scholarly journals Discovering Key Topics From Short, Real-World Medical Inquiries via Natural Language Processing

2021 ◽  
Vol 3 ◽  
Author(s):  
A. Ziletti ◽  
C. Berns ◽  
O. Treichel ◽  
T. Weber ◽  
J. Liang ◽  
...  
Keyword(s):  
Language Processing ◽  
Real World ◽  
Medical Information ◽  
Pharmaceutical Companies ◽  
Medicinal Products ◽  
Medical Treatments ◽  
Key Topics ◽  
Linear Dimensionality Reduction ◽  
Improving Patient Care ◽  
Insight Into

Millions of unsolicited medical inquiries are received by pharmaceutical companies every year. It has been hypothesized that these inquiries represent a treasure trove of information, potentially giving insight into matters regarding medicinal products and the associated medical treatments. However, due to the large volume and specialized nature of the inquiries, it is difficult to perform timely, recurrent, and comprehensive analyses. Here, we combine biomedical word embeddings, non-linear dimensionality reduction, and hierarchical clustering to automatically discover key topics in real-world medical inquiries from customers. This approach does not require ontologies nor annotations. The discovered topics are meaningful and medically relevant, as judged by medical information specialists, thus demonstrating that unsolicited medical inquiries are a source of valuable customer insights. Our work paves the way for the machine-learning-driven analysis of medical inquiries in the pharmaceutical industry, which ultimately aims at improving patient care.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

Upcoming Drifts In Bio-Similars

2020 ◽  
Vol 15 ◽  
Author(s):  
Geeta Aggarwal ◽  
Manju Nagpal ◽  
Ameya Sharma ◽  
Vivek Puri ◽  
Gitika Arora Dhingra
Keyword(s):  
Metabolic Diseases ◽  
Market Competition ◽  
Current Status ◽  
Medicinal Products ◽  
The Past ◽  
Biologic Activity ◽  
Emerging Trends ◽  
Regulatory Guidelines ◽  
Food Drug Administration ◽  
Insight Into

Background: Biopharmaceuticals such as Biologic medicinal products have been in clinical use over the past three decades and have benefited towards the therapy of degenerative and critical metabolic diseases. It is forecasted that market of biologics will be going to increase at a rate of 20% per year, and by 2025, more than ˃ 50% of new drug approvals may be biological products. The increasing utilization of the biologics necessitates for cost control, especially for innovators products that have enjoyed a lengthy period of exclusive use. As the first wave of biopharmaceuticals is expired or set to expire, it has led to various opportunities for the expansion of bio-similars i.e. copied versions of original biologics with same biologic activity. Development of biosimilars is expected to promote market competition, meet worldwide demand, sustain the healthcare systems and maintain the incentives for innovation. Methods: Appraisal of published articles from peer reviewed journals, PubMed literature, latest news and guidelines from European Medicine Agency, US Food Drug Administration (FDA) and India are used to identify data for review. Results: Main insight into the quality requirements concerning biologics, current status of regulation of biosimilars and upcoming challenges lying ahead for the upgrading of marketing authorization of bio-similars has been incorporated. Compiled literature on therapeutic status, regulatory guidelines and the emerging trends and opportunities of biosimilars has been thoroughly stated. Conclusion: Updates on biosimilars will support to investigate the possible impact of bio-similars on healthcare market.

scholarly journals Using NLP in openEHR archetypes retrieval to promote interoperability: a feasibility study in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bo Sun ◽  
Fei Zhang ◽  
Jing Li ◽  
Yicheng Yang ◽  
Xiaolin Diao ◽  
...  
Keyword(s):  
Information Sharing ◽  
Language Processing ◽  
Medical Information ◽  
Medical Professional ◽  
Semantic Interoperability ◽  
Medical Information System ◽  
Search Term ◽  
Search Terms ◽  
Novel Approach ◽  
Test Sets

Abstract Background With the development and application of medical information system, semantic interoperability is essential for accurate and advanced health-related computing and electronic health record (EHR) information sharing. The openEHR approach can improve semantic interoperability. One key improvement of openEHR is that it allows for the use of existing archetypes. The crucial problem is how to improve the precision and resolve ambiguity in the archetype retrieval. Method Based on the query expansion technology and Word2Vec model in Nature Language Processing (NLP), we propose to find synonyms as substitutes for original search terms in archetype retrieval. Test sets in different medical professional level are used to verify the feasibility. Result Applying the approach to each original search term (n = 120) in test sets, a total of 69,348 substitutes were constructed. Precision at 5 (P@5) was improved by 0.767, on average. For the best result, the P@5 was up to 0.975. Conclusions We introduce a novel approach that using NLP technology and corpus to find synonyms as substitutes for original search terms. Compared to simply mapping the element contained in openEHR to an external dictionary, this approach could greatly improve precision and resolve ambiguity in retrieval tasks. This is helpful to promote the application of openEHR and advance EHR information sharing.

Leveraging Twitter Using Artificial Intelligence to Explore Mental Health Insights in the UK During the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Christopher Marshall ◽  
Kate Lanyi ◽  
Rhiannon Green ◽  
Georgie Wilkins ◽  
Fiona Pearson ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Language Processing ◽  
Mental Health Problems ◽  
Well Being ◽  
Research Activity ◽  
Health Crisis ◽  
Key Topics ◽  
The Uk ◽  
The Impact

BACKGROUND There is increasing need to explore the value of soft-intelligence, leveraged using the latest artificial intelligence (AI) and natural language processing (NLP) techniques, as a source of analysed evidence to support public health research activity and decision-making. OBJECTIVE The aim of this study was to further explore the value of soft-intelligence analysed using AI through a case study, which examined a large collection of UK tweets relating to mental health during the COVID-19 pandemic. METHODS A search strategy comprising a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a specialist NLP platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. Qualitative document analysis was carried out to further explore and expand upon the results generated by the NLP platform. All collated tweets were anonymised RESULTS We identified and analysed 286,902 tweets posted from UK user accounts from 23 July 2020 to 6 January 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume and sentiment appeared to coincide with key changes to any local and/or national social-distancing measures. Tweets around mental health were polarising, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. CONCLUSIONS Through the primary use of an AI-based NLP platform, we were able to rapidly mine and analyse emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analysed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

Probing Patient Messages Enhanced by Natural Language Processing: A Top-Down Message Corpus Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
George Mastorakos ◽  
Aditya Khurana ◽  
Ming Huang ◽  
Sunyang Fu ◽  
Ahmad P. Tafti ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Corpus Analysis ◽  
Entity Recognition ◽  
Message Content ◽  
Named Entity ◽  
Medical Concepts ◽  
Insight Into

Background. Patients increasingly use asynchronous communication platforms to converse with care teams. Natural language processing (NLP) to classify content and automate triage of these messages has great potential to enhance clinical efficiency. We characterize the contents of a corpus of portal messages generated by patients using NLP methods. We aim to demonstrate descriptive analyses of patient text that can contribute to the development of future sophisticated NLP applications. Methods. We collected approximately 3,000 portal messages from the cardiology, dermatology, and gastroenterology departments at Mayo Clinic. After labeling these messages as either Active Symptom, Logistical, Prescription, or Update, we used NER (named entity recognition) to identify medical concepts based on the UMLS library. We hierarchically analyzed the distribution of these messages in terms of departments, message types, medical concepts, and keywords therewithin. Results. Active Symptom and Logistical content types comprised approximately 67% of the message cohort. The “Findings” medical concept had the largest number of keywords across all groupings of content types and departments. “Anatomical Sites” and “Disorders” keywords were more prevalent in Active Symptom messages, while “Drugs” keywords were most prevalent in Prescription messages. Logistical messages tended to have the lower proportions of “Anatomical Sites,”, “Disorders,”, “Drugs,”, and “Findings” keywords when compared to other message content types. Conclusions. This descriptive corpus analysis sheds light on the content and foci of portal messages. The insight into the content and differences among message themes can inform the development of more robust NLP models.

The “Doctrine of Love” in ʿAbd Allāh al-Anṣārī’s Manāzil al-sāʾirīn with Critical Paraphrase of ʿAbd al-Razzāq Kāshānī’s Commentary

2016 ◽  
Vol 5 (2) ◽  
pp. 140-155
Author(s):  
Mukhtar H. Ali
Keyword(s):  
English Translation ◽  
Guided Reading ◽  
Classic Work ◽  
Key Topics ◽  
Insight Into ◽  
Commentarial Tradition ◽  
Profound Insight

This article represents a preliminary inquiry into a little known and understudied commentarial tradition upon ʿAbd Allāh al-Anṣārī’s classic work on the stations of Sufism, the Manāzil al-sāʾirīn (Stations of the Wayfarers). After briefly taking stock of the considerably late commentarial tradition which this important text engendered, we will take as our case study one of the Manāzil ’s key topics, namely its sixty-first chapter on the station of love. This pivotal section on love gives profound insight into early Sufism and into the minds of two of its greatest exponents. Anṣārī discusses the station of love in detail, as he does with every chapter, in three aspects, each pertaining to the three types of wayfarers: the initiates, the elect, and the foremost of the elect. Then, we shall turn our attention to perhaps the most important Sufi commentary upon this work by an important follower of the school of Ibn al-ʿArabī, ʿAbd al-Razzāq Kāshānī, offering a guided reading of his commentary upon Anṣarī’s chapter on love in the Manāzil. A complete English translation of this chapter will be offered and appropriately contextualized.

scholarly journals Usability in the real world: assessing medical information technologies in patients’ homes

2003 ◽  
Vol 36 (1-2) ◽  
pp. 45-60 ◽  
Author(s):  
David R Kaufman ◽  
Vimla L Patel ◽  
Charlyn Hilliman ◽  
Philip C Morin ◽  
Jenia Pevzner ◽  
...  
Keyword(s):  
Real World ◽  
Information Technologies ◽  
Medical Information ◽  
The Real

scholarly journals Real World of Artificial Intelligence - A Review

2020 ◽  
Vol 11 (2) ◽  
pp. 41-47
Author(s):  
Amandeep Kaur ◽  
Madhu Dhiman ◽  
Mansi Tonk ◽  
Ramneet Kaur
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Face Recognition ◽  
Language Processing ◽  
Real World ◽  
Review Paper ◽  
General Concept ◽  
Research Trends ◽  
Human Intelligence

Artificial Intelligence is the combination of machine and human intelligence, which are in research trends from the last many years. Different Artificial Intelligence programs have become capable of challenging humans by providing Expert Systems, Neural Networks, Robotics, Natural Language Processing, Face Recognition and Speech Recognition. Artificial Intelligence brings a bright future for different technical inventions in various fields. This review paper shows the general concept of Artificial Intelligence and presents an impact of Artificial Intelligence in the present and future world.

scholarly journals Twitter content classification

First Monday ◽  
2010 ◽  
Author(s):  
Stephen Dann
Keyword(s):  
Grounded Theory ◽  
Real World ◽  
Classification Scheme ◽  
Theory Analysis ◽  
The Real ◽  
Real World Application ◽  
Classification Framework ◽  
Content Classification ◽  
Insight Into

This paper delivers a new Twitter content classification framework based sixteen existing Twitter studies and a grounded theory analysis of a personal Twitter history. It expands the existing understanding of Twitter as a multifunction tool for personal, profession, commercial and phatic communications with a split level classification scheme that offers broad categorization and specific sub categories for deeper insight into the real world application of the service.

scholarly journals Current Situation and Prospects of Evidence-Based Medicine in Russia Assessed by Physicians and Patients

2019 ◽  
Vol 47 (3) ◽  
pp. 123-143
Author(s):  
Natalia A. Vyatkina
Keyword(s):  
Evidence Based Medicine ◽  
Current Status ◽  
Pharmaceutical Companies ◽  
Criminal Prosecution ◽  
Evidence Based ◽  
Sources Of Information ◽  
Critical Situation ◽  
Based Medicine ◽  
Insight Into

The term "evidence-based medicine" is being increasingly used by various sources of information today, and becomes a discussion subject of professional communities and ordinary citizens. Apart from a brief insight into the origin and development of evidence-based medicine in the world and in Russia, the article deals with the anthropological analysis of the attitudes of the modern Russian physicians and patients towards both the understanding of the term and the current status, prospects and possible risks of the development of this discipline in our country. The views of respondents about the role of pharmaceutical companies, the state and the balance between the development and implementation of clinical guidelines and individual cases are considered. The article presents the arguments of patients about whether there is still a "physician blessed by God" and whether it is important for them that the person who they address for help works in the paradigm of evidence-based medicine. Physicians question whether healing itself is still an art, or evidence-based medicine has finally turned it into a business and well-organized mechanism, which could protect them from criminal prosecution in a critical situation.

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