A Téllez Santoyo, C Lopera, A Ladino Vásquez, F Seguí Fernández, I Grafiá Pérez, M Chumbita, T F Aiello, P Monzó, O Peyrony, P Puerta-Alcalde, C Cardozo, N Garcia-Pouton, P Castro, S Fernández Méndez, J M Nicolas Arfelis, A Soriano, C Garcia-Vidal
{"title":"识别传染病领域最重要的研究数据:基于人工智能的思考。","authors":"A Téllez Santoyo, C Lopera, A Ladino Vásquez, F Seguí Fernández, I Grafiá Pérez, M Chumbita, T F Aiello, P Monzó, O Peyrony, P Puerta-Alcalde, C Cardozo, N Garcia-Pouton, P Castro, S Fernández Méndez, J M Nicolas Arfelis, A Soriano, C Garcia-Vidal","doi":"10.37201/req/032.2023","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Clinical data on which artificial intelligence (AI) algorithms are trained and tested provide the basis to improve diagnosis or treatment of infectious diseases (ID). We aimed to identify important data for ID research to prioritise efforts being undertaken in AI programmes.</p><p><strong>Methods: </strong>We searched for 1,000 articlesfrom high-impact ID journals on PubMed, selecting 288 of the latest articles from 10 top journals. We classified them into structured or unstructured data. Variables were homogenised and grouped into the following categories: epidemiology, admission, demographics, comorbidities, clinical manifestations, laboratory, microbiology, other diagnoses, treatment, outcomes and other non-categorizable variables.</p><p><strong>Results: </strong>4,488 individual variables were collected, from the 288 articles. 3,670 (81.8%) variables were classified as structured data whilst 818 (18.2%) as unstructured data. From the structured data, 2,319 (63.2%) variables were classified as direct-retrievable from electronic health records-whilst 1,351 (36.8%) were indirect. The most frequent unstructured data were related to clinical manifestations and were repeated across articles. Data on demographics, comorbidities and microbiology constituted the most frequent group of variables.</p><p><strong>Conclusions: </strong>This article identified that structured variables have comprised the most important data in research to generate knowledge in the field of ID. Extracting these data should be a priority when a medical centre intends to start an AI programme for ID. We also documented that the most important unstructured data in this field are those related to clinical manifestations. Such data could easily undergo some structuring with the use of semi-structured medical records focusing on a few symptoms.</p>","PeriodicalId":21232,"journal":{"name":"Revista Espanola De Quimioterapia","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10710675/pdf/","citationCount":"0","resultStr":"{\"title\":\"Identifying the most important data for research in the field of infectious diseases: thinking on the basis of artificial intelligence.\",\"authors\":\"A Téllez Santoyo, C Lopera, A Ladino Vásquez, F Seguí Fernández, I Grafiá Pérez, M Chumbita, T F Aiello, P Monzó, O Peyrony, P Puerta-Alcalde, C Cardozo, N Garcia-Pouton, P Castro, S Fernández Méndez, J M Nicolas Arfelis, A Soriano, C Garcia-Vidal\",\"doi\":\"10.37201/req/032.2023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>Clinical data on which artificial intelligence (AI) algorithms are trained and tested provide the basis to improve diagnosis or treatment of infectious diseases (ID). We aimed to identify important data for ID research to prioritise efforts being undertaken in AI programmes.</p><p><strong>Methods: </strong>We searched for 1,000 articlesfrom high-impact ID journals on PubMed, selecting 288 of the latest articles from 10 top journals. We classified them into structured or unstructured data. Variables were homogenised and grouped into the following categories: epidemiology, admission, demographics, comorbidities, clinical manifestations, laboratory, microbiology, other diagnoses, treatment, outcomes and other non-categorizable variables.</p><p><strong>Results: </strong>4,488 individual variables were collected, from the 288 articles. 3,670 (81.8%) variables were classified as structured data whilst 818 (18.2%) as unstructured data. From the structured data, 2,319 (63.2%) variables were classified as direct-retrievable from electronic health records-whilst 1,351 (36.8%) were indirect. The most frequent unstructured data were related to clinical manifestations and were repeated across articles. Data on demographics, comorbidities and microbiology constituted the most frequent group of variables.</p><p><strong>Conclusions: </strong>This article identified that structured variables have comprised the most important data in research to generate knowledge in the field of ID. Extracting these data should be a priority when a medical centre intends to start an AI programme for ID. We also documented that the most important unstructured data in this field are those related to clinical manifestations. Such data could easily undergo some structuring with the use of semi-structured medical records focusing on a few symptoms.</p>\",\"PeriodicalId\":21232,\"journal\":{\"name\":\"Revista Espanola De Quimioterapia\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2023-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10710675/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Revista Espanola De Quimioterapia\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.37201/req/032.2023\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/8/12 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Revista Espanola De Quimioterapia","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.37201/req/032.2023","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/12 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
Identifying the most important data for research in the field of infectious diseases: thinking on the basis of artificial intelligence.
Objective: Clinical data on which artificial intelligence (AI) algorithms are trained and tested provide the basis to improve diagnosis or treatment of infectious diseases (ID). We aimed to identify important data for ID research to prioritise efforts being undertaken in AI programmes.
Methods: We searched for 1,000 articlesfrom high-impact ID journals on PubMed, selecting 288 of the latest articles from 10 top journals. We classified them into structured or unstructured data. Variables were homogenised and grouped into the following categories: epidemiology, admission, demographics, comorbidities, clinical manifestations, laboratory, microbiology, other diagnoses, treatment, outcomes and other non-categorizable variables.
Results: 4,488 individual variables were collected, from the 288 articles. 3,670 (81.8%) variables were classified as structured data whilst 818 (18.2%) as unstructured data. From the structured data, 2,319 (63.2%) variables were classified as direct-retrievable from electronic health records-whilst 1,351 (36.8%) were indirect. The most frequent unstructured data were related to clinical manifestations and were repeated across articles. Data on demographics, comorbidities and microbiology constituted the most frequent group of variables.
Conclusions: This article identified that structured variables have comprised the most important data in research to generate knowledge in the field of ID. Extracting these data should be a priority when a medical centre intends to start an AI programme for ID. We also documented that the most important unstructured data in this field are those related to clinical manifestations. Such data could easily undergo some structuring with the use of semi-structured medical records focusing on a few symptoms.
期刊介绍:
The official journal of the Sociedad Española de Quimioterapia (Spanish Society of Chemotherapy), publishes articles that further knowledge and advance the science and application of antimicrobial chemotherapy with antibiotics and antifungal, antiviral and antiprotozoal agents primarily in human medicine. Authors sign an exclusive license agreement, where authors have copyright but license exclusive rights in their article to the Publisher. All manuscripts are free open access. Revista Española de Quimioterapia includes the following sections: reviews, original articles, brierf reports, letters, and consensus documents.