In today’s advanced applications, such as memory interfaces, feature-based detection, and sensory games, human-computer interaction (HCI) plays a pivotal role. A medical decision support system (MDSS) emerges from the integration of a data system with resources for medical decision-making. Within MDSS, human-computer interaction and perceptual medical decision-making stand out as two highly valuable technologies. Systems enabled by the Internet of Things (IoT), which leverage decentralized, diverse communication and networking technology to cater to a wide range of end-users, are referred to as pervasive computing. A challenging aspect of pervasive computing is ensuring transparency in interaction, managing administration levels, and accommodating varying tolerance levels for widely dispersed users. This paper presents a uniquely flexible MDSS framework designed to enhance end-user confidence in the availability of MDSS through ubiquitous IoT devices within the context of HCI. This architecture utilizes recurring training to assess resource allocation based on demand and collaborative characteristics. Projected resource requirements enable pervasive computing to better serve end-users by reducing latency and increasing communication speeds for MDSS in HCI. The primary goal of this framework is to simplify the management of terminal transitions by facilitating the allocation and utilization of resources for data transfer from peripheral technology. Experimental analysis is employed to estimate the framework’s performance, utilizing various metrics to demonstrate its consistency. These metrics encompass responsiveness, transaction success rates, processed demands, application caseloads, capacity utilization, and memory usage. The uniquely flexible and distributed computing framework optimizes request handling, network accuracy, and memory utilization, resulting in reduced transaction failures and lower latency, ultimately leading to shorter response times. The proposed UFDSS maintains a transaction failure rate below 25% with increasing requests and achieves 100 MHz bandwidth utilization, surpassing other techniques capped at 80 MHz. UFDSS exhibits a lower average latency of around 30 ms for a range of energy data inputs. This uniquely flexible MDSS framework showcases its potential to enhance MDSS availability through IoT devices within HCI contexts. By optimizing resource allocation and utilization, it successfully reduces latency, improves communication speeds, and ultimately leads to shorter response times, contributing to more efficient and reliable medical decision support. Further, integrating generative AI into MDSS for IoT-based HCI could also enhance data-driven decision support.

在当今的高级应用中，例如内存接口、基于特征的检测和感官游戏，人机交互（HCI）发挥着关键作用。医疗决策支持系统（MDSS）是数据系统与医疗决策资源集成的产物。在MDSS中，人机交互和感知医疗决策作为两项非常有价值的技术脱颖而出。物联网 (IoT) 支持的系统利用分散、多样化的通信和网络技术来满足广泛的最终用户需求，被称为普适计算。普适计算的一个具有挑战性的方面是确保交互的透明度、管理管理级别以及适应广泛分散的用户的不同容忍级别。本文提出了一种独特灵活的 MDSS 框架，旨在通过 HCI 环境中无处不在的物联网设备增强最终用户对 MDSS 可用性的信心。该架构利用重复训练来根据需求和协作特征评估资源分配。预计的资源需求使普适计算能够通过减少延迟并提高 HCI 中 MDSS 的通信速度来更好地服务最终用户。该框架的主要目标是通过促进外围技术数据传输资源的分配和利用来简化终端转换的管理。采用实验分析来估计框架的性能，并利用各种指标来证明其一致性。这些指标包括响应能力、交易成功率、处理的需求、应用程序案例负载、容量利用率和内存使用情况。独特的灵活分布式计算框架优化了请求处理、网络准确性和内存利用率，从而减少了事务失败并降低了延迟，最终缩短了响应时间。所提出的 UFDSS 随着请求的增加将事务失败率保持在 25% 以下，并实现 100 MHz 带宽利用率，超过了上限为 80 MHz 的其他技术。对于一系列能源数据输入，UFDSS 的平均延迟较低，约为 30 毫秒。这种独特灵活的 MDSS 框架展示了其通过 HCI 环境中的物联网设备增强 MDSS 可用性的潜力。通过优化资源分配和利用，它成功地减少了延迟，提高了通信速度，并最终缩短了响应时间，有助于更高效、更可靠的医疗决策支持。此外，将生成式人工智能集成到 MDSS 中以实现基于物联网的人机交互还可以增强数据驱动的决策支持。