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Posted: April 4th, 2022

Implementation of Machine Learning Technology in Mobile App for Patient Tracking and Monitoring

Implementation of Machine Learning Technology in Mobile App for Patient Tracking and Monitoring

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data and make predictions without being explicitly programmed. ML has various applications in different domains, such as healthcare, education, finance, and entertainment. One of the emerging areas where ML can have a significant impact is mobile health (mHealth), which refers to the use of mobile devices and wireless technologies to deliver health services and information.

One of the challenges that mHealth faces is how to track and monitor patients effectively and efficiently, especially those with chronic conditions or who need continuous care. Traditional methods of patient tracking and monitoring, such as phone calls, visits, or surveys, are often costly, time-consuming, and prone to errors. Moreover, they may not capture the full picture of the patient’s health status, behavior, and preferences.

This is where ML can play a vital role. By applying ML techniques to the data collected from mobile devices, such as sensors, cameras, GPS, or user inputs, mHealth apps can provide personalized and timely feedback, recommendations, and interventions to patients and their caregivers. ML can also help analyze large-scale data from multiple sources and identify patterns, trends, and anomalies that can inform clinical decisions and improve health outcomes.

In this blog post, we will discuss some of the benefits and challenges of implementing ML technology in mobile apps for patient tracking and monitoring. We will also provide some examples of existing mHealth apps that use ML to enhance their functionality and performance.

Benefits of Implementing ML Technology in Mobile Apps for Patient Tracking and Monitoring

ML technology can offer several advantages for mHealth apps that aim to track and monitor patients, such as:

– Improved accuracy and reliability: ML algorithms can learn from data and improve their predictions over time, reducing errors and uncertainties. For example, ML can help detect abnormal vital signs, such as heart rate or blood pressure, and alert the patient or the caregiver accordingly.
– Enhanced personalization and adaptation: ML algorithms can tailor their outputs to the specific needs and preferences of each patient, taking into account their medical history, lifestyle, goals, and feedback. For example, ML can help customize the frequency and content of reminders, notifications, or messages that the app sends to the patient or the caregiver.
– Increased engagement and motivation: ML algorithms can provide interactive and gamified features that can make the app more appealing and enjoyable for the patient or the caregiver. For example, ML can help create challenges, rewards, or social comparisons that can encourage the patient or the caregiver to adhere to the prescribed treatment or behavior change plan.
– Enhanced scalability and efficiency: ML algorithms can process large amounts of data from multiple sources and generate insights that can be useful for both individual and population-level interventions. For example, ML can help identify risk factors, trends, or gaps in care that can inform policy making or resource allocation.

Challenges of Implementing ML Technology in Mobile Apps for Patient Tracking and Monitoring

Despite its potential benefits, implementing ML technology in mHealth apps also poses some challenges that need to be addressed carefully, such as:

– Data quality and availability: ML algorithms depend on the quality and quantity of data that they receive from the mobile devices or other sources. However, data collection may be affected by various factors, such as user consent, privacy, security, connectivity, battery life, or device compatibility. Therefore, mHealth apps need to ensure that they collect relevant, reliable, and representative data that can support their ML functions.
– Ethical and legal issues: ML algorithms may raise some ethical and legal concerns regarding their transparency, accountability, fairness, and safety. For example, how can users know how the app makes decisions or recommendations based on their data? How can users control or correct their data or opt out of certain features? How can users be protected from potential harms or biases that may arise from the app’s actions or outputs? Therefore, mHealth apps need to adhere to ethical principles and legal regulations that govern their use of ML technology.
– User acceptance and trust: ML algorithms may face some resistance or skepticism from users who may not understand or appreciate their value or functionality. For example, users may perceive the app as intrusive, impersonal, or unreliable. Users may also have different expectations or preferences regarding the app’s interaction style or feedback mode. Therefore, mHealth apps need to design user-friendly interfaces and provide clear explanations and instructions that can enhance user acceptance and trust.

Examples of Mobile Apps that Use ML Technology for Patient Tracking and Monitoring

There are many examples of mHealth apps that use ML technology to track and monitor patients in various domains. Here are some of them:

– Ada: Ada is a personal health companion app that uses natural language processing (NLP) and clinical reasoning to help users understand their symptoms and find possible causes. The app also provides personalized advice on what to do next based on the user’s condition and location.
– Ginger: Ginger is a mental health app that uses natural language understanding (NLU) and sentiment analysis to assess the user’s mood and provide emotional support. The app also connects the user with licensed therapists or coaches who can offer counseling or coaching sessions via text, video, or audio.
– Livongo: Livongo is a chronic care management app that uses machine learning and behavioral science to help users manage their diabetes, hypertension, or weight. The app collects data from connected devices, such as glucose meters or blood pressure monitors, and provides real-time feedback, tips, and coaching to the user. The app also shares the data with the user’s care team who can provide further guidance or intervention.
– SkinVision: SkinVision is a skin cancer detection app that uses computer vision and deep learning to analyze the user’s skin lesions and provide a risk assessment. The app also provides recommendations on whether to visit a doctor or monitor the lesion over time. The app also creates a personal archive of the user’s skin images and tracks any changes over time.


Machine learning technology can offer many benefits for mobile apps that aim to track and monitor patients, such as improved accuracy, personalization, engagement, and scalability. However, implementing ML technology also poses some challenges, such as data quality, ethical issues, user acceptance, and trust. Therefore, mHealth apps need to address these challenges carefully and ensure that they use ML technology in a responsible and effective way.


– Alshurafa N., Eastwood J.A., Pourhomayoun M., Liu J.J. (2017) Designing and Evaluating mHealth Interventions for Vulnerable Populations: A Systematic Review. In: Bamidis P., Ziefle M., Maciaszek L., Maciaszek L. (eds) eHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-319-49655-9_1
– Davenport T.H., Kalakota R. (2019) The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/fhj.2019-0004
– Kvedar J., Coye M.J., Everett W. (2014) Connected health: a review of technologies and strategies write my essay to improve patient care with telemedicine and telehealth. Health Affairs, 33(2), 194–199. https://doi.org/10.1377/hlthaff.2013.0992
– Shatte A.B.R., Hutchinson D.M., Teague S.J. (2019) Machine learning in mental health: a scoping review of methods and applications. Psychological Medicine, 49(9), 1426–1448. https://doi.org/10.1017/S0033291719000151


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