Advanced natural language processing technique to predict patient disposition based on emergency triage notes

Abstract

Objective To demonstrate the potential of machine learning and capability of natural language processing (NLP) to predict disposition of patients based on triage notes in the ED. Methods A retrospective cohort of ED triage notes from St Vincent's Hospital (Melbourne) was used to develop a deep‐learning algorithm that predicts patient disposition. Bidirectional Encoder Representations from Transformers, a recent language representation model developed by Google, was utilised for NLP. Eighty percent of the dataset was used for training the model and 20% was used to test the algorithm performance. Ktrain library, a wrapper for TensorFlow Keras, was employed to develop the model. Results The accuracy of the algorithm was 83% and the area under the curve was 0.88. Sensitivity, specificity, precision and F1‐score of the algorithm were 72%, 86%, 56% and 63%, respectively. Conclusion Machine learning and NLP can be together applied to the ED triage note to predict patient disposition with a high level of accuracy. The algorithm can potentially assist ED clinicians in early identification of patients requiring admission by mitigating the cognitive load, thus optimises resource allocation in EDs.

Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management

“…By combining the rapid development of artificial intelligence technology with emergency diagnosis and treatment, a medical information processing system with emergency characteristics is constructed to greatly improve the efficiency of emergency first aid. In this study, based on the neural network algorithm, semantic network, natural language processing (NLP) algorithm, and the national diagnosis and treatment guidelines and related literature as data resources, it builds a set of medical information processing system based on ontology semantic medical knowledge base and AI reasoning engine as the core [19][20][21][22]. The system will follow the national clinical diagnosis and treatment norms, through big data to simulate the emergency first aid nursing manage-ment of clinicians, compare the input patient information with the database information, and provide all patients with auxiliary management including inquiry (for conscious patients), examination, diagnosis, and treatment.…”

Natural Language Processing for Smart Healthcare

“…Before the interpretability issue is fully explored, the role of decision support systems in clinical practice can only be auxiliary from the perspectives of medical ethics and practical application. [133], [134] machine translation clinical documentation [135]- [137] speech recognition clinical decision support build QA-based clinical decision support systems [88], [138], [139] information extraction build clinical decision support systems with extracted information: family history information [140], entities and relations [141], [142], treatment and prognosis data [143], clinical data concepts and features [144], causal relations [113], [114] question answering healthcare quality control: assess clinical procedures [145], [146], warning of ADE [147], disease symptoms [148], [149], and outcome-related causal effects [150] information extraction, causal inference provide supporting evidence for decisions under evidence-based fashion [112], [151]- [154] information retrieval, causal inference Hospital Management medical resource allocation patient triage [155], [156] information extraction enable users to communicate and control intelligent systems through virtual assistants [157]- [159], hospital automation systems [160], [161] and collaborative robots [162], [163] speech recognition, natural language understanding predict and reduce readmission rate [164]- [166] information extraction free medical staff from routine text writing [74], [167] information extraction data management m...…”

“…Due to limited medical resources, including hospital spaces, personnel, and materials, efficient resource allocation is critical in hospitals and other medical facilities. By building patient triage systems, medical resources can attend to critical cases with priority and enhance medical resource allocation effectiveness and efficiency [155], [156]. Virtual assistants [157]- [159], hospital automation systems [160], [161] and collaborative robots [162], [163] with voice control can further reduce the burden on medical staff, thereby improving hospital management efficiency.…”

The Feasibility of Using Machine Learning to Classify Calls to South African Emergency Dispatch Centres According to Prehospital Diagnosis, by Utilising Caller Descriptions of the Incident

“…Machine learning (ML) and natural language processing (NLP) have been repeatedly used to boost productivity [ 8 , 9 , 10 ] and are, thus, viable solutions to manual transcription. A recent study found that ML and NLP can be applied to the emergency department triage, and noted to predict patient disposition with a high level of accuracy [ 11 ]. ML can be described as a sub-field of artificial intelligence which attempts to endow computers with the capacity of learning from data, so that explicit programming is not necessary to perform a task [ 9 ].…”