This dataset provides counts and percentages of diagnoses broken down by each patient’s Healthy Places Index percentile ranking (based on ZIP code of residence). Healthcare encounters are categorized into four diagnosis groups: mental health disorders, substance use disorders, co-occurring disorders, and all other diagnoses. To view and interact with a fully functioning version of the HPI map and data used in these HCAI analyses of behavioral health, please click the link to visit https://map.healthyplacesindex.org/.

This dataset includes Medicaid Managed Care, Commercial HMO, and Commercial PPO performance data from the Quality Assurance Reporting Requirements (QARR) by member demographic characteristics. QARR is largely based on measures of quality developed and published by the National Committee for Quality Assurance (NCQA) Healthcare Effectiveness Data and Information Set (HEDIS®). Plans are required to submit quality performance data each year. Demographic information analyzed in this report includes members’ sex, age, race/ethnicity, Medicaid aid category, cash assistance status, behavioral health conditions including serious mental illness (SMI) and substance use disorder (SUD), payer status, and region of residence. Measuring the quality of care, and the ability to measure disparities in care is an important first step to a better understanding of the underlying factors that drive differences in care among certain populations within Medicaid Managed Care, Commercial HMO, and Commercial PPO. These data are published annually for Medicaid Managed Care in the Health Care Disparities in New York State Report and on the NYSDOH web site: https://www.health.ny.gov/health_care/managed_care/reports/

HeAL benchmark (HEalth Advice in LLMs), a health-advice benchmark dataset that has been manually curated and annotated to evaluate LLMs’ capability in recognizing health-advice.Some of the sentences in this data are sampled from: https://figshare.com/articles/dataset/MedRed/12039609/1, originally under MIT license (https://opensource.org/license/MIT).THE Data IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE Data.

This dataset includes Medicaid Managed Care, Commercial HMO, and Commercial PPO performance data from the Quality Assurance Reporting Requirements (QARR) by member demographic characteristics. QARR is largely based on measures of quality developed and published by the National Committee for Quality Assurance (NCQA) Healthcare Effectiveness Data and Information Set (HEDIS®). Plans are required to submit quality performance data each year. Demographic information analyzed in this report includes members’ sex, age, race/ethnicity, Medicaid aid category, cash assistance status, behavioral health conditions including serious mental illness (SMI) and substance use disorder (SUD), payer status, and region of residence. Measuring the quality of care, and the ability to measure disparities in care is an important first step to a better understanding of the underlying factors that drive differences in care among certain populations within Medicaid Managed Care, Commercial HMO, and Commercial PPO.

These data are published annually for Medicaid Managed Care in the Health Care Disparities in New York State Report and on the NYSDOH web site: http://www.health.ny.gov/health_care/managed_care/reports/quality_performance_improvement.htm#link6

Abstract Objective: To evaluate the association between triglycerides and HDL-cholesterol (TG/HDL-c) ratio and cardiovascular risk factors among the elderly. Method: A cross-sectional epidemiological study with a random sample of elderly persons (n=349) of both genders, who received care under the Family Health Strategy in the municipality of Viçosa, in the state of Minas Gerais, was performed. Cardiovascular risk was calculated by the relationship between the TG and the HDL-c levels, with values greater than 3.5 considered a risk. Social and economic variables, lifestyle, noncommunicable chronic diseases, serum glucose levels, waist circumference (WC) and body mass index were evaluated. Multiple linear regression was used to evaluate the association between the TG/HDL-c ratio and other variables. Variables associated with the dependent variable with a level of significance lower than 0.20 in univariate regression analysis were included in the final model (stepwise-forward), applying a significance level of p

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Will the Medical Equipment Index Heal the Market?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

ObjectiveThe prevalence of obesity and type 2 diabetes is rapidly increasing worldwide, posing serious threats to human health. This study aimed to evaluate the role of FMT in the treatment of obesity and/or metabolic syndrome and its impact on clinically important parameters.MethodsWe searched Medline, Embase, and Cochrane Library databases up to April 31, 2022 and further assessed articles that met the eligibility criteria. Mean differences and 95% confidence intervals were used to analyze continuous data. The I2 statistic was used to measure study heterogeneity. Univariate meta-regression or subgroup analyses were performed to explore the covariates that might contribute to heterogeneity. Potential publication bias was assessed using the Egger’s test. We used the GRADEpro guideline development tool to assess the quality of the evidence.ResultsNine studies, comprising 303 participants, were included in the meta-analysis. In the short-term outcomes (