Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAutomatic Speech-Based Checklist for Medical Simulations
قائمة المراجعة القائمة على الكلام التلقائي للمحاكاة الطبية
357
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
توفر المحاكاة الطبية بيئة تسيطر عليها لتدريب وتقييم المهارات السريرية. ومع ذلك، كمنصة تقييم، فإنه يتطلب وجود فاحص من ذوي الخبرة لتوفير ملاحظات الأداء، والتي تشمل عادة باستخدام قائمة مرجعية محددة المهام. هذا يجعل عملية التقييم غير فعالة ومكلفة. علاوة على ذلك، فإن طريقة التقييم هذه لا توفر الممارسين الطبيين الفرصة للتدريب المستقل. من الناحية المثالية، يجب إجراء عملية ملء قائمة التحقق بواسطة نظام موضوعي تدرك كامل، قادر على الاعتراف بمراقبة الأداء السريرية ومراقبتها. تحقيقا لهذه الغاية، قمنا بتطوير نظام مرجعي تلقائي بالكامل ونظام مرئي تلقائي بالكامل، قادر على تحديد تصرفات سكان التخدير بشكل موضوعي وتحقيق صحة في بيئة محاكاة. بناء على النتائج التي تم تحليلها، يكون نظامنا قادرا على الاعتراف بمعظم المهام في قائمة المراجعة: درجة F1 من 0.77 لجميع المهام، ونتيجة F1 من 0.79 للمهام اللفظية. تطوير نظام يستند إلى تحسين تجربة مجموعة واسعة من منصات المحاكاة. علاوة على ذلك، في المستقبل، يجوز تنفيذ هذا النهج في غرفة التشغيل وغرفة الطوارئ. هذا يمكن أن يسهل تطوير التقنيات المساعدة التلقائية لهذه المجالات.
Medical simulators provide a controlled environment for training and assessing clinical skills. However, as an assessment platform, it requires the presence of an experienced examiner to provide performance feedback, commonly preformed using a task specific checklist. This makes the assessment process inefficient and expensive. Furthermore, this evaluation method does not provide medical practitioners the opportunity for independent training. Ideally, the process of filling the checklist should be done by a fully-aware objective system, capable of recognizing and monitoring the clinical performance. To this end, we have developed an autonomous and a fully automatic speech-based checklist system, capable of objectively identifying and validating anesthesia residents' actions in a simulation environment. Based on the analyzed results, our system is capable of recognizing most of the tasks in the checklist: F1 score of 0.77 for all of the tasks, and F1 score of 0.79 for the verbal tasks. Developing an audio-based system will improve the experience of a wide range of simulation platforms. Furthermore, in the future, this approach may be implemented in the operation room and emergency room. This could facilitate the development of automatic assistive technologies for these domains.
References used
https://aclanthology.org/
rate research
Read More
In general, the aim of an automatic speech recognition system is to write down what is said. State of the art continuous speech recognition systems consist of four basic modules: the signal processing, the acoustic modeling, the language modeling and
the search engine. While isolated word recognition systems do not contain language modeling, which is responsible for connecting words together to form understandable sentences.
Logical Observation Identifiers Names and Codes (LOINC) is a standard set of codes that enable clinicians to communicate about medical tests. Laboratories depend on LOINC to identify what tests a doctor orders for a patient. However, clinicians often
use site specific, custom codes in their medical records systems that can include shorthand, spelling mistakes, and invented acronyms. Software solutions must map from these custom codes to the LOINC standard to support data interoperability. A key challenge is that LOINC is comprised of six elements. Mapping requires not only extracting those elements, but also combining them according to LOINC logic. We found that character-based deep learning excels at extracting LOINC elements while logic based methods are more effective for combining those elements into complete LOINC values. In this paper, we present an ensemble of machine learning and logic that is currently used in several medical facilities to map from
In recent years, automatic speech-to-speech and speech-to-text translation has gained momentum thanks to advances in artificial intelligence, especially in the domains of speech recognition and machine translation. The quality of such applications is
commonly tested with automatic metrics, such as BLEU, primarily with the goal of assessing improvements of releases or in the context of evaluation campaigns. However, little is known about how the output of such systems is perceived by end users or how they compare to human performances in similar communicative tasks. In this paper, we present the results of an experiment aimed at evaluating the quality of a real-time speech translation engine by comparing it to the performance of professional simultaneous interpreters. To do so, we adopt a framework developed for the assessment of human interpreters and use it to perform a manual evaluation on both human and machine performances. In our sample, we found better performance for the human interpreters in terms of intelligibility, while the machine performs slightly better in terms of informativeness. The limitations of the study and the possible enhancements of the chosen framework are discussed. Despite its intrinsic limitations, the use of this framework represents a first step towards a user-centric and communication-oriented methodology for evaluating real-time automatic speech translation.
This paper investigates the effectiveness of automatic annotator assignment for text annotation in expert domains. In the task of creating high-quality annotated corpora, expert domains often cover multiple sub-domains (e.g. organic and inorganic che
mistry in the chemistry domain) either explicitly or implicitly. Therefore, it is crucial to assign annotators to documents relevant with their fine-grained domain expertise. However, most of existing methods for crowdsoucing estimate reliability of each annotator or annotated instance only after the annotation process. To address the issue, we propose a method to estimate the domain expertise of each annotator before the annotation process using information easily available from the annotators beforehand. We propose two measures to estimate the annotator expertise: an explicit measure using the predefined categories of sub-domains, and an implicit measure using distributed representations of the documents. The experimental results on chemical name annotation tasks show that the annotation accuracy improves when both explicit and implicit measures for annotator assignment are combined.
The main purpose of the present research is to support Arabic Text- to - Speech synthesizers, with
natural prosody, based on linguistic analysis of texts to synthesize, and automatic prosody generation,
using rules which are deduced from recorded s
ignals analysis, of different types of sentences in Arabic. All
the types of Arabic sentences (declarative and constructive) were enumerated with the help of an expert in
Arabic linguistics . A textual corpus of about 2500 sentences covering most of these types was built and
recorded both in natural prosody and without prosody. Later, these sentences were analyzed to extract
prosody effect on the signal parameters, and to build prosody generation rules. In this paper, we present
the results on negation sentences, applied on synthesized speech using the open source tool MBROLA. The
results can be used with any parametric Arabic synthesizer. Future work will apply the rules on a new
Arabic synthesizer based on semi-syllables units, which is under development in the Higher Institute for
Applied Sciences and Technology.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
