What is the Algorithm of Life2vec AI?

What is the Algorithm of Life2vec AI? Life2vec is an artificial intelligence system that can predict an individual’s remaining life expectancy and risk of dying in a given timeframe. It was created by researchers at Stanford University and can generate highly personalized predictions by analyzing a combination of biometric, genetic, activity, and survey data.

The goal of life2vec

The overarching goal of the life2vec AI system is to more accurately predict life expectancy and mortality risk on an individual level. Most existing models rely on population-level data and cannot account for each person’s unique combination of risk factors. By incorporating more granular data, the creators of life2vec aimed to develop personalized predictions that could empower individuals to make more informed lifestyle and healthcare decisions.

The algorithm behind life2vec:

The life2vec algorithm comprises two main components:

  • A deep neural network model for making mortality predictions
  • Preprocessing pipelines for converting raw input data into embeddings consumable by the neural network

The entire system was developed using TensorFlow and Python.

Neural network architecture:

The core of life2vec is a deep neural network for mapping patient data to life expectancy predictions. The network contains multiple dense layers interspersed with dropout layers to prevent overfitting.

The input layer accepts embeddings generated from the raw patient data. These embeddings encode information such as demographics, family history, lab tests results, and wearable data into a dense vector representation.

The output layer contains a single node that generates a real-valued number indicating the patient’s remaining life expectancy. During training, this output is compared to actual mortality data to learn the correlation between input data patterns and mortality outcomes.

Data preprocessing pipeline:

Since neural networks can only process numeric data, several data preprocessing steps are required to convert raw patient data like lab tests, wearable data, surveys etc. into embeddings readable by the network. The pipelines used by life2vec include:

Demographic embedding

  • Encode categorical features like gender, ethnicity, education level into numeric representations using one-hot encoding
  • Standardize continuous features like age and income
  • Join categorical and continuous encodings into a single dense demographic embedding vector

Family history embedding

  • Extract relevant conditions from unstructured family history text data using natural language processing
  • Assign risk scores to extracted conditions based on their heritability and mortality impact
  • Summarize risk scores into a single family history embedding vector

Lab test embedding

  • For each lab test, build a longitudinal timeseries tracking the patient’s results over time
  • Run statistical feature extraction to extract interpretable timeseries features like averages and standard deviations
  • Encode extracted features into a dense lab test embedding vector

Wearable data embedding

  • Preprocess raw timeseries data from devices like Fitbits and Apple Watches
  • Apply signal processing transforms like Fourier Transforms to extract relevant health signals
  • Summarize extracted signals into an embedding vector

Survey data embedding

  • For surveys assessing factors like lifestyle, mental health, physical activity etc. encode responses numerically
  • Run dimensionality reduction algorithms like PCA toCondense survey responses into an embedding vector

The output of each pipeline is fed into the neural network, which learns associations between data patterns and mortality without directly processing raw patient data.

How life2vec is trained?

The life2vec model is trained using a dataset compiled from several large-scale longitudinal human studies including the Framingham Heart Study, NHANES, and UK Biobank.

The training data

The training dataset contains both input data and targets:


  • Demographic information
  • Detailed family medical history
  • Results from periodic lab tests like cholesterol panels and CBCs
  • Multi-year timeseries data from wearables
  • Extensive lifestyle and mental health surveys


  • Death register data indicating the age at which a patient died
  • Right-censored data for patients who were still alive at the end of the study

By training on over 100,000 patients with upto 30 years of granular input data each, life2vec learns to accurately correlate inputs to mortality outcomes.

The training process

During training, the neural network adjusts its internal weights through backpropagation to minimize the difference between its predictions and actual recorded deaths in the training dataset.

The key steps are:

  1. Generate embeddings for all input data about a patient
  2. Pass embeddings through the neural network to generate a predicted remaining life expectancy
  3. Compare the prediction against the patient’s observed age of death
  4. Calculate the deviation between prediction and observation
  5. Backpropagate the deviation through the network to adjust weights
  6. Iterate through training examples to minimize overall error

Through multiple epochs of training data, the network learns a complex function mapping inputs to accurate remaining life expectancy predictions.

How life2vec predicts longevity?

After training, life2vec can generate personalized longevity predictions for new patients. The process works as follows:

  • Collect demographic details, family history, lab tests, wearable data, and lifestyle surveys for a patient
  • Preprocess raw data into embeddings using the same pipelines as during training
  • Pass embeddings through the trained neural network
  • The network’s output node activation indicates the patient’s predicted remaining life expectancy

By accounting for granular data, life2vec is able to detect protective and risk factors in a patient’s profile missed by conventional predictive models. This enables more accurate and personalized estimates.

The system can also identify which elements of a patient’s profile are most positively or negatively influencing its prediction. This explainability allows patients to take actions to potentially increase longevity by modifying relevant risk factors.

Applications of life2vec:

The authors of life2vec highlight several potential applications for their personalized longevity predictor:

Personalized preventative healthcare

By identifying individual risk factors influencing expected longevity, life2vec can guide patients to take actions like changing diet, increasing exercise, or scheduling relevant checkup exams to mitigate risks and promote longevity.

Optimized medical decision making

Life expectancy predictions can help patients and doctors make better informed decisions about potential procedures, therapies, and interventions based on their expected benefit given a patient’s lifespan.

Accelerated research

Pharmaceutical companies can use life2vec predictions to better select participants for clinical trials testing new medications and interventions intended to promote longevity.

The unprecedented accuracy and explainability of life2vec offers potential to both extend lifespans as well as simply empower individuals with more information about a fundamental aspect of their health.

Limitations and ethical considerations:

While life2vec aims to improve longevity prediction accuracy, its reliance on personal data prompts important ethical considerations:

Data privacy

Collecting extensive health data from wearables and genetic tests raises patient privacy concerns. Strict controls around consent, data sharing, and securing sensitive biometric datasets are necessary.

Algorithmic transparency

The complexity of deep neural networks reduces interpretability compared to simpler statistical models. Extra work is required to enable explainability about why the model makes certain predictions.

Unintended consequences

Precise longevity predictors must be carefully vetted to avoid unintended psychological consequences from patients learning their probable age of death. Delivery of predictions requires sensitivity.

Like many biometric AI systems, realizing the benefits of life2vec necessitates balancing model accuracy with frameworks that establish ethics and trustworthiness among affected individuals.

Proactive mitigation of these concerns supplements life2vec’s technological innovations and supports unlocking its potential to provide patients and practitioners with powerful new predictive insights.

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