natural language processing
Dotaz
Zobrazit nápovědu
Natural Language processing (NLP) umožňuje prostřednictvím sémantické organizace informací a znalostí v oblasti zdravotnické informatiky: 1)snadnější a rychlejší přístup k informacím jak ve zdravotnické dokumentaci, tak v jiných zdrojích 2) spolehlivější a komplexnější ukládání dat do zdravotnické dokumentace v přirozeně mluveném jazyce, nikoli za použití omezeného počtu předem definovaných frází a kódů 3) automatické vyhledávání pacientů vhodných pro klinické studie 4) automatické kódování a klasifikaci údajů jako vedlejší produkt záznamu dat.
INTRODUCTION: Behavioural support has been shown to possess high efficacy in aiding smokers to stop smoking at a level at least similar to nicotine replacement therapies (NRT) while causing no adverse physiological effects. Furthermore, the effects of behavioural interventions and NRT appear to be roughly additive. The effectiveness of interventions appears to increase with the frequency of contact with an eHealth application, with daily contact showing higher efficacy compared to weekly sessions with a trained stop-smoking specialist. Previous automated counsellors based on a motivational interview approach, lapse preparation, and lapse management are rigid in their therapeutic path, with limited ability to reflect the needs and the specific situation of a given patient. AIMS: This report aims to describe a possible approach to developing a more engaging, patient-tailored automated counsellor based on recent advances in the natural language processing (NLP) field that should make remote chat-based counselling easier for professionals while gathering data for the NLP model, which should ultimately be able to conduct the therapy on its own. METHODS: The core of the approach lies in utilizing the Text-to-text transfer transformer (T5). T5 is, in essence, a set of neural network models aimed at tasks formulated as an expected textual response to a given textual input. These models can be utilized to – at first – suggest answers to counsellors in live chat sessionswith patients. Actual answers from these sessions would subsequently be used to fine-tune the models and ultimately provide high-quality counselling without human intervention on the therapist’s side. CONCLUSION: The article presents a novel approach to internet-delivered smoking cessation cognitive-behavioural therapy utilizing a powerful artificial neural network NLP model acting as a conversational agent and a data collection protocol with usage incentives for both smoking cessation experts and smokers.
BACKGROUND: Impairment of higher language functions associated with natural spontaneous speech in multiple sclerosis (MS) remains underexplored. OBJECTIVES: We presented a fully automated method for discriminating MS patients from healthy controls based on lexical and syntactic linguistic features. METHODS: We enrolled 120 MS individuals with Expanded Disability Status Scale ranging from 1 to 6.5 and 120 age-, sex-, and education-matched healthy controls. Linguistic analysis was performed with fully automated methods based on automatic speech recognition and natural language processing techniques using eight lexical and syntactic features acquired from the spontaneous discourse. Fully automated annotations were compared with human annotations. RESULTS: Compared with healthy controls, lexical impairment in MS consisted of an increase in content words (p = 0.037), a decrease in function words (p = 0.007), and overuse of verbs at the expense of noun (p = 0.047), while syntactic impairment manifested as shorter utterance length (p = 0.002), and low number of coordinate clause (p < 0.001). A fully automated language analysis approach enabled discrimination between MS and controls with an area under the curve of 0.70. A significant relationship was detected between shorter utterance length and lower symbol digit modalities test score (r = 0.25, p = 0.008). Strong associations between a majority of automatically and manually computed features were observed (r > 0.88, p < 0.001). CONCLUSION: Automated discourse analysis has the potential to provide an easy-to-implement and low-cost language-based biomarker of cognitive decline in MS for future clinical trials.
- Publikační typ
- časopisecké články MeSH
Hlavním cílem příspěvku je uvedení do problematiky vývoje sebeobrazu (Self) u dětí s vývojovou dysfázií. Abychom lépe pochopili emoční prožívání a modifikace kontaktu ve vývoji Self u dětí, které jsou limitovány aberantním neurobiologickým zráním, uvádíme i stručný teoretický základ vývojových teorií a attachmentu. Pokud v klinicko-logopedické praxi chceme maximalizovat efektivitu terapeutického působení, je nasnadě pohlížet a reagovat na dítě jako na celostní bytost, která má vlastní prožívání, uvědomování, potřeby a patří do konkrétního rodinného systému. Podpoříme tím interpersonální vztahování, pocit bezpečí a sebeuvědomování. Kvalitním kontaktem dítě-terapeut můžeme nejen zvýšit motivovanost dítěte pro vzájemnou spolupráci, ale také stimulovat seberegulaci a přiměřeně zdravý vývoj Self.
The contribution introduces into the self-image (Self) development in children with specific language impairment. A brief survey of the developmental theories and attachment theory is also provided in order to understand better the emotional experience and contact modification in the Self development in children that are limited by aberrant neurobiological maturing. If one aims at maximizing an effect of clinical speech therapy, it is natural to perceive and reflect a child as a holistic personality with its own experience, needs etc. and belongs to a given family system. Such approach supports interpersonal relationship, feeling of safety and self-awareness. Good contact between a child and a speech therapist may increase not only child's motivation for mutual collaboration, but also stimulate self-regulation mechanism and proportionately health development of Self.
Příchod velkých jazykových modelů (LLMs) založených na neuronových sítích představuje zásadní změnu v akademickém psaní, zejména v lékařských vědách. Tyto modely, např. GPT-4 od OpenAI, Google’s Bard či Claude od Anthropic, umožňují efektivnější zpracování textu díky architektuře transformátorů a mechanismu pozornosti. LLMs jsou schopny generovat koherentní texty, které se těžko rozeznávají od lidských. V medicíně mohou přispět k automatizaci rešerší, extrakci dat a formulaci hypotéz. Současně však vyvstávají etické otázky týkající se kvality a integrity vědeckých publikací a rizika generování zavádějícího obsahu. Článek poskytuje přehled o tom, jak LLMs mění psaní odborných textů, etická dilemata a možnosti detekce generovaného textu. Závěrem se zaměřuje na potenciální budoucnost LLMs v akademickém publikování a jejich dopad na lékařskou komunitu.
The advent of large language models (LLMs) based on neural networks marks a significant shift in academic writing, particularly in medical sciences. These models, including OpenAI's GPT-4, Google's Bard, and Anthropic’s Claude, enable more efficient text processing through transformer architecture and attention mechanisms. LLMs can generate coherent texts that are indistinguishable from human-written content. In medicine, they can contribute to the automation of literature reviews, data extraction, and hypothesis formulation. However, ethical concerns arise regarding the quality and integrity of scientific publications and the risk of generating misleading content. This article provides an overview of how LLMs are changing medical writing, the ethical dilemmas they bring, and the possibilities for detecting AI-generated text. It concludes with a focus on the potential future of LLMs in academic publishing and their impact on the medical community.
- MeSH
- jazyk (prostředek komunikace) MeSH
- lidé MeSH
- neuronové sítě MeSH
- publikování * etika MeSH
- zpracování přirozeného jazyka * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
Artificial Intelligence (AI) has evolved significantly over the past decades, from its early concepts in the 1950s to the present era of deep learning and natural language processing. Advanced large language models (LLMs), such as Chatbot Generative Pre-Trained Transformer (ChatGPT) is trained to generate human-like text responses. This technology has the potential to revolutionize various aspects of gastroenterology, including diagnosis, treatment, education, and decision-making support. The benefits of using LLMs in gastroenterology could include accelerating diagnosis and treatment, providing personalized care, enhancing education and training, assisting in decision-making, and improving communication with patients. However, drawbacks and challenges such as limited AI capability, training on possibly biased data, data errors, security and privacy concerns, and implementation costs must be addressed to ensure the responsible and effective use of this technology. The future of LLMs in gastroenterology relies on the ability to process and analyse large amounts of data, identify patterns, and summarize information and thus assist physicians in creating personalized treatment plans. As AI advances, LLMs will become more accurate and efficient, allowing for faster diagnosis and treatment of gastroenterological conditions. Ensuring effective collaboration between AI developers, healthcare professionals, and regulatory bodies is essential for the responsible and effective use of this technology. By finding the right balance between AI and human expertise and addressing the limitations and risks associated with its use, LLMs can play an increasingly significant role in gastroenterology, contributing to better patient care and supporting doctors in their work.
- MeSH
- deep learning MeSH
- gastroenterologie * MeSH
- lidé MeSH
- umělá inteligence * MeSH
- zpracování přirozeného jazyka * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
BACKGROUND AND OBJECTIVES: Patients with synucleinopathies such as multiple system atrophy (MSA) and Parkinson's disease (PD) frequently display speech and language abnormalities. We explore the diagnostic potential of automated linguistic analysis of natural spontaneous speech to differentiate MSA and PD. METHODS: Spontaneous speech of 39 participants with MSA compared to 39 drug-naive PD and 39 healthy controls matched for age and sex was transcribed and linguistically annotated using automatic speech recognition and natural language processing. A quantitative analysis was performed using 6 lexical and syntactic and 2 acoustic features. Results were compared with human-controlled analysis to assess the robustness of the approach. Diagnostic accuracy was evaluated using sensitivity analysis. RESULTS: Despite similar disease duration, linguistic abnormalities were generally more severe in MSA than in PD, leading to high diagnostic accuracy with an area under the curve of 0.81. Compared to controls, MSA showed decreased grammatical component usage, more repetitive phrases, shorter sentences, reduced sentence development, slower articulation rate, and increased duration of pauses, whereas PD had only shorter sentences, reduced sentence development, and longer pauses. Only slower articulation rate was distinctive for MSA while unchanged for PD relative to controls. The highest correlation was found between bulbar/pseudobulbar clinical score and sentence length (r = -0.49, p = 0.002). Despite the relatively high severity of dysarthria in MSA, a strong agreement between manually and automatically computed results was achieved. DISCUSSION: Automated linguistic analysis may offer an objective, cost-effective, and widely applicable biomarker to differentiate synucleinopathies with similar clinical manifestations.
- MeSH
- diferenciální diagnóza MeSH
- lidé středního věku MeSH
- lidé MeSH
- multisystémová atrofie * diagnóza patofyziologie komplikace MeSH
- Parkinsonova nemoc * diagnóza komplikace patofyziologie MeSH
- řeč fyziologie MeSH
- senioři MeSH
- zpracování přirozeného jazyka MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Covering: up to the end of 2020. The machine learning field can be defined as the study and application of algorithms that perform classification and prediction tasks through pattern recognition instead of explicitly defined rules. Among other areas, machine learning has excelled in natural language processing. As such methods have excelled at understanding written languages (e.g. English), they are also being applied to biological problems to better understand the "genomic language". In this review we focus on recent advances in applying machine learning to natural products and genomics, and how those advances are improving our understanding of natural product biology, chemistry, and drug discovery. We discuss machine learning applications in genome mining (identifying biosynthetic signatures in genomic data), predictions of what structures will be created from those genomic signatures, and the types of activity we might expect from those molecules. We further explore the application of these approaches to data derived from complex microbiomes, with a focus on the human microbiome. We also review challenges in leveraging machine learning approaches in the field, and how the availability of other "omics" data layers provides value. Finally, we provide insights into the challenges associated with interpreting machine learning models and the underlying biology and promises of applying machine learning to natural product drug discovery. We believe that the application of machine learning methods to natural product research is poised to accelerate the identification of new molecular entities that may be used to treat a variety of disease indications.
- MeSH
- biologické přípravky * chemie farmakologie MeSH
- biosyntetické dráhy genetika MeSH
- genomika * MeSH
- lidé MeSH
- mikrobiota MeSH
- objevování léků MeSH
- strojové učení * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
