Predicting the future has come a long way. It used to be perceived as a pseudoscience but now it’s an actual one – data science.
When it comes to disease predictions, epidemiology is the scientific discipline. It identifies the distribution of diseases, their determinants, and risk factors, and offers tools for their control and prevention. It’s not a new kid on the block. It has been around since the Age of Pericles in the 5th century B.C. Since then it has saved millions of lives through interventions and preventative measures.
However, predicting the exact incidence rates of infectious diseases is no easy job. There is a myriad of characteristics that must be considered. Such as the nature of the disease, geographical traits, people’s habits, and the risk of the disease spreading.
Luckily, this (like many other sciences) has been improving with the better accessibility of bigger and bigger data sets.
Classical Diagnosis vs. Digital Healthcare Solutions
Classical diagnosis is when a patient visits a doctor, reports their symptoms, undergoes examinations, and receives a diagnosis. This method is not only time-consuming but it tends to be inaccurate and often unreliable.
To tackle the roadblocks of classical diagnosis, more and more solutions are born. They make use of patient data and medical records to fine-tune the process of diagnostics. So, they can go beyond its optimisation to the next level: predicting diseases.
5 Digital Healthcare Solutions
How can data science predict health or death?
1. Analysing news articles to detect infectious disease outbreaks
A study published in Nature analysed media data and carried out a keyword search from articles published on the internet to track the global spread of infectious diseases. Based on various machine learning methods they built a prediction model to find patterns and predict outbreaks.
The massive study (115,279 articles published in 237 different countries) showed accurate and reasonable prediction performance by all the three machine learning models used: semi-supervised learning (SSL), support vector machine (SVM), and deep neural network (DNN)
2. Wastewater monitoring to prevent epidemics
Monitoring samplings of sewage water is a technique that has prevented outbreaks before. In 2013 a robust environmental surveillance program in Israel detected a polio epidemic before its outbreak.
Since the COVID-19 pandemic, we are witnessing increasing enthusiasm in this field. Today, more people than ever before benefit from wastewater monitoring that detects community health threats such as SARS-CoV-2. The virus that causes COVID. It’s also a more cost-effective way than individual screening. As more people are represented in the sewage water sample, the cheaper the technology gets.
Getting an early alert on the next possible outbreak could help prepare policymakers. Also, it causes to mitigate the public health consequences with adequate preventative measures.
3. Predicting cancer outcomes by Digital Healthcare Solutions
Cancer does not need to be a death sentence anymore – highlights the WHO. They claim that the world will see a 60% increase in cancer cases over the next two decades. As more people are affected, scientists are surging to find methods to establish a more accurate prognosis of outcomes across multiple cancers.
AI-based cancer imaging analysis has been successfully used for diagnostic tasks. As cancer detection and tumour classification since the early 2000s.
The advent of digital imaging methods enabled researchers to address more complex clinical needs. As predicting cancer outcomes and response to different treatments. With the help of AI in radiology, clinicians could categorise patients by disease severity and prognosis, predict treatment response and identify unfavourable treatment outcomes. In the near future, AI tools used in complex decision-making tasks could empower oncologists to provide better treatment. It will cause to more accurate prognosis for their patients.
4. Predicting diseases at the patients’ home with AI, Machin Learning, or Data Analytics
These are large-scale initiatives but there are a myriad of research projects and startups that are working with AI, machine learning or data analytics to diagnose earlier and predict outcomes faster and more accurately. For example, Current Health, a health tech company, collects a huge amount of information from patients, and biomarkers from wearable devices in order to identify who needs to see a doctor, who has a higher risk to develop a disease, and how the disease is going to progress. It all happens in the comfort of the patient’s home, saving costs and time for both patients and healthcare practitioners.
Such care-at-home programs demonstrate that AI in healthcare is getting more and more accessible not only for clinicians but also for the end-users and they can reap the benefits of early diagnosis, prognosis, and personalised care without leaving their home
5. Detecting and tackling mental health issues by Digital Healthcare Solutions
Autumn is a Toronto-based startup that applies natural language processing to social media posts, messages, and emails to determine the psychological health of a population, track the effectiveness of preventative interventions, and predict trends in mental well-being. They measure burn-out risks of employees to aid employers in preventing it.
Autumn uses AI to measure the psychological health of teams or companies, detecting early signs of increasing stress, anxiety, and burnout days or weeks before it would happen. The tool can be simply integrated into Slack or G Suite to track and analyse messages and avoid the hurdles and reporting biases of surveys. They are combining AI with psychology, psycholinguistics, and natural language processing to create a tool that empowers companies to provide preventative mental healthcare to their employees.
These predictive models do not exist in silos. To create powerful and accurate systems they need to be combined (both the algorithms and data sources) and linked with each other. Companies working towards the same goal but not sharing information may lead to wasted time and missed opportunities. Forging partnerships and reaching out to companies with similar objectives can enhance knowledge sharing and even commercial success.
Have you come across cool predictive models or digital healthcare solutions that we should write about?
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