Clinical Decision Support System for the Respiratory Diseases Diagnosis

Shakhmametova, G. R.; Zulkarneev, Rustem; Evgrafov, Alexander

doi:10.2991/itids-19.2019.19

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…V. Poberezhets highlighted interesting future developments in AI such as clinical decision support systems that will aggregate all data obtained in a patient's pathway [ 27 ]. The current systems are limited by having to process unstructured data such as patient complaints or observations made in clinical examinations and recorded in notes; AI may address these issues.…”

Section: Group 901: Respiratory Function Technologists and Scientistmentioning

confidence: 99%

ERS International Congress 2020 Virtual: highlights from the Allied Respiratory Professionals Assembly

Smith

Thomas²,

Çalık-Kütükçü

et al. 2020

ERJ Open Res

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This article provides an overview of outstanding sessions that were (co)organised by the Allied Respiratory Professionals (ARP) Assembly during the European Respiratory Society (ERS) International Congress 2020, which this year assumed a virtual format. The content of the sessions was mainly targeted at ARP, including respiratory function technologists and scientists, physiotherapists and nurses, and is summarised in this highlights article. Short take-home messages related to spirometry and exercise testing are provided, highlighting the importance of quality control. In addition, the need for quality improvement in sleep interventions is underlined as it may enhance patient outcomes and the working capacity of healthcare services. The promising role of digital health in chronic disease management is discussed, with emphasis on the value of end-user participation in the development of these technologies. Evidence on the effectiveness of airway clearance techniques in chronic respiratory conditions is provided along with the rationale for its use and challenges to be addressed in future research. Furthermore, the importance of assessing, preventing and reversing frailty in respiratory patients is discussed, with a clear focus on exercise-based interventions. Research on the impact of disease-specific fear and anxiety on patient outcomes draws attention to the need for early assessment and intervention. Finally, advances in nursing care related to treatment adherence, self-management and patients’ perspective in asthma and COPD are provided, highlighting the need for patient engagement and shared decision making. This highlights article provides readers with valuable insight into the latest scientific data and emerging areas affecting clinical practice of ARP.

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Section: Group 901: Respiratory Function Technologists and Scientistmentioning

confidence: 99%

ERS International Congress 2020 Virtual: highlights from the Allied Respiratory Professionals Assembly

Smith

Thomas²,

Çalık-Kütükçü

et al. 2020

ERJ Open Res

View full text Add to dashboard Cite

show abstract

“…The [2] describes how the architecture of the system for making clinical decisions in the diagnosis of respiratory diseases was developed. This system involves such functions as analysis of patient data in the form of text, graphic and sound in-formation, presentation of the expected diagnosis on the basis of the analysis carried out and the detected patterns, formation and issuance of medical recommendations to increase the patient's treatment efficiency [3].…”

Section: Introductionmentioning

confidence: 99%

Unstructured Text and Tabular Information Processing in the Clinical Decision Making System for the Respiratory Diseases Diagnosis

Shakhmametova¹,

Evgrafov²,

Zulkarneev³

2020

Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)

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The article considers the basic algorithm of text and table data extraction used in the developed system of clinical decision-making in diagnosis of respiratory diseases, methods of formation of the data structure of an individual patient, a set of data from all patients for further application in models of machine learning as well as construction of ML models which provide detection of disease in patients. Data extraction and generation processes are performed in the Python programming language using additional libraries: "docx" and "pandas" for data processing and "sklearn", "lightgbm" and "catboost" for building machine learning models. The relevance of the task is due to large volumes of unstructured data received by the CDSS input and necessary for its effective functioning. The novelty of development lies in application of a set of existing and development of new algorithms of extraction and primary processing of text and table information.

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Development of Data Storage and User Interface in the Clinical Decision Support System

Shakhmametova

Evgrafov²,

Zulkarneev

2023

Lecture Notes in Networks and Systems

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Clinical Decision Support System for the Respiratory Diseases Diagnosis

Cited by 5 publications

References 5 publications

ERS International Congress 2020 Virtual: highlights from the Allied Respiratory Professionals Assembly

ERS International Congress 2020 Virtual: highlights from the Allied Respiratory Professionals Assembly

Unstructured Text and Tabular Information Processing in the Clinical Decision Making System for the Respiratory Diseases Diagnosis

Development of Data Storage and User Interface in the Clinical Decision Support System

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