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Medical Intelligence with Interoperability and Standard (APAMI 2022)

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 15954

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Special Issue Editors

Prof. Dr. Chien-Yeh Hsu

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Guest Editor

Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei 112303, Taiwan
Interests: electronic medical record and hospital information system; cloud computing, standards, and block chain architecture in clinical informatics; information & bio-signal processing; biomedical database and data analysis of bio-bank
Special Issues, Collections and Topics in MDPI journals

Dr. Hsiu-An Lee

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Guest Editor

National Institute of Cancer Research, National Health Research Institutes, Tainan 704, Taiwan
Interests: healthcare system design; artificial intelligence applications; medical big data analysis; personalized medical artificial intelligence; decision-making framework; international standard; interoperability
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chi-Ming Chu

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Guest Editor

Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114201, Taiwan
Interests: mHealth; wearable device; personal health record; eGFR correction; SNPs; GWAS; gene annotation; gene expression profile; natural language processing; artificial intelligence; biomedical informatics; biostatistics; epidemiology; public health
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kuo-Chung Chu

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Guest Editor

Department of Information Management, National Taipei University of Nursing and Health Sciences (NTUNHS), Taipei 112303, Taiwan
Interests: big data analytics; computational intelligence; data mining; data visualization; decision modeling; deep learning; optimization approach; simulation; and their applications on medical; nursing and healthcare informatics

Special Issue Information

Dear Colleagues,

The Asia-Pacific Association for Medical Informatics (APAMI 2022) covers a wide range of fields in medical and healthcare technology and aims to bring together medical and technology expertise. During the conference, there should be substantial time for presentation and discussion. In addition, poster sessions and exhibitions offer ample opportunity for information exchange among delegates and participants, especially for those who are looking for new opportunities between presenters and participants.

The conference theme is “Medical Intelligence with Interoperability and Standard” which WHO has been promoting globally, but we also invite a lot of paper in any field in medical informatics from all over the world.

This conference will provide you an excellent opportunity to establish new relationships with professional colleagues from various nations including the Asia-Pacific region.

Aside from these conference papers, general Special Issue papers can be accepted for submission to our Special Issue.

Prof. Dr. Chien-Yeh Hsu
Dr. Hsiu-An Lee
Prof. Dr. Chi-Ming Chu
Prof. Dr. Kuo-Chung Chu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

medical intelligence
interoperability
standard
medical information

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Published Papers (4 papers)

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Research

18 pages, 28423 KiB

Open AccessArticle

Classifying Microscopic Images of Reactive Lymphocytosis Using Two-Step Tandem AI Models

by Hiroyuki Nozaka, Mihoko Kushibiki, Kosuke Kamata and Kazufumi Yamagata

Appl. Sci. 2023, 13(9), 5296; https://doi.org/10.3390/app13095296 - 23 Apr 2023

Cited by 1 | Viewed by 6623

Abstract

The practical applications of automatic recognition and categorization technology for next-generation systems are desired in the clinical laboratory. We approached the identification of reactive lymphocytosis using artificial intelligence (AI) technology and studied its clinical usefulness for blood smear screening. This study created one- and two-step AI models for the identification of reactive lymphocytosis. The ResNet-101 model was applied for deep learning. The original image set for supervised AI training consisted of 5765 typical nucleated blood cell images. The subjects for clinical assessment were 25 healthy cases, 25 erythroblast cases, and 25 reactive lymphocytosis cases. The total accuracy (mean ± standard deviation) of the one- and two-step models were 0.971 ± 0.047 and 0.977 ± 0.024 in healthy, 0.938 ± 0.040 and 0.978 ± 0.018 in erythroblast, and 0.856 ± 0.056 and 0.863 ± 0.069 in reactive lymphocytosis cases, respectively. The two-step AI model showed a sensitivity of 0.960 and a specificity of 1.000 between healthy and reactive lymphocytosis cases. As our two-step tandem AI model showed high performance for identifying reactive lymphocytosis in blood smear screening, we plan to apply this method to the development of AI models to differentiate reactive and neoplastic lymphocytosis. Full article

(This article belongs to the Special Issue Medical Intelligence with Interoperability and Standard (APAMI 2022))

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12 pages, 1232 KiB

Open AccessCommunication

Potential Improvement in a Portable Health Clinic for Community Health Service to Control Non-Communicable Diseases in Indonesia

by Hanifah Wulandari, Lutfan Lazuardi, Nurholis Majid, Fumihiko Yokota, Guardian Yoki Sanjaya, Tika Sari Dewi, Andreasta Meliala, Rafiqul Islam and Naoki Nakashima

Appl. Sci. 2023, 13(3), 1623; https://doi.org/10.3390/app13031623 - 27 Jan 2023

Cited by 4 | Viewed by 2485

Abstract

The COVID-19 pandemic has limited routine community health services, including screening for non-communicable diseases (NCDs). An adaptive and innovative digital approach is needed in the health technology ecosystem. A portable health clinic (PHC) is a community-based mobile health service equipped with telemonitoring and teleconsultation using portable medical devices and an Android application. The aim of this study was to assess the challenges and potential improvement in PHC implementation in Indonesia. This study was conducted in February–April 2021 in three primary health centers, Mlati II in Sleman District, Samigaluh II in Kulon Progo, and Kalikotes in Klaten. In-depth interviews were conducted with 11 health workers and community health workers. At the baseline, 268 patients were examined, and 214 patients were successfully followed-up until the third month. A proportion of 32% of the patients required teleconsultations based on automatic triage. Implementation challenges included technical constraints such as complexity of applications; unstable networks; and non-technical constraints, such as the effectivity of training, the availability of doctors, and the workload at the primary health center. PHCs were perceived as an added value in addition to existing community-based health services. The successful implementation of PHCs should not only be considered with respect to technology but also in terms of human impact, organization, and legality. Full article

(This article belongs to the Special Issue Medical Intelligence with Interoperability and Standard (APAMI 2022))

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14 pages, 47877 KiB

Open AccessArticle

Delirium Prediction Using Machine Learning Interpretation Method and Its Incorporation into a Clinical Workflow

by Koutarou Matsumoto, Yasunobu Nohara, Mikako Sakaguchi, Yohei Takayama, Shota Fukushige, Hidehisa Soejima and Naoki Nakashima

Appl. Sci. 2023, 13(3), 1564; https://doi.org/10.3390/app13031564 - 25 Jan 2023

Cited by 1 | Viewed by 2384

Abstract

Delirium in hospitalized patients is a worldwide problem, causing a burden on healthcare professionals and impacting patient prognosis. A machine learning interpretation method (ML interpretation method) presents the results of machine learning predictions and promotes guided decisions. This study focuses on visualizing the predictors of delirium using a ML interpretation method and implementing the analysis results in clinical practice. Retrospective data of 55,389 patients hospitalized in a single acute care center in Japan between December 2017 and February 2022 were collected. Patients were categorized into three analysis populations, according to inclusion and exclusion criteria, to develop delirium prediction models. The predictors were then visualized using Shapley additive explanation (SHAP) and fed back to clinical practice. The machine learning-based prediction of delirium in each population exhibited excellent predictive performance. SHAP was used to visualize the body mass index and albumin levels as critical contributors to delirium prediction. In addition, the cutoff value for age, which was previously unknown, was visualized, and the risk threshold for age was raised. By using the SHAP method, we demonstrated that data-driven decision support is possible using electronic medical record data. Full article

(This article belongs to the Special Issue Medical Intelligence with Interoperability and Standard (APAMI 2022))

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13 pages, 1575 KiB

Open AccessArticle

Machine Learning Approach for Chronic Kidney Disease Risk Prediction Combining Conventional Risk Factors and Novel Metabolic Indices

by Amadou Wurry Jallow, Adama N. S. Bah, Karamo Bah, Chien-Yeh Hsu and Kuo-Chung Chu

Appl. Sci. 2022, 12(23), 12001; https://doi.org/10.3390/app122312001 - 24 Nov 2022

Cited by 2 | Viewed by 2203

Abstract

Patients at risk of chronic kidney disease (CKD) must be identified early and precisely in order to prevent complications, save lives, and limit expenditures for patients and health systems. This study aimed to develop a simple, high-precision machine learning model to identify individuals at risk of developing CKD in the near future, using a novel metabolic index with or without creatinine. This retrospective cohort study used data from the MJ medical record database collected between 2001 and 2015 in Taiwan. We used Cox hazard regression to identify potential predictors, including the novel metabolic index, for use as variables in the models. To develop a machine learning-based CKD risk model with fewer variables, we performed several experimental analyses to combine interacting variables into subsets. Those subsets were used to train three models, random forest, logistic regression, and XGBoost, with or without adding creatinine. The study included 12,189 participants, 20% with and 80% without CKD. The most important conventional predictors of CKD are age and gender. The novel metabolic index, TyG-Index, TG/HDL-ratio and VAI, had stronger predictive power than the conventional risk factors. Without including creatinine data, the XGBoost provided the best predictive performance. After adding creatinine, the performance of all the models was excellent, outperforming both conventional indicators and existing clinical algorithms for CKD. Using novel metabolic index in machine learning-based CKD risk prediction can accurately identify individuals at risk of diagnosis with CKD in the next year, with or without including creatinine. Full article

(This article belongs to the Special Issue Medical Intelligence with Interoperability and Standard (APAMI 2022))

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