Health Disparities through Generative AI Models: A Comparison Study Using A Domain Specific large language model

Health disparities are differences in health outcomes and access to healthcare between different groups, including racial and ethnic minorities, low-income people, and rural residents. An artificial intelligence (AI) program called large language models (LLMs) can understand and generate human language, improving health communication and reducing health disparities. There are many challenges in using LLMs in human-doctor interaction, including the need for diverse and representative data, privacy concerns, and collaboration between healthcare providers and technology experts. We introduce the comparative investigation of domain-specific large language models such as SciBERT with a multi-purpose LLMs BERT. We used cosine similarity to analyze text queries about health disparities in exam rooms when factors such as race are used alone. Using text queries, SciBERT fails when it doesn't differentiate between queries text: "race" alone and "perpetuates health disparities." We believe clinicians can use generative AI to create a draft response when communicating asynchronously with patients. However, careful attention must be paid to ensure they are developed and implemented ethically and equitably

MSI-CIEC: MSI Cyberinfrastructure Empowerment Coalition and the TeraGrid

Paper written as a collaboration of the following institutions and presented at the 2006 TeraGrid Conference, Indianapolis, IN June 12-16: 1. University of Houston Downtown 2. NAFEO: National Association for Equal Opportunity in Higher Education 3. SDSC: San Diego Supercomputer Center 4. Indiana University, Computer Science Department 5. AIHEC: The American Indiana Highter Education Consortium 6. HACU: Hispanic Association of Colleges and Universitie

The fourth phase of the radiative transfer model intercomparison (RAMI) exercise : Actual canopy scenarios and conformity testing

The RAdiative transfer Model Intercomparison (RAMI) activity focuses on the benchmarking of canopy radiative transfer (RT) models. For the current fourth phase of RAMI, six highly realistic virtual plant environments were constructed on the basis of intensive field data collected from (both deciduous and coniferous) forest stands as well as test sites in Europe and South Africa. Twelve RT modelling groups provided simulations of canopy scale (directional and hemispherically integrated) radiative quantities, as well as a series of binary hemispherical photographs acquired from different locations within the virtual canopies. The simulation results showed much greater variance than those recently analysed for the abstract canopy scenarios of RAMI-IV. Canopy complexity is among the most likely drivers behind operator induced errors that gave rise to the discrepancies. Conformity testing was introduced to separate the simulation results into acceptable and non-acceptable contributions. More specifically, a shared risk approach is used to evaluate the compliance of RI model simulations on the basis of reference data generated with the weighted ensemble averaging technique from ISO-13528. However, using concepts from legal metrology, the uncertainty of this reference solution will be shown to prevent a confident assessment of model performance with respect to the selected tolerance intervals. As an alternative, guarded risk decision rules will be presented to account explicitly for the uncertainty associated with the reference and candidate methods. Both guarded acceptance and guarded rejection approaches are used to make confident statements about the acceptance and/or rejection of RT model simulations with respect to the predefined tolerance intervals. (C) 2015 The Authors. Published by Elsevier Inc.Peer reviewe

Implementation of corticosteroids in treating COVID-19 in the ISARIC WHO Clinical Characterisation Protocol UK:prospective observational cohort study

BACKGROUND: Dexamethasone was the first intervention proven to reduce mortality in patients with COVID-19 being treated in hospital. We aimed to evaluate the adoption of corticosteroids in the treatment of COVID-19 in the UK after the RECOVERY trial publication on June 16, 2020, and to identify discrepancies in care. METHODS: We did an audit of clinical implementation of corticosteroids in a prospective, observational, cohort study in 237 UK acute care hospitals between March 16, 2020, and April 14, 2021, restricted to patients aged 18 years or older with proven or high likelihood of COVID-19, who received supplementary oxygen. The primary outcome was administration of dexamethasone, prednisolone, hydrocortisone, or methylprednisolone. This study is registered with ISRCTN, ISRCTN66726260. FINDINGS: Between June 17, 2020, and April 14, 2021, 47 795 (75·2%) of 63 525 of patients on supplementary oxygen received corticosteroids, higher among patients requiring critical care than in those who received ward care (11 185 [86·6%] of 12 909 vs 36 415 [72·4%] of 50 278). Patients 50 years or older were significantly less likely to receive corticosteroids than those younger than 50 years (adjusted odds ratio 0·79 [95% CI 0·70–0·89], p=0·0001, for 70–79 years; 0·52 [0·46–0·58], p80 years), independent of patient demographics and illness severity. 84 (54·2%) of 155 pregnant women received corticosteroids. Rates of corticosteroid administration increased from 27·5% in the week before June 16, 2020, to 75–80% in January, 2021. INTERPRETATION: Implementation of corticosteroids into clinical practice in the UK for patients with COVID-19 has been successful, but not universal. Patients older than 70 years, independent of illness severity, chronic neurological disease, and dementia, were less likely to receive corticosteroids than those who were younger, as were pregnant women. This could reflect appropriate clinical decision making, but the possibility of inequitable access to life-saving care should be considered. FUNDING: UK National Institute for Health Research and UK Medical Research Council

Towards Intelligent Virtual Environment for Training Medical Doctors in Surgical Pain Relief

Chronic pain is a serious health problem affecting millions of people worldwide. Spinal cord stimulation is one of the most effective methods of easing the chronic pain. For most patients, a careful selection of weak electric currents drastically decreases the pain level. Engineering progress leads to more and more flexible devices that offer a wide variety of millions of possible simulation regimes. It is not possible to test all of them on each patient, we need an intelligent method of choosing an appropriate simulation regime. In this paper, we describe the need for an intelligent virtual environment for training medical doctors in surgical pain relief; specifically, we show that the design of such a system will drastically speed up the doctor\u27s training and enhance their training skills

Vector valued absolutely continuous functions on idempotent semigroups

Mathematics Technical Repor

Spinal Cord Stimulation for Chronic Pain Management: Towards an Expert System

Chronic pain is a serious health problem affecting millions of people worldwide. Currently, spinal cord simulation is one of the most effective methods of easing the chronic pain. For most patients, a careful selection of weak electric currents enables to drastically decrease the pain level. The first devices offered only a few possible regimes, and it was possible to choose an appropriate regime simply by exhaustive search. Continuous engineering progress leads to more and more flexible devices that offer a wide variety of millions of possible simulation regimes. With this variety, it is no longer possible to test all of them on each patient, we need an intelligent method of choosing an appropriate simulation regime. In this paper, we describe the design of an expert system for choosing appropriate simulations. We hope that our paper will be understandable both for the computer science readers interested in medical applications, and for medical researchers interested in using computers for pain relief

Towards Optimal Pain Relief: Acupuncture and Spinal Cord Stimulation

One of the important potential areas of application of intelligent virtual environment is to training medical doctors. One of the main problems in designing the corresponding intelligent system is the computational complexity of the corresponding computational problems. This computational complexity is especially high when the corresponding optimization is a discrete optimization problem, e.g., for pain relief methodologies such as acupuncture and spinal cord stimulation. In this paper, we show how to efficiently solve the corresponding discrete optimization problems. As a result, we get, e.g., a theoretical justification for the heuristic method of "guarded cathode". Formulation of the problem. One of the prospective applications of intelligent virtual environments is to training medical doctors. For this application, it is desirable to design an intelligent system which would: ffl simulate a patient, and ffl recommend, to a doctor, the optimal way of curing the given patient. Mai..

Can Computers Do the Job of Nobelist Physicists? Planck Formula Revisited

There exist several computer programs which successfully model the discovery process in science. There are successful expert systems in medicine and other areas. But one area is a real challenge for such systems: theoretical physics. The most advanced knowledge discovery programs (like BACON written under the supervision of the Nobelist Herbert A. Simon) successfully reproduce only 17, 18, and 19 century physics, but stop short of explaining the very first formula of the 20 century: Planck\u27s law of black body radiation. This law, discovered by an insight, led to the modern Quantum Physics. The programs stop short not because the computers are not fast enough: as Simon emphasized, we need new ideas -- not only new computers. In the present paper, we present the natural symmetry ideas which lead directly to Planck\u27s formula. Possible other applications of these ideas are discussed

A note on compactifications and semi normal spaces

Mathematics Technical Repor