Traditional machine learning methods face unique challenges when applied to healthcare
predictive analytics. The high-dimensional nature of healthcare data necessitates labor …

With a mortality rate of 5.4 million lives worldwide every year and a healthcare cost of more
than 16 billion dollars in the USA alone, sepsis is one of the leading causes of hospital …

The purpose of this study is to identify additional clinical features for sepsis detection
through the use of a novel mechanism for interpreting black-box machine learning models …

Early detection of sepsis can assist in clinical triage and decision-making, resulting in early
intervention with improved outcomes. This study aims to develop a machine learning …

This paper presents an 8× 8 channel optoelectronic readout array (ORA) realized in the
TSMC 180 nm 1P6M RF CMOS process for the applications of short-range light detection …

Utilizing administrative data to predict outcomes is an important application area of machine
learning, particularly in healthcare. Most administrative data records are timestamped and …

This paper presents a novel power-efficient topology for receivers in short-range LiDAR
sensors. Conventionally, LiDAR sensors exploit complex time-to-digital converters (TDCs) …

Purpose The purpose of this study is to identify additional clinical features for sepsis
detection through the use of a novel mechanism for interpreting black-box machine learning …

With the growth of administrative data due to increased storage and digitization, there is a
need for effective ways to process and analyze this information. This study examines the …