Pilar López-Úbeda1, Manuel Carlos Díaz-Galiano2, Teodoro Martín-Noguerol3, Antonio Luna4, L Alfonso Ureña-López5, M Teresa Martín-Valdivia6. 1. SINAI Group - CEATIC - Universidad de Jaén, Campus Las Lagunillas s/n, Jaén E-23071, Spain. Electronic address: plubeda@ujaen.es. 2. SINAI Group - CEATIC - Universidad de Jaén, Campus Las Lagunillas s/n, Jaén E-23071, Spain. Electronic address: mcdiaz@ujaen.es. 3. MRI Unit, Radiology Department, HT médica Carmelo Torres 2, Jaén 23007, Spain. Electronic address: t.martin.f@htime.org. 4. MRI Unit, Radiology Department, HT médica Carmelo Torres 2, Jaén 23007, Spain. Electronic address: aluna70@htime.org. 5. SINAI Group - CEATIC - Universidad de Jaén, Campus Las Lagunillas s/n, Jaén E-23071, Spain. Electronic address: laurena@ujaen.es. 6. SINAI Group - CEATIC - Universidad de Jaén, Campus Las Lagunillas s/n, Jaén E-23071, Spain. Electronic address: maite@ujaen.es.
Abstract
BACKGROUND AND OBJECTIVE: Assignment of medical imaging procedure protocols requires extensive knowledge about patient's data, usually included in radiological request forms and radiological reports. Assignment of protocol is required prior to radiological study acquisition, determining procedure for each patient. The automation of this protocol assignment process could improve the efficiency of patient's diagnosis. Artificial intelligence has proven to be of great help in these healthcare-related problems, and specifically the application of Natural Language Processing (NLP) techniques for extracting information from text reports has been successfully used in automatic text classification tasks. METHODS: In this paper, machine learning classification models based on NLP have been developed using patient's data present in radiological reports and radiological imaging protocols. We have used a real corpus provided by the private medical center "HT medica" composed of almost 700,000 Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) examinations obtained during routine clinical use. We have compared several models including traditional machine learning methods such as support vector machine and random forest, neural networks and transfer language techniques. RESULTS: The results obtained are encouraging taking into account that the system is performing a complex text multiclass classification task. Specifically, for the best proposed system we obtain 92.2% accuracy in the CT dataset and 86.9% in the MRI dataset. CONCLUSIONS: The best machine learning system is potentially efficient, quality and cost effective. For this reason it is currently used in real scenarios by radiologists as decision support tool for assigning protocols of CT and MRI studies.
BACKGROUND AND OBJECTIVE: Assignment of medical imaging procedure protocols requires extensive knowledge about patient's data, usually included in radiological request forms and radiological reports. Assignment of protocol is required prior to radiological study acquisition, determining procedure for each patient. The automation of this protocol assignment process could improve the efficiency of patient's diagnosis. Artificial intelligence has proven to be of great help in these healthcare-related problems, and specifically the application of Natural Language Processing (NLP) techniques for extracting information from text reports has been successfully used in automatic text classification tasks. METHODS: In this paper, machine learning classification models based on NLP have been developed using patient's data present in radiological reports and radiological imaging protocols. We have used a real corpus provided by the private medical center "HT medica" composed of almost 700,000 Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) examinations obtained during routine clinical use. We have compared several models including traditional machine learning methods such as support vector machine and random forest, neural networks and transfer language techniques. RESULTS: The results obtained are encouraging taking into account that the system is performing a complex text multiclass classification task. Specifically, for the best proposed system we obtain 92.2% accuracy in the CT dataset and 86.9% in the MRI dataset. CONCLUSIONS: The best machine learning system is potentially efficient, quality and cost effective. For this reason it is currently used in real scenarios by radiologists as decision support tool for assigning protocols of CT and MRI studies.