In recent years, researchers have made great strides in developing artificial intelligence (AI) systems for predicting various life outcomes. One notable example is Life2vec – an AI-powered “death prediction calculator” that can estimate a person’s remaining lifespan based on personal attributes like income, occupation, location, and health history.
How Does It Work?
Life2vec was created by researchers from Stanford University and the University of Copenhagen using a concept from natural language processing called “word embedding.” In NLP, word embedding assigns vectors (numeric representations) to words based on their semantic meaning. Life2vec implements this technique on life event data instead.
Each factor associated with mortality risk – things like medical conditions, lifestyle behaviors, demographics etc. – is assigned a vector. By analyzing these vectors relative to one another, Life2vec can estimate the probability of death in a given timeframe. The system was trained on Danish population registries containing medical history, income, education, marital status and other data for over 6 million people.
Accuracy of Death Predictions
In validation tests, Life2vec achieved 78% accuracy at predicting whether a person would die within the next year. This compares favorably to predictions from clinical experts. Accuracy remained similar across different demographic groups.
Predictions further into the future are less reliable. But even 10-year estimates proved more accurate than conventional methodology used by actuaries. So while Life2vec cannot pinpoint the exact date of death, it provides good insight into short and medium-term probability of dying.
Data Sources Used for Life2vec
The creators of Life2vec had access to comprehensive Danish medical, social, and governmental data sets with hundreds of attributes on each citizen. This allowed them to uncover connections that would not be detectable from smaller population samples.
Specific data sources utilized include:
- The Danish National Patient Registry – Records on all hospital visits and diagnoses since 1977
- Integrated Database for Labor Market Research (IDA) – Employment status, income, education level, marital status
- Danish Register of Causes of Death – Official records of date and cause of death
In total, Life2vec used 9 broad categories of historical data on over 6 million Danish individuals to detect mortality patterns.
Interpreting Life2vec Predictions
Researchers involved with Life2vec emphasize that its predictions show correlation – not necessarily causation – between certain attributes and lifespan. So while it can identify individuals with higher probability of dying, it does not necessarily single out causes.
Life2vec also gives results at a population level. It cannot account for sudden unexpected events that could lead to premature death of a particular individual. So predictions should be considered in context rather than taken as a definitive expiration date.
Applications of Life2vec
Since Life2vec remains an experimental research initiative, the creators do not intend to make it available as a consumer product any time soon. However, they suggest several valuable applications if developed responsibly:
Public Health Practice
Life2vec could efficiently stratify groups by mortality risk to guide decisions on resource allocation and preventative programs. Communities with lower life expectancy could be identified and studied.
Predictions could be provided to doctors to augment understanding of patient prognosis. Though physicians may be wary of algorithms dictating care decisions.
Insurance agencies and pension programs could incorporate Life2vec into pricing and sustainability models. Though anti-discrimination safeguards would need to be rigorous.
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Ethics of AI Death Prediction Systems
Predicting mortality inherently involves complex ethical considerations regarding privacy, consent, and fair use. The creators of Life2vec prohibited access to individually identifiable records to alleviate privacy concerns. They also intentionally built the tool using employment data instead of genetic or biometric identifiers.
But ethical questions remain on employing Life2vec predictions in real-world decisions that impact people’s lives, such as setting insurance premiums. There are also transparency issues around AI black boxes. If Life2vec outputs a high probability of dying, the complex system cannot easily explain why in an interpretable manner. Researchers need to determine what constitutes fair and adequately transparent use.
For now, the Life2vec researchers aim to keep developing the technology as an open academic initiative, while partnering with medical ethicists to address these pressing issues. They maintain the tool should never be commercialized in its current form. Consumer access would require extreme care and consideration.
The Future of AI Life Prediction
Life2vec represents the cutting edge of efforts to apply AI to human mortality data. The results demonstrate the viability of training deep learning algorithms to derive meaningful correlations across a breadth of life events and conditions.
However, accurately forecasting an individual’s precise remaining lifespan may remain beyond reach. Human lives are too complex, unexpected and fragile to model definitively with data-driven tools. While AI can parse probabilities, the randomness of life eludes predictability.
Nonetheless, technologies like Life2vec showcase AI’s potential for unlocking insights into health and longevity at a population scale. If developed conscientiously under proper oversight, AI mortality prediction tools could greatly benefit medical research for generations to come. The future remains uncertain, but sometimes looking ahead can help guide us to live better in the present.