Association of Diagnostic Discrepancy with Length of Stay and Mortality in Congestive Heart Failure Patients Admitted to the Emergency Department.

The goal of this study was to analyze diagnostic discrepancies between emergency department (ED) and hospital discharge diagnoses in patients with congestive heart failure admitted to the ED. Using a synthetic dataset from the Department of Veterans Affairs, the patients' primary diagnoses were compared at two levels: diagnostic category and body system. With 12,621 patients and 24,235 admission cases, the study found a 58% mismatch rate at the category level, which was reduced to 30% at the body system level. Diagnostic categories associated with higher levels of mismatch included aplastic anemia, pneumonia, and bacterial infections. In contrast, diagnostic categories associated with lower levels of mismatch included alcohol-related disorders, COVID-19, cardiac dysrhythmias, and gastrointestinal hemorrhage. Further investigation revealed that diagnostic mismatches are associated with longer hospital stays and higher mortality rates. These findings highlight the importance of reducing diagnostic uncertainty, particularly in specific diagnostic categories and body systems, to improve patient care following ED admission.

Identifying Determinants of Survival Disparities in Multiple Myeloma Patients Using Electronic Health Record Data.

Multiple myeloma (MM) is one of the most common hematological malignancies. The goal of this study was to analyze the sociodemographic, economic, and genetic characteristics of long-term and short-term survival of multiple myeloma patients using EHR data from an academic medical center in New York City. The de-identified analytical dataset comprised 2,111 patients with MM who were stratified based on the length of survival into two groups. Demographic variables, cancer stage, income level, and genetic mutations were analyzed using descriptive statistics and logistic regression. Age, race, and cancer stage were all significant factors that affected the length of survival of multiple myeloma patients. In contrast, gender and income level were not significant factors based on the multivariate adjusted analysis. Older adults, African American patients, and patients who were diagnosed with stage III of multiple myeloma were the people most likely to exhibit short-term survival after the MM diagnosis.

Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors.

This research aimed to develop a model for real-time prediction of aerobic exercise exertion levels. ECG signals were registered during 16-minute cycling exercises. Perceived ratings of exertion (RPE) were collected each minute from the study participants. Based on the reported RPE, each consecutive minute of the exercise was assigned to the "high exertion" or "low exertion" class. The characteristics of heart rate variability (HRV) in time and frequency domains were used as predictive features. The top ten ranked predictive features were selected using the minimum redundancy maximum relevance (mRMR) algorithm. The support vector machine demonstrated the highest accuracy with an F1 score of 82%.

Quality of reporting of integrative Chinese and Western medicine intervention in randomized controlled trials of ulcerative colitis: a review.

BACKGROUND: Integrative Chinese and Western medicine (ICWM) is commonly used for the treatment of ulcerative colitis (UC) in clinical practice. However, it is unclear whether the details of ICWM interventions, such as selection rationale, implementation design, and potential interactions, were adequately reported. Therefore, this study aimed to assess the quality of reporting in the ICWM interventional randomized controlled trials (RCTs) of UC and to identify the common problems if any. METHODS: Through a search of 10 international electronic databases, we identified RCTs of UC with ICWM interventions published in English or Chinese from the inception date of each database up to 16 June 2023. Literature screening was strictly conducted based on the inclusion and exclusion criteria of the Population, Concept, and Context (PCC) framework. The general characteristics of the included studies were described. The quality of reporting was assessed according to three checklists, including the CONSORT (Consolidated Standards of Reporting Trials) with 36 items (except for one item 1b about abstract), the CONSORT for Abstracts (17 items), and a self-designed ICWM-related checklist (27 items covering design rationale, intervention details, outcome assessments, and analysis). The reporting scores of RCTs published before and after 2010 were compared. RESULTS: A total of 1458 eligible RCTs were included. For the reporting compliance, the median score (interquartile ranges) of the CONSORT (72 score in total), the CONSORT for Abstract (34 score), and ICWM-related (54 score) items was 21 (18-25), 13 (12-15), and 18 (15-21), respectively. Although the time period comparisons showed that reporting quality of included publications improved significantly after the CONSORT 2010 issued (P < 0.01), more than 50% of items were evaluated as poor quality (reporting rate < 65%) among each checklist, especially in the CONSORT for Abstract and ICWM-specific items. CONCLUSION: Although CONSORT appears to have enhanced the reporting of RCTs in UC, the quality of ICWM specifics is variable and in need of improvement. Reporting guidelines of the ICWM recommendations should be developed to improve their quality.

Exercise Exertion Levels Prediction Based on Real-Time Wearable Physiological Signal Monitoring.

The real-time revolutions per minute (RPM) data, ECG signal, pulse rate, and oxygen saturation levels were collected during 16-minute cycling exercises. In parallel, ratings of perceived exertion (RPE) were collected each minute from the study participants. A 2-minute moving window, with one minute shift, was applied to each 16-minute exercise session to divide it into a total of fifteen 2-minute windows. Based on the self-reported RPE, each exercise window was labeled as "high exertion" or "low exertion" classes. The heart rate variability (HRV) characteristics in time and frequency domains were extracted from the collected ECG signals for each window. In addition, collected oxygen saturation levels, pulse rate, and RPMs were averaged for each window. The best predictive features were then selected using the minimum redundancy maximum relevance (mRMR) algorithm. Top selected features were then used to assess the accuracy of five ML classifiers to predict the level of exertion. The Naïve Bayes model demonstrated the best performance with an accuracy of 80% and an F1 score of 79%.

Using Electronic Medical Records and Clinical Notes to Predict the Outcome of Opioid Treatment Program.

Opioid addiction is a serious public health problem in the US, and this study aimed to explore how natural language processing (NLP) can be used to identify factors that contribute to distress in individuals with opioid addiction, and then use this information along with structured data to predict the outcome of opioid treatment programs (OTP). The study analyzed medical records data and clinical notes of 1,364 patients, out of which 136 succeeded in the program and 1,228 failed. The results showed that several factors influenced the success of patients in the program, including sex, race, education, employment, secondary substance, tobacco use, and type of residences. XGBoost with down sampling was the best model. The accuracy of the model was 0.71 and the AUC score was 0.64. The study highlights the importance of using both structured and unstructured data to evaluate the effectiveness of OTP.

Automated Classification of Exercise Exertion Levels Based on Real-Time Wearable Physiological Signal Monitoring.

This study aimed to build machine learning (ML) algorithms for the automated classification of cycling exercise exertion levels using data from wearable devices. The best predictive features were selected using the minimum redundancy maximum relevance algorithm (mRMR). Top selected features were then used to build and assess the accuracy of five ML classifiers to predict the level of exertion. The Naïve Bayes showed the best F1 score of 79%. The proposed approach may be used for real-time monitoring of exercise exertion.

Development and Evaluation of Wireless Interfaces to Monitor and Control Cycling Exercise During Home Telerehabilitation.

Purpose: This paper focuses on developing and testing three versions of interactive bike (iBikE) interfaces for remote monitoring and control of cycling exercise sessions to promote upper and lower limb rehabilitation. Methods: Two versions of the system, which consisted of a portable bike and a tablet PC, were designed to communicate through either Bluetooth low energy (BLE) or Wi-Fi interfaces for real-time monitoring of exercise progress by both the users and their clinical team. The third version of the iBikE system consisted of a motorized bike and a tablet PC. It utilized conventional Bluetooth to implement remote control of the motorized bike's speed during an exercise session as well as to provide real-time visualization of the exercise progress. We developed three customized tablet PC apps with similar user interfaces but different communication protocols for all the platforms to provide a graphical representation of exercise progress. The same microcontroller unit (MCU), ESP-32, was used in all the systems. Results: Each system was tested in 1-minute exercise sessions at various speeds. To evaluate the accuracy of the measured data, in addition to reading speed values from the iBikE app, the cycling speed of the bikes was measured continuously using a tachometer. The mean differences of averaged RPMs for both data sets were calculated. The calculated values were 0.38 ± 0.03, 0.25 ± 0.27, and 6.7 ± 3.3 for the BLE system, the Wi-Fi system, and the conventional Bluetooth system, respectively. Conclusion: All interfaces provided sufficient accuracy for use in telerehabilitation.

Exercise Exertion Level Prediction Using Data from Wearable Physiologic Monitors.

This study aims to develop machine learning (ML) algorithms to predict exercise exertion levels using physiological parameters collected from wearable devices. Real-time ECG, oxygen saturation, pulse rate, and revolutions per minute (RPM) data were collected at three intensity levels during a 16-minute cycling exercise. Parallel to this, throughout each exercise session, the study subjects' ratings of perceived exertion (RPE) were gathered once per minute. Each 16-minute exercise session was divided into a total of eight 2-minute windows. Each exercise window was labeled as "high exertion," or "low exertion" classes based on the self-reported RPEs. For each window, the gathered ECG data were used to derive the heart rate variability (HRV) features in the temporal and frequency domains. Additionally, each window's averaged RPMs, heart rate, and oxygen saturation levels were calculated to form all the predictive features. The minimum redundancy maximum relevance algorithm was used to choose the best predictive features. Top selected features were then used to assess the accuracy of ten ML classifiers to predict the next window's exertion level. The k-nearest neighbors (KNN) model showed the highest accuracy of 85.7% and the highest F1 score of 83%. An ensemble model showed the highest area under the curve (AUC) of 0.92. The suggested method can be used to automatically track perceived exercise exertion in real-time.

Using Natural Language Processing of Clinical Notes to Predict Outcomes of Opioid Treatment Program.

Potential of natural language processing (NLP) in extracting patient's information from clinical notes of opioid treatment programs (OTP) and leveraging it in development of predictive models has not been fully explored. The goal of this study was to assess potential of NLP in identifying legal, social, mental, medical and family environment-based determinants of distress from clinical narratives of patients with opioid addiction, and then using this information in predicting OTP outcomes. Around 63% of patients reported improvements after completing OTP. We compared the results of logistics regression and random forest for predictive modeling. Random forest model performed slightly better than logistic regression (75% F1 score) with 74% accuracy. Clinical Relevance- Psychiatric and medical disorders, social, legal and family-based distress are important determinants of distress in patients enrolled in OTP. These information are often recorded in clinical notes. Extraction of this information and their utilization as features in machine learning models will lead to the enhancement of the performance of the OTP outcome predictive models.

Genetic subtypes and phenotypic characteristics of 110 patients with Prader-Willi syndrome.

BACKGROUND: Prader-Willi syndrome (PWS) is a complex disorder caused by impaired paternally expressed genes on chromosome 15q11-q13. Variable findings have been reported about the phenotypic differences among PWS genetic subtypes. METHODS: A total of 110 PWS patients were diagnosed from 8,572 pediatric patients included from July 2013 to December 2021 by MLPA and MS-MLPA assays. Atypical deletions were defined by genomic CNV-sequencing. Maternal uniparental disomy (UPD) was subgrouped by microsatellite genotyping. Clinical data were collected for phenotype-genotype associations. Twenty-one patients received growth hormone (GH) treatment, and the anthropometric and laboratory parameters were evaluated and compared. RESULTS: Genetically, the 110 patients with PWS included 29 type I deletion, 56 type II deletion, 6 atypical deletion, 11 heterodisomy UPD, and 8 isodisomy UPD. The UPD group had significantly higher maternal age (31.4 ± 3.4 vs 27.8 ± 3.8 years), more anxiety (64.29% vs 26.09%) and autistic traits (57.14% vs 26.09%), and less hypopigmentation (42.11% vs 68.24%) and skin picking (42.86% vs 71.01%) than the deletion group. The type I deletion group was diagnosed at earlier age (3.7 ± 3.3 vs 6.2 ± 3.2 years) and more common in speech delay (95.45% vs 63.83%) than the type II. The isodisomy UPD group showed a higher tendency of anxiety (83.33% vs 50%) than the heterodisomy. GH treatment for 1 year significantly improved the SDS of height (- 0.43 ± 0.68 vs - 1.32 ± 1.19) and IGF-I (- 0.45 ± 0.48 vs - 1.97 ± 1.12). No significant changes were found in thyroid function or glucose/lipid metabolism. CONCLUSION: We explored the physical, psychological and behavioral phenotype-genotype associations as well as the GH treatment effect on PWS from a large cohort of Chinese pediatric patients. Our data might promote pediatricians' recognition and early diagnosis of PWS.

Assessing Disparities in COVID-19 Testing Using National COVID Cohort Collaborative.

With NCATS National COVID Cohort Collaborative (N3C) dataset, we evaluated 14 billion medical records and identified more than 12 million patients tested for COVID-19 across the US. To assess potential disparities in COVID-19 testing, we chose ten US states and then compared each state's population distribution characteristics with distribution of corresponding characteristics from N3C. Minority racial groups were more prevalent in the N3C dataset as compared to census data. The proportion of Hispanics and Latinos in N3C was slightly lower than in the state census. Patients over 65 years old had higher representation in the N3C dataset and patients under 18 were underrepresented. Proportion of females in the N3C was higher compared with the state data. All ten states in N3C showed a higher representation of urban population versus rural population compared to census data.

Using Machine Learning to Identify No-Show Telemedicine Encounters in a New York City Hospital.

No-show visits are a serious problem for healthcare centers. It costs a major hospital over 15 million dollars annually. The goal of this paper was to build machine learning models to identify potential no-show telemedicine visits and to identify significant factors that affect no-show visits. 257,293 telemedicine sessions and 152,164 unique patients were identified in Mount Sinai Health System between March 2020 and December 2020. 5,124 (2%) of these sessions were no-show encounters. Extreme Gradient Boosting (XGB) with under-sampling was the best machine learning model to identify no-show visits using telemedicine service. The accuracy was 0.74, with an AUC score of 0.68. Patients with previous no-show encounters, non-White or non-Asian patients, and patients living in Bronx and Manhattan were all important factors for no-show encounters. Furthermore, providers' specialties in psychiatry and nutrition, and social workers were more susceptible to higher patient no-show rates.

Using EHR Data to Identify Social Determinants of Health Affecting Disparities in Cancer Survival.

The aim of this pilot study was to identify social determinants of health (SDH) that affect disparities in cancer survival. A limited dataset was generated by querying electronic medical records (EHR) from an academic medical center in New York City between January 2003 and November 2020. Socio-demographic characteristics that affected survival in 22,096 cancer patients were analyzed using descriptive statistics and logistic regression analyses. Two subsets of adult patients were identified: patients who were deceased less than 1 year after diagnosis and patients who survived over 5 years after diagnosis. Percentage of individuals with short survival in Blacks and Whites was respectively 41.4% and 22.2% for lung cancer, 9.8% and 7.1% for colorectal cancer, 2.9% and 0.7% for breast cancer, 6.8% and 4.0% for multiple myeloma, and 1.4% and 0.8% for prostate cancer. Logistic regression identified SDH factors increasing likelihood of shorter survival that included older age, and being male, Black or Hispanic. We concluded that further analysis of a broader spectrum of SDH factors is warranted.

Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19.

The goal of this paper was to assess if mortality in COVID-19 positive patients is affected by a history of asthma in anamnesis. A total of 48,640 COVID-19 positive patients were included in our analysis. A propensity score matching was carried out to match each asthma patient with two patients without history of chronic respiratory diseases in one stratum. Matching was based on age, comorbidity score, and gender. Conditional logistics regression was used to compute within each strata. There were 5,557 strata in this model. We included asthma, ethnicity, race, and BMI as risk factors. The results showed that the presence of asthma in anamnesis is a statistically significant protective factor from mortality in COVID-19 positive patients.

Identifying Determinants of Disparities in Lung Cancer Survival Rates from Electronic Health Record Data.

The goal of this pilot study was to identify significant factors that affect disparities in lung cancer survival. A de-identified dataset was generated by querying electronic health records (EHR) from an academic medical center in New York City between January 2003 and November 2020. Socio-demographic characteristics, cancer stage, and genetic profile were analyzed using logistic regression. Two subsets of adult patients were identified: patients who were deceased less than 1 year after diagnosis and patients who survived over 5 years after diagnosis. Male, Black and Hispanic patients and those who were diagnosed in later stages were the people most susceptible to a shorter length of survival after cancer diagnoses. In addition, we identified three genetic oncodrivers (KRAS, EGFR and TP53) which were highly correlated with the length of survival after lung cancer diagnoses and their distribution was associated with race. We concluded that EHR data provide important insights on cancer survival disparities.

Comparative Analysis of Patient Distress in Opioid Treatment Programs using Natural Language Processing.

Psychiatric and medical disorders, social and family environment, and legal distress are important determinants of distress that impact the effectiveness of the treatment in opioid treatment program (OTP). This information is not routinely captured in electronic health record, but may be found in clinical notes. This study aims to explore the feasibility and effectiveness of natural language processing (NLP) strategy for identifying legal, social, mental and medical determinates of distress along with emotional pain rooted in family environment from clinical narratives of patients with opioid addiction, and then using this information to find its impact on OTP outcomes. Analysis in this study showed that mental and legal distress significantly impact the result of the treatment in OTP.

Machine Learning Approaches for Early Prostate Cancer Prediction Based on Healthcare Utilization Patterns.

The goal of this study was to build a machine learning model for early prostate cancer prediction based on healthcare utilization patterns. We examined the frequency and pattern changes of healthcare utilization in 2916 prostate cancer patients 3 years prior to their prostate cancer diagnoses and explored several supervised machine learning techniques to predict possible prostate cancer diagnosis. Analysis of patients' medical activities between 1 year and 2 years prior to their prostate cancer diagnoses using XGBoost model provided the best prediction accuracy with high F1 score (0.9) and AUC score (0.73). These pilot results indicated that application of machine learning to healthcare utilization patterns may result in early identification of prostate cancer diagnosis.

Latent COVID-19 Clusters in Patients with Opioid Misuse.

The goal of this paper is to apply unsupervised machine learning techniques in order to discover latent clusters in patients who have opioid misuse and also undergone COVID-19 testing. Target dataset has been constructed based on COVID-19 testing results at Mount Sinai Health System and opioid treatment program (OTP) information from New York State Office of Addiction Service and Support (OASAS). The dataset was preprocessed using factor analysis for mixed data (FAMD) method and then K-means algorithm along with elbow method were used to determine the number of optimal clusters. Four patient clusters were identified among which the fourth cluster constituted the maximum percentage of positive COVID-19 test results (20%). Compared to the other clusters, this cluster has the highest percentage of African Americans. This cluster has also the highest mortality rate (16.52%), hospitalization rate after receiving the COVID-19 test result (72.17%, use of ventilator (7.83%) and ICU admission rate (47.83%). In addition, this cluster has the highest percentage of patients with at least one chronic disease (99.13%) and age-adjusted comorbidity score more than 1 (83.48%). Longer participation in OTP was associated with the highest morbidity and mortality from COVID-19.