Simulation Case Study of Future Proactive Medical Scenarios for Early-Stage Cataract Patients

—Full-Chain Health Management Based on the DIKWP Artificial Consciousness Ecosystem

Yucong Duan

International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com)

The following is a detailed simulation case design for future active medicine scenarios for patients with early cataract based on the DIKWP artificial awareness operating system, chips, semantic communication and semantic-driven programming. This case strives to comprehensively cover: personal health history, family daily life, intelligent data link, personality goal generation, intelligent medical treatment and multidisciplinary collaboration, whole-process tracking, knowledge self-evolution, security and trust, social ecology, expanding future scenarios, in-depth dialogue and human-computer interaction. 

1. Character setting and background foreshadowing

Ms. Li, 62 years old, is a retired teacher who is accustomed to self-discipline, loves reading and remote video communication with her children. She and her husband live in a digital home with a smart home and active health management terminals. Physical examination indicators have been stable for the past five years, with the only change being a slight decrease in visual acuity – especially blurred vision at night and reduced reading endurance. In my family, my mother was diagnosed with cataracts at the age of 70, and she usually pays attention to diet and exercise.

Ms Lee has a cheerful personality and is a positive embracer of digital technology:

·Use the smart health mirror, get used to getting up in the morning and checking your health data in front of the mirror at night;

·Wear a multi-modal smart health bracelet (to monitor heart rate, blood pressure, blood sugar, step count, etc.) and smart eye health sensors (to detect pupil dynamics, light response, blink frequency, reading distance, light sensitivity, etc.).

Her family and community doctors supported her approach to proactive health management.           
The community has a smart health station and a holographic medical cloud platform, and neighbors and peer groups have a certain awareness and positive feedback on active medicine. 

2. Holographic perception of family health ecology

2.1 Multi-source health data link

Every morning, Ms. Li washes in front of the health mirror. The health mirror automatically recognizes her iris, facial expressions, and pupil size, and calls the built-in DIKWP chip to collect multimodal data such as vision, psychology, expression, and skin. The bracelet synchronously records last night's sleep quality, the number of night turnovers, early morning heart rate and blood pressure, and the eye sensor detects early morning light sensitivity and ciliary muscle response.

After breakfast, she used her voice to talk to the AI health manager: "I struggled to read last night, and the lights don't seem to be bright enough at night. "The AI automatically converts its voice into multi-level information labels (eye fatigue, night lighting discomfort) and incorporates it into a health log.

2.2 Intelligent scene linkage

·The kitchen light is linked with the health mirror, and the system automatically adjusts the indoor illumination and spectrum according to the morning inspection results (such as detecting the decrease in light sensitivity, automatically brightening the warm color lamp);

·Health Calendar: ACOS automatically generates "Today's Health Advice": appropriately extend outdoor light time and reduce screen reading time; 

·Bedtime habit tracking: The health mirror at night automatically detects the distance between the eyes and the reading time, and sends a flexible reminder that "it is recommended to close your eyes for 5 minutes every 30 minutes". 

3. Active collection of multimodal data and multi-level semantic processing

3.1 Collection Mechanism

·Physiological layer: blood pressure, heart rate, body temperature, intraocular pressure, tear secretion, fundus image

·Behavioral layer: average daily reading time, blue light exposure on mobile phone screens, travel time, walking distance, blink frequency, light adaptation response

·Psychological level: expression recognition, anxiety/depression emotional tendencies, frequency of social interaction

·Environmental layer: living environment light, temperature and humidity, PM2.5, noise

3.2 Chip-side preprocessing and semantic extraction

·The DIKWP chip locally encrypts and features the raw data to generate a structured information stream (e.g., labels such as decreased eye sensitivity, excessive eye load, poor night rest, etc.)

·The data is aggregated in the local health mirror, and the ACOS platform is compared with historical health records and peer group data in the cloud to achieve multi-dimensional semantic integration

3.3 Multi-level inference of intelligent semantic tags

·First-order labels: "decreased vision for reading at night", "dry eyes in the afternoon", "prolonged reading time at night".

·Second-order labels: "high risk of visual fatigue" and "initial downward trend in photosensitivity".

·Third-order labels: "increased likelihood of early cataracts" and "increased risk of nighttime life (fall/driving hazard)".

4. Personalized health profiles and risk knowledge networks

4.1 Dynamic modeling of holographic health portraits

·The system summarizes the changes in Ms. Li's health status weekly/monthly, and draws a multi-dimensional health radar chart (including visual, metabolic, cardiovascular, behavioral, psychological and other sub-dimensions)

·The portrait automatically adapts to the rhythm of personal life, health goals and family risks, forming a three-dimensional knowledge mapping of "healthy individuals-families-groups".

4.2 Real-time evolution of risk knowledge networks

·The health portrait is linked with the global cataract medical knowledge base and domestic epidemic trends in real time, and the DIKWP knowledge layer automatically extracts the latest high-risk signals (such as the recent increase in the incidence of cataract in the local elderly group)

·The system automatically analyzed Ms. Li's multiple risk nodes such as "local high incidence - family history - eye behavior - age", and deduced that her probability of cataract in the next 1-2 years was higher than that of her peers

·The health knowledge network not only shows medical risks, but also reflects subjective dimensions such as "life satisfaction" and "social participation", reminding doctors and families to pay attention to whole-person health

5. Intelligent risk reasoning and active early warning mechanism

5.1 Chip-side active reasoning and early warning

·The DIKWP chip automatically analyzes all health data streams across layers, and if multiple visual fatigue, decreased photosensitivity, and family medical history overlap, the chip's local inference engine generates a "high risk in the early stage of cataract" signal

·After local reasoning, the risk information will be informed to Ms. Li through the ACOS Health Mirror/APP in the form of gentle prompts (for example, the pop-up window of the Health Mirror "Recently detected abnormal vision and light sensitivity, it is recommended to make an appointment for professional screening")

5.2 Personalized expression and active incentive of risk warning

·The early warning information adopts "scenario-based expression" - for example, before Ms. Li prepares to drive at night, the health mirror actively reminds her to "pay attention to night driving safety tonight, and if there is blur or glare, it is recommended to reduce night travel"

·The system combines the content of early warning and health incentives to generate "active action tasks" points (such as adjusting lighting as recommended, making appointments for screening, and insisting on eye exercises, etc., can obtain health points and redeem them for health services)

6. Semantic goal generation and dynamic health task system

6.1 Automatic Target Generation Mechanism

·Based on the DIKWP model, ACOS automatically interprets Ms. Li's health data, life goals, and family needs, and generates multi-dimensional active health goals

·The goals are hierarchical and phased, such as short-term (complete eye screening), medium-term (adjust eye behavior), long-term (delay the progression of cataracts, improve self-care ability)

6.2 Dynamic task decomposition and personalized planning

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