Operationalizing GPT-3 in Healthcare: An outlook of compliance, trust, and access for pretrained large AI linguistic models (Preprint)

2021 ◽  
Author(s):  
Emre Sezgin ◽  
Joseph Sirrianni ◽  
Simon L Linwood
Keyword(s):  
Question Answering ◽  
Healthcare Providers ◽  
Training Data ◽  
Question Mark ◽  
Building Trust ◽  
Hipaa Compliance ◽  
Operational Factors ◽  
And Performance ◽  
Linguistic Models ◽  
Complex Question

UNSTRUCTURED Generative Pre-trained Transformer (GPT) models have been popular recently with their enhanced capability and performance. In contrast to many existing Artificial Intelligence (AI) models, GPT can perform with very limited training data. GPT-3 is one of the latest releases in this pipeline, demonstrating human-like logical and intellectual responses to prompts: some examples are including writing essays, complex question answering, matching pronouns to their noun, and sentiment analysis. However, its implementation in healthcare is still a question mark in terms of operationalization and its use in clinical practice and research. In this viewpoint paper, we outlined three major operational factors that drive the adoption of GPT-3 in healthcare: (1) Health Insurance Portability and Accountability Act (HIPAA) compliance, (2) building trust with healthcare providers, and (3) establishing the broader access to the GPT-3 tools.

scholarly journals Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform

2021 ◽  
Vol 11 (6) ◽  
pp. 2535
Author(s):  
Bruno E. Silva ◽  
Ramiro S. Barbosa
Keyword(s):  
Neural Networks ◽  
Neural Control ◽  
Magnetic Levitation ◽  
Low Cost ◽  
Training Data ◽  
Neural Controllers ◽  
Levitation System ◽  
Internal Model Controller ◽  
Magnetic Levitation System ◽  
And Performance

In this article, we designed and implemented neural controllers to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The objective was to evaluate the implementation and performance of neural control algorithms in a low-cost hardware. In a first phase, we designed two classical controllers with the objective to provide the training data for the neural controllers. After, we identified several neural models of the levitation system using Nonlinear AutoRegressive eXogenous (NARX)-type neural networks that were used to emulate the forward dynamics of the system. Finally, we designed and implemented three neural control structures: the inverse controller, the internal model controller, and the model reference controller for the control of the levitation system. The neural controllers were tested on a low-cost Arduino control platform through MATLAB/Simulink. The experimental results proved the good performance of the neural controllers.

scholarly journals DiagnosisQA: A semi-automated pipeline for developing clinician validated diagnosis specific QA datasets.

2021 ◽  
Author(s):  
Shreya Mishra ◽  
Raghav Awasthi ◽  
Frank Papay ◽  
Kamal Maheshawari ◽  
Jacek B Cywinski ◽  
...  
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Healthcare Providers ◽  
Unstructured Data ◽  
Significant Progress ◽  
Improve Performance ◽  
Healthcare Data ◽  
Research Areas ◽  
Benchmark Datasets ◽  
Automated Pipeline

Question answering (QA) is one of the oldest research areas of AI and Compu- national Linguistics. QA has seen significant progress with the development of state-of-the-art models and benchmark datasets over the last few years. However, pre-trained QA models perform poorly for clinical QA tasks, presumably due to the complexity of electronic healthcare data. With the digitization of healthcare data and the increasing volume of unstructured data, it is extremely important for healthcare providers to have a mechanism to query the data to find appropriate answers. Since diagnosis is central to any decision-making for the clinicians and patients, we have created a pipeline to develop diagnosis-specific QA datasets and curated a QA database for the Cerebrovascular Accident (CVA). CVA, also commonly known as Stroke, is an important and commonly occurring diagnosis amongst critically ill patients. Our method when compared to clinician validation achieved an accuracy of 0.90(with 90% CI [0.82,0.99]). Using our method, we hope to overcome the key challenges of building and validating a highly accurate QA dataset in a semiautomated manner which can help improve performance of QA models.

scholarly journals Enhancing Natural Language Inference Using New and Expanded Training Data Sets and New Learning Models

2020 ◽  
Vol 34 (05) ◽  
pp. 8504-8511
Author(s):  
Arindam Mitra ◽  
Ishan Shrivastava ◽  
Chitta Baral
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Training Data ◽  
Data Sets ◽  
Learning Models ◽  
New Learning ◽  
Word Attention ◽  
Attention Function

Natural Language Inference (NLI) plays an important role in many natural language processing tasks such as question answering. However, existing NLI modules that are trained on existing NLI datasets have several drawbacks. For example, they do not capture the notion of entity and role well and often end up making mistakes such as “Peter signed a deal” can be inferred from “John signed a deal”. As part of this work, we have developed two datasets that help mitigate such issues and make the systems better at understanding the notion of “entities” and “roles”. After training the existing models on the new dataset we observe that the existing models do not perform well on one of the new benchmark. We then propose a modification to the “word-to-word” attention function which has been uniformly reused across several popular NLI architectures. The resulting models perform as well as their unmodified counterparts on the existing benchmarks and perform significantly well on the new benchmarks that emphasize “roles” and “entities”.

scholarly journals Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base

2020 ◽  
Author(s):  
Yongrui Chen ◽  
Huiying Li ◽  
Yuncheng Hua ◽  
Guilin Qi
Keyword(s):  
Knowledge Base ◽  
Structure Prediction ◽  
Question Answering ◽  
State Transition ◽  
Knowledge Bases ◽  
Second Stage ◽  
Transition Strategy ◽  
Query Structure ◽  
Two Stages ◽  
Complex Question

Formal query building is an important part of complex question answering over knowledge bases. It aims to build correct executable queries for questions. Recent methods try to rank candidate queries generated by a state-transition strategy. However, this candidate generation strategy ignores the structure of queries, resulting in a considerable number of noisy queries. In this paper, we propose a new formal query building approach that consists of two stages. In the first stage, we predict the query structure of the question and leverage the structure to constrain the generation of the candidate queries. We propose a novel graph generation framework to handle the structure prediction task and design an encoder-decoder model to predict the argument of the predetermined operation in each generative step. In the second stage, we follow the previous methods to rank the candidate queries. The experimental results show that our formal query building approach outperforms existing methods on complex questions while staying competitive on simple questions.

scholarly journals Relevance-guided Supervision for OpenQA with ColBERT

2021 ◽  
Vol 9 ◽  
pp. 929-944
Author(s):  
Omar Khattab ◽  
Christopher Potts ◽  
Matei Zaharia
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Training Data ◽  
Coarse Grained ◽  
Retrieval Model ◽  
Open Domain ◽  
Weak Supervision ◽  
Fine Grained ◽  
Vector Representations ◽  
Large Corpus

Abstract Systems for Open-Domain Question Answering (OpenQA) generally depend on a retriever for finding candidate passages in a large corpus and a reader for extracting answers from those passages. In much recent work, the retriever is a learned component that uses coarse-grained vector representations of questions and passages. We argue that this modeling choice is insufficiently expressive for dealing with the complexity of natural language questions. To address this, we define ColBERT-QA, which adapts the scalable neural retrieval model ColBERT to OpenQA. ColBERT creates fine-grained interactions between questions and passages. We propose an efficient weak supervision strategy that iteratively uses ColBERT to create its own training data. This greatly improves OpenQA retrieval on Natural Questions, SQuAD, and TriviaQA, and the resulting system attains state-of-the-art extractive OpenQA performance on all three datasets.

Knowledge of Nurses Regarding Disaster Management in Tertiary Health Care Hospitals of Lahore

2020 ◽  
pp. 24-28
Author(s):  
OJS Admin
Keyword(s):  
Health Care ◽  
Disaster Management ◽  
Question Mark ◽  
And Performance

This is universally accepted that nurses have inadequate awareness and knowledge regarding disaster management and dealing which is the question mark on the competence and performance of Nurses to deal the disaster in an a competent way.

Performance Degradation Detection of Virtual Machines Via Passive Measurement and Machine Learning

2014 ◽  
Vol 5 (2) ◽  
pp. 40-56 ◽  
Author(s):  
Toshiaki Hayashi ◽  
Satoru Ohta
Keyword(s):  
Machine Learning ◽  
Virtual Machines ◽  
Performance Degradation ◽  
Training Data ◽  
Efficient Operation ◽  
Traffic Measurement ◽  
Passive Measurement ◽  
And Performance ◽  
Complex Relationships ◽  
Heavy Loads

Virtualization is commonly used for efficient operation of servers in datacenters. The autonomic management of virtual machines enhances the advantages of virtualization. Therefore, for the development of such management, it is important to establish a method to accurately detect the performance degradation in virtual machines. This paper proposes a method that detects degradation via passive measurement of traffic exchanged by virtual machines. Using passive traffic measurement is advantageous because it is robust against heavy loads, non-intrusive to the managed machines, and independent of hardware/software platforms. From the measured traffic metrics, performance state is determined by a machine learning technique that algorithmically determines the complex relationships between traffic metrics and performance degradation from training data. The feasibility and effectiveness of the proposed method are confirmed experimentally.

Attitudes Toward and Beliefs About Family Presence: A Survey of Healthcare Providers, Patients’ Families, and Patients

2007 ◽  
Vol 16 (3) ◽  
pp. 270-279 ◽  
Author(s):  
Christine R. Duran ◽  
Kathleen S. Oman ◽  
Jenni Jordan Abel ◽  
Virginia M. Koziel ◽  
Deborah Szymanski
Keyword(s):  
Neonatal Intensive Care ◽  
Healthcare Providers ◽  
Family Members ◽  
Emotional Responses ◽  
Neonatal Intensive Care Units ◽  
Family Presence ◽  
Positive Attitudes ◽  
The Family ◽  
And Performance ◽  
Accepted Practice

Background Although some healthcare providers remain hesitant, family presence, defined as the presence of patients’ family members during resuscitation and/or invasive procedures, is becoming an accepted practice. Evidence indicates that family presence is beneficial to patients and their families. Objectives To describe and compare the beliefs about and attitudes toward family presence of clinicians, patients’ families, and patients. Methods Clinicians, patients’ families, and patients in the emergency department and adult and neonatal intensive care units of a 300-bed urban academic hospital were surveyed. Results Surveys were completed by 202 clinicians, 72 family members, and 62 patients. Clinicians had positive attitudes toward family presence but had concerns about safety, the emotional responses of the family members, and performance anxiety. Nurses had more favorable attitudes toward family presence than physicians did. Patients and their families had positive attitudes toward family presence. Conclusions Family presence is beneficial to patients, patients’ families, and healthcare providers. As family presence becomes a more accepted practice, healthcare providers will need to accommodate patients’ families at the bedside and address the barriers that impede the practice.

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