Frontal Lobe Function and Risk of Hip Fracture in Patient With Alzheimer Disease: An Analysis of Linked Data

Gender differences in the effect of depression and cognitive impairment on risk of falls among community-dwelling older adults

BACKGROUND

The impact of the interrelationship between depression and cognitive impairment on risk of falls remains unclear. In addition, gender differences should be considered to further understand the relationships between depression, cognitive impairment, and risk of falls.

METHODS

Older adults who completed the Living Profiles of Older People Survey in Korea were included. In total, 14,055 participants completed the baseline survey, and 7,150 participants completed a follow-up survey after 3 years. We classified participants into 4 groups based on depression and cognitive impairment.

RESULTS

Using generalized estimating equation models, older adults with depression but without cognitive impairment had a higher risk of falls (OR=1.49, 95% CI=1.35-1.63), compared to a reference group with neither depression nor cognitive impairment. However, older adults who had cognitive impairment but not depression had a similar risk of falls (OR=1.03, 95% CI=0.91-1.16) to the reference group. Finally, older adults who had both depression and cognitive impairment had the highest risk of falls (OR=1.95, 95% CI=1.73-2.20) compared to the reference group. In addition, depression seemed to have a bigger effect on the risk of falls in men compared to women (p for interaction < 0.001).

LIMITATIONS

These results should be interpreted with caution, considering the use of screening tool for defining depression or cognitive impairment, and lack of information on psychotropic medication use.

CONCLUSIONS

Depression alone or depression combined with cognitive impairment was associated with higher risk of falls in older adults. In addition, gender differences in the risk of falls was noted.

Quad-phased data mining modeling for dementia diagnosis

BACKGROUND

The number of people with dementia is increasing along with people's ageing trend worldwide. Therefore, there are various researches to improve a dementia diagnosis process in the field of computer-aided diagnosis (CAD) technology. The most significant issue is that the evaluation processes by physician which is based on medical information for patients and questionnaire from their guardians are time consuming, subjective and prone to error. This problem can be solved by an overall data mining modeling, which subsidizes an intuitive decision of clinicians.

METHODS

Therefore, in this paper we propose a quad-phased data mining modeling consisting of 4 modules. In Proposer Module, significant diagnostic criteria are selected that are effective for diagnostics. Then in Predictor Module, a model is constructed to predict and diagnose dementia based on a machine learning algorism. To help clinical physicians understand results of the predictive model better, in Descriptor Module, we interpret causes of diagnostics by profiling patient groups. Lastly, in Visualization Module, we provide visualization to effectively explore characteristics of patient groups.

RESULTS

The proposed model is applied for CREDOS study which contains clinical data collected from 37 university-affiliated hospitals in republic of Korea from year 2005 to 2013.

CONCLUSIONS

This research is an intelligent system enabling intuitive collaboration between CAD system and physicians. And also, improved evaluation process is able to effectively reduce time and cost consuming for clinicians and patients.