Health insurance and managed care
7. Digital health coach for physical activities
Physical inactivity increases the risk significantly for diabetes, cardiovascular diseases, cancer, hypertension, obesity, and mental disorders. On the other hand, regular physical activities of more than one hour a week can prevent these diseases and halve the mortality rates.
This saves a lot of costs for health insurers but especially for society as a whole. So, there are a lot of efforts to motivate people for regular physical activities.
On the other side, there is the trend of all the wearables. The benefit of wearables is that they measure health-related factors continuously and enable data-driven coaching support.
An important aspect is that the recommendation is individualized. For example, a physically inactive person for several years needs other recommendations and motivations than an athlete who already has a high level of activities but would maybe need some coaching in sleep or nutrition management.
Machine learning is an essential tool to provide individualized coaching and incentive system, ongoing and in real-time, such that recommendations are given based on the daily performance of activities.
First, the people must be classified into groups of responsiveness. E.g., people who would like to start with activities but need an extrinsic nudge, people who are already doing little activities but need to be motivated to do more, etc. The standard classification algorithms are applied.
Further, based on the individual progress, further recommendations must be given. Forecasting of physical activities is done with logistic regressions, AdaBoost, decision trees, Random Forest, Support Vector Machine, and neural networks. Especially for recommendations of behavioral changes, recurrent neural networks methods like long short-term memory (LSTM) are used.
8. Quality improvement in pediatric care
The quality of health care services and the possibility to treat complex diseases is enhancing continuously. Nevertheless, many challenges remain, especially, dosage and duration of therapies based on individual characteristics or for patient groups where not many clinical studies are available like children.
The dosage of drugs and therapies for small children remains challenging. A too high dose can lead to permanent injuries, while a too low one delay recovery and can cause secondary diseases. Both cases can lead to death. Also, children’s immune system is not fully trained, and reactions to therapies can differ from reactions of adults. Further, children can often not verbally express the state of illness such that an adverse development is detected too late.
So, over the last years, machine learning has been incorporated into pediatric care with great success to predict the right and individualized treatments for children. It is still in its infancy and bears a lot of development potential.
One of the main obstacles to fully leveraging its potential is the missing or insufficient data available for methods that can detect and predict complex patterns. So, currently, simpler methods are used.
First, cluster algorithms like k-means are applied to determine different cohorts. Then, the characteristics of the different cohorts, like the length of treatment, mortality rate, etc., are analyzed. As we are working in critical health fields, the characteristics of the cohorts are tested against each other with, e.g., Chi-squared test (categorical variables) and, e.g., Wilcoxon-Mann-Whitney test (continuous variables).
After, various classification algorithms are applied, whereas Random Forest with cross-validation is the most used approach.
There are already a few applications in pediatric care with genomic data where deep learning methods are used. But this is only at the beginning.