Arash Kia
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View article: Exploring Named Entity Recognition Potential and the Value of Tailored Natural Language Processing Pipelines for Radiology, Pathology, and Progress Notes in Clinical Decision Support: Quantitative Study
Exploring Named Entity Recognition Potential and the Value of Tailored Natural Language Processing Pipelines for Radiology, Pathology, and Progress Notes in Clinical Decision Support: Quantitative Study Open
Background Clinical notes house rich, yet unstructured, patient data, making analysis challenging due to medical jargon, abbreviations, and synonyms causing ambiguity. This complicates real-time extraction for decision support tools. Objec…
View article: Counseling and Training: Transformation of Higher Education through Outcome Based Education (OBE) Curriculum Development at STT Arastamar Wamena
Counseling and Training: Transformation of Higher Education through Outcome Based Education (OBE) Curriculum Development at STT Arastamar Wamena Open
The implementation method is through counseling and training attended by 38 participants from STT Arastamar Wamena, STKIP Abdi Wacana Wamena, STKIP Kristen Wamena, STAK Dispora Wamena, STT Reformasi Wamena, and Sekolah Tinggi Agama Kristen…
View article: Machine Learning Multimodal Model for Delirium Risk Stratification
Machine Learning Multimodal Model for Delirium Risk Stratification Open
Importance Automating the identification of risk for developing hospital delirium with models that use machine learning (ML) could facilitate more rapid prevention, identification, and treatment of delirium. However, there are very few rep…
View article: Electronic-Medical-Record-Driven Machine Learning Predictive Model for Hospital-Acquired Pressure Injuries: Development and External Validation
Electronic-Medical-Record-Driven Machine Learning Predictive Model for Hospital-Acquired Pressure Injuries: Development and External Validation Open
Background: Hospital-acquired pressure injuries (HAPIs) affect approximately 2.5 million patients annually in the United States, leading to increased morbidity and healthcare costs. Current rule-based screening tools, such as the Braden Sc…
View article: Biases in Artificial Intelligence Application in Pain Medicine
Biases in Artificial Intelligence Application in Pain Medicine Open
Artificial Intelligence (AI) has the potential to optimize personalized treatment tools and enhance clinical decision-making. However, biases in AI, arising from sex, race, socioeconomic status (SES), and statistical methods, can exacerbat…
View article: Development and Validation of Natural Language Processing (NLP)‐Based Risk Prediction Model for Cognitive Impairment in Geriatric Patients
Development and Validation of Natural Language Processing (NLP)‐Based Risk Prediction Model for Cognitive Impairment in Geriatric Patients Open
Background Dementia poses a significant global crisis, yet 60% of cases go undetected, particularly among specific sub‐populations. Timely diagnosis is crucial for implementing early intervention strategies. Challenges of current screening…
View article: Development and Validation of Natural Language Processing (NLP)‐Based Risk Prediction Model for Cognitive Impairment in Geriatric Patients
Development and Validation of Natural Language Processing (NLP)‐Based Risk Prediction Model for Cognitive Impairment in Geriatric Patients Open
Background Dementia poses a significant global crisis, yet 60% of cases go undetected, particularly among specific sub‐populations. Timely diagnosis is crucial for implementing early intervention strategies. Challenges of current screening…
View article: Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management
Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management Open
Background Predicting hospitalization from nurse triage notes has the potential to augment care. However, there needs to be careful considerations for which models to choose for this goal. Specifically, health systems will have varying deg…
View article: Fairness in Predicting Cancer Mortality Across Racial Subgroups
Fairness in Predicting Cancer Mortality Across Racial Subgroups Open
Importance Machine learning has potential to transform cancer care by helping clinicians prioritize patients for serious illness conversations. However, models need to be evaluated for unequal performance across racial groups (ie, racial b…
View article: Speech markers of depression dimensions across cognitive status
Speech markers of depression dimensions across cognitive status Open
Introduction Depression and its components significantly impact dementia prediction and severity, necessitating reliable objective measures for quantification. Methods We investigated associations between emotion‐based speech measures (val…
View article: Development and Validation of a Deep Learning Classifier Using Chest Radiographs to Predict Extubation Success in Patients Undergoing Invasive Mechanical Ventilation
Development and Validation of a Deep Learning Classifier Using Chest Radiographs to Predict Extubation Success in Patients Undergoing Invasive Mechanical Ventilation Open
The decision to extubate patients on invasive mechanical ventilation is critical; however, clinician performance in identifying patients to liberate from the ventilator is poor. Machine Learning-based predictors using tabular data have bee…
View article: Assessing calibration and bias of a deployed machine learning malnutrition prediction model within a large healthcare system
Assessing calibration and bias of a deployed machine learning malnutrition prediction model within a large healthcare system Open
Malnutrition is a frequently underdiagnosed condition leading to increased morbidity, mortality, and healthcare costs. The Mount Sinai Health System (MSHS) deployed a machine learning model (MUST-Plus) to detect malnutrition upon hospital …
View article: Speech markers of depression dimensions across the spectrum of cognitive functions
Speech markers of depression dimensions across the spectrum of cognitive functions Open
Background Depression is a common among patients with cognitive impairment and encompass different dimensions, which may vary in their association with cognitive outcomes and biological substrates. Previous studies have shown a shift towar…
View article: Assessing Calibration and Bias of a Deployed Machine Learning Malnutrition Prediction Model within a Large Healthcare System
Assessing Calibration and Bias of a Deployed Machine Learning Malnutrition Prediction Model within a Large Healthcare System Open
Introduction Malnutrition is a frequently underdiagnosed condition leading to increased morbidity, mortality and healthcare costs. The Mount Sinai Health System (MSHS) deployed a machine learning model (MUST-Plus) to detect malnutrition up…
View article: A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation
A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation Open
Background Early prediction of the need for invasive mechanical ventilation (IMV) in patients hospitalized with COVID-19 symptoms can help in the allocation of resources appropriately and improve patient outcomes by appropriately monitorin…
View article: Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management (Preprint)
Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management (Preprint) Open
BACKGROUND Predicting hospitalization from nurse triage notes has the potential to augment care. However, there needs to be careful considerations for which models to choose for this goal. Specifically, health systems will have varying de…
View article: Comparative Analysis of a Large Language Model and Machine Learning Method for Prediction of Hospitalization from Nurse Triage Notes: Implications for Machine Learning-based Resource Management
Comparative Analysis of a Large Language Model and Machine Learning Method for Prediction of Hospitalization from Nurse Triage Notes: Implications for Machine Learning-based Resource Management Open
Predicting hospitalization from nurse triage notes has significant implications in health informatics. To this end, we compared the performance of the deep-learning transformer-based model, bio-clinical-BERT, with a bag-of-words logistic r…
View article: A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation (Preprint)
A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation (Preprint) Open
BACKGROUND Early prediction of the need for invasive mechanical ventilation (IMV) in patients hospitalized with COVID-19 symptoms can help in the allocation of resources appropriately and improve patient outcomes by appropriately monitori…
View article: Implementing a Machine Learning Screening Tool for Malnutrition: Insights From Qualitative Research Applicable to Other Machine Learning–Based Clinical Decision Support Systems
Implementing a Machine Learning Screening Tool for Malnutrition: Insights From Qualitative Research Applicable to Other Machine Learning–Based Clinical Decision Support Systems Open
Background Machine learning (ML)–based clinical decision support systems (CDSS) are popular in clinical practice settings but are often criticized for being limited in usability, interpretability, and effectiveness. Evaluating the implemen…
View article: Real-time Machine Learning Alerts to Prevent Escalation of Care: A Pragmatic Clinical Trial
Real-time Machine Learning Alerts to Prevent Escalation of Care: A Pragmatic Clinical Trial Open
Importance Automated machine learning algorithms have been shown to outperform older methods in predicting clinical deterioration requiring escalation of care, but rigorous prospective data on their real-world efficacy are limited. Objecti…
View article: Predicting Adult Hospital Admission from Emergency Department Using Machine Learning: An Inclusive Gradient Boosting Model
Predicting Adult Hospital Admission from Emergency Department Using Machine Learning: An Inclusive Gradient Boosting Model Open
Background and aim: We analyzed an inclusive gradient boosting model to predict hospital admission from the emergency department (ED) at different time points. We compared its results to multiple models built exclusively at each time point…
View article: Implementing a Machine Learning Screening Tool for Malnutrition: Insights From Qualitative Research Applicable to Other Machine Learning–Based Clinical Decision Support Systems (Preprint)
Implementing a Machine Learning Screening Tool for Malnutrition: Insights From Qualitative Research Applicable to Other Machine Learning–Based Clinical Decision Support Systems (Preprint) Open
BACKGROUND Machine learning (ML)–based clinical decision support systems (CDSS) are popular in clinical practice settings but are often criticized for being limited in usability, interpretability, and effectiveness. Evaluating the impleme…
View article: An Evaluation and Dissemination Model for the Machine Learning Embedded System Lifecycle in Clinical Practice Settings
An Evaluation and Dissemination Model for the Machine Learning Embedded System Lifecycle in Clinical Practice Settings Open
Machine learning (ML) algorithms are gaining popularity in clinical practice settings due to their ability to process information in ways that augment human reasoning. While tools that rely on output from ML algorithms in the healthcare se…
View article: Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19
Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19 Open
Background and objectives AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited. Design, setting, partic…
View article: SARS-CoV-2 and Stroke Characteristics
SARS-CoV-2 and Stroke Characteristics Open
Background and Purpose: Stroke is reported as a consequence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in several reports. However, data are sparse regarding the details of these patients in a multinational a…
View article: Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City
Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City Open
Objective The COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare…
View article: Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation Open
Background COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients.…