Priyanka Makhijani

👉🏼 Integrating Large Language Models in Bioinformatics Education for Medical Students: Opportunities and Challenges 🤓 Kai Kang 👇🏻 https://lnkd.in/gEEkehBw 🔍 Focus on data insights: - Large language models (LLMs) enhance educational outcomes by providing accessible code templates. - LLMs clarify the function of coding elements in bioinformatics education. - LLMs assist in error troubleshooting during data analysis processes. 💡 Main outcomes and implications: - The integration of LLMs in bioinformatics education improves student learning experiences. - LLMs can streamline data processing tasks in medical research. - Rigorous validation is essential to ensure the accuracy and reliability of LLM-generated information. 📚 Field significance: - Integrating innovative LLMs with traditional educational methods prepares medical students effectively for future challenges in bioinformatics. 🗄️: [#bioinformatics #medicalstudents #LLMs #datainsights]

DVA is not associated with this job posting Senior Data Scientist at Health Data Vision, Inc. https://lnkd.in/gmcBNyPa WHAT YOU’LL DO: Build proof-of-concept machine learning models Work with engineering teams to implement ML models into production and build ML pipelines Explore, find and implement new solutions to improve the performance of the existing models Bring in the cutting-edge ML and NLP technologies into the existing models (“R&D” style) Exploring LLM solutions for our AI models Be able to complete the entire end-to-end development cycle: define the problem, architect the solution, build and implement the solution #workfromhome #wfh #entrepreneur #stayathome #business #stayhome #motivation #success #money #work #onlinebusiness #love #makemoneyonline #digitalmarketing #homeoffice #networkmarketing #smallbusiness #marketing #financialfreedom #workfromhomelife #businessowner #affiliatemarketing #instagram #makemoney

👉🏼 Understanding the Impact of Large Language Models in Healthcare Communication 🤓 Zefeng Yang 👇🏻 https://lnkd.in/eAtYVexV 🔍 Focus on data insights: - 📊 Large language models (LLMs) demonstrate advanced capabilities in natural language comprehension and generation, enhancing communication in healthcare settings. - 🧠 The evolution of LLMs parallels significant advancements in deep learning, particularly through transformer architecture, which improves their functionality. - 🔄 With extensive training data, LLMs can retain memory and comprehend complex medical information, facilitating better interactions between clinicians and patients. 💡 Main outcomes and implications: - 📝 LLMs can automate medical documentation, potentially reducing administrative burdens on healthcare providers. - 🤖 Future applications may include autonomous diagnosis and treatment suggestions, improving efficiency in patient care. - 📚 LLMs can assist in triage, summarizing medical documents, and personalizing patient education materials, thereby enhancing patient understanding and engagement. 📚 Field significance: - 🌐 The integration of LLMs in healthcare could revolutionize communication, leading to improved patient outcomes and satisfaction. - 🔍 Ongoing research is necessary to address limitations and ensure the safe and effective use of LLMs in real-world clinical settings. - 🚀 As technology advances, LLMs are poised to play a critical role in the future of healthcare delivery, particularly in specialized fields like ophthalmology. 🗄️: [#LargeLanguageModels #HealthcareCommunication #NaturalLanguageProcessing #DeepLearning #PatientCare #MedicalDocumentation #Ophthalmology #AIinHealthcare]

👉🏼 Towards automated phenotype definition extraction using large language models 🤓 Ramya Tekumalla 👇🏻 https://lnkd.in/ezhYqxVd 🔍 Focus on data insights: 🌟 Electronic phenotyping leverages both structured and unstructured data to enhance accuracy. 📊 The study highlights the inefficiency of current methods, which rely heavily on manual literature reviews. 🤖 Large language models present a potential solution, moving towards automation in phenotype extraction. 💡 Main outcomes and implications: ✅ A standard evaluation set was developed to ensure the reliability of outputs from large language models. 🧪 Evaluation of various prompting approaches showed promise in extracting more nuanced phenotype definitions. ⚠️ Despite progress, human validation remains crucial to mitigate risks of non-factual data and misleading results. 📚 Field significance: 🔬 The proposed advancements could significantly reduce the time spent in literature review and evaluation. 🚀 Automation in electronic phenotyping may lead to broader application in clinical settings, improving efficiency and scalability. 🌐 Heightened interest in the intersection of machine learning and clinical informatics could foster further innovations. 🗄️: [#genomics] [#phenotyping] [#machinelearning] [#naturallanguagemodels] [#dataextraction] [#clinicalinformatics]

👉🏼 How ChatGPT Enhances Computational Biology Programming Efficiency 🤓 Chowdhury Rafeed Rahman https://lnkd.in/eAUwUpN7 🔍 Focus on data insights: - ChatGPT streamlines coding tasks for computational biologists. - Enhances data analysis efficiency. - Facilitates script creation and machine learning modeling. 💡 Main outcomes and implications: - Improved productivity in bioinformatics algorithm development. - Enhanced accuracy in data interpretation. - Simplified pipeline creation and maintenance. 📚 Field significance: - ChatGPT addresses coding resource limitations in computational biology. - Mitigates sensitivity and bias issues in medical data analysis. - Provides essential coding support for individuals from diverse backgrounds entering the field. 🗄️: [#computationalbiology #ChatGPT #dataanalysis #bioinformatics #machinelearning]

👉🏼 Evaluating the strengths and weaknesses of large language models in answering neurophysiology questions 🤓 Hassan Shojaee-Mend 👇🏻 https://lnkd.in/eZEbA_zF 🔍 Focus on data insights: - Large language models (LLMs) showcase remarkable natural language processing capabilities. - Evaluation of LLMs' proficiency in neurophysiology is crucial for research, education, and clinical applications. - Assessment of LLMs in answering neurophysiology questions in English and Persian (Farsi) reveals insights into their utility. 💡 Main outcomes and implications: - LLMs demonstrate good performance in answering neurophysiology questions. - No significant difference observed between English and Persian languages or cognitive levels. - Models excel in motor system topics but show weaknesses in integrative topics. 📚 Field significance: - Understanding LLMs' capabilities and limitations in neurophysiology research. - Highlighting the need for targeted training to address knowledge gaps and enhance reasoning skills. - Importance of domain-specific assessments for evaluating advancements in LLMs' performance. 🗄️: [#naturallanguageprocessing #neurophysiology #LLMs #research #datainsights]

👉🏼 Developing large language models to detect adverse drug events in posts on X 🤓 Yu Deng 👇🏻 https://lnkd.in/eVVYKA8b 🔍 Focus on data insights: - 📊 The study developed several large language models

👉🏼 ChatGPT-based Biological and Psychological Data Imputation 🤓 Anam Nazir https://lnkd.in/ezAXTbzp 🔍 Focus on data insights: - ChatGPT utilized for biological and psychological data imputation - Evaluation metrics include Pearson correlation coefficient, relative accuracy, and mean absolute error 💡 Main outcomes and implications: - ChatGPT shows superior efficacy compared to traditional imputation methods - Customized data-to-text prompting enhances accuracy in imputations 📚 Field significance: - Improved accuracy in imputing biological and psychological data - Potential for enhancing research outcomes in large cohort studies 🗄️: #datainsights #imputation #biologicaldata #psychologicaldata

👉🏼 Criteria2Query 3.0: Leveraging generative large language models for clinical trial eligibility query generation 🤓 Jimyung Park 👇🏻 https://lnkd.in/eEvH3Xhi 🔍 Focus on data insights: - GPT-4 achieved an F1-score of 0.891 in concept extraction. - 29 errors were detected in SQL generation, with logic errors being the most common. - Reasoning evaluations showed high coherence but relatively lower readability. 💡 Main outcomes and implications: - GPT-4 significantly improves the accuracy of extracting clinical trial eligibility criteria concepts in C2Q 3.0. - Continued research is warranted to ensure the reliability of large language models. 📚 Field significance: - Automation of patient identification in clinical research. - Advancements in leveraging large language models for query generation. 🗄️: #clinicalresearch #GPT-4 #datainsights #automation

👉🏼 Developing large language models to detect adverse drug events in posts on X 🤓 Yu Deng 👇🏻 https://lnkd.in/emQta8y3 🔍 Focus on data insights: 🌐 Social media platforms like X

👉🏼 Fine-tuning large language models for rare disease concept normalization 🤓 Andy Wang 👇🏻 https://lnkd.in/eJts4fut 🔍 Focus on data insights: - Fine-tuning Llama 2, an open-source large language model (LLM), using a domain-specific corpus sourced from the Human Phenotype Ontology (HPO). - Developing an in-house template-based script to generate two corpora for fine-tuning, one containing standardized HPO names and identifiers, and the other including HPO names, synonyms, and identifiers. - Achieving nearly perfect performance with over 99% accuracy when phenotype terms were included in the fine-tuning corpora. 💡 Main outcomes and implications: - Demonstrating the ability to normalize phenotype terms unseen in the fine-tuning corpus, including misspellings, synonyms, terms from other ontologies, and laymen's terms. - Providing a solution for using LLMs to identify named medical entities from clinical narratives and normalizing them to standard concepts in a controlled vocabulary. 📚 Field significance: - Advancing the field of rare disease concept normalization through the fine-tuning of large language models. - Enhancing the accuracy and efficiency of identifying and normalizing medical entities in clinical contexts. 🗄️: [#raredisease #conceptnormalization #LLM #datainsights]

👉🏼 Accuracy, readability, and understandability of large language models for prostate cancer information to the public 🤓 Jacob S Hershenhouse 👇🏻 https://lnkd.in/ek8wbVfg 🔍 Focus on data insights: - GPT-generated output was deemed correct by 71.7% to 94.3% of raters across 9 scenarios. - GPT-generated simplified layperson summaries were rated as accurate in 8 of 9 scenarios. - Mean readability of layperson summaries was significantly higher than original GPT outputs. 💡 Main outcomes and implications: - GPT shows promise for correct patient education for prostate cancer-related content. - Enhancing accuracy, completeness, clarity, and readability of GPT responses can improve its utility in medical chatbots. 📚 Field significance: - Improving the quality of information provided by large language models can enhance patient education and decision-making in healthcare. - Optimizing language model outputs for patient consumption can bridge the gap between complex medical information and public understanding. 🗄️: [#prostatecancer #GPT #patienteducation #healthcare #medicalchatbots]

👉🏼 Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination 🤓 Ananya Bhattacharjee 👇🏻 https://lnkd.in/eJDYa222 🔍 Focus on data insights: - LLMs offer potential for personalized interventions - Customization to individual needs is facilitated - User expectations and limitations need further exploration 💡 Main outcomes and implications: - Structured, deadline-oriented steps are crucial - Enhanced user support mechanisms are needed - Adaptive questioning based on factors like busyness is essential 📚 Field significance: - Design implications for LLM-based tools in managing procrastination - Caution advised in using LLMs for therapeutic guidance 🗄️: [#academicprocrastination #LLMs #personalizedinterventions]