A New Natural Language Processing–Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study
Authors: Paiva Bruno , Gonçalves Marcos André , da Rocha Leonardo Chaves Dutra , Marcolino Milena Soriano , Lana Fernanda Cristina Barbosa , Souza-Silva Maira Viana Rego , Almeida Jussara M , Pereira Polianna Delfino , de Andrade Claudio Moisés Valiense , Gomes Angélica Gomides dos Reis , Ferreira Maria Angélica Pires , Bartolazzi Frederico , Sacioto Manuela Furtado , Boscato Ana Paula , Guimarães-Júnior Milton Henriques , dos Reis Priscilla Pereira , Costa Felício Roberto , Jorge Alzira de Oliveira , Coelho Laryssa Reis , Carneiro Marcelo , Sales Thaís Lorenna Souza , Araújo Silvia Ferreira , Silveira Daniel Vitório , Ruschel Karen Brasil , Santos Fernanda Caldeira Veloso , Cenci Evelin Paola de Almeida , Menezes Luanna Silva Monteiro , Anschau Fernando , Bicalho Maria Aparecida Camargos , Manenti Euler Roberto Fernandes , Finger Renan Goulart , Ponce Daniela , de Aguiar Filipe Carrilho , Marques Luiza Margoto , de Castro Luís César , Vietta Giovanna Grünewald , Godoy Mariana Frizzo de , Vilaça Mariana do Nascimento , Morais Vivian Costa 
Published: July 2024

Background
Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes.

Objective
This study aims to propose detection, initial characterization, and semantic characterization (DIS), a new methodology for analyzing changes in health outcomes and variables over time while discovering contextual changes for outcomes in large volumes of data.

Methods
The DIS methodology involves 3 steps: detection, initial characterization, and semantic characterization. Detection uses metrics such as Jensen-Shannon divergence to identify significant data drifts. Initial characterization offers a global analysis of changes in data distribution and predictive feature significance over time. Semantic characterization uses natural language processing–inspired techniques to understand the local context of these changes, helping identify factors driving changes in patient outcomes. By integrating the outcomes from these 3 steps, our results can identify specific factors (eg, interventions and modifications in health care practices) that drive changes in patient outcomes. DIS was applied to the Brazilian COVID-19 Registry and the Medical Information Mart for Intensive Care, version IV (MIMIC-IV) data sets.

Results
Our approach allowed us to (1) identify drifts effectively, especially using metrics such as the Jensen-Shannon divergence, and (2) uncover reasons for the decline in overall mortality in both the COVID-19 and MIMIC-IV data sets, as well as changes in the cooccurrence between different diseases and this particular outcome. Factors such as vaccination during the COVID-19 pandemic and reduced iatrogenic events and cancer-related deaths in MIMIC-IV were highlighted. The methodology also pinpointed shifts in patient demographics and disease patterns, providing insights into the evolving health care landscape during the study period.

Conclusions
We developed a novel methodology combining machine learning and natural language processing techniques to detect, characterize, and understand temporal shifts in health care data. This understanding can enhance predictive algorithms, improve patient outcomes, and optimize health care resource allocation, ultimately improving the effectiveness of machine learning predictive algorithms applied to health care data. Our methodology can be applied to a variety of scenarios beyond those discussed in this paper.