Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Objectives: This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to “classical" knowledge-based ones, and to consider the issues raised and their possible solutions. Methods: We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies. Results: We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data. Conclusions: Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.

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A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare

Sensors ◽

10.3390/s21186209 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6209

Author(s):

Andrei Velichko

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Decision Support ◽

Data Analysis ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Classification Accuracy ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Medical Data

Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3–10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.

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The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

Robotic Systems ◽

10.4018/978-1-7998-1754-3.ch008 ◽

2020 ◽

pp. 167-186

Author(s):

Steven Walczak

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Clinical Decision Making ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

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Artificial intelligence in medical devices and clinical decision support systems

Clinical Engineering Handbook ◽

10.1016/b978-0-12-813467-2.00084-5 ◽

2020 ◽

pp. 556-568 ◽

Cited By ~ 1

Author(s):

Elliot B. Sloane ◽

Ricardo J. Silva

Keyword(s):

Artificial Intelligence ◽

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Medical Devices ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision

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Clinical Decision Support Systems

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1952264 ◽

2019 ◽

pp. 993-995 ◽

Cited By ~ 1

Author(s):

Pratibha Tiwari ◽

Nishant Shah ◽

Evanglelin Samuel ◽

Poojan Shah ◽

Yask Patel ◽

...

Keyword(s):

Health Care ◽

Decision Support ◽

Clinical Decision Support ◽

Prediction Models ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Health Care Sector ◽

Easy Access ◽

Disease Prediction ◽

Knowledge Based

Nowadays, every field is digitizing their data for easy access at anytime and anywhere or even for enclosed cabinet servers, especially the health care sector. But, that is not the only reason health care sector is computerizing its data. These huge chucks of records are used for research purposes. Many hospitals are working with education institutes with research departments (Damian Borbolla et.al 2010).CDSS performs Knowledge-based analyses on these EHRs and running disease prediction models on these data is done. There may be many complications. We have reviewed the problems faced by such system from previous researches and implemented systems.

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Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study (Preprint)

10.2196/preprints.25929 ◽

2020 ◽

Author(s):

Mengting Ji ◽

Georgi Z Genchev ◽

Hengye Huang ◽

Ting Xu ◽

Hui Lu ◽

...

Keyword(s):

Artificial Intelligence ◽

Decision Support ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision Support System ◽

Measurement Instrument ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

User Acceptance ◽

Evaluation Framework

BACKGROUND Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice. OBJECTIVE The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework. METHODS An artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents. RESULTS The Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ262=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension. CONCLUSIONS User acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

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Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study

Journal of Medical Internet Research ◽

10.2196/25929 ◽

2021 ◽

Vol 23 (6) ◽

pp. e25929

Author(s):

Mengting Ji ◽

Georgi Z Genchev ◽

Hengye Huang ◽

Ting Xu ◽

Hui Lu ◽

...

Keyword(s):

Artificial Intelligence ◽

Decision Support ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision Support System ◽

Measurement Instrument ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

User Acceptance ◽

Evaluation Framework

Background Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice. Objective The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework. Methods An artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents. Results The Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ262=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension. Conclusions User acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

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The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

Data Analytics in Medicine ◽

10.4018/978-1-7998-1204-3.ch021 ◽

2020 ◽

pp. 390-409

Author(s):

Steven Walczak

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Clinical Decision Making ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Classification Framework

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Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

Diagnostics ◽

10.3390/diagnostics9020052 ◽

2019 ◽

Vol 9 (2) ◽

pp. 52 ◽

Cited By ~ 6

Author(s):

Yamid Fabián Hernández-Julio ◽

Martha Janeth Prieto-Guevara ◽

Wilson Nieto-Bernal ◽

Inés Meriño-Fuentes ◽

Alexander Guerrero-Avendaño

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Fuzzy Inference ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Data Driven ◽

Kappa Statistics ◽

Levels Of Abstraction

Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.

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