OpenMedLM: prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models.

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

Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer's Disease.

Mild cognitive impairment (MCI) is cognitive decline that can indicate future risk of Alzheimer's disease (AD). We developed and validated a machine learning algorithm (MLA), based on a gradient-boosted tree ensemble method, to analyze phenotypic data for individuals 55-88 years old ( = 493) diagnosed with MCI. Data were analyzed within multiple prediction windows and averaged to predict progression to AD within 24-48 months.

Machine learning determination of applied behavioral analysis treatment plan type.

Background: Applied behavioral analysis (ABA) is regarded as the gold standard treatment for autism spectrum disorder (ASD) and has the potential to improve outcomes for patients with ASD. It can be delivered at different intensities, which are classified as comprehensive or focused treatment approaches. Comprehensive ABA targets multiple developmental domains and involves 20-40 h/week of treatment.

Early prediction of prostate cancer risk in younger men using polygenic risk scores and electronic health records.

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) and patient data applied toward early prediction of PCa may lead to earlier interventions and increased survival. We have developed machine learning (ML) models to predict PCa risk in men 55 and under using PRSs combined with patient data.

Multitask Learning With Recurrent Neural Networks for Acute Respiratory Distress Syndrome Prediction Using Only Electronic Health Record Data: Model Development and Validation Study.

Background: Acute respiratory distress syndrome (ARDS) is a condition that is often considered to have broad and subjective diagnostic criteria and is associated with significant mortality and morbidity. Early and accurate prediction of ARDS and related conditions such as hypoxemia and sepsis could allow timely administration of therapies, leading to improved patient outcomes.

Objective: The aim of this study is to perform an exploration of how multilabel classification in the clinical setting can take advantage of the underlying dependencies between ARDS and related conditions to improve early prediction of ARDS in patients.

Massive external validation of a machine learning algorithm to predict pulmonary embolism in hospitalized patients.

Background: Pulmonary embolism (PE) is a life-threatening condition associated with ~10% of deaths of hospitalized patients. Machine learning algorithms (MLAs) which predict the onset of pulmonary embolism (PE) could enable earlier treatment and improve patient outcomes. However, the extent to which they generalize to broader patient populations impacts their clinical utility.

Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.

Background: Strokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified.

Mortality, disease progression, and disease burden of acute kidney injury in alcohol use disorder subpopulation.

Background: The aim of the study was to quantify the relationship between acute kidney injury (AKI) and alcohol use disorder (AUD).

Methods: We used a large academic medical center and the MIMIC-III databases to quantify AKI disease and mortality burden as well as AKI disease progression in the AUD and non-AUD subpopulations. We used the MIMIC-III dataset to compare two different methods of encoding AKI: ICD-9 codes, and the Kidney Disease: Improving Global Outcomes scheme (KDIGO) definition.