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Recently, artificial intelligence (AI)-powered “death calculators” have emerged that aim to predict an individual’s risk of death within a specific time frame, such as the next year or five years. These predictive models analyze personal health data and patterns in populations to generate mortality risk scores.
As this technology continues to develop, many have questions about how AI mortality algorithms work to predict life expectancy, as well as concerns about the responsible development and use of such sensitive predictive systems.
What is Life2vec?
Life2vec is an AI system trained on anonymized electronic medical records of millions of patients over decades from the US and UK. The neural networks analyze how combinations of different demographics, vital signs, diagnoses, medications, procedures and laboratory tests correlate with patient deaths over the next twelve months.
Based on these risk factor and mortality patterns, Life2vec provides each patient with a risk score of 0-100%, indicating the likelihood of dying within the next year.
How did Death Calculator AI work?
AI-powered mortality risk calculators like Life2vec work similarly to other predictive healthcare algorithms, but focus on predicting doom rather than the onset of disease.
The Death Calculator AI Process
- Data collection: Comprehensive, multi-year medical datasets are being compiled containing health and personal data on large patient populations spanning decades. Details range from demographics to diagnoses, vitals, medications, procedures, labs, socio-economics and more.
- Anonymization of data: Before model development, all protected health information and identifying details are removed to respect patient privacy. Only anonymized data is accessible to modeling algorithms.
- Neural network analysis: Deep learning neural networks process the massive data sets and evaluate the correlations between thousands of data variables and whether the death occurred within 12 months.
- Identification of mortality risk: Based on pattern recognition between factors such as age, circumstances, income, laboratories and mortality rates, the AI model learns risk profiles that are predictive of death within a year.
- Model validation and tuning: The AI model is tested and refined until its predictions accurately match real-world results based on input data. Trustworthiness is evaluated across all demographics to minimize unfairness.
- Generating risk scores: Once deployed, the tuned AI model takes an individual’s data and generates a personalized percent risk score of dying within the next twelve months, based on their similarities to patterns for both survivors and decedents.
Interpretation of Death Calculator AI risk scores
The AI outputs percentage scores ranging from 0 to 100%:
- <10%: low risk of death in the short term
- 10-30%: Moderate risk
- >30%: High risk
However, these scores represent probabilistic guesses rather than a definitive forecast. Many contextual factors also influence mortality beyond the data available to algorithms.
The risks projected by AI may evolve as new health data is analyzed over time or interventions occur. Although risk instruments can stimulate positive change, relying solely on their outcomes is not medically advisable.
What data does Death Calculator AI use?
Leading mortality algorithms often use aggregated electronic health records from millions of patients. Specific data analyzed may include:
- Demographics – age, gender, ethnicity, income, education level, marital status
- Diagnoses – medical conditions, mental health history
- Medicines and therapies
- Vital signs – blood pressure, temperature, oxygen saturation
- Laboratory tests – cholesterol panels, blood cell count
- Procedures and immunizations
- Family history
- Socio-economic status – occupation, location of residence
- Health behavior – smoking, exercise patterns
All protected health information will be deleted prior to modeling. Hundreds to thousands of data variables are evaluated to determine associations with mortality.
Applications for AI prediction of life and death
Predictive analytics tools like Life2vec are intended to support several use cases:
Clinical decision support
- Inform preventive interventions for high-risk patients
- Guide screening protocols, testing based on risk factors
- Optimize treatment plans tailored to mortality profiles
Patient empowerment
- Increase awareness of health risks
- Motivate lifestyle changes to reduce risks
- Make informed decisions about health priorities
Population health management
- Segment populations by mortality risk
- Targeted outreach, support for high-risk groups
- Guide resource planning to manage cohorts as needed
Drug development and clinical trials
- Identify individuals at increased risk of mortality
- Assess the efficacy of new therapies to reduce risks
- Monitor safety signals and results in studies
Concerns surrounding Death Calculator AI
While there is promise, ethical questions remain about the appropriate development and deployment of probabilistic models for predicting deaths, including:
Data privacy and consent
- Patient data requires careful de-identification and management to respect autonomy and prevent misuse.
Algorithmic bias and fairness
- Predictive patterns should be assessed across populations to minimize unfair or skewed projections.
Clinical validity and usefulness
- Predictions may remain insufficient to guide medical decisions without contextual interpretation and additional laboratory testing.
Psychological damage
- Visibility of mortality risk can increase fear or exacerbate despair among already vulnerable groups.
Access to shares
- Those who do not have digital health records may be disadvantaged by risk models that rely on robust data sets.
Conservation of human action
- Overreliance on AI forecasts could erode person-centered professional judgment in medicine.
While AI will increasingly have a voice in predicting longevity, its ethical application requires that its guidance be supported rather than displace complementary holistic human judgments.
The future of AI calculators for life and death
As algorithms continue to collect more diverse health data across broader populations over time, their ability to predict mortality risks for particular individuals based on correlating patterns will continue to improve.
Over time, the accuracy of some leading models is likely to reach levels sufficient to guide certain personalized prevention and end-of-life care decisions when applied judiciously. However, many experts argue that AI will never match the ability of human doctors to make nuanced healthcare judgments, taking into account social determinants unaffected by data.
Therefore, rather than replacing physicians, future AI mortality predictors may play a limited role within healthcare, flagging patients at highest risk for human intervention while synthesizing knowledge from millions of case histories to better inform human expertise.
The interfaces and directions for this technology remain undetermined and require an inclusive discussion about ethical application. But if used responsibly, AI prognostic models can increase access to personalized preventive knowledge and life-saving interventions – ultimately shifting medicine from reactive treatment to a deeply personalized understanding of the risks each unique life faces.
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