AIMC Topic: Cohort Studies

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Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We aimed to investigate bias in applying machine learning to predict real-world individual treatment effects.

Machine Learning Algorithm Identifies Patients at High Risk for Early Complications After Intracranial Tumor Surgery: Registry-Based Cohort Study.

Neurosurgery
INTRODUCTION: Reliable preoperative identification of patients at high risk for early postoperative complications occurring within 24 h (EPC) of intracranial tumor surgery can improve patient safety and postoperative management. Statistical analysis ...

Machine-learned analysis of the association of next-generation sequencing-based genotypes with persistent pain after breast cancer surgery.

Pain
Cancer and its surgical treatment are among the most important triggering events for persistent pain, but additional factors need to be present for the clinical manifestation, such as variants in pain-relevant genes. In a cohort of 140 women undergoi...

Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases.

Science translational medicine
Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical i...

Relationship Between Very Cold Outside Weather and Surgical Outcome: Integrating Shallow and Deep Artificial Neural Nets.

Studies in health technology and informatics
Patients' hospital length of stay (LOS) as a surgical outcome is important indicator of quality of care. We used EMR data to build artificial neural network models to better understand the impact of cold weather on outcome of first surgeries in a day...

Measuring Exposure to Incarceration Using the Electronic Health Record.

Medical care
BACKGROUND: Electronic health records (EHRs) are a rich source of health information; however social determinants of health, including incarceration, and how they impact health and health care disparities can be hard to extract.

Machine Learning-Based Model for Prediction of Outcomes in Acute Stroke.

Stroke
Background and Purpose- The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. Machine learning techniques are being increasingly adapted for use in the medical field because of their high accuracy. Thi...

Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study.

The Journal of antimicrobial chemotherapy
BACKGROUND: Infection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood par...

Discovery of Distinct Immune Phenotypes Using Machine Learning in Pulmonary Arterial Hypertension.

Circulation research
RATIONALE: Accumulating evidence implicates inflammation in pulmonary arterial hypertension (PAH) and therapies targeting immunity are under investigation, although it remains unknown if distinct immune phenotypes exist.