Using Sequences of Life-events to Predict Human Lives

Abstract: Over the past decade, machine learning has revolutionized computers' ability to analyze text through flexible computational models. Due to their structural similarity to written language, transformer-based architectures have also shown promise as tools to make sense of a range of multi-variate sequences from protein-structures, music, electronic health records to weather-forecasts. We can also represent human lives in a way that shares this structural similarity to language. From one perspective, lives are simply sequences of events: People are born, visit the pediatrician, start school, move to a new location, get married, and so on. Here, we exploit this similarity to adapt innovations from natural language processing to examine the evolution and predictability of human lives based on detailed event sequences. We do this by drawing on arguably the most comprehensive registry data in existence, available for an entire nation of more than six million individuals across decades. Our data include information about life-events related to health, education, occupation, income, address, and working hours, recorded with day-to-day resolution. We create embeddings of life-events in a single vector space showing that this embedding space is robust and highly structured. Our models allow us to predict diverse outcomes ranging from early mortality to personality nuances, outperforming state-of-the-art models by a wide margin. Using methods for interpreting deep learning models, we probe the algorithm to understand the factors that enable our predictions. Our framework allows researchers to identify new potential mechanisms that impact life outcomes and associated possibilities for personalized interventions.

• The paper introduces the life2vec model that leverages transformer-based methods to predict human lives from sequences of life-events.
• It employs a two-stage approach with MLM and SOP pre-training on extensive Danish register data, achieving an 11% improvement in mortality prediction.
• The model’s detailed concept space reveals meaningful clusters of events, paving the way for personalized interventions and ethical AI applications.

Analyzing Human Lives: Sequence-Based Predictive Modeling

The research paper, "Using Sequences of Life-events to Predict Human Lives," explores the application of machine learning, specifically transformer-based models, to analyze and predict human life trajectories. This work builds upon innovations in NLP by viewing human life as sequences of events, similar in structure to language. The study leverages an extensive dataset from Danish national registers, offering detailed records of various life-events across health, education, occupation, and more, for over six million individuals.

Methodology and Model Architecture

The authors developed a model named life2vec, inspired by BERT architectures, to predict diverse life outcomes, such as early mortality and personality nuances. The model encompasses a transformer encoder to capture complex patterns within life-sequences, using a common embedding space to represent life-events vectorially. The life2vec model's efficacy is demonstrated through its superior performance in prediction tasks compared to state-of-the-art models.

For model training, the researchers employed a two-stage strategy: a pre-training phase involving Masked Language Modeling (MLM) and Sequence Order Prediction (SOP) tasks to establish a robust concept space, followed by domain-specific fine-tuning. This approach effectively combines temporal, contextual, and structural data features into compact life representations, called person-summaries.

Results and Insights

The study highlights the model's prediction prowess with impressive results, particularly in the mortality prediction task, achieving an 11% improvement over existing models. Furthermore, life2vec successfully predicts personality nuances, underscoring its adaptability across different prediction domains.

A detailed analysis of the embeddings reveals a globally and locally meaningful structure in the concept space, where life-events cluster logically based on their semantic relationships. The model's spatial organization of concepts such as health diagnoses and job categories validates its ability to capture intricate event interactions.

Implications and Future Prospects

The implications of this research are multifaceted. Practically, the life2vec framework can advance personalized interventions and strategies in healthcare, education, and social services by providing insights into critical life determinants. Theoretically, it opens avenues for exploring the causal mechanisms by which life-events influence outcomes. Future developments may involve integrating causal inference tools to enhance the interpretability and applicability of the model's predictions in real-world settings.

Moreover, this research prompts further exploration into the ethical use of predictive models on socio-economic data, emphasizing the need for regulated deployment to ensure fairness and respect for individual privacy.

In conclusion, this paper illustrates a significant stride in using AI to decipher life dynamics, leveraging vast chronological datasets to address the complexity of human existences. As AI technologies evolve, such innovations hold promise for transforming our understanding and anticipation of human developmental trajectories.