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A New Natural Language Processing–Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study

A New Natural Language Processing–Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study

JSD(P||Q) = 1/2 KL(P||M) + 1/2 KL(Q||M) (2) In equation 2, KL is the KL divergence, M is 1/(P + Q), and P and Q are the distributions of the variables we compared. Figure 4 presents the results of our drift detection metrics, applied to the various “anchor_year_groups” in the MIMIC-IV data set. The figure depicts the normalized magnitude of the drift signal calculated per “anchor_year_group.” The drift signals were normalized in the 0 range for visualization, as shown in equation 2.

Bruno Paiva, Marcos André Gonçalves, Leonardo Chaves Dutra da Rocha, Milena Soriano Marcolino, Fernanda Cristina Barbosa Lana, Maira Viana Rego Souza-Silva, Jussara M Almeida, Polianna Delfino Pereira, Claudio Moisés Valiense de Andrade, Angélica Gomides dos Reis Gomes, Maria Angélica Pires Ferreira, Frederico Bartolazzi, Manuela Furtado Sacioto, Ana Paula Boscato, Milton Henriques Guimarães-Júnior, Priscilla Pereira dos Reis, Felício Roberto Costa, Alzira de Oliveira Jorge, Laryssa Reis Coelho, Marcelo Carneiro, Thaís Lorenna Souza Sales, Silvia Ferreira Araújo, Daniel Vitório Silveira, Karen Brasil Ruschel, Fernanda Caldeira Veloso Santos, Evelin Paola de Almeida Cenci, Luanna Silva Monteiro Menezes, Fernando Anschau, Maria Aparecida Camargos Bicalho, Euler Roberto Fernandes Manenti, Renan Goulart Finger, Daniela Ponce, Filipe Carrilho de Aguiar, Luiza Margoto Marques, Luís César de Castro, Giovanna Grünewald Vietta, Mariana Frizzo de Godoy, Mariana do Nascimento Vilaça, Vivian Costa Morais

JMIR Med Inform 2024;12:e54246

Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers

Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers

isquêmica (ischemic) actp (short for percutaneous transluminal coronary angioplasty) dp crm (short for myocardial revascularization surgery) iam (short for acute myocardial infarction) 2014 infarto (short for acute myocardial infarction) mm sf Dyslipidemia dislipidemia (dyslipidemia) comorbidades (comorbidities) 1hora cesária (cesarean) morbidades (morbidities) puerpera (puerperal) has (short for high blood pressure) fêmur (fêmur) tep previas (previous) Obesity BMI (short for body mass index) obesidade (obesity) m²

Bruna Stella Zanotto, Ana Paula Beck da Silva Etges, Avner dal Bosco, Eduardo Gabriel Cortes, Renata Ruschel, Ana Claudia De Souza, Claudio M V Andrade, Felipe Viegas, Sergio Canuto, Washington Luiz, Sheila Ouriques Martins, Renata Vieira, Carisi Polanczyk, Marcos André Gonçalves

JMIR Med Inform 2021;9(11):e29120