Uri Kartoun
IBM Research, IBM T. J. Watson Research Center, Department Member
Insomnia remains under-diagnosed and poorly treated despite its high economic and social costs. Though previous work has examined how patient characteristics affect sleep medication prescriptions, the role of physician characteristics... more
Insomnia remains under-diagnosed and poorly treated despite its high economic and social costs. Though previous work has examined how patient characteristics affect sleep medication prescriptions, the role of physician characteristics that influence this clinical decision remains unclear. We sought to understand patient and physician factors that influence sleep medication prescribing patterns by analyzing Electronic Medical Records (EMRs) including the narrative clinical notes as well as codified data. Zolpidem and trazodone were the most widely prescribed initial sleep medication in a cohort of 1,105 patients. Some providers showed a historical preference for one medication, which was highly predictive of their future prescribing behavior. Using a predictive model (AUC = 0.77), physician preference largely determined which medication a patient received (OR = 3.13; p = 3 × 10 −37). In addition to the dominant effect of empirically determined physician preference, discussion of depression in a patient's note was found to have a statistically significant association with receiving a prescription for trazodone (OR = 1.38, p = 0.04). EMR data can yield insights into physician prescribing behavior based on real-world physician-patient interactions.
Research Interests:
→The available machine learning text-classification methods show only fair levels of accuracy in extracting patients’ medical conditions and behavioral descriptors. →An easily adaptable human in-the-loop big-data method with an... more
→The available machine learning text-classification methods show only fair levels of accuracy in extracting patients’ medical conditions
and behavioral descriptors.
→An easily adaptable human in-the-loop big-data method with an interactive front end may improve classification accuracy of widely used text classification techniques.
and behavioral descriptors.
→An easily adaptable human in-the-loop big-data method with an interactive front end may improve classification accuracy of widely used text classification techniques.
Research Interests:
OBJECTIVES: Among adults with nonalcoholic fatty liver disease (NAFLD), 25% of deaths are attributable to cardiovascular disease (CVD). CVD risk reduction in NAFLD requires not only modifi cation of traditional CVD risk factors but... more
OBJECTIVES: Among adults with nonalcoholic fatty liver disease (NAFLD), 25% of deaths are attributable to
cardiovascular disease (CVD). CVD risk reduction in NAFLD requires not only modifi cation of
traditional CVD risk factors but identifi cation of risk factors unique to NAFLD.
METHODS: In a NAFLD cohort, we sought to identify non-traditional risk factors associated with CVD. NAFLD
was determined by a previously described algorithm and a multivariable logistic regression model
determined predictors of CVD.
RESULTS: Of the 8,409 individuals with NAFLD, 3,243 had CVD and 5,166 did not. On multivariable analysis,
CVD among NAFLD patients was associated with traditional CVD risk factors including family history
of CVD (OR 4.25, P =0.0007), hypertension (OR 2.54, P =0.0017), renal failure (OR 1.59, P =0.04),
and age (OR 1.05, P <0.0001). Several non-traditional CVD risk factors including albumin, sodium,
and Model for End-Stage Liver Disease (MELD) score were associated with CVD. On multivariable
analysis, an increased MELD score (OR 1.10, P <0.0001) was associated with an increased risk of
CVD. Albumin (OR 0.52, P <0.0001) and sodium (OR 0.96, P =0.037) were inversely associated with
CVD. In addition, CVD was more common among those with a NAFLD fi brosis score >0.676 than
those with a score ≤0.676 (39 vs. 20%, P <0.0001).
CONCLUSIONS: CVD in NAFLD is associated with traditional CVD risk factors, as well as higher MELD scores and
lower albumin and sodium levels. Individuals with evidence of advanced fi brosis were more likely to
have CVD. These fi ndings suggest that the drivers of NAFLD may also promote CVD development and
progression.
cardiovascular disease (CVD). CVD risk reduction in NAFLD requires not only modifi cation of
traditional CVD risk factors but identifi cation of risk factors unique to NAFLD.
METHODS: In a NAFLD cohort, we sought to identify non-traditional risk factors associated with CVD. NAFLD
was determined by a previously described algorithm and a multivariable logistic regression model
determined predictors of CVD.
RESULTS: Of the 8,409 individuals with NAFLD, 3,243 had CVD and 5,166 did not. On multivariable analysis,
CVD among NAFLD patients was associated with traditional CVD risk factors including family history
of CVD (OR 4.25, P =0.0007), hypertension (OR 2.54, P =0.0017), renal failure (OR 1.59, P =0.04),
and age (OR 1.05, P <0.0001). Several non-traditional CVD risk factors including albumin, sodium,
and Model for End-Stage Liver Disease (MELD) score were associated with CVD. On multivariable
analysis, an increased MELD score (OR 1.10, P <0.0001) was associated with an increased risk of
CVD. Albumin (OR 0.52, P <0.0001) and sodium (OR 0.96, P =0.037) were inversely associated with
CVD. In addition, CVD was more common among those with a NAFLD fi brosis score >0.676 than
those with a score ≤0.676 (39 vs. 20%, P <0.0001).
CONCLUSIONS: CVD in NAFLD is associated with traditional CVD risk factors, as well as higher MELD scores and
lower albumin and sodium levels. Individuals with evidence of advanced fi brosis were more likely to
have CVD. These fi ndings suggest that the drivers of NAFLD may also promote CVD development and
progression.
Research Interests:
Background and Aims—NAFLD is the most common cause of chronic liver disease worldwide. Risk factors for NAFLD disease progression and liver-related outcomes remain incompletely understood due to the lack of computational identification... more
Background and Aims—NAFLD is the most common cause of chronic liver disease worldwide. Risk factors for NAFLD disease progression and liver-related outcomes remain incompletely understood due to the lack of computational identification methods. The present study sought to design a classification algorithm for NAFLD within the Electronic Medical Record (EMR) for the development of large-scale longitudinal cohorts.
Methods—We implemented feature selection using logistic regression with adaptive LASSO. A training set of 620 patients was randomly selected from the Research Patient Data Registry at Partners Healthcare. To assess a true diagnosis for NAFLD we performed chart reviews and considered either a documentation of a biopsy or a clinical diagnosis of NAFLD. We included in our model variables including laboratory measurements, diagnosis codes, and concepts extracted from medical notes. Variables with P<0.05 were included in the multivariable analysis.
Results—The NAFLD classification algorithm included number of natural language mentions of NAFLD in the EMR, lifetime number of ICD-9 codes for NAFLD and triglyceride level. This classification algorithm was superior to an algorithm using ICD-9 data alone with AUC of 0.85 vs. 0.75 (P<0.0001) and lead to the creation of a new independent cohort of 8,458 individuals with a high probability for NAFLD.
Conclusions—The NAFLD classification algorithm is superior to ICD-9 billing data alone. This approach is simple to develop, deploy and can be applied across different institutions to create EMR based cohorts of individuals with NAFLD.
Methods—We implemented feature selection using logistic regression with adaptive LASSO. A training set of 620 patients was randomly selected from the Research Patient Data Registry at Partners Healthcare. To assess a true diagnosis for NAFLD we performed chart reviews and considered either a documentation of a biopsy or a clinical diagnosis of NAFLD. We included in our model variables including laboratory measurements, diagnosis codes, and concepts extracted from medical notes. Variables with P<0.05 were included in the multivariable analysis.
Results—The NAFLD classification algorithm included number of natural language mentions of NAFLD in the EMR, lifetime number of ICD-9 codes for NAFLD and triglyceride level. This classification algorithm was superior to an algorithm using ICD-9 data alone with AUC of 0.85 vs. 0.75 (P<0.0001) and lead to the creation of a new independent cohort of 8,458 individuals with a high probability for NAFLD.
Conclusions—The NAFLD classification algorithm is superior to ICD-9 billing data alone. This approach is simple to develop, deploy and can be applied across different institutions to create EMR based cohorts of individuals with NAFLD.
Research Interests:
Background and aims Accurate assessment of the risk of mortality following a cirrhosis-related admission can enable health-care providers to identify high-risk patients and modify treatment plans to decrease the risk of mortality.... more
Background and aims
Accurate assessment of the risk of mortality following a cirrhosis-related admission can enable health-care providers to identify high-risk patients and modify treatment plans to decrease the risk of mortality.
Methods
We developed a post-discharge mortality prediction model for patients with a cirrhosis-related admission using a population of 314,292 patients who received care either at Massachusetts General Hospital (MGH) or Brigham and Women’s Hospital (BWH) between 1992 and 2010. We extracted 68 variables from the electronic medical records (EMRs), including demographics, laboratory values, diagnosis codes, and medications. We then used a regularized logistic regression to select the most informative variables and created a risk score that comprises the selected variables. To evaluate the potential for generalizability of our score, we applied it on all cirrhosis-related admissions between 2010 and 2015 at an independent EMR data source of more than 18 million patients, pooled from different health-care systems with EMRs. We calculated the areas under the receiver operating characteristic curves (AUROCs) to assess prediction performance.
Results
We identified 4,781 cirrhosis-related admissions at MGH/BWH hospitals, of which 778 resulted in death within 90 days of discharge. Nine variables were the most effective predictors for 90-day mortality, and these included all MELD-Na’s components, as well as albumin, total cholesterol, white blood cell count, age, and length of stay. Applying our nine-variable risk score (denoted as “MELD-Plus”) resulted in an improvement over MELD and MELD-Na scores in several prediction models. On the MGH/BWH 90-day model, MELD-Plus improved the performance of MELD-Na by 11.4% (0.78 [95% CI, 0.75–0.81] versus 0.70 [95% CI, 0.66–0.73]). In the MGH/BWH approximate 1-year model, MELD-Plus improved the performance of MELD-Na by 8.3% (0.78 [95% CI, 0.76–0.79] versus 0.72 [95% CI, 0.71–0.73]). Performance improvement was similar when the novel MELD-Plus risk score was applied to an independent database; when considering 24,042 cirrhosis-related admissions, MELD-Plus improved the performance of MELD-Na by 16.9% (0.69 [95% CI, 0.69–0.70] versus 0.59 [95% CI, 0.58–0.60]).
Conclusions
We developed a new risk score, MELD-Plus that accurately stratifies the short-term mortality of patients with established cirrhosis, following a hospital admission. Our findings demonstrate that using a small set of easily accessible structured variables can help identify novel predictors of outcomes in cirrhosis patients and improve the performance of widely used traditional risk scores.
Accurate assessment of the risk of mortality following a cirrhosis-related admission can enable health-care providers to identify high-risk patients and modify treatment plans to decrease the risk of mortality.
Methods
We developed a post-discharge mortality prediction model for patients with a cirrhosis-related admission using a population of 314,292 patients who received care either at Massachusetts General Hospital (MGH) or Brigham and Women’s Hospital (BWH) between 1992 and 2010. We extracted 68 variables from the electronic medical records (EMRs), including demographics, laboratory values, diagnosis codes, and medications. We then used a regularized logistic regression to select the most informative variables and created a risk score that comprises the selected variables. To evaluate the potential for generalizability of our score, we applied it on all cirrhosis-related admissions between 2010 and 2015 at an independent EMR data source of more than 18 million patients, pooled from different health-care systems with EMRs. We calculated the areas under the receiver operating characteristic curves (AUROCs) to assess prediction performance.
Results
We identified 4,781 cirrhosis-related admissions at MGH/BWH hospitals, of which 778 resulted in death within 90 days of discharge. Nine variables were the most effective predictors for 90-day mortality, and these included all MELD-Na’s components, as well as albumin, total cholesterol, white blood cell count, age, and length of stay. Applying our nine-variable risk score (denoted as “MELD-Plus”) resulted in an improvement over MELD and MELD-Na scores in several prediction models. On the MGH/BWH 90-day model, MELD-Plus improved the performance of MELD-Na by 11.4% (0.78 [95% CI, 0.75–0.81] versus 0.70 [95% CI, 0.66–0.73]). In the MGH/BWH approximate 1-year model, MELD-Plus improved the performance of MELD-Na by 8.3% (0.78 [95% CI, 0.76–0.79] versus 0.72 [95% CI, 0.71–0.73]). Performance improvement was similar when the novel MELD-Plus risk score was applied to an independent database; when considering 24,042 cirrhosis-related admissions, MELD-Plus improved the performance of MELD-Na by 16.9% (0.69 [95% CI, 0.69–0.70] versus 0.59 [95% CI, 0.58–0.60]).
Conclusions
We developed a new risk score, MELD-Plus that accurately stratifies the short-term mortality of patients with established cirrhosis, following a hospital admission. Our findings demonstrate that using a small set of easily accessible structured variables can help identify novel predictors of outcomes in cirrhosis patients and improve the performance of widely used traditional risk scores.