A phrase-based questionnaire-answering approach for automatic initial frailty assessment based on clinical notes.

Frailty stands out as a particularly challenging multidimensional geriatric syndrome in the elderly population, often resulting in diminished quality of life and heightened mortality risk. Negative consequences encompass a heightened likelihood of hospitalization and institutionalization, as well as suboptimal post-hospitalization outcomes and elevated mortality rates. Using a questionnaire-based approach for assessing frailty has been shown to be an effective method for early diagnosis of frailty. Nonetheless, the majority of current frailty assessment tools necessitate in-person consultations. This poses a significant challenge for elderly patients residing in rural areas, who often encounter difficulties in accessing healthcare compared to their urban or suburban counterparts. Additionally, elderly patients face an elevated risk of contracting diseases as a result of frequent hospital visits, given that many of them are immunocompromised. An automated initial frailty assessment approach can help mitigate the challenges mentioned above and conserve clinical resources by circumventing the need for extensive manual assessments. The primary aim of this paper is to introduce an automatic initial frailty assessment method. This method efficiently identifies individuals who may necessitate further frailty evaluation by automatically extracting relevant information from a patient's clinical notes and using it to complete the Tillburg Frailty Indicator (TFI) questionnaire. The introduced phrase-based query expansion technique is designed to identify the most pertinent phrases related to the frailty assessment questionnaire using Unified Medical Language System (UMLS) ontology and incorporates information from clinical notes to enhance its accuracy. Additionally, a method for retrieving pertinent clinical notes to automatically facilitate the frailty assessment process based on the identified phrases was also proposed. The proposed approaches are evaluated using a dataset containing a collection of clinical notes from elderly patients, assessing their effectiveness in terms of automating frailty assessment and question-answering tasks. This research underscores the significance of incorporating phrases as features in the automated frailty assessment process using clinical notes. The research empowers clinicians to conduct automatic frailty assessments utilizing medical data, thereby reducing the need for frequent hospital visits and in-patient consultations. This becomes particularly valuable during unusual or unexpected situations, such as the COVID-19 pandemic, where minimizing in-person interactions is crucial.