LLMs can accomplish specialized medical knowledge tasks, however, equitable access is hindered by the extensive fine-tuning, data requirement, and limited to proprietary models. Open-source (OS) show performance improvements provide transparency compliance required in healthcare. We present OpenMedLM, a prompting platform delivering state-of-the-art (SOTA) for OS on benchmarks. evaluated foundation (7B-70B) benchmarks (MedQA, MedMCQA, PubMedQA, MMLU medical-subset) selected Yi34B developing...

Abstract Background Applied behavioral analysis (ABA) is regarded as the gold standard treatment for autism spectrum disorder (ASD) and has potential to improve outcomes patients with ASD. It can be delivered at different intensities, which are classified comprehensive or focused approaches. Comprehensive ABA targets multiple developmental domains involves 20–40 h/week of treatment. Focused individual behaviors typically 10–20 Determining appropriate intensity patient assessment by trained...

Pulmonary embolisms (PE) are life-threatening medical events, and early identification of patients experiencing a PE is essential to optimizing patient outcomes. Current tools for risk stratification limited unable predict events before their occurrence.We developed machine learning algorithm (MLA) designed identify at the clinical detection onset in an inpatient population.Three (ML) models were on electronic health record data from 63,798 surgical inpatients large US center. These included...

Mild cognitive impairment (MCI) is decline that can indicate future risk of Alzheimer’s disease (AD). We developed and validated a machine learning algorithm (MLA), based on gradient-boosted tree ensemble method, to analyze phenotypic data for individuals 55–88 years old (n = 493) diagnosed with MCI. Data were analyzed within multiple prediction windows averaged predict progression AD 24–48 months. The MLA outperformed the mini-mental state examination (MMSE) three comparison models at all...

Acute respiratory distress syndrome (ARDS) is a condition that often considered to have broad and subjective diagnostic criteria associated with significant mortality morbidity. Early accurate prediction of ARDS related conditions such as hypoxemia sepsis could allow timely administration therapies, leading improved patient outcomes.The aim this study perform an exploration how multilabel classification in the clinical setting can take advantage underlying dependencies between improve early...

Pulmonary embolism (PE) is a life-threatening condition associated with ~10% of deaths hospitalized patients. Machine learning algorithms (MLAs) which predict the onset pulmonary could enable earlier treatment and improve patient outcomes. However, extent to they generalize broader populations impacts their clinical utility.To conduct first large-scale external validation machine learning-based PE prediction model uses EHR data from three hours patient's hospital stay occurrence within next...

Abstract Background Prostate cancer (PCa) screening is not routinely conducted in men aged 55 and younger, although this age group accounts for more than 10% of cases. Polygenic risk scores (PRSs) patient data applied toward early prediction PCa may lead to earlier interventions increased survival. We have developed machine learning (ML) models predict under using PRSs combined with data. Methods a retrospective study on 91,106 male patients 35–55 the UK Biobank database. Five gradient...

The objective of this study was to assess seven configurations six convolutional deep neural network architectures for classification chest X-rays (CXRs) as COVID-19 positive or negative.The primary dataset consisted 294 and negative CXRs, the latter comprising roughly equally many pneumonia, emphysema, fibrosis, healthy images. We used common architectures, VGG16, DenseNet121, DenseNet201, MobileNet, NasNetMobile InceptionV3. studied models (one each architecture) which were pre-trained on...

Strokes represent a leading cause of mortality globally. The evolution developing new therapies is subject to safety and efficacy testing in clinical trials, which operate limited timeframe. To maximize the impact these patient cohorts for whom ischemic stroke likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods order trials prevention treatment safety.A retrospective study was performed using 41,970...

LLMs have become increasingly capable at accomplishing a range of specialized-tasks and can be utilized to expand equitable access medical knowledge. Most involved extensive fine-tuning, leveraging specialized data significant, thus costly, amounts computational power. Many the top performing are proprietary their is limited very few research groups. However, open-source (OS) models represent key area growth for due significant improvements in performance an inherent ability provide...

The aim of the study was to quantify relationship between acute kidney injury (AKI) and alcohol use disorder (AUD).We used a large academic medical center MIMIC-III databases AKI disease mortality burden as well progression in AUD non-AUD subpopulations. We dataset compare two different methods encoding AKI: ICD-9 codes, Kidney Disease: Improving Global Outcomes scheme (KDIGO) definition. In addition subpopulation, we also present analyses for hepatorenal syndrome (HRS) alcohol-related...

In this work, we present a machine learning method to guide an ultrasound operator towards selected area of interest. Unlike other automatic medical imaging methods, is one the few modalities where operator's skill and training are critical in obtaining high quality images. Additionally, due recent advances affordability portability technology, its utilization by non-experts has increased. Thus, there growing need for intelligent systems that have ability assist operators both clinical...

<sec> <title>BACKGROUND</title> Acute respiratory distress syndrome (ARDS) is a condition that often considered to have broad and subjective diagnostic criteria associated with significant mortality morbidity. Early accurate prediction of ARDS related conditions such as hypoxemia sepsis could allow timely administration therapies, leading improved patient outcomes. </sec> <title>OBJECTIVE</title> The aim this study perform an exploration how multilabel classification in the clinical setting...

Timely recognition of sepsis in hospital patients increases the likelihood patient survival. The value alert systems clinical settings is diminished if these alerts are generated after clinically relevant times: clinician suspicion (clinical evaluation or treatment for sepsis) onset (defined by SOFA score). We evaluate and compare two models - standard early using traditional time-agnostic methods (area under curve: AUC; true positive rate: TPR) a time-dependent approach with redefined...

ABSTRACT Objective The objective of this study is to quantify the relationship between acute kidney injury (AKI) and alcohol use disorder (AUD), in terms disease burden, mortality burden progression. Methods We used University California, San Francisco Medical Center Francisco, CA (UCSF) Information Mart for Intensive Care (MIMIC-III) databases AKI as well progression AUD non-AUD subpopulations. MIMIC-III dataset compare two different methods encoding AKI: ICD-9 codes, 2012 Kidney Disease:...

Abstract Background Despite the emergence of several promising machine learning models for prediction patients at risk sepsis, investigation factors that contribute to false positive rates has not been performed. Here, we conducted a analysis determine sources alerts and examine mitigation methodology reduction in sepsis prediction. Methods Analysis predictions from our model was patient populations were stratified by underlying conditions positives. Sensitivity specificity results each...

Abstract Background Prostate cancer (PCa) screening is not routinely conducted in men 55 and younger, although this age group accounts for more than 10% of cases. Polygenic risk scores (PRSs) patient data applied towards early prediction PCa may lead to earlier interventions increased survival. We have developed machine learning models predict under using PRSs combined with data. Methods a retrospective study on 91,106 male patients aged 35 the UK Biobank database. Five gradient boosting...

Abstract Purpose Fractures in older adults are a significant cause of morbidity and mortality, particularly for post-menopausal women with osteoporosis. Prevention is key managing fractures this population may include identifying individuals at high fracture risk providing therapeutic treatment to mitigate risk. This study aimed develop machine learning prediction tool overcome the limitations existing methods by incorporating additional factors short-term predictions. Methods We developed...

Abstract When training neural networks (NNs) on time-series inpatient data, as the number of outcomes predicted diversifies, NN both generalizes better external validation and reaches higher performance in similar numbers epochs. We demonstrated this context predicting decompensation Acute Respiratory Distress Syndrome (ARDS). The outperformed gradient boosted trees, achieving an area under receiver operating characteristic 0.86 hold out test set hospitals not included set. estimated real...