Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine.

One of the major barriers to using large language models (LLMs) in medicine is the perception they use uninterpretable methods to make clinical decisions that are inherently different from the cognitive processes of clinicians. In this manuscript we develop diagnostic reasoning prompts to study whether LLMs can imitate clinical reasoning while accurately forming a diagnosis. We find that GPT-4 can be prompted to mimic the common clinical reasoning processes of clinicians without sacrificing diagnostic accuracy.

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.

Real-time alerting system for COVID-19 and other stress events using wearable data.

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals.

Real-time Alerting System for COVID-19 Using Wearable Data.

Early detection of infectious disease is crucial for reducing transmission and facilitating early intervention. We built a real-time smartwatch-based alerting system for the detection of aberrant physiological and activity signals (e.g.

Deriving High Performance Alerts from Reduced Sensor Data for Timely Intervention in Acute Hypotensive Episodes.

Alerting critical health conditions ahead of time leads to reduced mortality rates. Recently wirelessly enabled medical sensors have become pervasive in both hospital and ambulatory settings. These sensors pour out voluminous data that are generally not amenable to direct interpretation.

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.