Tarun Chawdhury, MSCS

>>>New #Research Published Model Complexity Reduction for #ZKML #Healthcare Applications New research has been published in #Blockchain in Health Today Platform Approaches in Healthcare open access peer reviewed journal. Authors > Sathya Krishnasamy, MS > Ilangovan G., MD The author discusses the barriers and the mitigations through specific technologies maturing to solve issues including privacy-preserving technologies, off-chain and on-chain design optimizations, and the multi-dimensional approach needed in planning and adopting these technologies. The paper discusses how one enabler, ZKML, combines two streams of technology that are merging in unique ways to address problems in privacy and the cost of inference. The authors have conceptualized the technical and operational feasibility and implemented a reference healthcare implementation using the synthetic ICHOM in the evaluation phase of a global healthcare setting for high-volume data collection, including patient-reported outcomes. Model complexity reduction is researched and reported for the ICHOM #diabetesdataset to advance the usage of ML models in global standards of healthcare data collection in network decentralized architectures for increased data protection and efficiencies. *****The deadline to submit your research, reviews, editorial, is November 1, 2024. Upload your work AND cover letter to the submission portal at https://lnkd.in/dpRQZYw

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As Gen AI based applications ramp up, validation at scale becomes critical. Even more so in domains like Healthcare where cost and effort to do so are high. Interesting article on how this process can be supplemented. https://lnkd.in/g4rSjkuQ Arjun Bansal lots of synergies with what you are up to! More than the data itself, how we close the loop on these models and enable validation and continuous learning will determine how robust they can become.

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👉🏼 ChatGPT's Inconsistency in the Diagnosis of Alzheimer's Disease 🤓 ArunSundar MohanaSundaram 👇🏻 https://lnkd.in/gyhpr7mn 🔍 Focus on data insights: - ChatGPT's responses in diagnosing AD and MCI stages varied. - Reassessment of ChatGPT's accuracy and reproducibility in diagnosis. - Discussion on reasons for differing results and suggestions for future studies. 💡 Main outcomes and implications: - Potential limitations in using ChatGPT for AD and MCI diagnosis. - Need for further research to enhance ChatGPT's diagnostic reliability. - Importance of understanding AI tools' nuances in clinical applications. 📚 Field significance: - Enhancing AI tools for medical diagnosis. - Impact of AI inconsistencies on clinical decision-making. - Future directions for improving AI-assisted healthcare. 🗄️: [#AlzheimersDisease #AI #MedicalDiagnosis #ClinicalApplications]

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I’m thrilled to share our latest research on predictive analytics in healthcare! 🏥📊 This study introduces a predictive framework for ICU length of stay (LOS) using hospital EHR data and machine learning models. Utilizing explainable AI (xAI), it achieves 98% AUC with XGBoost, enhancing hospital efficiency and patient outcomes by providing trustworthy LOS predictions and explaining model outputs. Belal Alsinglawi Omar Mubin #HealthcareInnovation #MachineLearning #PredictiveAnalytics #AIHospitalManagement #UAEU

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🚀 Transforming Healthcare with AI: Precision and Progress 🚀 * Rapid Drug Development: AI reduced the drug discovery process to just 30 months with Insilico Medicine, compared to the traditional decade-long timeline. *Enhanced Diagnostic Accuracy: In diagnosing complex cases, AI accurately identified tricky diagnoses 39% of the time and provided the correct diagnosis as a secondary option 64% of the time, surpassing traditional methods. *Focused Patient Care: Ambient AI allows doctors to focus more on patients by handling administrative tasks, shown in a study where 3,442 physicians adopted this technology for over 303,266 visits. *Empathetic Patient Communication: AI proved to make patients feel more heard, offering emotional support effectively, though it showed a reduction in perceived empathy when users knew the communication was AI-generated. #AIinHealthcare #HealthTech #PatientCare #Innovation Summary from: https://lnkd.in/gyqbBf5R

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See Current Standings: 👆🏽 UPDATE 5 of 6 of the OCI Foundation's VLSS SOCIAL MEDIA CONTEST (Correct as of 11.08 AM, 04/05/2024; Nigerian Time). Just over 24 hours to go: KWARA (96%), GOMBE (90.0%; 7,123) and BAUCHI (85.0%) maintain the top 3, and any of them can still finish top!!! We wait to see who has the guts to push through to the final day . . . Any of the 9 states can still finish in the cash-winning top 3. IMPORTANT NOTES: 1. To Vote, follow this link: https://lnkd.in/eTXQyvYh. 2. A balanced approach on all VLSS components gives more points than a very high score in one component. 3. We strongly recommend that contestants should target all of the Views (20%), Like (20%), Share (30%) and Subscribe (30%), not just the views. 4. To win easily, see where the points are, and watch some winning strategies here: https://lnkd.in/gJB8v6EF. 5. This all ends at midnight on Saturday, May 4, 2024. Results will be announced within 48 hours after that, to allow for updates on the Subscription Scores (data on this lags by 48 hours from TikTok) All others (Views, Likes and Shares) will be frozen at this time. 6. A minimum of 60% VLSS Percentage Score AND a Top 3 finish are needed for the main 50k rewards. 7. Any amount won by any State will be shared at the ratio of 50% to the participating Corps Member(s) and 50% to the Anchors(s). 8. If there is a tie in the PERCENTAGE VLSS SCORE, the RAW VLSS SCORES are used to separate contestants. #werisebyliftingothers #ocifoundation

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ChatGPT's Role in Supporting Clinical Pharmacists with Herbal-Drug Interaction Assessments: Benefits and Challenges #AI #Pharmacy #HerbalMedicine #ClinicalDecisionMaking #HealthcareInnovation #ChatGPT #DrugInteractions #Pharmacology #PatientSafety Link: https://lnkd.in/eUuQTrHc

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👉🏼 The Digital Technology in Clinical Medicine: From Calculators to ChatGPT 🤓 Somarajan Anandan 👇🏻 https://lnkd.in/gtRmmq8z 🔍 Focus on data insights: - Utilization of digital technology in clinical medicine - Impact of digital information technology on healthcare delivery 💡 Main outcomes and implications: - Advancements in clinical medicine due to digital technology - Need for balance between technology, clinical expertise, and humanistic values 📚 Field significance: - Regulatory push for improvements through clinical trials - Projects like "World Health Organizations Guidelines for Digital Interventions" and "Food and Drug Administration's Digital Health Center of Excellence" highlighting efforts in digital health services 🗄️: [#digitaltechnology #clinicalmedicine #healthcaredelivery]

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Here is my column on the #TiEcon2024 conference in #indicanews:

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At the heart of the intersection of AI & ML with healthcare is an area of growing potential. Join Mark Sendak and Suresh Balu from Duke University Health System while they explore their work in "Accelerating Healthcare AI/ML/Advanced Analytics through Advanced Data Curation" at #HDAA2024. "Unlocking the value of data and making it actionable is critical to achieve healthcare transformation. This encompasses access to the right data, ensuring that data is high-quality and accurate, and establishing the right governance, infrastructure, and analytics to make the data actionable and useful. Most of this data is unstructured, inconsistent, incomplete, or inaccurate, which limits its immediate potential for application. Data curation is the process of transforming raw data into clean, standardized, and usable data that can support various purposes, such as clinical care, research, innovation, and policy making. Data curation involves tasks such as data extraction, validation, transformation, integration, annotation, and documentation. Data curation is essential for ensuring the quality, reliability, and utility of healthcare data, but it is also a complex, time-consuming, and labor-intensive process that requires significant human expertise and resources. We present the design and implementation of data curation platform at Fullsteam Health, which is based on the insights and feedback from Duke University Health System. We describe the technical architecture and components of the platform, such as the data extraction, validation, transformation, integration, annotation, and documentation modules. We also discuss the challenges and solutions of data curation in healthcare, such as dealing with data variety, veracity, velocity, and volume. We demonstrate the effectiveness and impact of the platform by showing how it has improved the quality and usability of healthcare data for Duke University Health System and other customers. We also share the lessons learned and future directions of the platform development and deployment.By facilitating real-time decision support, improved clinical outcomes, and operational efficiency, Duke Health is setting new standards for data utility in healthcare." #healthcare #healthcareAI #artificialintelligence #machinelearning #ML #AIML #datascience

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AI in finance as in other domains, it is promising tool, but has yet to be proved. https://lnkd.in/dPv6mKDa #ai #artificicalinteligence #finance #machinelearning #datascience

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Updates to #RxVenture by OpenClinTech! Check it out here: https://lnkd.in/egKyCGkD - Modernized landing page a bit more with additional key stats - Added a dynamic search in organizations to enable easy filtering on name - Finished a first draft of my python script that ingests source data from ASHP, converts it to FHIR, and subsequently converts it to a schema that aligns with my database. Ran script successfully against all positions & organizations from Alabama state and imported into database - Implemented lazy loading to career hub to avoid pagination for a better user experience For next steps, I'm planning on... 1. Refactoring the python script to handle bulk import of all positions from ASHP and extracting additional fields of interest 2. Enriching data to introduce some fun filters (e.g. 'VA' positions, top rankings by U.S. News & World Report) 3. Adding a content management system to enable end users to add events If you have any feedback, especially suggestions for new features you'd like to see, I'd love to hear them in the comments below! Or just email me at [email protected] #healthcareonlinkedin #careers #residency #fellowship #postgraduate

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👉🏼 Evaluation of a Large Language Model on the American Academy of Pediatrics' PREP Emergency Medicine Question Bank 🤓 Sriram Ramgopal 👇🏻 https://lnkd.in/ezHKQ6UX 🔍 Focus on data insights: - 📊 ChatGPT achieved a correct answer rate of 74.5% across 215 questions, indicating effectiveness in PEM knowledge assessment. - 🔄 The model demonstrated consistent performance across three years, suggesting reliability in its responses over time. - 📚 Topic-specific performance revealed strengths in research and medical specialties, while highlighting weaknesses in procedures and toxicology. 💡 Main outcomes and implications: - 🏆 ChatGPT exceeded the minimum passing threshold of 65%, supporting potential integration into medical education curricula. - 💬 Detailed explanations provided by the model can enhance understanding among learners, fostering a deeper grasp of pediatric emergency medicine concepts. - ⚠️ Identified limitations in specific topics may guide future enhancements of LLMs for more comprehensive educational tools. 📚 Field significance: - 🧠 The application of LLMs like ChatGPT could revolutionize medical education, offering dynamic learning resources tailored to various specialties. - 🌍 The findings suggest broader implications for using AI in clinical settings, potentially improving diagnostic capabilities and patient care. - 🔍 Continuous evaluation of LLM performance is essential to ensure quality and adaptivity in rapidly evolving medical landscapes. 🗄️: [#LargeLanguageModels #PediatricEmergencyMedicine #AIinHealthcare #MedicalEducation #DataInsights]

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