scholarly journals TPGen: Prescription Generation Using Knowledge-guided Translator

2021 ◽  
Author(s):  
Chunyang Ruan ◽  
Hua Luo ◽  
Yingpei Wu ◽  
Yun Yang
Keyword(s):  
Decision Making ◽  
Machine Translation ◽  
Language Processing ◽  
Clinical Decision Making ◽  
Clinical Information ◽  
Clinical Decision ◽  
Proposed Model ◽  
Translation Mechanism ◽  
Mining Complex ◽  
Model Improvement

Abstract Background: Prescriptions contain a lot of clinical information and play a pivotal role in the clinical diagnosis of Traditional Chinese Medicine (TCM), which is a combination of herb to treat the symptoms of a patient from decision-making of doctors. In the process of clinical decision-making, a large number of prescriptions have been invented and accumulated based on TCM theories. Mining complex and the regular relationships between symptoms and herbs in the prescriptions are important for both clinical practice and novel prescription development. Previous work used several machine learning methods to discover regularities and generate prescriptions but rarely used TCM knowledge to guide prescription generation and described why each herb is predicted for treating a symptom. Methods: In this work, we employ a machine translation mechanism and propose a novel sequence-to-sequence (seq2seq) architecture termed TPGen to generate prescriptions. TPGen consisting of an encoder and a decoder is a well-known framework for resolving the machine translation problem in the natural language processing (NLP) domain. We use the lite transformer and Bi-directional Gate Recurrent Units(Bi-GRUS) as a fundamental model in TPGen, and integrate TCM clinical knowledge to guide the model improvement termed TPGen+. Results: We conduct extensive experiments on a public TCM dataset and clinical data. The experimental results demonstrate that our proposed model is effective and outperforms other state-of-the-art methods in TCM expert evaluation. The approach will be beneficial for clinical prescription discovery and diagnosis

scholarly journals Health record systems that meet clinical needs

2012 ◽  
Author(s):  
Gabriella Negrini
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Patient Safety ◽  
Health Care Professionals ◽  
Clinical Decision Making ◽  
Clinical Information ◽  
Clinical Decision ◽  
Health Record ◽  
Record System ◽  
Health Records

Introduction Increased attention has recently been focused on health record systems as a result of accreditation programs, a growing emphasis on patient safety, and the increase in lawsuits involving allegations of malpractice. Health-care professionals frequently express dissatisfaction with the health record systems and complain that the data included are neither informative nor useful for clinical decision making. This article reviews the main objectives of a hospital health record system, with emphasis on its roles in communication and exchange among clinicians, patient safety, and continuity of care, and asks whether current systems have responded to the recent changes in the Italian health-care system.Discussion If health records are to meet the expectations of all health professionals, the overall information need must be carefully analyzed, a common data set must be created, and essential specialist contributions must be defined. Working with health-care professionals, the hospital management should define how clinical information is to be displayed and organized, identify a functionally optimal layout, define the characteristics of ongoing patient assessment in terms of who will be responsible for these activities and how often they will be performed. Internet technology can facilitate data retrieval and meet the general requirements of a paper-based health record system, but it must also ensure focus on clinical information, business continuity, integrity, security, and privacy.Conclusions The current health records system needs to be thoroughly revised to increase its accessibility, streamline the work of health-care professionals who consult it, and render it more useful for clinical decision making—a challenging task that will require the active involvement of the many professional classes involved.

scholarly journals Use of EHR-based simulation to diagnose aetiology of information gathering issues in struggling learners: a proof of concept study

2017 ◽  
Vol 4 (2) ◽  
pp. 92-94 ◽  
Author(s):  
Vishnu Mohan ◽  
Gretchen Scholl ◽  
Jeffrey A Gold
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Large Fraction ◽  
Clinical Information ◽  
Clinical Decision ◽  
Information Gathering ◽  
High Fidelity ◽  
Struggling Learners ◽  
Effective Decision Making ◽  
Main Component

Learners who struggle with clinical decision making are often the most challenging to identify and remediate. While for some learners, struggles can be directly traced to a poor knowledge base, for many others, it is more difficult to understand the reason for their struggles. One of the main component of effective decision making is access to accurate and complete clinical information. The electronic health record (EHR) is the main source of clinical information and, with its widespread adoption, has come increased realisation that a large fraction of users have difficulty in effectively gathering and subsequently processing information out of the EHR. We previously documented that high-fidelity EHR-based simulation improves EHR usability and, when combined with eye and screen tracking, generates important measures of usability. We hypothesised that the same simulation exercise could help distinguish whether learners had difficulty in knowledge, information gathering or information processing. We report the results of the first three struggling learners who participated in this exercise. In each case, the simulation was able to ‘diagnose’ the aetiology for the learners’ struggle and assist in formulating an appropriate solution. We suggest that high-fidelity EHR-based simulation can be a powerful tool in the standard approach to understanding struggling learners.

Modelling in Clinical Practice with Web Services and BPEL

2011 ◽  
pp. 208-220
Author(s):  
Iain Morrison ◽  
Bryn Lewis ◽  
Sony Nugrahanto
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Information System ◽  
Clinical Information ◽  
Clinical Decision ◽  
Quality Of Healthcare ◽  
Critical Areas ◽  
Service Modelling ◽  
Tools And Techniques ◽  
Traditional Approaches

The aim of increasing the quality of healthcare has led to the development of a number of ‘guideline’ systems whereby clinicians receive assistance in decision making in a given care context – for example in areas such as prescribing or therapeutics. These guidelines range in complexity and functionality from simple textual references through to executable modules which can subsume some of the clinical decision making process. In the latter case, ensuring consistent and interoperable engagement between the guideline engine, clinical information system and patient record can become problematic. Critical areas include vocabulary and terminology (in differing use contexts) and the interfaces and interaction between different sub-systems where traditional approaches have been focussed on tightly coupling of sub-systems and in the generation of special purpose ‘glue’ languages and logic. In this paper, we briefly describe an approach to clinical, information and service modelling. This approach uses tools and techniques gaining increasing acceptance in the e-Commerce domain, which shares many of the technical and interoperability problems present in e-Health.

scholarly journals Automatic Identification of High Impact Relevant Articles to Support Clinical Decision Making Using Attention-Based Deep Learning

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1364
Author(s):  
Beomjoo Park ◽  
Muhammad Afzal ◽  
Jamil Hussain ◽  
Asim Abbas ◽  
Sungyoung Lee
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
High Impact ◽  
Biomedical Literature ◽  
Classification Model ◽  
Evidence Based ◽  
Proposed Model

To support evidence-based precision medicine and clinical decision-making, we need to identify accurate, appropriate, and clinically relevant studies from voluminous biomedical literature. To address the issue of accurate identification of high impact relevant articles, we propose a novel approach of attention-based deep learning for finding and ranking relevant studies against a topic of interest. For learning the proposed model, we collect data consisting of 240,324 clinical articles from the 2018 Precision Medicine track in Text REtrieval Conference (TREC) to identify and rank relevant documents matched with the user query. We built a BERT (Bidirectional Encoder Representations from Transformers) based classification model to classify high and low impact articles. We contextualized word embedding to create vectors of the documents, and user queries combined with genetic information to find contextual similarity for determining the relevancy score to rank the articles. We compare our proposed model results with existing approaches and obtain a higher accuracy of 95.44% as compared to 94.57% (the next best performer) and get a higher precision by about 14% at P@5 (precision at 5) and about 12% at P@10 (precision at 10). The contextually viable and competitive outcomes of the proposed model confirm the suitability of our proposed model for use in domains like evidence-based precision medicine.

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