of 1

A Theory-Based, Multidisciplinary Approach to Cocreate a Patient-Centric Digital Solution to Enhance Perioperative Health Outcomes Among Colorectal Cancer Patients and Their Family Caregivers: Development and Evaluation Study.

J Med Internet Res 2021 12 7;23(12):e31917. Epub 2021 Dec 7.

Alice Lee Centre for Nursing Studies, National University of Singapore, Singapore, Singapore.

Background: Elective colorectal cancer (CRC) surgeries offer enhanced surgical outcomes but demand high self-efficacy in prehabilitation and competency in self-care and disease management postsurgery. Conventional strategies to meet perioperative needs have not been pragmatic, and there remains a pressing need for novel technologies that could improve health outcomes.

Objective: The aim of this paper was to describe the development of a smartphone-based interactive CRC self-management enhancement psychosocial program (iCanManage) in order to improve health outcomes among patients who undergo elective CRC surgeries and their family caregivers.

Methods: A multidisciplinary international team comprising physicians, specialist nurses, a psychologist, software engineers, academic researchers, cancer survivors, patient ambassadors, and ostomy care medical equipment suppliers was formed to facilitate the development of this patient-centric digital solution. The process occurred in several stages: (1) review of current practice through clinic visits and on-site observations; (2) review of literature and findings from preliminary studies; (3) content development grounded in an underpinning theory; (4) integration of support services; and (5) optimizing user experience through improving interface aesthetics and customization. In our study, 5 participants with CRC performed preliminary assessments on the quality of the developed solution using the 20-item user version of the Mobile App Rating Scale (uMARS), which had good psychometric properties.

Results: Based on the collected uMARS data, the smartphone app was rated highly for functionality, aesthetics, information quality, and perceived impact, and moderately for engagement and subjective quality. Several limiting factors such as poor agility in the adoption of digital technology and low eHealth literacy were identified despite efforts to promote engagement and ensure ease of use of the mobile app. To overcome such barriers, additional app-training sessions, an instruction manual, and regular telephone calls will be incorporated into the iCanManage program during the trial period.

Conclusions: This form of multidisciplinary collaboration is advantageous as it can potentially streamline existing care paths and allow the delivery of more holistic care to the CRC population during the perioperative period. Should the program be found to be effective and sustainable, hospitals adopting this digital solution may achieve better resource allocation and reduce overall health care costs in the long run.

Trial Registration: ClinicalTrials.gov NCT04159363; https://clinicaltrials.gov/ct2/show/NCT04159363.
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http://dx.doi.org/10.2196/31917DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8693179PMC
December 2021

Development and validation of the SARICA score to predict survival after return of spontaneous circulation in out of hospital cardiac arrest using an interpretable machine learning framework.

Resuscitation 2022 01 26;170:126-133. Epub 2021 Nov 26.

Pre-hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore. Electronic address:

Background: Accurate and timely prognostication of patients with out-of-hospital cardiac arrest (OHCA) who achieved the return of spontaneous circulation (ROSC) is crucial in clinical decision-making, resource allocation, and communications with next-of-kins. We aimed to develop the Survival After ROSC in Cardiac Arrest (SARICA), a practical clinical decision tool to predict survival in OHCA patients who attained ROSC.

Methods: We utilized real-world Singapore data from the population-based Pan-Asian Resuscitation Outcomes Study between 2010-2018. We excluded patients without ROSC. The dataset was segmented into training (60%), validation (20%) and testing (20%) cohorts. The primary endpoint was survival (to 30-days or hospital discharge). AutoScore, an interpretable machine-learning based clinical score generation algorithm, was used to develop SARICA. Candidate factors were chosen based on objective demographic and clinical factors commonly available at the time of admission. Performance of SARICA was evaluated based on receiver-operating curve (ROC) analyses.

Results: 5970 patients were included, of which 855 (14.3%) survived. A three-variable model was determined to be most parsimonious. Prehospital ROSC, age, and initial heart rhythm were identified for inclusion via random forest selection. Finally, SARICA consisted of these 3 variables and ranged from 0 to 10 points, achieving an area under the ROC (AUC) of 0.87 (95% confidence interval: 0.84-0.90) within the testing cohort.

Conclusion: We developed and internally validated the SARICA score to accurately predict survival of OHCA patients with ROSC at the time of admission. SARICA is clinically practical and developed using an interpretable machine-learning framework. SARICA has unknown generalizability pending external validation studies.
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http://dx.doi.org/10.1016/j.resuscitation.2021.11.029DOI Listing
January 2022

Improved Prediction Model of Protein Lysine Crotonylation Sites Using Bidirectional Recurrent Neural Networks.

J Proteome Res 2022 01 23;21(1):265-273. Epub 2021 Nov 23.

Institute of Systems Science, National University of Singapore, 29 Heng Mui Keng Terrace, Singapore 119620, Singapore.

Histone lysine crotonylation (Kcr) is a post-translational modification of histone proteins that is involved in the regulation of gene transcription, acute and chronic kidney injury, spermatogenesis, depression, cancer, and so forth. The identification of Kcr sites in proteins is important for characterizing and regulating primary biological mechanisms. The use of computational approaches such as machine learning and deep learning algorithms have emerged in recent years as the traditional wet-lab experiments are time-consuming and costly. We propose as part of this study a deep learning model based on a recurrent neural network (RNN) termed as Sohoko-Kcr for the prediction of Kcr sites. Through the embedded encoding of the peptide sequences, we investigate the efficiency of RNN-based models such as long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and bidirectional gated recurrent unit (BiGRU) networks using cross-validation and independent tests. We also established the comparison between Sohoko-Kcr and other published tools to verify the efficiency of our model based on 3-fold, 5-fold, and 10-fold cross-validations using independent set tests. The results then show that the BiGRU model has consistently displayed outstanding performance and computational efficiency. Based on the proposed model, a webserver called Sohoko-Kcr was deployed for free use and is accessible at https://sohoko-research-9uu23.ondigitalocean.app.
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http://dx.doi.org/10.1021/acs.jproteome.1c00848DOI Listing
January 2022

Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture.

Comput Struct Biotechnol J 2019 25;17:1245-1254. Epub 2019 Oct 25.

Medical Humanities Research Cluster, School of Humanities, Nanyang Technological University, 48 Nanyang Ave, 639818, Singapore.

Protein function prediction is one of the most well-studied topics, attracting attention from countless researchers in the field of computational biology. Implementing deep neural networks that help improve the prediction of protein function, however, is still a major challenge. In this research, we suggested a new strategy that includes gated recurrent units and position-specific scoring matrix profiles to predict vesicular transportation proteins, a biological function of great importance. Although it is difficult to discover its function, our model is able to achieve accuracies of 82.3% and 85.8% in the cross-validation and independent dataset, respectively. We also solve the problem of imbalance in the dataset via tuning class weight in the deep learning model. The results generated showed sensitivity, specificity, MCC, and AUC to have values of 79.2%, 82.9%, 0.52, and 0.861, respectively. Our strategy shows superiority in results on the same dataset against all other state-of-the-art algorithms. In our suggested research, we have suggested a technique for the discovery of more proteins, particularly proteins connected with vesicular transport. In addition, our accomplishment could encourage the use of gated recurrent units architecture in protein function prediction.
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http://dx.doi.org/10.1016/j.csbj.2019.09.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6944713PMC
October 2019

Time series classification using a modified LSTM approach from accelerometer-based data: A comparative study for gait cycle detection.

Gait Posture 2019 10 5;74:128-134. Epub 2019 Sep 5.

Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, 169608, Singapore.

Background: Gait event detection (GED) is an important aspect in identifying and interpret a user's gait to assess gait abnormalities and design intelligent assistive devices.

Research Question: There is a need to develop robust GED models that can accurately detect various gait instances in different scenarios and environments.

Methods: This paper presents a novel method of detecting heel strikes (HS) and toe offs (TO) during the user's gait cycle using a modified Long Short-Term Memory (LSTM) networks approach. The method was tested on a database from Movement Analysis in Real-world Environments using Accelerometers (MAREA) (n = 20 healthy subjects) that consisted of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. Modifications include oversampling, composite accelerations and optimizing the LSTM network architecture were made.

Results: Performance of our modified model was found to be better than six state-of-the-art GED algorithms, with a median F1 score of 0.98 for Heel Strikes and 0.98 for Toe Offs in the scenario of steady walking in an indoor environment, and a median F1 score of 0.94 for Heel Strikes and 0.68 for Toe-offs in the scenario of walking and running in an outdoor environment.

Significance: This paper highlights the potential of the single proposed model to be an alternative to the six GED models in gait detection under various conditions.
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http://dx.doi.org/10.1016/j.gaitpost.2019.09.007DOI Listing
October 2019

Ensemble of Deep Recurrent Neural Networks for Identifying Enhancers via Dinucleotide Physicochemical Properties.

Cells 2019 07 23;8(7). Epub 2019 Jul 23.

Institute of Systems Science, 25 Heng Mui Keng Terrace, National University of Singapore, Singapore 119615, Singapore.

Enhancers are short deoxyribonucleic acid fragments that assume an important part in the genetic process of gene expression. Due to their possibly distant location relative to the gene that is acted upon, the identification of enhancers is difficult. There are many published works focused on identifying enhancers based on their sequence information, however, the resulting performance still requires improvements. Using deep learning methods, this study proposes a model ensemble of classifiers for predicting enhancers based on deep recurrent neural networks. The input features of deep ensemble networks were generated from six types of dinucleotide physicochemical properties, which had outperformed the other features. In summary, our model which used this ensemble approach could identify enhancers with achieved sensitivity of 75.5%, specificity of 76%, accuracy of 75.5%, and MCC of 0.51. For classifying enhancers into strong or weak sequences, our model reached sensitivity of 83.15%, specificity of 45.61%, accuracy of 68.49%, and MCC of 0.312. Compared to the benchmark result, our results had higher performance in term of most measurement metrics. The results showed that deep model ensembles hold the potential for improving on the best results achieved to date using shallow machine learning methods.
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http://dx.doi.org/10.3390/cells8070767DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6678823PMC
July 2019

Self-Administered Auricular Acupressure Integrated With a Smartphone App for Weight Reduction: Randomized Feasibility Trial.

JMIR Mhealth Uhealth 2019 05 29;7(5):e14386. Epub 2019 May 29.

School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China (Hong Kong).

Background: Obesity is a common global health problem and increases the risk of many chronic illnesses. Given the adverse effects of antiobesity agents and bariatric surgeries, the exploration of noninvasive and nonpharmacological complementary methods for weight reduction is warranted.

Objective: The study aimed to determine whether self-administered auricular acupressure (AA) integrated with a smartphone app was more effective than using AA alone or the controls for weight reduction.

Methods: This study is a 3-arm randomized waitlist-controlled feasibility trial. A total of 59 eligible participants were randomly divided into either group 1 (AA group, n=19), group 2 (AA plus smartphone app, n=19), or group 3 (waitlist control, n=21). A total of 6 reflective zones or acupoints for weight reduction were chosen. The smartphone app could send out daily messages to the subjects to remind them to perform self-pressing on the 6 ear acupoints. A "date picker" of the 8-week treatment course was used to enable the users to input the compliance of pressing and the number of bowel movement daily instead of using the booklet for recordings. The app also served as a reminder for the subjects regarding the dates for returning to the center for acupoint changing and assessments. Treatment was delivered 2 times a week, for 8 weeks. Generalized estimating equations were used to examine the interactions among the groups before and after intervention.

Results: Subjects in group 2 expressed that the smartphone app was useful (7.41 out of 10). The most popular features were the daily reminders for performing self-pressing (88%), the ear diagram indicating the locations and functions of the 6 ear points (71%), and ear pressing method demonstrated in the video scripts (47%). Nearly 90% of the participants completed the 8-week intervention, with a high satisfaction toward the overall arrangement (8.37 out of 10). The subjects in group 1 and 2 achieved better therapeutic effects in terms of body weight, body mass index (BMI), waist circumference, and hip circumference and perceived more fullness before meals than the waitlist controls. Although no significant differences in the pairwise comparisons between the 2 groups were detected (P>.05), the decrease in body weight, BMI, body fat, visceral fat rating and leptin level, and increase in adiponectin level were notable in group 2 before and after the intervention.

Conclusions: The high compliance rate and high satisfaction toward the trial arrangement indicate that AA can be used to achieve weight reduction and applied in future large-scale studies. AA integrated with the smartphone app has a more notable effect than using AA alone for weight reduction. Larger sample size should be considered in future trials to determine the causal relationship between treatment and effect.

Trial Registration: ClinicalTrials.gov NCT03442712; https://clinicaltrials.gov/ct2/show/NCT03442712 (Archived by WebCite at http://www.webcitation.org/78L2tO8Ql).
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http://dx.doi.org/10.2196/14386DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6658225PMC
May 2019

Design and Characterization of a Soft Robotic Therapeutic Glove for Rheumatoid Arthritis.

Assist Technol 2019 3;31(1):44-52. Epub 2017 Aug 3.

a Biomedical Engineering , National University of Singapore , Singapore , Singapore.

The modeling and experimentation of a pneumatic actuation system for the development of a soft robotic therapeutic glove is proposed in this article for the prevention of finger deformities in rheumatoid arthritis (RA) patients. The Rehabilitative Arthritis Glove (RA-Glove) is a soft robotic glove fitted with two internal inflatable actuators for lateral compression and massage of the fingers and their joints. Two mechanical models to predict the indentation and bending characteristics of the inflatable actuators based on their geometrical parameters will be presented and validated with experimental results. Experimental validation shows that the model was within a standard deviation of the experimental mean for input pressure range of 0 to 2 bars. Evaluation of the RA-Glove was also performed on six healthy human subjects. The stress distribution along the fingers of the subjects using the RA-Glove was also shown to be even and specific to the finger sizes. This article demonstrates the modeling of soft pneumatic actuators and highlights the potential of the RA-Glove as a therapeutic device for the prevention of arthritic deformities of the fingers.
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http://dx.doi.org/10.1080/10400435.2017.1346000DOI Listing
April 2020

Effects of Mattress Material on Body Pressure Profiles in Different Sleeping Postures.

J Chiropr Med 2017 Mar 21;16(1):1-9. Epub 2016 Oct 21.

Department of Biomedical Engineering, National University of Singapore, Singapore.

Objectives: This study compared the body contact pressure profiles of 2 types of mattresses: latex and polyurethane.

Methods: Twenty participants were required to lie down on the different mattresses in 3 different postures for 6 minutes, and their body contact pressure profiles were recorded with a pressure mat sensor.

Results: The data indicated that the latex mattress was able to reduce the peak body pressure on the torso and buttocks and achieve a higher proportion of low-pressure regions compared with the polyurethane mattress.

Conclusions: Latex mattress reduced peak body pressure and achieved a more even distribution of pressure compared with polyurethane mattress across different sleeping postures.
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http://dx.doi.org/10.1016/j.jcm.2016.09.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5310954PMC
March 2017
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