Domiciliary care for oncologic patients may have clear advantage in terms of improved quality of life, psychological well-being and cost effectiveness. Coordination and collaboration between oncologists and patients are essential in order to guarantee the healthcare provision and treatment adherence in cancer home therapy. FILO BLU is a software application that aims to improve the communication in the doctor-patient relationship composed of two messaging APPs for smartphone, one for the patient/caregiver and one for the medical team, it is equipped with a module for the interoperability with portable medical monitoring systems and it is integrated with the patients’ electronic medical records. This allows the doctor to respond to requests having always available all the clinical information. To improve decision making and workload management we develop an expert system for the analysis of medical-patient communications that aims to score the patient's clinical status and that analyzes the flow of communications in order to signal to doctors, through an "attention" score, potential critical situations keeping into account both the written texts and any physiological values monitored. We generate synthetic data, composed of a simulation of the patient physiology and semi-automatically generated sentences, to test operative workflow. To easily combine numerical and textual data sources for the classification we choose a deep learning approach. We tested multiple neural networks architectures used in sentiment analysis to classify patients’ messages according to the severity of the clinical status both alone and in conjunction with physiological parameters recordings. While we do not expect that our synthetic data can replace data gathered through the usage in clinical settings, it creates a controlled test environment for classification and context sensitive spelling correction algorithm.

Filoblu: Sentiment Analysis Application to Doctor-Patient Interactions / Ciardiello, Andrea; Curti, Nico; Castellani, Gastone; Giagu, Stefano; Mancini Terracciano, Carlo; Remondini, Daniel; Vistoli, Cristina; Voena, Cecilia; Faccini, Riccardo. - (2019). ((Intervento presentato al convegno International Conference on the Use of Computers in Radiation Therapy tenutosi a Montreal, CA.

Filoblu: Sentiment Analysis Application to Doctor-Patient Interactions

Andrea Ciardiello;Stefano Giagu Giagu;Carlo Mancini Terracciano;Cecilia Voena;Riccardo Faccini
2019

Abstract

Domiciliary care for oncologic patients may have clear advantage in terms of improved quality of life, psychological well-being and cost effectiveness. Coordination and collaboration between oncologists and patients are essential in order to guarantee the healthcare provision and treatment adherence in cancer home therapy. FILO BLU is a software application that aims to improve the communication in the doctor-patient relationship composed of two messaging APPs for smartphone, one for the patient/caregiver and one for the medical team, it is equipped with a module for the interoperability with portable medical monitoring systems and it is integrated with the patients’ electronic medical records. This allows the doctor to respond to requests having always available all the clinical information. To improve decision making and workload management we develop an expert system for the analysis of medical-patient communications that aims to score the patient's clinical status and that analyzes the flow of communications in order to signal to doctors, through an "attention" score, potential critical situations keeping into account both the written texts and any physiological values monitored. We generate synthetic data, composed of a simulation of the patient physiology and semi-automatically generated sentences, to test operative workflow. To easily combine numerical and textual data sources for the classification we choose a deep learning approach. We tested multiple neural networks architectures used in sentiment analysis to classify patients’ messages according to the severity of the clinical status both alone and in conjunction with physiological parameters recordings. While we do not expect that our synthetic data can replace data gathered through the usage in clinical settings, it creates a controlled test environment for classification and context sensitive spelling correction algorithm.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1477499
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