Identifying Heterogeneity of Diabetics Mellitus Based on the Demographical and Clinical Characteristics

Islam, Linta; Islam, Md. Rafiqul; Akter, Shanjita; Hasan, Md. Zobaer; Moni, Mohammad Ali; Uddin, Mohammed Nasir

doi:10.1007/s44230-022-00003-1

Cited by 4 publications

(1 citation statement)

References 43 publications

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“…Several advancements have enabled new and beneficial applications of IT in the medical field. The interdisciplinary discipline of medical informatics (MI) combines software, computer science, medicine, information science, statistics, cognitive sciences, and mathematics [ 2 ]. This field’s task and mission is to reduce costs while improving health care services, and also care errors by using concepts, tools, methods, software techniques, and modeling [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

et al. 2022

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Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system’s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field.

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Section: Introductionmentioning

confidence: 99%

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

et al. 2022

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A Semi-Automated Solution Approach Recommender for a Given Use Case: a Case Study for AI/ML in Oncology via Scopus and OpenAI

Kılıç,

Vasegaard,

Desoeuvres

et al. 2024

Hum-Cent Intell Syst

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Nowadays, literature review is a necessary task when trying to solve a given problem. However, an exhaustive literature review is very time-consuming in today’s vast literature landscape. It can take weeks, even if looking only for abstracts or surveys. Moreover, choosing a method among others, and targeting searches within relevant problem and solution domains, are not easy tasks. These are especially true for young researchers or engineers starting to work in their field. Even if surveys that provide methods used to solve a specific problem already exist, an automatic way to do it for any use case is missing, especially for those who don’t know the existing literature. Our proposed tool, SARBOLD-LLM, allows discovering and choosing among methods related to a given problem, providing additional information about their uses in the literature to derive decision-making insights, in only a few hours. The SARBOLD-LLM comprises three modules: (1: Scopus search) paper selection using a keyword selection scheme to query Scopus API; (2: Scoring and method extraction) relevancy and popularity scores calculation and solution method extraction in papers utilizing OpenAI API (GPT 3.5); (3: Analyzes) sensitivity analysis and post-analyzes which reveals trends, relevant papers and methods. Comparing the SARBOLD-LLM to manual ground truth using precision, recall, and F1-score metrics, the performance results of AI in the oncology case study are 0.68, 0.9, and 0.77, respectively. SARBOLD-LLM demonstrates successful outcomes across various domains, showcasing its robustness and effectiveness. The SARBOLD-LLM addresses engineers more than researchers, as it proposes methods and trends without adding pros and cons. It is a useful tool to select which methods to investigate first and comes as a complement to surveys. This can limit the global search and accumulation of knowledge for the end user. However, it can be used as a director or recommender for future implementation to solve a problem.

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A review: Data pre-processing techniques used for diabetes prediction

Isnan,

Elwirehardja,

Pardamean

2024

Procedia Computer Science

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Identifying Heterogeneity of Diabetics Mellitus Based on the Demographical and Clinical Characteristics

Cited by 4 publications

References 43 publications

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

A Semi-Automated Solution Approach Recommender for a Given Use Case: a Case Study for AI/ML in Oncology via Scopus and OpenAI

A review: Data pre-processing techniques used for diabetes prediction

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