Neural network-supported patient-adaptive fall prevention system


Patient falls due to unattended bed-exits are costly to patients, healthcare personnel and hospitals. Numerous researches based on up to three predetermined factors have been conducted for preventing falls. The present comprehensive proposal is based on four sub-systems that synthesize six factors. A parameter is assigned to each factor with a coefficient specifically determined for each individual patient and per admittance. The parameters are aggregated in equations that lead to an early warning about a probable bed-exit, or an alarm about an imminent bed-exit. The ultimate aim of our proposal is the generation of the earliest possible warning to grant the longest time for nurse intervention. Thus, the probable fall of high-risk patients can be prevented, by stopping the unattended bed-exits. The proposal is supported by a prototype multi-tier system design and the results of laboratory patient bed-exit scenarios, carried out using the design. Comparison of the obtained results with previous work shows that our proposed solution is unmatched in providing the longest time for nurse intervention (up to 15.7 ± 1.1 s), because of the comprehensive six-factor synthesis, specific to each individual patient and each admittance.

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Correspondence to Semih Utku.

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The authors Mehmet Hilal Özcanhan, Semih Utku and Mehmet Suleyman Unluturk did not at any time receive payment or services from a third party for any aspect of the submitted work (including but not limited to grants, data monitoring board, study design, manuscript preparation, statistical analysis, etc.). The authors Mehmet Hilal Özcanhan, Semih Utku and Mehmet Suleyman Unluturk do not have any financial relationships regardless of amount with any entities. The authors Mehmet Hilal Özcanhan, Semih Utku and Mehmet Suleyman Unluturk declare that they have no conflict of interest.

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Özcanhan, M.H., Utku, S. & Unluturk, M.S. Neural network-supported patient-adaptive fall prevention system. Neural Comput & Applic 32, 9369–9382 (2020).

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  • Fall prevention
  • Medical systems
  • Patient safety
  • Wearable sensors