Prescreening in Oncology Using Data Sciences: The PreScIOUS Study

Ansoborlo, Marie; Dhalluin, Thibault; Gaborit, Christophe; Cuggia, Marc; Grammatico-Guilllon, Leslie

doi:10.3233/shti210133

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…Data were collected using eHOP®, the Clinical Data Warehouse software of the tertiary care hospital of Tours; it integrates all electronic medical documents produced in the hospital information system [ 15 , 16 ]. We developed a research algorithm to detect the keywords “sweat” and its synonyms in French, including plural spelling, which were subsequently followed, within fewer than four words, by the French words for “prolonged”, “chronic”, “recurrent”, “night”, “day(s)”, “week(s)”, “month(s)”, or “year(s)”.…”

Section: Methodsmentioning

confidence: 99%

When to investigate for secondary hyperhidrosis: data from a retrospective cohort of all causes of recurrent sweating

et al. 2022

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Background: Identification of underlying diseases is crucial for secondary hyperhidrosis management, but data are lacking to guide appropriate investigation. Objective: To describe aetiologies of recurrent sweating in a hospital setting and the diagnostic performance parameters of their respective clinical/biological features. Patients and Methods: We performed a monocentric evaluative study in a tertiary care centre. Patients with recurrent generalised sweating were selected via the Clinical Data Warehouse (CDW) by screening all electronic hospital documents from the year 2018 using a keyword-based algorithm. All in and out-patients aged ≥ 18 years having reported recurrent sweating for at least 2 weeks in 2018 were included, with a minimum one-year follow-up after symptoms’ onset. Results: A total of 420 patients were included. Over 130 different aetiologies were identified; 70 patients (16.7%) remained without diagnosis. Solid organ cancers (14.3% with 13 lung cancers), haematologic malignancies (14.0% with 35 non-Hodgkin’s lymphomas) and Infectious Diseases (10.5% including 13 tuberculosis) were the most frequent diagnoses. Other aetiologies were gathered into inflammatory (16.9%) and non-inflammatory (27.6%) conditions. To distinguish non-inflammatory and undiagnosed hyperhidrosis from other causes, fever had a specificity of 94%, impaired general condition a sensitivity of 78%, and C-reactive protein (CRP) > 5.6 mg/l a positive predictive value of 0.86. Symptoms’ duration over 1 year was in favour of non-infectious and non-malignant causes (94% specificity). Conclusions: We identified fever, impaired general condition, duration, and CRP as helpful orientation parameters to assess the need for complementary explorations for hyperhidrosis. The study provides a diagnostic algorithm for the investigation of recurrent sweating. KEY MESSAGES In a hospital setting, malignancies and infections are the most frequently associated diseases, but 1/5 remain without diagnosis. Fever is a specific but not sensitive sign to distinguish inflammatory conditions. Over 1 year duration of symptoms significantly reduce the probability of malignancy or infection as the underlying diagnosis.

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Section: Methodsmentioning

confidence: 99%

When to investigate for secondary hyperhidrosis: data from a retrospective cohort of all causes of recurrent sweating

et al. 2022

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“…It is also possible to intervene upstream by applying corrective actions on the source applications, which is sometimes facilitated by the fact that the end users are also the data producers. Secondary reuse involves defining the dimensions of interest in terms of data quality in relation to intended uses in order to put in place indicators to assess and monitor data quality [ 22 , 23 , 24 ].…”

Section: Dwh In French Cccs: the Challenge Of Structuring The Datamentioning

confidence: 99%

Opportunities and Obstacles to the Development of Health Data Warehouses in Hospitals in France: The Recent Experience of Comprehensive Cancer Centers

Raimbourg

Bigot

Simmet

et al. 2023

IJERPH

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Big Data and Artificial Intelligence can profoundly transform medical practices, particularly in oncology. Comprehensive Cancer Centers have a major role to play in this revolution. With the purpose of advancing our knowledge and accelerating cancer research, it is urgent to make this pool of data usable through the development of robust and effective data warehouses. Through the recent experience of Comprehensive Cancer Centers in France, this article shows that, while the use of hospital data warehouses can be a source of progress by taking into account multisource, multidomain and multiscale data for the benefit of knowledge and patients, it nevertheless raises technical, organizational and legal issues that still need to be addressed. The objectives of this article are threefold: 1. to provide insight on public health stakes of development in Comprehensive Cancer Centers to manage cancer patients comprehensively; 2. to set out a challenge of structuring the data from within them; 3. to outline the legal issues of implementation to carry out real-world evidence studies. To meet objective 1, this article firstly proposed a discussion on the relevance of an integrated approach to manage cancer and the formidable tool that data warehouses represent to achieve this. To address objective 2, we carried out a literature review to screen the articles published in PubMed and Google Scholar through the end of 2022 on the use of data warehouses in French Comprehensive Cancer Centers. Seven publications dealing specifically with the issue of data structuring were selected. To achieve objective 3, we presented and commented on the main aspects of French and European legislation and regulations in the field of health data, hospital data warehouses and real-world evidence.

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“…Multiple pieces of information about patients were extracted from clinical texts for application in retrospective studies [56]. Ansoborlo et al [89] extracted 52 pieces of bioclinical information from French multidisciplinary team meeting reports concerning lung cancer by applying regular expressions and then compared this approach with a Bayesian classifier method.…”

Section: Information Extractionmentioning

confidence: 99%

“…Publications, n (%) Data language [15-17,19-25,27-38,41-48,50,51,53-68,71,72,74,75,78-80,83-88,90-92,96,97,99-112,114,116,119-124,126-135, 137,140,142-149,151-154,156-159,161,162,165-176,179,181,184-187,189-192,194-196,198,200-208] 153 (78.9) English [39,49,52,73,76,77,81,89,93,94,113,115,118,125,136,138,139,155,163,164,177,178,182,183,188,193,197] 27 (13.9) French [18,26,69,95,117,150,160,180,199] 9 (4.6) German [40,65,82] 3 (1.5) Korean…”

Section: Referencesmentioning

confidence: 99%

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

Bazoge,

Morin,

Daille

et al. 2023

JMIR Med Inform

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Background In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. Objective The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. Methods This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. Results We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). Conclusions CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.

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Prescreening in Oncology Using Data Sciences: The PreScIOUS Study

Cited by 4 publications

References 12 publications

When to investigate for secondary hyperhidrosis: data from a retrospective cohort of all causes of recurrent sweating

When to investigate for secondary hyperhidrosis: data from a retrospective cohort of all causes of recurrent sweating

Opportunities and Obstacles to the Development of Health Data Warehouses in Hospitals in France: The Recent Experience of Comprehensive Cancer Centers

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

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