2022 International Conference on Healthcare Engineering (ICHE)最新文献

{"title":"A Heuristic Approach to Optimal Combinational Target-Drug Therapy for Melanoma","authors":"P. Partha Koundinya, Y. Sai Krishna Reddy, K. Rutwesh, Anuj Deshpande","doi":"10.1109/ICHE55634.2022.10179892","DOIUrl":"https://doi.org/10.1109/ICHE55634.2022.10179892","url":null,"abstract":"At the system level, cancer is viewed as the malfunctioning of proteins synthesized by the mutated genes. One of the effective ways of cancer treatment is personalized combinational drug therapy, where a best-suited mixture of drugs is given to the patient. Finding the most effective combination requires the study of mutations which is an exponentially complex task. In this work, we considered case study of melanoma. Signal transduction pathways for melanoma are mapped into Boolean networks (BNs), the plausible mutation sights in the biological pathway are marked as stuck-at faults, and drugs are marked as inhibitory inputs. Finding mutations or stuck-at faults in the BN is an NP-complete problem, which needs heuristic algorithms to determine the solutions. We used the Boolean satisfiability (or SAT) algorithm MiniSAT2.2 to determine such possible fault locations and consequently, the optimal drugs. In this work, we modified the previously published algorithm for SAT-based drugs therapy to obtain faster results and freshly applied the methods on melanoma pathways. We expect the best therapy in minimum time, which is a crucial factor in the rapidly growing disease like cancer.","PeriodicalId":289905,"journal":{"name":"2022 International Conference on Healthcare Engineering (ICHE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131882697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ICHE 2022 Cover Page","authors":"","doi":"10.1109/iche55634.2022.10179881","DOIUrl":"https://doi.org/10.1109/iche55634.2022.10179881","url":null,"abstract":"","PeriodicalId":289905,"journal":{"name":"2022 International Conference on Healthcare Engineering (ICHE)","volume":"20 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132722690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Depression Risk Model Among Malaysians","authors":"Mohamad Aizat Mohd Radzman, Leena Abdu Ali Al-Hazmi, Abdelrahman Zaian, E. Supriyanto","doi":"10.1109/ICHE55634.2022.10179888","DOIUrl":"https://doi.org/10.1109/ICHE55634.2022.10179888","url":null,"abstract":"Depression is a debilitative disease that affects over 300 million people all around the globe. It affects the functionality of people suffering from it, which implicates to socioeconomic burden to individual, families and societal levels. The subjectivity symptoms and signs in diagnosing depression on patients is a great problem among psychiatrists and psychologists. By building a depression risk model, it helps physician to identify depression with higher efficiency, accuracy and specificity. Healthcare will be improved in terms of cutting costs, time of service and energy to serve the patients. By Machine Learning, specifically Supervised Learning uses classifiers and feature extraction tools to identify what are the most significant factors to diagnose depression. This method helps to build a risk model which helps to improve in identifying depression among liable patients.","PeriodicalId":289905,"journal":{"name":"2022 International Conference on Healthcare Engineering (ICHE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123355515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of K-Means Algorithm on Normalized and Standardized Data for Type 2 Diabetes Subclusters","authors":"N. Omar, E. Supriyanto, Asnida Abdul Wahab, R. Al-ashwal, N. N. N. Nazirun","doi":"10.1109/ICHE55634.2022.10179877","DOIUrl":"https://doi.org/10.1109/ICHE55634.2022.10179877","url":null,"abstract":"Current discovery indicates that Type 2 Diabetes (T2D) could be categorized into many sub-clusters, which is a step towards precision medicine. Implementation of feature scaling to cluster T2D into subgroups is crucial, aiming to transform and fit the data within a specific scale. This paper aims to compare the differences between cleaned, normalized, and standardized data in k-means clustering using T2D patient data. Two data transformation approaches were applied on the clustering algorithm, namely data normalization and data standardization. By comparing the clustering analysis results, normalized data (Elbow method (EM) =435.63) illustrates the best data point distribution to form clusters and good internal clustering validation scores in comparison to cleaned data (EM =502,254.97) and standardized data (EM = 23,518.82). We concluded that data normalization in the k-means clustering algorithm is the best method for data transformation for T2D sub-clustering compared to cleaned data and standardized data.","PeriodicalId":289905,"journal":{"name":"2022 International Conference on Healthcare Engineering (ICHE)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128451364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Based Multimodality Clinical Alarm in ICU/CCU","authors":"S. Thangavelu, E. Supriyanto, A. Yahya, M. H. A. Yazid, Shaman Kalearasu, Aishriah Kalearasu","doi":"10.1109/ICHE55634.2022.10179878","DOIUrl":"https://doi.org/10.1109/ICHE55634.2022.10179878","url":null,"abstract":"In high workload areas such as the Intensive or Critical Care Units (ICU/CCU), clinicians are burdened with too many alarms and false alarms, leading to poor user response or no response to alarm signals, which in turn leads to serious patient safety concerns, adverse events such as injury and deaths. Even with the implementation of the IEC international alarm standard, alarm hazards are still seen as the top health technology hazard in healthcare institutions. There are numerous new developments in alarm technology, including in the areas of alarm detection and smart alarm design, aimed at improving the sensitivity and performance of alarm systems. However, there is still a lack of studies on the application of Human Factors Engineering (HFE) principles and AI in designing alarms for medical devices that could improve user response and ensure patient safety. This research aims to develop a fuzzy logic base multimodality clinical alarm monitor software to improve alarm response among the clinicians in ICU/CCU and the performance of the clinical alarm. The research involves testing, verifying, and validating the fuzzy-based multimodality clinical alarm to improve the performance of the alarm system. The proposed fuzzy alarm is compared to the medical professional interpretation of a patient physiological condition extracted from the MIMIC II database. The results show that the proposed fuzzy alarm can match the interpretation of medical professionals with high accuracy. In terms of sensitivity and specificity, the proposed alarms achieve good performance with blood pressure and heart rate specificity and sensitivity at 100%. Meanwhile, sensitivity and specificity for respiratory rate are at 97.59% and 99.68%, while sensitivity and specificity for oxygen saturation are at 100% and 98.04%, respectively. The research concluded that incorporating alarm information systems based on risk, human factor engineering principles, and fuzzy logic into the alarm system significantly improves user response while reducing alarm hazards and optimising the performance of the alarm system.","PeriodicalId":289905,"journal":{"name":"2022 International Conference on Healthcare Engineering (ICHE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127803850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of Advanced Imaging Software Fusion Techniques on The Detection of Prostate Cancer: A Review Paper","authors":"H. N. Abduljabbar, Ameer Sardar Kwekha-Rashid, Ezamin Abdulrahim","doi":"10.1109/ICHE55634.2022.10179867","DOIUrl":"https://doi.org/10.1109/ICHE55634.2022.10179867","url":null,"abstract":"Diagnostic imaging particularly image-guided biopsy is a necessary fundamental for making the diagnosis, performing stratification of risks, and planning the treatment of prostate cancers (PCa). The protocol of the American and European Urological Association confirms the systematic trans-rectal ultrasound (TRUS 10–12-core)-guided biopsy as the gold standard for primary diagnosis, but with growing knowledge and advanced imaging technologies; a multimodal approach of MRI/TRUS can improve the detection of PCa. This study aims to find and highlight the role of biomedical engineering development in imaging modalities for PCa detection. Eleven (11) articles used in this research, the imagine modalities that involved in all the studies were TRUS, MRI techniques, and CT/PET fusion methods. The results showed that most of the studies identified MRI-guided biopsy as superior to diagnosing PCa, compared to other modalities, and PSA level illustrated a significant decrease from (2008–2018), so that PSA test is strongly recommended for aged or patients with an enlarged prostate and or BPH. The correlation between age and PSA level also presented (0.43) which means that age is one of the factors that involved in raising PSA level. In conclusion, findings discovered that there has been tremendous improvement in PCa detection due to significant advancements in medical imaging software fusion engineering especially MRI techniques that enable better targeting of PCa. The rapid development in biomedical engineering specifically in imaging modalities and software fusion field.","PeriodicalId":289905,"journal":{"name":"2022 International Conference on Healthcare Engineering (ICHE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129883703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review on Human Stress Detection using Biosignal Based on Image Processing Technique","authors":"Atika Hendryani, M. Rizkinia, D. Gunawan","doi":"10.1109/ICHE55634.2022.10179880","DOIUrl":"https://doi.org/10.1109/ICHE55634.2022.10179880","url":null,"abstract":"Stress is a problem in human life today. The pandemic caused by COVID-19 has also caused increased stress. Some people are aware of the stress they are experiencing and can control it, but some are unaware. Subsequently, it is vital to identify it early to anticipate worsening the condition. A noninvasive, easy, and convenient method is needed to predict daily life stress. One widely developed noninvasive stress detection method is the image processing technique. This technique uses images captured by cameras. Various image processing techniques are applied, i.e., extracting temperature, biosignal, and respiration parameters. This study investigated research on stress detection using biosignals based on image processing techniques. This research found that stress detection can be done using a webcam, a cheap and easy method to implement. Several limitations, such as accuracy and minimizing environmental influences, are still challenging to improve.","PeriodicalId":289905,"journal":{"name":"2022 International Conference on Healthcare Engineering (ICHE)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121540080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}