Monday, Aug 10, 2020 | Last Update : 05:28 AM IST

139th Day Of Lockdown

Maharashtra51533235171017757 Tamil Nadu2969012386384927 Andhra Pradesh2278601387122036 Karnataka178087939083198 Delhi1454271305874111 Uttar Pradesh122609726502069 West Bengal95554671202059 Bihar7972051315429 Telangana7949555999627 Gujarat71064542382652 Assam5883842326145 Rajasthan5249738235789 Odisha4592731785321 Haryana4163534781483 Madhya Pradesh3902529020996 Kerala3433121832109 Jammu and Kashmir2489717003472 Punjab2390315319586 Jharkhand185168998177 Chhatisgarh12148880996 Uttarakhand96326134125 Goa871259575 Tripura6161417641 Puducherry5382320187 Manipur3752204411 Himachal Pradesh3371218114 Nagaland27819048 Arunachal Pradesh215514823 Chandigarh151590425 Meghalaya10624906 Sikkim8664971 Mizoram6082980
  Life   Health  16 Dec 2019  Risk adjustment model predicts how often elderly people seek treatment in hospital

Risk adjustment model predicts how often elderly people seek treatment in hospital

ANI
Published : Dec 16, 2019, 11:16 am IST
Updated : Dec 16, 2019, 11:16 am IST

Researchers develop risk adjustment model for elderly.

The research results were published in the scientific publication series of Proceedings of Machine Learning Research. (Photo: ANI)
 The research results were published in the scientific publication series of Proceedings of Machine Learning Research. (Photo: ANI)

Washington: Researchers have developed a so-called risk adjustment model to predict how often elderly people seek treatment in a healthcare centre or hospital. The results suggest that the new model is more accurate than traditional regression models commonly used for this task, and can reliably predict how the situation changes over the years.

Risk-adjustment models make use of data from previous years and are used to allocate healthcare funds in a fair and effective way. These models are already used in countries like Germany, the Netherlands, and the US. However, this is the first proof-of-concept that deep neural networks have the potential to significantly improve the accuracy of such models.

 

According to Pekka Marttinen, Assistant Professor at Aalto University and FCAI, "Without a risk adjustment model, healthcare providers whose patients are ill more often than average people would be treated unfairly." Elderly people are a good example of such a patient group. The goal of the model is to take these differences between patient groups into account when making funding decisions.

According to Yogesh Kumar, the main author of the research article and a doctoral candidate at Aalto University and FCAI, the results show that deep learning may help design more accurate and reliable risk adjustment models. 'Having an accurate model has the potential to save several millions of dollars,' Kumar points out.

 

The results show that training a deep model does not necessarily require an enormous dataset in order to produce reliable results. Instead, the new model worked better than simpler, count-based models even when it made use of only one-tenth of all available data. In other words, it provides accurate predictions even with a relatively small dataset, which is a remarkable finding, as acquiring large amounts of medical data is always difficult.

"Our goal is not to put the model developed in this research into practice as such but to integrate features of deep learning models to existing models, combining the best sides of both. In the future, the goal is to make use of these models to support decision-making and allocate funds in a more reasonable way," explains Marttinen.

 

Tags: old age, alzheimers, depression