A review on intelligent methods used in medicine and life science

Soni, Saksham; Khunteta, Ajay; Gupta, Michèle

doi:10.1145/1980022.1980173

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…Their original and still typical task is to suggest a patient's possible diagnosis on the basis of signs, findings, test results, history and systems. The inference techniques of clinical diagnosis expert system [10,11] include rule-based, case-based, artificial neural network, fuzzy logic, Bayesian Network, and so on. Those inference techniques cannot simplistically and intuitively express a large number of medical knowledge and cannot interpret the diagnosis results by the doctor with easily understandable manner.…”

Section: Introductionmentioning

confidence: 99%

Clinical diagnosis expert system based on dynamic uncertain causality graph

Geng

Zhang

2014

2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference

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Clinical diagnosis expert system is the focus and hotspots of research from the beginning of the 1960s, many inference techniques have been applied to disease diagnosis. Dynamic Uncertain Causality Graph (DUCG) is the model of graphical probability reasoning.It can represent the quantitative and qualitative causal knowledge by the way of causal graph and can reason in the case of incomplete knowledge. According to DUCG theory, we developed the clinical diagnosis expert system. Using the system, the clinical knowledge can be easily represented as a causal graph. The knowledge base construction can be done by more than one people separately. The consistence check of the so constructed knowledge base is encoded in this system. In the case of incomplete knowledge representation, this system still works well. The examples of hiatal hernia and infectious disease are provided, which demonstrates that our clinical diagnosis expert system is a powerful tool for the clinical diagnosis.

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Section: Introductionmentioning

confidence: 99%

Clinical diagnosis expert system based on dynamic uncertain causality graph

Geng

Zhang

2014

2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference

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“…The enormous amount of clinical data generated both by ICU patients and staff, as well as the constant estimations of patients' health outcomes, can provide AI calculations with an amazing opportunity. The detection approach [1] and hereditary data extraction [2] are only two examples of current clinical examinations that deeply rely on AI technologies. This creates a great opportunity for clinicians because of the growing use of Electronic Health Records (EHR).…”

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confidence: 99%

Risk assessment of ICU patients through deep learning technique: A big data approach

et al. 2022

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Background Intensive Care Unit (ICU) patients are exposed to various medications, especially during infusion, and the amount of infusion drugs and the rate of their application may negatively affect their health status. A deep learning model can monitor a patient's continuous reaction to tranquillizer therapy, analyze the treatment plans of experts to avoid severe situations such as reverse medication associations, work with a convenient mediator, and change the treatment plans of specialists as needed.Methods Generally, patients' treatment histories are linked together via a period grouping connection, which is usually burdened by missing information. Displaying time-succession via Repetitive Neural Organization (RNO) is the best available solution. However, it's possible that a patient's treatment may be prolonged, which RNN may not be able to demonstrate in this manner. ResultsWe propose the use of the LSTM-RNN driven by heterogeneous medicine events to predict the patient's outcome, as well as the Regular Language Handling and Gaussian Cycle, which can handle boisterous, deficient, inadequate, heterogeneous, and unevenly tested prescription records of patients while addressing the missing value issue using a piece-based Gaussian cycle. ConclusionsWe emphasize the semantic relevance of every medication event and the grouping of drug events on patients in our study. We will focus specifically on LSTM-RNN and Phased LSTM-RNN for showing treatment results and information attribution using bit-based Gaussian cycles. We worked on Staged LSTM-RNN.

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