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Ambee's pollen app uses machine learning-based AI algorithms to track pollen. To ensure accuracy, it combines pollen data from several sources with on-ground sensors, satellite imaging, and statistical inference. We are Asia's only pollen-tracking API to give real-time pollen data sets across the world. The pollen dataset offers pollen counts and danger levels for many categories. It provides overall risk alerts for over 90 different pollen species. The dataset provides information on different types of pollen, grass, weeds, and trees.
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How Advanced Can Pollen App Forecasts Maintain Quality in an eHealth service?
Introduction Allergies to pollen are a major global health issue. They have a sizable impact on a sizeable portion of the population in many nations. Pollen forecast have proven to be a very useful tool for managing and treating allergies to pollen. People consume pollen information more frequently during the season, indicating that such services are necessary. The public is now informed about pollen forecasts via mobile technology (phones, tablets, and other wireless devices), so mobile health (mHealth) is becoming more significant. To prevent unexpected outcomes, norms and an ethical framework still need to be developed for eHealth. Additionally, the World Health Organization conducted a global survey on eHealth, which revealed a rise in eHealth and mHealth activity in nations with higher incomes.
mHealth The treatment of asthma and allergy disorders may be improved through web applications (apps), wearables, and other personal monitoring tools. The capacity to longitudinally gather data on symptoms & inhaler usage is one of the benefits of asthma applications that have been suggested since it enables the identification of shifts over time to assist patients and caregivers in determining if the symptoms are getting worse. It is possible to incorporate information from outside sources, such as weather, allergy load, and air quality reports. The allergy clinicians can receive the data gathered by the apps either in-person or through telemedicine portals.
Digital health records Large amounts of actual data are produced due to the widespread adoption of electronic health records in the healthcare industry. The artificial intelligence (AI) method called natural language processing uses clinical narratives from electronic health records to extract information. Electronic health records can enable temporal condition pattern mining, which may reveal undetected relationships between illnesses and guide future causality studies. The use of these technologies in allergy treatment is still in its early phases of development. Studies have used electronic health data to estimate the frequency of drug or food allergies in various populations. Analyzing this information, a natural language recognition algorithm has been proven to identify asthma. AI may someday be used to identify allergy patients and treat them before their conditions worsen. This may be especially pertinent to patients who missed consultations or had to stop receiving therapy because of the COVID-19 outbreak.
Recommendations And Potential Future Routes To support people with pollen forecasts allergies, mobile apps that deal with allergen avoidance must meet several requirements and functionalities, including easy-to-understand pollen forecasts, at least a few forecasted aeroallergens, herbal information, symptom diaries, allergic rhinitis risk questionnaires, and an imprint from the app's publisher identifying the institution that is, ideally, free from conflicts of interest. It is to be noted that pollen information applications frequently contain advertisements, particularly if the publisher is a business and not a company involved directly in pollen forecasting. The applications "Biowetter," "Pollenflug," and "Hayfever" all contain direct advertisements. Information that is vital to know is represented by the app’s publisher. In terms of incidence in the imprint, a forecasting institution appears in the backdrop of the applications "Pollen," "DWD," and "Pollen News," but not in the other six apps. The findings of this study highlight the need for pollen forecasts to be improved and held to higher standards.
Ambee pollen app More than 50 million Americans have various sorts of allergies each year, making them the sixth most common chronic ailment, according to the AAFA (Asthma & allergy foundation of America). As per survey, pollen negatively impacts people's health by causing various seasonal allergies, including respiratory conditions. "The issue is that accurate results must be gathered using a complicated process. Currently, most pollen forecasts give a fairly broad estimate, and part of the issue is that there aren't enough stations for pollen counting." Ambee's pollen app uses machine learning-based AI algorithms to track pollen. To ensure accuracy, it combines pollen data from several sources with on-ground sensors, satellite imaging, and statistical inference. We are Asia's only pollen-tracking API to give real-time pollen data sets across the world. Healthcare, pharmaceutical, and weather companies use Ambee's pollen data. When marketing its anti-allergy product, Kleenex, a company that sells paper-based items, had a 200% boost in website traffic. Ambee's pollen app swiftly and easily delivers real-time data for a seamless user experience. It is simple to incorporate into any program, app, or product and is user-friendly for developers. The pollen dataset offers pollen counts and danger levels for many categories. It provides overall risk alerts for over 90 different pollen species. The dataset provides information on different types of pollen, grass, weeds, and trees.
