A comprehensive review of computational diagnostic techniques for lymphedema.

Lymphedema is localized swelling due to lymphatic system dysfunction, often affecting arms and legs due to fluid accumulation. It occurs in 20% to 94% of patients within 2 to 5 years after breast cancer treatment, with around 20% of women developing breast cancer-related lymphedema (BCRL). This condition involves the accumulation of protein-rich fluid in interstitial spaces, leading to symptoms like swelling, pain, and reduced mobility that significantly impact quality of life.

Automatic Diagnostic Tool for Detection of Regional Wall Motion Abnormality from Echocardiogram.

The echocardiogram is an ultrasound imaging modality, employed to assess cardiac abnormalities. The Regional Wall Motion Abnormality (RWMA) is the occurrence of abnormal or absent contractility of a region of the heart muscle. Conventional assessment of RWMA is based on visual interpretation of endocardial excursion and myocardial thickening from the echocardiogram videos.

Performance effectiveness of vital parameter combinations for early warning of sepsis-an exhaustive study using machine learning.

Objective: To carry out exhaustive data-driven computations for the performance of noninvasive vital signs heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO), and temperature (Temp), considered both independently and in all possible combinations, for early detection of sepsis.

Materials And Methods: By extracting features interpretable by clinicians, we applied Gradient Boosted Decision Tree machine learning on a dataset of 2630 patients to build 240 models. Validation was performed on a geographically distinct dataset.

Data to diagnosis in global health: a 3P approach.

Background: With connected medical devices fast becoming ubiquitous in healthcare monitoring there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge.

Methods: To address this challenge, we present a 3P approach: personalized patient monitoring, precision diagnostics, and preventive criticality alerts.

Consensus motifs as adaptive and efficient predictors for acute hypotensive episodes.

Acute hypotensive episodes (AHE) are characterized by continuously low blood pressure for prolonged time, and could be potentially fatal. We present a novel AHE detection system, by first quantizing the blood pressure data into clinically accepted severity ranges and then identifying most frequently occurring blood pressure pattern among these which we call consensus motifs. We apply machine learning techniques (support vector machine) on these consensus motifs.

Severity Summarization and Just in Time Alert Computation in mHealth Monitoring.

Mobile health is fast evolving into a practical solution to remotely monitor high-risk patients and deliver timely intervention in case of emergencies. Building upon our previous work on a fast and power efficient summarization framework for remote health monitoring applications, called RASPRO (Rapid Alerts Summarization for Effective Prognosis), we have developed a real-time criticality detection technique, which ensures meeting physician defined interventional time. We also present the results from initial testing of this technique.