Optimitron is an AI assistant that asks you about your symptoms and potential factors. Then she applies pharmacokinetic predictive analysis to inform you of the most important things you can do to minimize symptom severity.
A half-billion people suffer from autoimmune diseases like Crone's disease, psoriasis, and Fibromyalgia. There’s also a great deal of mounting evidence that treatment-resistant depression and other mental illnesses are strongly related to immune dysregulation. What all these diseases have in common is that they can be exacerbated or improved by hundreds of factors in daily life.
Probably the most significant is the 3 pounds of thousands of chemicals that we consume every day through our diets. Since the thousands of chemicals in our food have been GRAS or “Generally Recognized as Safe” by the FDA, there’s no incentive to do research.
We've built a connector framework that imports data on diet, physical activity, sleep, social interaction, environmental factors, symptom severity, vital signs, and others.
Then we apply pharmacokinetic predictive analysis that accounts for onset delays and durations of action. This enables her to obtain personalized effectiveness values (similar to recommended daily values) for treatments and reveal potential root causes of chronic conditions.
The accuracy of the results obtained from quasi-experimental techniques and observational data is highly dependent on the quantity and quality of the data. To maximize the amount of available data, we're currently creating a decentralized autonomous organization called CureDAO. Its mission is to create an open-source platform for crowd-sourced clinical research. It incentivizes collaboration and data sharing by competing entities by issuing non-fungible tokens to any contributor of intellectual property or data.
Contributors of data and intellectual property will receive ongoing royalties for their contributions linked to their non-fungible tokens.
Opening an app to record your symptoms, diet, or treatment sucks. Our dream is to turn this web-based robot into a physical reality. Longevitron is a robot that can follow you around and reminds you to take your medication, what and when to eat, track your symptoms, and even contribute your data to clinical trials.
We're looking at various implementation options here, but Loona below is an example of one such robot that could include such functionality. Loona has visual and auditory emotion recognition and speech recognition that allows you to converse with AI large language models such as ChatGPT.
Longevitron collects data on various factors that can influence your health, from your diet and exercise habits to your sleep patterns and stress levels. It also asks for patient-reported outcomes, such as how you're feeling and any symptoms you're experiencing.
This data is fed into a predictive control model system, a concept borrowed from behavioral medicine and control systems engineering. This system uses the data to continually refine its suggestions, helping you optimize your health and well-being.
Adaptive intervention is a strategy used in behavioral medicine to create individually tailored strategies for the prevention and treatment of chronic disorders. It involves intensive measurement and frequent decision-making over time, allowing the intervention to adapt to the individual's needs.
Predictive control models are a control system that uses data to predict future outcomes and adjust actions accordingly. In the context of Longevitron, this means using the data it collects to predict your future health outcomes and adjust its suggestions to optimize your health.
Consider a hypothetical scenario where you're dealing with a chronic condition like fibromyalgia. Longevitron would collect data on your symptoms, medication intake, stress levels, sleep quality, and other relevant factors. It would then feed this data into its predictive control model system, which would use it to predict your future symptoms and adjust your treatment plan accordingly.
This could involve suggesting changes to your medication dosage, recommending lifestyle changes, or even alerting your healthcare provider if it detects a potential issue. The goal is to optimize your health and well-being based on your needs and circumstances.
The future of personalized preventative medicine hinges on the ability to collect high-frequency data on diet, lifestyle, and medical interventions. This data is crucial for causal inference, which is the process of determining the cause-and-effect relationships that drive individual health outcomes. However, collecting this data has been a significant bottleneck in the field.
The future of personalized preventative medicine depends on the ability to collect high-frequency data on diet, lifestyle, and medical interventions. This data is crucial for causal inference, which is the process of determining the cause-and-effect relationships that drive individual health outcomes. However, collecting this data has been a significant bottleneck in the field.
Various sensors allow it to collect data passively on various health-related factors. For example, it could use
This passive data collection is done continuously and unobtrusively, providing a wealth of information without requiring any effort.
In addition to passive data collection, Longevitron actively interacts with you to gather more detailed information. For instance, it could ask you
Gut Microbiome Health and Water Intake
Longevitron could also ask you to describe your bowel movements or urine hue, as these can be major health indicators. The Bristol stool form scale (BSFS), which categorizes the form of stool, can be used as an assessment tool for the diagnosis of various bowel diseases or evaluation of treatment efficacy.
By combining passive and active data collection, Longevitron overcomes the bottleneck in personalized preventative medicine. It collects high-frequency data on diet, lifestyle, and medical interventions, enabling causal inference on an individual level. This allows Longevitron to determine the cause-and-effect relationships that drive individual health outcomes and use this information to provide personalized health recommendations.
One of the most exciting aspects of Longevitron is its potential to contribute to the development of AI alignment - ensuring that AI systems act in ways that benefit humans.
Longevitron is designed to collect a wealth of data on individual health, happiness, and stated human preferences. This data can be used to:
Preferences could be encrypted and stored in a personal Digital Twin Safe.
These personal digital twin AI models can, in turn, be used to inform a global AI safety bot. This bot could:
Moreover, the global AI safety bot could use this understanding to:
By supporting Longevitron, you're not just investing in a personal health assistant - you're also contributing to the future of AI safety and alignment. Your support will help us make this vision a reality, improving the lives of people around the world and ensuring that AI technology is used in a way that is beneficial to all.