Cloud Hosting

DUBLIN, Oct. 12, 2022 /PRNewswire/ --The "Global Markets for Machine Learning in the Life Sciences" report has been added to ResearchAndMarkets.com's offering.

Research and Markets Logo

This report highlights the current and future market potential for machine learning in life sciences and provides a detailed analysis of the competitive environment, regulatory scenario, drivers, restraints, opportunities and trends in the market. The report also covers market projections from 2022 through 2027 and profiles key market players.

The publisher analyzes each technology in detail, determines major players and current market status, and presents forecasts of growth over the next five years. Scientific challenges and advances, including the latest trends, are highlighted. Government regulations, major collaborations, recent patents and factors affecting the industry from a global perspective are examined.

Key machine learning in life sciences technologies and products are analyzed to determine present and future market status, and growth is forecast from 2022 to 2027. An in-depth discussion of strategic alliances, industry structures, competitive dynamics, patents and market driving forces is also provided.

Artificial intelligence (AI) is a term used to identify a scientific field that covers the creation of machines (e.g., robots) as well as computer hardware and software aimed at reproducing wholly or in part the intelligent behavior of human beings. AI is considered a branch of cognitive computing, a term that refers to systems able to learn, reason and interact with humans. Cognitive computing is a combination of computer science and cognitive science.

ML algorithms are designed to perform tasks such data browsing, extracting information that is relevant to the scope of the task, discovering rules that govern the data, making decisions and predictions, and accomplishing specific instructions. As an example, ML is used in image recognition to identify the content of an image after the machine has been instructed to learn the differences among many different categories of images.

There are several types of ML algorithms, the most common of which are nearest neighbor, naive Bayes, decision trees, a priori algorithms, linear regression, case-based reasoning, hidden Markov models, support vector machines (SVMs), clustering, and artificial neural networks. Artificial neural networks (ANN) have achieved great popularity in recent years for high-level computing.

Story continues

They are modeled to act similarly to the human brain. The most basic type of ANN is the feedforward network, which is formed by an input layer, a hidden layer and an output layer, with data moving in one direction from the input layer to the output layer, while being transformed in the hidden layer.

Report Includes

32 data tables and 28 additional tables

A comprehensive overview and up-to-date analysis of the global markets for machine learning in life sciences industry

Analyses of the global market trends, with historic market revenue data for 2020 and 2021, estimates for 2022, and projections of compound annual growth rates (CAGRs) through 2027

Highlights of the current and future market potential for ML in life sciences application, and areas of focus to forecast this market into various segments and sub-segments

Estimation of the actual market size for machine learning in life sciences in USD million values, and corresponding market share analysis based on solutions offering, mode of deployment, application, and geographic region

Updated information on key market drivers and opportunities, industry shifts and regulations, and other demographic factors that will influence this market demand in the coming years (2022-2027)

Discussion of the viable technology drivers through a holistic review of various platform technologies for new and existing applications of machine learning in the life sciences areas

Identification of the major stakeholders and analysis of the competitive landscape based on recent developments and segmental revenues

Emphasis on the major growth strategies adopted by leading players of the global machine learning in life sciences market, their product launches, key acquisitions, and competitive benchmarking

Profile descriptions of the leading market players, including Alteryx Inc., Canon Medical Systems Corp., Hewlett Packard Enterprise (HPE), KNIME AG, Microsoft Corp., and Phillips Healthcare

Key Topics Covered:

Chapter 1 Introduction

Chapter 2 Summary and Highlights

Chapter 3 Market Overview 3.1 Introduction 3.1.1 Understanding Artificial Intelligence in Healthcare 3.1.2 Artificial Intelligence in Healthcare Evolution and Transition

Chapter 4 Impact of the Covid-19 Pandemic 4.1 Introduction 4.1.1 Impact of Covid-19 on the Market

Chapter 5 Market Dynamics 5.1 Market Drivers 5.1.1 Investment in Ai Health Sector 5.1.2 Rising Chronic Diseases 5.1.3 Advanced, Precise Results 5.1.4 Increasing Research and Development Budget 5.2 Market Restraints and Challenges 5.2.1 Reluctance Among Medical Practitioners to Adopt Ai-Based Technologies 5.2.2 Privacy and Security of User Data 5.2.3 Hackers and Machine Learning 5.2.4 Ambiguous Regulatory Guidelines for Medical Software 5.3 Market Opportunities 5.3.1 Untapped Potential in Emerging Markets 5.4 Value Chain Analysis

Chapter 6 Market Breakdown by Offering 6.1 Software 6.1.1 Market Size and Forecast 6.2 Services 6.2.1 Market Size and Forecast

Chapter 7 Market Breakdown by Deployment Mode 7.1 Cloud 7.1.1 Market Size and Forecast 7.2 On-Premises 7.2.1 Market Size and Forecast

Chapter 8 Market Breakdown by Application 8.1 Diagnosis 8.1.1 Market Size and Forecast 8.2 Therapy 8.2.1 Market Size and Forecast 8.3 Healthcare Management 8.3.1 Market Size and Forecast

Chapter 9 Market Breakdown by Region 9.1 Global Market 9.2 North America 9.2.1 U.S. 9.2.1 Canada 9.3 Europe 9.3.1 Germany 9.3.2 U.K. 9.3.3 France 9.3.4 Italy 9.3.5 Spain 9.3.6 Rest of Europe 9.4 Asia-Pacific 9.4.1 China 9.4.2 Japan 9.4.3 India 9.4.4 Rest of Asia-Pacific 9.5 Rest of the World

Chapter 10 Regulations and Finance 10.1 Regulatory Framework 10.1.1 American Diabetes Association's Standards of Medical Care in Diabetes 10.1.2 Ata Guidelines for Artificial Intelligence 10.1.3 Indian Ai Guidelines, Strategy, and Standards

Chapter 11 Competitive Landscape 11.1 Overview 11.1.1 Development 11.1.2 Cloud 11.1.3 Users 11.1.4 Parent Market: Global Artificial Intelligence Market

Chapter 12 Company Profiles

For more information about this report visit https://www.researchandmarkets.com/r/qc8qjo

Media Contact:

Research and MarketsLaura Wood, Senior Managerpress@researchandmarkets.com

For E.S.T Office Hours Call +1-917-300-0470For U.S./CAN Toll Free Call +1-800-526-8630For GMT Office Hours Call +353-1-416-8900

U.S. Fax: 646-607-1904Fax (outside U.S.): +353-1-481-1716

Logo: https://mma.prnewswire.com/media/539438/Research_and_Markets_Logo.jpg

Cision

View original content:https://www.prnewswire.com/news-releases/outlook-on-the-machine-learning-in-life-sciences-global-market-to-2027---featuring-alteryx-anaconda-canon-medical-systems-and-imagen-technologies-among-others-301647240.html

SOURCE Research and Markets

Read the rest here:
Outlook on the Machine Learning in Life Sciences Global Market to 2027 - Featuring Alteryx, Anaconda, Canon Medical Systems and Imagen Technologies...

Read More..

WHO releases country estimates on air pollution exposure and health impact, <https://www.who.int/news/item/27-09-2016-who-releases-country-estimates-on-air-pollution-exposure-and-health-impact> (2016).

Faridi, S. et al. Long-term trends and health impact of PM2.5 and O3 in Tehran, Iran, 20062015. Environ. Int. 114, 3749. https://doi.org/10.1016/j.envint.2018.02.026 (2018).

CAS PubMed Google Scholar

Sun, G. et al. Association between air pollution and the development of rheumatic disease: A systematic review. Int. J. Rheumatol. 2016, 111 (2016).

Google Scholar

Zhang, H. et al. Ambient air pollution exposure and gestational diabetes mellitus in Guangzhou, China: A prospective cohort study. Sci. Total Environ. 699, 134390. https://doi.org/10.1016/j.scitotenv.2019.134390 (2020).

ADS CAS PubMed Google Scholar

Rovira, J., Domingo, J. L. & Schuhmacher, M. Air quality, health impacts and burden of disease due to air pollution (PM10, PM2.5, NO2 and O3): Application of AirQ+ model to the Camp de Tarragona County Catalonia. Spain. Sci. Total Environ. 703, 135538. https://doi.org/10.1016/j.scitotenv.2019.135538 (2020).

ADS CAS PubMed Google Scholar

Mullen, C., Grineski, S. E., Collins, T. W. & Mendoza, D. L. Effects of PM2.5 on third grade students proficiency in math and english language arts. Int. J. Environ. Res. Public Health. 17, 6931. https://doi.org/10.3390/ijerph17186931 (2020).

PubMed Central Google Scholar

Delgado-Saborit, J. M. et al. A critical review of the epidemiological evidence of effects of air pollution on dementia, cognitive function and cognitive decline in adult population. Sci. Total Environ. 757, 143734 (2021).

ADS CAS PubMed Google Scholar

Peters, R. et al. Air pollution and dementia: A systematic review. J. Alzheimers Dis. 70, S145S163 (2019).

CAS PubMed PubMed Central Google Scholar

Shi, L. et al. A national cohort study (20002018) of long-term air pollution exposure and incident dementia in older adults in the United States. Nat. Commun. 12, 19 (2021).

ADS Google Scholar

Weuve, J. et al. Exposure to air pollution in relation to risk of dementia and related outcomes: An updated systematic review of the epidemiological literature. Environ. Health Perspect. 129, 096001 (2021).

CAS PubMed Central Google Scholar

Chen, J.-H. et al. Long-term exposure to air pollutants and cognitive function in taiwanese community-dwelling older adults: A four-year cohort study. J. Alzheimers Dis. 8, 115 (2020).

Google Scholar

Gao, Q. et al. Long-term ozone exposure and cognitive impairment among Chinese older adults: A cohort study. Environ. Int. 160, 107072 (2022).

CAS PubMed Google Scholar

He, F. et al. Impact of air pollution exposure on the risk of Alzheimers disease in China: A community-based cohort study. Environ. Res. 205, 112318 (2022).

CAS PubMed Google Scholar

Ran, J. et al. Long-term exposure to fine particulate matter and dementia incidence: A cohort study in Hong Kong. Environ. Pollut. 271, 116303 (2021).

CAS PubMed Google Scholar

Garcia, C. A., Yap, P.-S., Park, H.-Y. & Weller, B. L. Association of long-term PM2.5 exposure with mortality using different air pollution exposure models: Impacts in rural and urban California. Int J. Environ. Health Res. 26, 145157. https://doi.org/10.1080/09603123.2015.1061113 (2016).

CAS PubMed Google Scholar

Wang, B. et al. The impact of long-term PM2. 5 exposure on specific causes of death: exposure-response curves and effect modification among 53 million US Medicare beneficiaries. Environ. Health 19, 112 (2020).

CAS PubMed PubMed Central Google Scholar

Yu, W., Guo, Y., Shi, L. & Li, S. The association between long-term exposure to low-level PM2.5 and mortality in the state of Queensland, Australia: A modelling study with the difference-in-differences approach. PLOS Med. 17, e1003141. https://doi.org/10.1371/journal.pmed.1003141 (2020).

CAS PubMed PubMed Central Google Scholar

Bellinger, C., Jabbar, M. S. M., Zaane, O. & Osornio-Vargas, A. A systematic review of data mining and machine learning for air pollution epidemiology. BMC Public Health 17, 119 (2017).

Google Scholar

Belotti, J. T. et al. Air pollution epidemiology: A simplified Generalized Linear Model approach optimized by bio-inspired metaheuristics. Environ. Res. 191, 110106. https://doi.org/10.1016/j.envres.2020.110106 (2020).

CAS PubMed Google Scholar

Stingone, J. A., Pandey, O. P., Claudio, L. & Pandey, G. Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children. Environ. Pollut. 230, 730740. https://doi.org/10.1016/j.envpol.2017.07.023 (2017).

CAS PubMed PubMed Central Google Scholar

Chang, F.-J., Chang, L.-C., Kang, C.-C., Wang, Y.-S. & Huang, A. Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques. Sci. Total Environ. 736, 139656. https://doi.org/10.1016/j.scitotenv.2020.139656 (2020).

ADS CAS PubMed Google Scholar

Silibello, C. et al. Spatial-temporal prediction of ambient nitrogen dioxide and ozone levels over Italy using a random forest model for population exposure assessment. Air Qual. Atmos. Health 14, 817829. https://doi.org/10.1007/s11869-021-00981-4 (2021).

CAS Google Scholar

Fecho, K. et al. A novel approach for exposing and sharing clinical data: The translator integrated clinical and environmental exposures service. J. Am. Med. Inform. Assoc. 26, 10641073. https://doi.org/10.1093/jamia/ocz042 (2019).

PubMed PubMed Central Google Scholar

Chang, V., Ni, P. & Li, Y. K-clustering methods for investigating social-environmental and natural-environmental features based on air quality index. IT Prof. 22, 2834. https://doi.org/10.1109/MITP.2020.2993851 (2020).

Google Scholar

Wu, X., Cheng, C., Zurita-Milla, R. & Song, C. An overview of clustering methods for geo-referenced time series: From one-way clustering to co- and tri-clustering. Int. J. Geogr. Inf. Sci. 34, 18221848. https://doi.org/10.1080/13658816.2020.1726922 (2020).

Google Scholar

Karri, R., Chen, Y.-P.P. & Drummond, K. J. Using machine learning to predict health-related quality of life outcomes in patients with low grade glioma, meningioma, and acoustic neuroma. PLoS ONE 17, e0267931. https://doi.org/10.1371/journal.pone.0267931 (2022).

CAS PubMed PubMed Central Google Scholar

Hautamki, M. et al. The association between charlson comorbidity index and mortality in acute coronary syndromethe MADDEC study. Scand. Cardiovasc. J. 54, 146152. https://doi.org/10.1080/14017431.2019.1693615 (2020).

PubMed Google Scholar

Kantidakis, G. et al. Survival prediction models since liver transplantationcomparisons between Cox models and machine learning techniques. BMC Med. Res. Methodol. 20, 277. https://doi.org/10.1186/s12874-020-01153-1 (2020).

PubMed PubMed Central Google Scholar

Blom, M. C. et al. Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: A retrospective, population-based registry study. BMJ Open 9, e028015. https://doi.org/10.1136/bmjopen-2018-028015 (2019).

PubMed PubMed Central Google Scholar

Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M. & Qureshi, N. Can machine-learning improve cardiovascular risk prediction using routine clinical data?. PLoS ONE 12, e0174944. https://doi.org/10.1371/journal.pone.0174944 (2017).

CAS PubMed PubMed Central Google Scholar

Weng, S. F., Vaz, L., Qureshi, N. & Kai, J. Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches. PLoS ONE 14, e0214365. https://doi.org/10.1371/journal.pone.0214365 (2019).

CAS PubMed PubMed Central Google Scholar

Chun, M. et al. Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults. J. Am. Med. Inf. Assoc. 28, 17191727. https://doi.org/10.1093/jamia/ocab068 (2021).

Google Scholar

Moncada-Torres, A., van Maaren, M. C., Hendriks, M. P., Siesling, S. & Geleijnse, G. Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci. Rep. 11, 6968. https://doi.org/10.1038/s41598-021-86327-7 (2021).

ADS CAS PubMed PubMed Central Google Scholar

Du, M., Haag, D. G., Lynch, J. W. & Mittinty, M. N. Comparison of the tree-based machine learning algorithms to cox regression in predicting the survival of oral and pharyngeal cancers: Analyses based on SEER database. Cancers 12, 2802. https://doi.org/10.3390/cancers12102802 (2020).

CAS PubMed Central Google Scholar

Kim, H., Park, T., Jang, J. & Lee, S. Comparison of survival prediction models for pancreatic cancer: Cox model versus machine learning models. Genomics Inform. 20, e23. https://doi.org/10.5808/gi.22036 (2022).

PubMed PubMed Central Google Scholar

Kattan Michael, W. Comparison of Cox Regression with other methods for determining prediction models and nomograms. J. Urol. 170, S6S10. https://doi.org/10.1097/01.ju.0000094764.56269.2d (2003).

CAS PubMed Google Scholar

Lin, J., Li, K. & Luo, S. Functional survival forests for multivariate longitudinal outcomes: Dynamic prediction of Alzheimers disease progression. Stat. Methods Med. Res. 30, 99111 (2021).

MathSciNet PubMed Google Scholar

Facal, D. et al. Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia. Int. J. Geriatr. Psychiatry 34, 941949 (2019).

PubMed Google Scholar

Spooner, A. et al. A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction. Sci. Rep. 10, 110 (2020).

MathSciNet Google Scholar

Wang, J. et al. Random forest model in the diagnosis of dementia patients with normal mini-mental state examination scores. J. Personal. Med. 12, 37. https://doi.org/10.3390/jpm12010037 (2022).

Google Scholar

Pinheiro, L. I. C. C. et al. Application of data mining algorithms for dementia in people with HIV/AIDS. Comput. Math. Methods Med. 2021, 4602465. https://doi.org/10.1155/2021/4602465 (2021).

PubMed PubMed Central Google Scholar

Brickell, E., Whitford, A., Boettcher, A., Pereira, C. & Sawyer, R. J. A-1 the influence of base rate and sample size on performance of a random forest classifier for dementia prediction: Implications for recruitment. Arch. Clin. Neuropsychol. 36, 10401040. https://doi.org/10.1093/arclin/acab062.19 (2021).

Google Scholar

Dauwan, M. et al. Random forest to differentiate dementia with Lewy bodies from Alzheimers disease. Alzheimers Dement. Diagn. Assess. Dis. Monit. 4, 99106. https://doi.org/10.1016/j.dadm.2016.07.003 (2016).

Google Scholar

Mar, J. et al. Validation of random forest machine learning models to predict dementia-related neuropsychiatric symptoms in real-world data. J. Alzheimers Dis. 77, 855864. https://doi.org/10.3233/JAD-200345 (2020).

PubMed PubMed Central Google Scholar

World Medical Association. World medical association declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA 310, 21912194. https://doi.org/10.1001/jama.2013.281053 (2013).

CAS Google Scholar

Taiwan Environmental Protection Administration (EPA) website, <https://airtw.epa.gov.tw/CHT/Query/His_Data.aspx>

Yu, H.-L. et al. Interactive spatiotemporal modelling of health systems: The SEKSGUI framework. Stoch. Env. Res. Risk Assess. 21, 555572. https://doi.org/10.1007/s00477-007-0135-0 (2007).

MathSciNet Google Scholar

Charlson, M. E., Pompei, P., Ales, K. L. & MacKenzie, C. R. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J. Chronic Dis. 40, 373383. https://doi.org/10.1016/0021-9681(87)90171-8 (1987).

CAS PubMed Google Scholar

Hude, Q. et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med. Care 43, 11301139 (2005).

Google Scholar

Heagerty, P. J. & Saha, P. SurvivalROC: Time-dependent ROC curve estimation from censored survival data. Biometrics 56, 337344 (2000).

CAS PubMed Google Scholar

Harrell Jr, F. E., Harrell Jr, M. F. E. & Hmisc, D. Package rms. Vanderbilt University, 229 (2017).

Harrell, F. E. Jr., Califf, R. M., Pryor, D. B., Lee, K. L. & Rosati, R. A. Evaluating the yield of medical tests. JAMA 247, 25432546. https://doi.org/10.1001/jama.1982.03320430047030 (1982).

Original post:
Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning...

Read More..