scholarly journals The method of primary processing of poorly structured medical data

2020 ◽  
Vol 8 ◽  
pp. 1-10
Author(s):  
Dmytro Bychko ◽  
◽  
Vira Shendryk ◽  
Yuliia Parfenenko ◽  
◽  
...  

The article deals with the approach to the primary processing of poorly structured medical protocol textual data stored and disseminated as pdf files. The relevance of this work is due to the lack of a universal structure for the presentation of medical protocols and methods of their processing. In the course of the work, the problem of primary processing of clinical protocol data was solved by the example of a unified clinical protocol of primary, secondary (specialized) and tertiary (highly specialized) medical care. The method of primary data processing was developed to create a clear structure of the symptoms of the disease. The first step in structuring clinical protocol data is to divide the protocol information into four basic parts, which allows it to be quickly converted to other formats. This process is implemented using an algorithm developed in C # programming language. The proposed algorithm parses the information from a pdf file and converts it to a txt file. After that, the received information is processed, which consists in the syntactic analysis of the text of the protocol and selection of the structural parts of the protocol corresponding to the headings of the sections: title page; introduction; a list of abbreviations used in the protocol; the main part of the protocol; list of literary sources. The identification of the disease name in the medical protocol is performed by comparing the protocol data and the list of disease names, presented in the world classification MKH-10. The headings “Introduction”, “List of abbreviations used in the protocol” and the main part of the protocol were analyzed and the algorithm for removing uninformed sections from the beginning of the protocol, for example, literature sources, was proposed. An algorithm for finding information in the main part of the medical protocol by processing input data by: tables, diagrams, headings, words, phrases and special symbols are also proposed. As a result of the clinical protocol processing algorithms, a new clinical protocol file is generated, which is three times smaller than the original file. It contains only meaningful information from clinical protocols that will speed up further work on this file, namely its use in medical decision support. The disease card based on a medical protocol in JSON format is presented.

2021 ◽  
Vol 66 (Special Issue) ◽  
pp. 38-38
Author(s):  
Sorana D. Bolboacă ◽  
◽  
Adriana Elena Bulboacă ◽  
◽  
◽  
...  

"The Clinical Decision Support (CDS), a form of artificial intelligence (AI), consider physician expertise and cognitive function along with patient’s data as the input and case-specific medical decision as an output. The improvements in physician’s performances when using a CDS ranges from 13% to 68%. The AI applications are of large interest nowadays, and a lot of effort is also put in the development of IT applications in healthcare. Medical decision support systems for non-medical staff users (MDSS-NMSF) as phone applications are nowadays available on the market. A MDSS-NMSF app is generally not accompanied by a scientific evaluation of the performances, even if they are freely available or not. Two clinical scenarios were created, and Doctor31 retrieved the diagnosis decisions. First scenario: man, 29 years old, and three symptoms: dysphagia, weight loss (normal body mass index), and tiredness. Second scenario: women, 47 years old with L5-S1 disk herniation, abnormal anti-TPO antibodies, lower back pain (burning sensations), constipation, and tiredness. The outcome possible effects and implications, as well as vulnerabilities induced on the used, are highlighted and discussed. "


2017 ◽  
Vol 05 (06) ◽  
pp. E477-E483 ◽  
Author(s):  
Anastasios Koulaouzidis ◽  
Dimitris Iakovidis ◽  
Diana Yung ◽  
Emanuele Rondonotti ◽  
Uri Kopylov ◽  
...  

Abstract Background and aims Capsule endoscopy (CE) has revolutionized small-bowel (SB) investigation. Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amounts of image annotations are required for training. Current databases lack graphic annotations of pathologies and cannot be used. A novel database, KID, aims to provide a reference for research and development of medical decision support systems (MDSS) for CE. Methods Open-source software was used for the KID database. Clinicians contribute anonymized, annotated CE images and videos. Graphic annotations are supported by an open-access annotation tool (Ratsnake). We detail an experiment based on the KID database, examining differences in SB lesion measurement between human readers and a MLA. The Jaccard Index (JI) was used to evaluate similarity between annotations by the MLA and human readers. Results The MLA performed best in measuring lymphangiectasias with a JI of 81 ± 6 %. The other lesion types were: angioectasias (JI 64 ± 11 %), aphthae (JI 64 ± 8 %), chylous cysts (JI 70 ± 14 %), polypoid lesions (JI 75 ± 21 %), and ulcers (JI 56 ± 9 %). Conclusion MLA can perform as well as human readers in the measurement of SB angioectasias in white light (WL). Automated lesion measurement is therefore feasible. KID is currently the only open-source CE database developed specifically to aid development of MDSS. Our experiment demonstrates this potential.


2010 ◽  
Vol 36 (1) ◽  
pp. 233-239 ◽  
Author(s):  
Kavishwar Wagholikar ◽  
Sanjeev Mangrulkar ◽  
Ashok Deshpande ◽  
Vijayraghavan Sundararajan

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