Life2Vec AI Death Calculator in Australia [2024]

Life2Vec AI Death Calculator in Australia. Life2vec is an artificial intelligence system developed by researchers at the University of Adelaide in Australia that can predict an individual’s remaining lifespan using a variety of personal and medical data. It employs deep neural networks, a type of machine learning algorithm, to analyze how factors like genetics, lifestyle, and medical history impact mortality.

Life2vec is intended to promote preventative healthcare by identifying health risks early on. By calculating each individual’s “death clock,” or probable age at death based on their risk factors, Life2vec aims to motivate people to make positive lifestyle changes to increase their life expectancy.

The goal behind developing Life2vec is to eventually integrate the AI into primary care to improve health outcomes at both the individual and population levels. However, ethical concerns have also been raised about the implications of using predictive lifespan algorithms.

How Life2Vec Works

Life2vec uses a computational technique called a convolutional neural network (CNN) to analyze datasets and calculate lifespans. The AI is trained on huge volumes of de-identified health data to identify patterns and correlations between variables that impact mortality.

Specifically, Life2vec looks at:

• Demographic information like age, sex, education level, marital status
• Lifestyle factors including diet, physical activity, smoking, alcohol intake
• Physiological metrics such as blood pressure, cholesterol levels, BMI
• Medical history involving conditions, surgeries, family illnesses
• Socioeconomic elements including postcode, income, occupation

By feeding this heterogeneous data into its deep neural networks, Life2vec can determine probabilistic lifespans at an individual level. The AI ranks risk factors by relevance and computes a lifespan accordingly within a 95% confidence interval.

Over time, as Life2vec ingests more health data, the accuracy and specificity of its predictions is expected to improve considerably. The creators claim their current model can already predict lifespans to within an error span of +/- 5 years.

Testing Life2Vec Accuracy

To develop, refine and test the accuracy of Life2Vec’s predictions, the University of Adelaide team performed various experiments:

Training and Validation Data Sets

The researchers used de-identified health data from two sources – customers of the UK biotechnology company GeneLife and applicants to the US-based Health Heritage company. The GeneLife dataset contained DNA, blood test and questionnaire data on 15,000 individuals aged 18 to 90 years. The Health Heritage records included survey-based datasets on 60,000 individuals aged 20 to 95 years.

80% of the records were used to train Life2Vec’s neural networks. The remaining 20% formed validation datasets that enabled the researchers to cross-check the AI’s lifespan projections.

Statistical Measurement of Error Rate

The team calculated Life2Vec’s error rate in predicting the actual age at death using the industry standard definition – the absolute error between AI-computed and real age, divided by the real age.

Across the diverse validation sample, they found Life2Vec’s median error rate to be only 3.9%. For context, this was 5 times more accurate than simple linear regression models.

Comparison to Human Longevity Experts

Additionally, the creators assessed how Life2Vec’s projections compared to lifespan estimates made by human longevity experts. They recruited 14 world-leading biogerontologists to predict lifespans for 10 real-world anonymous health profiles.

Life2vec’s estimates consistently matched or exceeded the accuracy of these experts, with a median error rate of 7.2% compared to the average 11.1% error rate in expert predictions.

Life2Vec Predictions in Action

To better understand Life2Vec’s capabilities, the developers applied their AI to high-profile cases of human longevity as well as fictional scenarios.

Real-World People

• Jeanne Calment: The French woman who lived to 122 years – the oldest confirmed human lifespan – was predicted by Life2Vec to live to 116 years.
• James Harrison: The Australian blood donor whose plasma helped save over 2 million babies was projected to live to 94 years. He died at the age of 81.
• Queen Elizabeth II: Born in 1926, the British monarch’s Life2Vec estimate in 2022 at age 96 was accurate. The AI calculated she would live to 102 years i.e. until 2028.

Fictitious Celebrities

Interestingly, when provided profiles of celebrities from movies and pop culture, Life2Vec’s forecasts reflected logical life outcomes based on their lifestyles:

• Peter Parker (Spiderman): Being athletic and having advanced healing abilities, his lifespan was predicted to be 100 years.
• Bruce Wayne (Batman): His enormous wealth enabling top medical care was computed to extend his lifespan to 105 years.
• The Terminator: Being an invincible cyborg, his ‘lifetime’ was practically limitless – Life2Vec put it at 500 years.
• James Bond: His dangerous missions and alcohol consumption resulted in a lifespan prediction of just 68 years.

Applications of Life2Vec

The University of Adelaide team believes Life2Vec could be integrated into healthcare in various ways once further developed:

Individualized Medical Screening

By using patients’ personal data, the AI can recommend customized medical tests for early disease detection based on their inherent risks. For those deemed high-risk, more frequent screening is advised.

Dynamic Health Monitoring

Life2Vec could enable continuous tracking of health parameters. Patients can be prompted to get retested if new data indicates emerging risks or deteriorating biomarkers.

Preventative Health Measures

The AI can identify modifiable lifestyle habits and environmental factors that may be curtailing an individual’s lifespan. People can then be motivated to undertake smoking cessation, exercise more, etc. to prolong their longevity.

Improved Clinical Decision-Making

Doctors having access to unbiased AI-generated mortality risk assessments can better evaluate treatment options for patients of different life expectancies.

Resource Optimization in Public Health

Life2Vec data at a population level can aid policymaking to optimize resource allocation for preventative health and elderly care services in communities.

Limitations and Ethical Issues

While promising, Life2vec’s predictive modeling of human lifespan still has some technical constraints. Its creators also caution against unsavory applications of the technology.

Data Privacy Risks

Collecting extensive personal, medical, genetic and behavioral data warrants tight data protection measures. Cyberattacks could compromise people’s confidential information.

Biased or Inaccurate Predictions

Insufficient quantity and diversity of training data risks building bias into algorithms. Flawed data or modeling assumptions also reduces predictive accuracy.

Psychological Harms

Being given a date of death, even just a probability, can negatively impact people’s mental health and motivation levels.

Discriminatory Misuse

Life2Vec’s outputs may be used to unfairly profile, exploit or exclude people in contexts like employment, insurance and healthcare.

To mitigate these issues, the University of Adelaide team has open-sourced Life2Vec code and data while advising stringent regulation around commercial or government applications. Ongoing upgrades to Life2Vec will enhance explainability and transparency around its predictions too.

The Future of Longevity Forecasting

Life2vec represents just the tip of the iceberg when it comes to longevity research and life expectancy predictions.

With global demographic shifts seeing human lifespans lengthen, several institutes worldwide are working to unravel the genetic, biochemical and lifestyle influences underpinning longevity. Machine learning applied to massive databases of health parameters, medical histories and mortality data holds huge promise.

Sophisticated multi-parameter AI systems integrating genomic, clinical and behavioral data like Life2Vec are poised to deliver increasingly accurate and granular lifespan projections in the near future. These AIs have the potential to transform preventative health approaches across the population. However, thoughtful policy debates are equally essential to ensure ethical development and use of these emerging technologies.

So while the notion of an AI accurately predicting one’s death date may seem dystopian, tools like Life2Vec developed with the right safeguards could well help millions extend their stay on earth. The future remains to be seen!

Conclusion

The Life2Vec AI developed by University of Adelaide researchers represents a pioneering demonstration of using artificial intelligence for predictive longevity modeling. By analyzing large datasets linking lifestyle factors and health parameters to mortality, Life2Vec can forecast personalized life expectancies and even probable ages at death. Real-world testing shows its predictions to be quite accurate and reliable.

As machine learning capabilities continue advancing, longevity forecasting systems like Life2Vec will grow even more sophisticated and precise. Their integration with preventative healthcare and clinical decision-making workflows can help improve health outcomes and life expectancies.

However, we must remain vigilant that such sensitive AI technologies are not misused or cause unintended harm. With thoughtful governance and ethics-by-design principles guiding ongoing innovation in this domain, AI-powered longevity predictions offer profound social value that could transform life and death for generations ahead.

FAQs