RESUMO
The pointwise mutual information statistic (PMI), which measures how often two words occur together in a document corpus, is a cornerstone of recently proposed popular natural language processing algorithms such as word2vec. PMI and word2vec reveal semantic relationships between words and can be helpful in a range of applications such as document indexing, topic analysis, or document categorization. We use probability theory to demonstrate the relationship between PMI and word2vec. We use the theoretical results to demonstrate how the PMI can be modeled and estimated in a simple and straight forward manner. We further describe how one can obtain standard error estimates that account for within-patient clustering that arises from patterns of repeated words within a patient's health record due to a unique health history. We then demonstrate the usefulness of PMI on the problem of predictive identification of disease from free text notes of electronic health records. Specifically, we use our methods to distinguish those with and without type 2 diabetes mellitus in electronic health record free text data using over 400 000 clinical notes from an academic medical center.
Assuntos
Diabetes Mellitus Tipo 2 , Processamento de Linguagem Natural , Algoritmos , Registros Eletrônicos de Saúde , HumanosRESUMO
BACKGROUND: Residential histories linked to cancer registry data provide new opportunities to examine cancer outcomes by neighborhood socioeconomic status (SES). We examined differences in regional stage colon cancer survival estimates comparing models using a single neighborhood SES at diagnosis to models using neighborhood SES from residential histories. METHODS: We linked regional stage colon cancers from the New Jersey State Cancer Registry diagnosed from 2006 to 2011 to LexisNexis administrative data to obtain residential histories. We defined neighborhood SES as census tract poverty based on location at diagnosis and across the follow-up period through 31 December 2016 based on residential histories (average, time-weighted average, time-varying). Using Cox proportional hazards regression, we estimated associations between colon cancer and census tract poverty measurements (continuous and categorical), adjusted for age, sex, race/ethnicity, regional substage, and mover status. RESULTS: Sixty-five percent of the sample was nonmovers (one census tract); 35% (movers) changed tract at least once. Cases from tracts with >20% poverty changed residential tracts more often (42%) than cases from tracts with <5% poverty (32%). Hazard ratios (HRs) were generally similar in strength and direction across census tract poverty measurements. In time-varying models, cases in the highest poverty category (>20%) had a 30% higher risk of regional stage colon cancer death than cases in the lowest category (<5%) (95% confidence interval [CI] = 1.04, 1.63). CONCLUSION: Residential changes after regional stage colon cancer diagnosis may be associated with a higher risk of colon cancer death among cases in high-poverty areas. This has important implications for postdiagnostic access to care for treatment and follow-up surveillance. See video abstract: http://links.lww.com/EDE/B705.