Machine Learning Application to Family Business Status Classification

Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques

Transactions in GIS ◽

10.1111/tgis.12485 ◽

2018 ◽

Vol 22 (6) ◽

pp. 1522-1549 ◽

Cited By ~ 5

Author(s):

Kangjae Lee ◽

Mei‐Po Kwan

Keyword(s):

Physical Activity ◽

Machine Learning ◽

Hierarchical Classification ◽

Machine Learning Techniques ◽

Accelerometer Data ◽

Classification Approach ◽

Learning Techniques ◽

Status Classification ◽

Vehicle Status

Download Full-text

Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification

Journal of Alzheimer s Disease ◽

10.3233/jad-190165 ◽

2019 ◽

Vol 70 (1) ◽

pp. 277-286 ◽

Cited By ~ 1

Author(s):

Michael F. Bergeron ◽

Sara Landset ◽

Franck Tarpin-Bernard ◽

Curtis B. Ashford ◽

Taghi M. Khoshgoftaar ◽

...

Keyword(s):

Machine Learning ◽

Health Status ◽

Episodic Memory ◽

Memory Performance ◽

Cognitive Health ◽

Status Classification

Download Full-text

Machine learning algorithm to perform ASA Physical Status Classification

10.1101/2021.10.05.21264585 ◽

2021 ◽

Author(s):

Alexander Pozhitkov ◽

Naini Seth ◽

Trilokesh D. Kidambi ◽

John Raytis ◽

Srisairam Achuthan ◽

...

Keyword(s):

Machine Learning ◽

Clinical Characteristics ◽

Learning Algorithm ◽

Physical Status ◽

Asa Score ◽

Subsequent Test ◽

Physical Status Classification ◽

Status Classification ◽

American Society ◽

American Society Of Anesthesiologists

AbstractBackgroundThe American Society of Anesthesiologists (ASA) Physical Status Classification System defines peri-operative patient scores as 1 (healthy) thru 6 (brain dead). The scoring is used by the anesthesiologists to classify surgical patients based on co-morbidities and various clinical characteristics. The classification is always done by an anesthesiologist prior operation. There is a variability in scoring stemming from individual experiences / biases of the scoring anesthesiologists, which impacts prediction of operating times, length of stay in the hospital, necessity of blood transfusion, etc. In addition, the score affects anesthesia coding and billing. It is critical to remove subjectivity from the process to achieve reproducible generalizable scoring.MethodsA machine learning (ML) approach was used to associate assigned ASA scores with peri-operative patients’ clinical characteristics. More than ten ML algorithms were simultaneously trained, validated, and tested with retrospective records. The most accurate algorithm was chosen for a subsequent test on an independent dataset. DataRobot platform was used to run and select the ML algorithms. Manual scoring was also performed by one anesthesiologist. Intra-class correlation coefficient (ICC) was calculated to assess the consistency of scoringResultsRecords of 19,095 procedures corresponding to 12,064 patients with assigned ASA scores by 17 City of Hope anesthesiologists were used to train a number of ML algorithms (DataRobot platform). The most accurate algorithm was tested with independent records of 2325 procedures corresponding to 1999 patients. In addition, 86 patients from the same dataset were scored manually. The following ICC values were computed: COH anesthesiologists vs. ML – 0.427 (fair); manual vs. ML – 0.523 (fair-to-good); manual vs. COH anesthesiologists – 0.334 (poor).ConclusionsWe have shown the feasibility of using ML for assessing the ASA score. In principle, a group of experts (i.e. physicians, institutions, etc.) can train the ML algorithm such that individual experiences and biases would cancel each leaving the objective ASA score intact. As more data are being collected, a valid foundation for refinement to the ML will emerge.

Download Full-text

Maturity status classification of papaya fruits based on machine learning and transfer learning approach

Information Processing in Agriculture ◽

10.1016/j.inpa.2020.05.003 ◽

2020 ◽

Cited By ~ 5

Author(s):

Santi Kumari Behera ◽

Amiya Kumar Rath ◽

Prabira Kumar Sethy

Keyword(s):

Machine Learning ◽

Transfer Learning ◽

Learning Approach ◽

Status Classification

Download Full-text

Inland water's trophic status classification based on machine learning and remote sensing data

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2020.100326 ◽

2020 ◽

Vol 19 ◽

pp. 100326

Author(s):

Fernanda S.Y. Watanabe ◽

Gabriela T. Miyoshi ◽

Thanan W.P. Rodrigues ◽

Nariane M.R. Bernardo ◽

Luiz H.S. Rotta ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Trophic Status ◽

Remote Sensing Data ◽

Sensing Data ◽

Status Classification

Download Full-text

Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Download Full-text