Resilience after a nuclear accident: readiness in using mobile phone applications to measure radiation and health indicators in various groups (SHAMISEN SINGS project).

An anonymous web-based survey was developed to check different aspects (SHAMISEN SINGS project): stakeholder awareness and perceptions of available mobile applications (apps) for measuring ionising radiation doses and health/well-being indicators; whether they would be ready to use them in the post-accidental recovery; and what are their preferred methodologies to acquire information etc. The results show that participation of the citizens would be most beneficial during post-accident recovery, providing individual measurements of external ionizing dose and health/well-being parameters, with possible follow-up. Also, participants indicated different preferences for sources to gain knowledge on ionising radiation and for the functions that an ideal app should have. The level of awareness and readiness to use apps to measure ionising radiation dose depended on two main aspects: individual differences (age & gender) and whether people were from countries affected by the previous major accidents. We concluded that stakeholders could have benefits from the data management plan: (1) it potentiates resilience at individual and community level; (2) citizens' measurements contribute to environmental monitoring and public health screening; (3) linkages between different types of data (environmental exposure, individual behavioural diaries, and measurements of health indicators) allow to perform more rigorous epidemiological studies.

Leveraging Rule-Based NLP to Translate Textual Reports as Structured Inputs Automatically Processed by a Clinical Decision Support System.

Using clinical decision support systems (CDSSs) for breast cancer management necessitates to extract relevant patient data from textual reports which is a complex task although efficiently achieved by machine learning but black box methods. We proposed a rule-based natural language processing (NLP) method to automate the translation of breast cancer patient summaries into structured patient profiles suitable for input into the guideline-based CDSS of the DESIREE project. Our method encompasses named entity recognition (NER), relation extraction and structured data extraction to systematically organize patient data. The method demonstrated strong alignment with treatment recommendations generated for manually created patient profiles (gold standard) with only 2% of differences. Moreover, the NER pipeline achieved an average F1-score of 0.9 across the main entities (patient, side, and tumor), of 0,87 for relation extraction, and 0.75 for contextual information, showing promising results for rule-based NLP.

Clinical Decision Support Systems Applied to the Management of Breast Cancer Patients: A Scoping Review.

Breast cancer is the most commonly diagnosed cancer worldwide, and its burden has been rising over the past decades. A significant advance in healthcare is the integration of Clinical Decision Support Systems (CDSSs) into medical practice, which support healthcare professionals improving clinical decisions, leading to recommended patient-specific treatments and enhanced patient care. Breast cancer CDSSs are thus currently expanding, whether applied to screening, diagnostic, therapeutic or follow-up tasks. We conducted a scoping review to study their availability and use in practice. Except risk calculators, very few CDSSs are currently routinely used.