Transforming unstructured voice and text data into insight for paramedic emergency service using recurrent and convolutional neural networks

• URL: http://arxiv.org/abs/2006.04946v1
• Date: Sat, 30 May 2020 06:47:02 GMT
• Title: Transforming unstructured voice and text data into insight for paramedic emergency service using recurrent and convolutional neural networks
• Authors: Kyongsik Yun, Thomas Lu, Alexander Huyen
• Abstract summary: Paramedics often have to make lifesaving decisions within a limited time in an ambulance. This study aims to automatically fuse voice and text data to provide tailored situational awareness information to paramedics.
• Score: 68.8204255655161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Paramedics often have to make lifesaving decisions within a limited time in an ambulance. They sometimes ask the doctor for additional medical instructions, during which valuable time passes for the patient. This study aims to automatically fuse voice and text data to provide tailored situational awareness information to paramedics. To train and test speech recognition models, we built a bidirectional deep recurrent neural network (long short-term memory (LSTM)). Then we used convolutional neural networks on top of custom-trained word vectors for sentence-level classification tasks. Each sentence is automatically categorized into four classes, including patient status, medical history, treatment plan, and medication reminder. Subsequently, incident reports were automatically generated to extract keywords and assist paramedics and physicians in making decisions. The proposed system found that it could provide timely medication notifications based on unstructured voice and text data, which was not possible in paramedic emergencies at present. In addition, the automatic incident report generation provided by the proposed system improves the routine but error-prone tasks of paramedics and doctors, helping them focus on patient care.

Related papers

• Medical Vision-Language Pre-Training for Brain Abnormalities [96.1408455065347]
  We show how to automatically collect medical image-text aligned data for pretraining from public resources such as PubMed. In particular, we present a pipeline that streamlines the pre-training process by initially collecting a large brain image-text dataset. We also investigate the unique challenge of mapping subfigures to subcaptions in the medical domain.
  arXiv  Detail & Related papers  (2024-04-27T05:03:42Z)
• Leveraging graph neural networks for supporting Automatic Triage of Patients [5.864579168378686]
  We propose an AI based module to manage patients emergency code assignments in emergency departments. Data containing relevant patient information, such as vital signs, symptoms, and medical history, are used to accurately classify patients into triage categories.
  arXiv  Detail & Related papers  (2024-03-11T09:54:35Z)
• Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models [9.879960506853145]
  Problem list summarization requires a model to understand, abstract, and generate clinical documentation. We propose a new NLP task that aims to generate a list of problems in a patient's daily care plan using input from the provider's progress notes during hospitalization.
  arXiv  Detail & Related papers  (2022-08-17T17:07:35Z)
• PoCaP Corpus: A Multimodal Dataset for Smart Operating Room Speech Assistant using Interventional Radiology Workflow Analysis [7.189635716814341]
  This paper presents a new multimodal interventional radiology dataset, called PoCaP (Port Catheter Placement) Corpus. This corpus consists of speech and audio signals in German, X-ray images, and system commands collected from 31 PoCaP interventions by six surgeons.
  arXiv  Detail & Related papers  (2022-06-24T14:39:11Z)
• A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets [70.32630628211803]
  We propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. A new large medical dialogue dataset with multi-level fine-grained annotations is introduced. We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies.
  arXiv  Detail & Related papers  (2022-04-19T16:43:21Z)
• Towards more patient friendly clinical notes through language models and ontologies [57.51898902864543]
  We present a novel approach to automated medical text based on word simplification and language modelling. We use a new dataset pairs of publicly available medical sentences and a version of them simplified by clinicians. Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning.
  arXiv  Detail & Related papers  (2021-12-23T16:11:19Z)
• Auto Response Generation in Online Medical Chat Services [0.0]
  We develop a smart auto-response generation mechanism for medical conversations that helps doctors respond to consultation requests efficiently. We explore over 900,000 anonymous, historical online messages between doctors and patients collected over nine months.
  arXiv  Detail & Related papers  (2021-04-26T17:45:10Z)
• Where's the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data [83.89578557287658]
  We propose a novel multi-channel deep convolutional neural network architecture, namely Quest-CNN, for the purpose of separating real questions. We conducted a comprehensive performance comparison analysis of the proposed network against other deep neural networks. The proposed Quest-CNN achieved the best F1 score both on a dataset of data entry-review dialogue in a dialysis care setting, and on a general domain dataset.
  arXiv  Detail & Related papers  (2020-10-15T15:11:22Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations [0.6875312133832078]
  We bridge the gap for classifying utterances from medical conversations according to (i) the SOAP section and (ii) the speaker role. We present a systematic analysis in which we adapt an existing deep learning architecture to the two aforementioned tasks. The results suggest that modelling context in a hierarchical manner, which captures both word and utterance level context, yields substantial improvements on both classification tasks.
  arXiv  Detail & Related papers  (2020-07-17T04:19:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.