: A relevant problem in medicine is the standardization of the diagnosis associated with a clinical case. Although diagnosis formulation is an intrinsically subjective and uncertain process, its standardization may take benefit from digital solutions automating the routines at the basis of such a decision. In this work, we propose ARGO 2.0: a framework for the development of decision support systems for diagnosis formulation. The framework can read free-text reports and store their clinically relevant information as personalized electronic Case Report Forms. A hybrid strategy, exploiting the synergy of Natural Language Processing and Machine Learning techniques, is used to automatically suggest a diagnosis in a standardized fashion. ARGO 2.0 has been designed to be template-independent and easily tailored to specific medical fields. We here demonstrate its feasibility in hemo lympho-pathology, by detailing its implementation, object of an ongoing validation campaign in a standing medical institute. ARGO 2.0 achieved an average Accuracy of 95.07%, an average precision of 94.85%, an average Recall of 96.31% and a F-Score of 95.32% onto the test set, outperforming both its embedded components, based on Natural Language Processing and Machine Learning.

ARGO 2.0: a Hybrid NLP/ML Framework for Diagnosis Standardization / Berloco, Francesco; Ciavarella, Sabino; Colucci, Simona; Grieco, Luigi Alfredo; Guarini, Attilio; Zaccaria, Gian Maria. - ELETTRONICO. - 2023:(2023), pp. 1-4. (Intervento presentato al convegno 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)) [10.1109/EMBC40787.2023.10340022].

ARGO 2.0: a Hybrid NLP/ML Framework for Diagnosis Standardization

Berloco, Francesco
;
Colucci, Simona;Grieco, Luigi Alfredo;Zaccaria, Gian Maria
2023-01-01

Abstract

: A relevant problem in medicine is the standardization of the diagnosis associated with a clinical case. Although diagnosis formulation is an intrinsically subjective and uncertain process, its standardization may take benefit from digital solutions automating the routines at the basis of such a decision. In this work, we propose ARGO 2.0: a framework for the development of decision support systems for diagnosis formulation. The framework can read free-text reports and store their clinically relevant information as personalized electronic Case Report Forms. A hybrid strategy, exploiting the synergy of Natural Language Processing and Machine Learning techniques, is used to automatically suggest a diagnosis in a standardized fashion. ARGO 2.0 has been designed to be template-independent and easily tailored to specific medical fields. We here demonstrate its feasibility in hemo lympho-pathology, by detailing its implementation, object of an ongoing validation campaign in a standing medical institute. ARGO 2.0 achieved an average Accuracy of 95.07%, an average precision of 94.85%, an average Recall of 96.31% and a F-Score of 95.32% onto the test set, outperforming both its embedded components, based on Natural Language Processing and Machine Learning.
2023
45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
979-8-3503-2447-1
ARGO 2.0: a Hybrid NLP/ML Framework for Diagnosis Standardization / Berloco, Francesco; Ciavarella, Sabino; Colucci, Simona; Grieco, Luigi Alfredo; Guarini, Attilio; Zaccaria, Gian Maria. - ELETTRONICO. - 2023:(2023), pp. 1-4. (Intervento presentato al convegno 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)) [10.1109/EMBC40787.2023.10340022].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/263540
Citazioni
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact