Samah Fodeh

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Samah Fodeh is an assistant professor at the Department of Emergency Medicine at Yale University. she obtained her Masters and Philosophy degree from Michigan State University. Her focus is to apply data mining and machine learning approaches to biomedical data. She develops algorithms that exploit complementary data modalities to better inform decision making in healthcare.

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Functional Impairments as Symptoms in the Symptom Cluster Analysis of Patients Newly Diagnosed With Advanced Cancer

Journal of Pain and Symptom Management, Oct 1, 2013

Context. Symptoms and subsequent functional impairment have been associated with the biological p... more Context. Symptoms and subsequent functional impairment have been associated with the biological processes of disease, including the interaction between disease and treatment in a measurement model of symptoms. However, hitherto cluster analysis has primarily focused on symptoms. Objectives. This study among patients within 100 days of diagnosis with advanced cancer explored whether self-reported physical symptoms and functional impairments formed clusters at the time of diagnosis. Methods. We applied cluster analysis to self-reported symptoms and activities of daily living of 111 patients newly diagnosed with advanced gastrointestinal (GI), gynecological, head and neck, and lung cancers. Based on content expert evaluations, the best techniques and variables were identified, yielding the best solution. Results. The best cluster solution used a K-means algorithm and cosine similarity and yielded five clusters of physical as well as emotional symptoms and functional impairments. Cancer site formed the predominant organizing principle of composition for each cluster. The top five symptoms and functional impairments in each cluster were Cluster 1 (GI): outlook, insomnia, appearance, concentration, and eating/feeding; Cluster 2 (GI): appetite, bowel, insomnia, eating/feeding, and appearance; Cluster 3 (gynecological): nausea, insomnia, eating/feeding, concentration, and pain; Cluster 4 (head and neck): dressing, eating/feeding, bathing, toileting, and walking; and Cluster 5 (lung): cough, walking, eating/feeding, breathing, and insomnia. Conclusion. Functional impairments in patients newly diagnosed with late-stage cancers behave as symptoms during the diagnostic phase. Health care providers need to expand their assessments to include both symptoms and functional impairments. Early recognition of functional changes may accelerate diagnosis at an earlier cancer stage.

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Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization

Chiropractic & Manual Therapies, Jul 17, 2020

Background: Chronic spinal pain conditions affect millions of US adults and carry a high healthca... more Background: Chronic spinal pain conditions affect millions of US adults and carry a high healthcare cost burden, both direct and indirect. Conservative interventions for spinal pain conditions, including chiropractic care, have been associated with lower healthcare costs and improvements in pain status in different clinical populations, including veterans. Little is currently known about predicting healthcare service utilization in the domain of conservative interventions for spinal pain conditions, including the frequency of use of chiropractic services. The purpose of this retrospective cohort study was to explore the use of supervised machine learning approaches to predicting one-year chiropractic service utilization by veterans receiving VA chiropractic care. Methods: We included 19,946 veterans who entered the Musculoskeletal Diagnosis Cohort between October 1, 2003 and September 30, 2013 and utilized VA chiropractic services within one year of cohort entry. The primary outcome was one-year chiropractic service utilization following index chiropractic visit, split into quartiles represented by the following classes: 1 visit, 2 to 3 visits, 4 to 6 visits, and 7 or greater visits. We compared the performance of four multiclass classification algorithms (gradient boosted classifier, stochastic gradient descent classifier, support vector classifier, and artificial neural network) in predicting visit quartile using 158 sociodemographic and clinical features. Results: The selected algorithms demonstrated poor prediction capabilities. Subset accuracy was 42.1% for the gradient boosted classifier, 38.6% for the stochastic gradient descent classifier, 41.4% for the support vector classifier, and 40.3% for the artificial neural network. The micro-averaged area under the precision-recall curve for each oneversus-rest classifier was 0.43 for the gradient boosted classifier, 0.38 for the stochastic gradient descent classifier, 0.43 for the support vector classifier, and 0.42 for the artificial neural network. Performance of each model yielded only a small positive shift in prediction probability (approximately 15%) compared to naïve classification.

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Guest Editorial for Selected Papers from BIOKDD 2018 and DMBIH 2018

IEEE/ACM Transactions on Computational Biology and Bioinformatics, Nov 1, 2020

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Understanding headache classification coding within the veterans health administration using ICD-9-CM and ICD-10-CM in fiscal years 2014–2017

PLOS ONE, Jan 4, 2023

Objectives Understand the continuity and changes in headache not-otherwise-specified (NOS), migra... more Objectives Understand the continuity and changes in headache not-otherwise-specified (NOS), migraine, and post-traumatic headache (PTH) diagnoses after the transition from ICD-9-CM to ICD-10-CM in the Veterans Health Administration (VHA). Background Headache is one of the most commonly diagnosed chronic conditions managed within primary and specialty care clinics. The VHA transitioned from ICD-9-CM to ICD-10-CM on October-1-2015. The effect transitioning on coding of specific headache diagnoses is unknown. Accuracy of headache diagnosis is important since different headache types respond to different treatments. Methods We mapped headache diagnoses from ICD-9-CM (FY 2014/2015) onto ICD-10-CM (FY 2016/2017) and computed coding proportions two years before/after the transition in VHA. We used queries to determine the change in transition pathways. We report the odds of

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Global spatiotemporal trends and determinants of COVID-19 vaccine acceptance on Twitter: a multilingual deep learning study in 135 countries and territories

medRxiv (Cold Spring Harbor Laboratory), Nov 17, 2022

doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by pee... more

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Complementary and Integrative Health Approaches Are Underused Among Older Veterans With Musculoskeletal Pain

Innovation in Aging, Dec 1, 2020

Both back pain and heart failure (HF) have negative influence on all aspects of life. Little is k... more Both back pain and heart failure (HF) have negative influence on all aspects of life. Little is known about the impact of back pain on older adults with HF. We include 1295 older adults who had data collected in the 11th year (2007-2008) of the Health, Aging and Body Composition (Health ABC) study to evaluate the effect of back pain on health status among older adults with and without HF. The participants aged 79-91, 54.8% were female and 34.8% were African American. Among 94 participants with HF, 63 (67.0%) had back pain; among 1201 participants without HF, 649 (54.0%) had back pain. Females reporting back pain had 4.76 (95% CI: 1.83, 12.37) times the odds of having HF compared to those without back pain. Male with back pain, compared to those without back pain, had 1.14 times (95% CI: 0.65, 2.02) the odds of having HF. Depressive symptoms were measured by the Center for Epidemiological Studies-Depression (CES-D) scale. Performance and functions were measured by the Established Populations for Epidemiologic Studies in the Elderly (EPESE) performance score, the Health ABC performance battery score and self-reported difficulty with functional tasks. These symptom and performance measures were significantly associated with both back pain and HF, but not the interaction terms of back pain and HF after adjusting demographic variables including gender, race, smoking status and BMI category. The high incidence and negative impact of back pain highlighted the needs of developing strategies in pain management among older adults with and without HF.

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Using the Adverse Event Reporting System: Can Analysis be Streamlined by Text Processing?

AMIA, 2015

Feature Selection Based LapSVM to Classify Medical Event Reports and Enhance Patient Safety

AMIA, 2015

Analysis of VA Telephone Call Notes using Topic Modeling

AMIA, 2012

Pain Assessment in Veterans Health Administration Chiropractic Clinic Documentation

AMIA, 2020

Presecription opioid tapering: Mining structured and unstructured data

AMIA, 2019

Classification of radiology reports for falls in an HIV study cohort

Journal of the American Medical Informatics Association, Nov 13, 2015

Objective To identify patients in a human immunodeficiency virus (HIV) study cohort who have fall... more Objective To identify patients in a human immunodeficiency virus (HIV) study cohort who have fallen by applying supervised machine learning methods to radiology reports of the cohort. Methods We used the Veterans Aging Cohort Study Virtual Cohort (VACS-VC), an electronic health record-based cohort of 146 530 veterans for whom radiology reports were available (N ¼2 977 739). We created a reference standard of radiology reports, represented each report by a feature set of words and Unified Medical Language System concepts, and then developed several support vector machine (SVM) classifiers for falls. We compared mutual information (MI) ranking and embedded feature selection approaches. The SVM classifier with MI feature selection was chosen to classify all radiology reports in VACS-VC. Results Our SVM classifier with MI feature selection achieved an area under the curve score of 97.04 on the test set. When applied to all the radiology reports in VACS-VC, 80 416 of these reports were classified as positive for a fall. Of these, 11 484 were associated with a fall-related external cause of injury code (E-code) and 68 932 were not, corresponding to 29 280 patients with potential fall-related injuries who could not have been found using E-codes. Discussion Feature selection was crucial to improving the classifier's performance. Feature selection with MI allowed us to select the number of discriminative features to use for classification, in contrast to the embedded feature selection method, in which the number of features is chosen automatically. Conclusion Machine learning is an effective method of identifying patients who have suffered a fall. The development of this classifier supplements the clinical researcher's toolkit and reduces dependence on under-coded structured electronic health record data.

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Unlocking echocardiogram measurements for heart disease research through natural language processing

BMC Cardiovascular Disorders, Jun 12, 2017

Background: In order to investigate the mechanisms of cardiovascular disease in HIV infected and ... more Background: In order to investigate the mechanisms of cardiovascular disease in HIV infected and uninfected patients, an analysis of echocardiogram reports is required for a large longitudinal multi-center study. Implementation: A natural language processing system using a dictionary lookup, rules, and patterns was developed to extract heart function measurements that are typically recorded in echocardiogram reports as measurement-value pairs. Curated semantic bootstrapping was used to create a custom dictionary that extends existing terminologies based on terms that actually appear in the medical record. A novel disambiguation method based on semantic constraints was created to identify and discard erroneous alternative definitions of the measurement terms. The system was built utilizing a scalable framework, making it available for processing large datasets. Results: The system was developed for and validated on notes from three sources: general clinic notes, echocardiogram reports, and radiology reports. The system achieved F-scores of 0.872, 0.844, and 0.877 with precision of 0.936, 0.982, and 0.969 for each dataset respectively averaged across all extracted values. Left ventricular ejection fraction (LVEF) is the most frequently extracted measurement. The precision of extraction of the LVEF measure ranged from 0.968 to 1.0 across different document types. Conclusions: This system illustrates the feasibility and effectiveness of a large-scale information extraction on clinical data. New clinical questions can be addressed in the domain of heart failure using retrospective clinical data analysis because key heart function measurements can be successfully extracted using natural language processing.

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Laplacian SVM Based Feature Selection Improves Medical Event Reports Classification

Timely reporting and analysis of adverse events and medical errors is critical to driving forward... more Timely reporting and analysis of adverse events and medical errors is critical to driving forward programs in patient-safety, however, due to the large numbers of event reports accumulating daily in health institutions, manually finding and labeling certain types of errors or events is becoming increasingly challenging. We propose to automatically classify/label event reports via semi-supervised learning which utilizes labeled as well as unlabeled event reports to complete the classification task. We focused on classifying two types of event reports: patient mismatches and weight errors. We downloaded 9405 reports from the Connecticut Children's Medical Center reporting system. We generated two samples of labeled and unlabeled reports containing 3155 and 255 for the patient mismatch and the weight error use cases respectively. We developed feature based Laplacian Support Vector machine (FS-LapSVM), a hybrid framework that combines feature selection with Laplacian Support Vector machine classifier (LapSVM). Superior performance of FS-LapSVM in finding patient weight error reports compared to LapSVM. Also, FS-LapSVM classifier outperformed standard LapSVM in classifying patient mismatch reports across all metrics.

Emergency care and the patient experience: Using sentiment analysis and topic modeling to understand the impact of the COVID-19 pandemic

Health and technology, Aug 13, 2021

The COVID-19 pandemic has presented many unique challenges to patient care especially in emergenc... more The COVID-19 pandemic has presented many unique challenges to patient care especially in emergency medicine. These challenges result in an altered patient experience. Patient experience refers to the cumulative impression made on patients during their medical visit and is measured by a standardized survey tool. Patient experience is considered a key measure of quality of care. The volume of survey data received makes it difficult to spot trends and concerns in patient comments. Topic modeling and sentiment analysis are well documented analytic techniques that can be used to gain insight into patient experience and make sense of vast quantities of data. This study examined three periods of time, pre, during and post-COVID-19 first wave in order to identify key trends in sentiment and topics related to patient experience. Previously collected, anonymized Press Ganey (PG) survey data was used from three northeastern emergency department that make up an academic emergency department. Data was collected for three contiguous time periods:

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Baseline Cluster Membership Demonstrates Positive Associations with First Occurrence of Multiple Gerontologic Outcomes Over 10 Years

Experimental Aging Research, Feb 27, 2015

Background: The potential of cluster analysis (CA) as a baseline predictor of multivariate geront... more Background: The potential of cluster analysis (CA) as a baseline predictor of multivariate gerontologic outcomes over a long period of time has not been previously demonstrated. Methods: Restricting candidate variables to a small group of established predictors of deleterious gerontologic outcomes, various CA methods were applied to baseline values from 754 nondisabled, community-living persons, aged 70 years or older. The best cluster solution yielded at baseline was subsequently used as a fixed explanatory variable in time-to-event models of the first occurrence of the following outcomes: any disability in four activities of daily living, any disability in four mobility measures, and death. Each outcome was recorded through a maximum of 129 months or death. Associations between baseline ordinal cluster level and first occurrence of all three outcomes were modeled over a 10 year period with proportional hazards regression and compared to the associations yielded by the analogous latent class analysis (LCA) solution. Results: The final cluster-defining variables were continuous measures of cognitive status and depressive symptoms, and dichotomous indicators of slow gait and exhaustion. The best solution yielded by baseline values of these variables was obtained with a K-means algorithm and cosine similarity, and consisted of three clusters representing increasing levels of impairment. After adjustment for age, gender, ethnic group, and number of chronic conditions, baseline ordinal cluster level demonstrated significantly positive association with all three outcomes over a 10 year period that were equivalent to those from the corresponding LCA solution. Conclusion: These findings suggest that baseline clusters based on previously established explanatory variables have potential to predict multivariate gerontologic outcomes over a long period of time.

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Mining Big Data in biomedicine and health care

Journal of Biomedical Informatics, Oct 1, 2016

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Identification of Patients with Nontraumatic Intracranial Hemorrhage Using Administrative Claims Data

Journal of stroke and cerebrovascular diseases, Dec 1, 2020

Introduction:Nontraumatic intracranial hemorrhage (ICH) is a neurological emergency of research i... more Introduction:Nontraumatic intracranial hemorrhage (ICH) is a neurological emergency of research interest; however, unlike ischemic stroke, has not been well studied in large datasets due to the lack of an established administrative claims-based definition. We aimed to evaluate both explicit diagnosis codes and machine learning methods to create a claims-based definition for this clinical phenotype.Methods:We examined all patients admitted to our tertiary medical center with a primary or secondary International Classification of Disease version 9 (ICD-9) or 10 (ICD-10) code for ICH in claims from any portion of the hospitalization in 2014-2015. As a gold standard, we defined the nontraumatic ICH phenotype based on manual chart review. We tested explicit definitions based on ICD-9 and ICD-10 that had been previously published in the literature as well as four machine learning classifiers including support vector machine (SVM), logistic regression with LASSO, random forest and xgboost. We report five standard measures of model performance for each approach.Results:A total of 1830 patients with 2145 unique ICD-10 codes were included in the initial dataset, of which 437 (24%) were true positive based on manual review. The explicit ICD-10 definition performed best (Sensitivity = 0.89 (95% Cl 0.85-0.92), Specificity = 0.83 (0.81-0.85), F-score = 0.73 (0.69-0.77)) and improves on an explicit ICD-9 definition (Sensitivity = 0.87 (0.83-0.90), Specificity = 0.77 (0.74-0.79), F-score = 0.67 (0.63-0.71). Among machine learning classifiers, SVM performed best (Sensitivity = 0.78 (0.75-0.82), Specificity = 0.84 (0.81-0.87), AUC = 0.89 (0.87-0.92), F-score = 0.66 (0.62-0.69)).Conclusions:An explicit ICD-10 definition can be used to accurately identify patients with a nontraumatic ICH phenotype with substantially better performance than ICD-9. An explicit ICD-10 based definition is easier to implement and quantitatively not appreciably improved with the additional application of machine learning classifiers. Future research utilizing large datasets should utilize this definition to address important research gaps.

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New JBI policy emphasizes clinically-meaningful novel machine learning methods

Journal of Biomedical Informatics, Mar 1, 2022

Suicide Risk on Twitter

International Journal of Knowledge Discovery in Bioinformatics, Jul 1, 2018

While many studies have explored the use of social media and behavioral changes ofindividuals,few... more While many studies have explored the use of social media and behavioral changes ofindividuals,fewexaminedtheutilityofusingsocialmediaforsuicidedetection and prevention. The study by Jashinsky et al. identified specific language patterns associatedwithasetoftwelvesuicideriskfactors.Theauthorsextendedthesemethods toassessthesignificanceofthelanguageusedonTwitterforsuicidedetection.This articlequantifiestheuseofTwittertoexpresssuiciderelatedlanguage,anditspotential todetectusersathighriskofsuicide.TheauthorssearchedTwitterfortweetsindicative of 12 suicide risk factors. This paper divided Twitter users into two groups: "high risk"and"atrisk"basedontwooftheriskfactors("self-harm"and"priorsuicide attempts")andexaminedlanguagepatternsbycomputingco-occurrencesoftermsin tweetswhichhelpedidentifyrelationshipsbetweensuicideriskfactorsinbothgroups.

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