A Hybrid Coupled Cluster -- Machine Learning Algorithm: Development of Various Regression Models and Benchmark Applications

Abstract Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student’s t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student’s t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of < 0.0001 and < 0.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation.

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Curating Archival Anatomic Pathology Material For Machine Learning Algorithm Development

American Journal of Clinical Pathology ◽

10.1093/ajcp/aqaa161.272 ◽

2020 ◽

Vol 154 (Supplement_1) ◽

pp. S124-S125

Author(s):

A Collins ◽

A Norgan ◽

J J Garcia

Keyword(s):

Machine Learning ◽

Cell Carcinoma ◽

Learning Algorithm ◽

Lessons Learned ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Algorithm ◽

Anatomic Site ◽

Algorithm Development ◽

Anatomic Pathology

Abstract Introduction/Objective Advances in whole slide imaging have enabled the application of machine learning algorithms to anatomic pathology. In the current state, the development of accurate algorithms requires robust training data with correctly assigned diagnostic and classification labels. Increasingly, institutions have looked to their archival slides as a source of “ground truth” for algorithm development. However, the curation and use of archival data poses several challenges. Here, we share lessons learned from reviewing head and neck pathology consult cases spanning a 10- year period at Mayo Clinic Rochester. Methods Archived surgical pathology slides from 2,590 consult cases were reviewed. Clinical and demographic information was recorded for each case, including surgical date, surgical procedure, anatomic site, age, gender and diagnosis. Cases were excluded from the curated archive if there was insufficient volume or quality of tissue to render a specific diagnosis (141 cases, 5.6%). Slides with a range of tissue size and quality, from numerable laboratories were included in the curated archive. Selected cases were collated by anatomic site: ear, gnathic, larynx, nasopharynx, neck, oral cavity, oropharynx, salivary gland and sinonasal tract. Results Common diagnostic reconciliations (115 cases, 4.4%) fell within the following categories: (1) novel entities (59 cases, 2.3%), including biphenotypic sinonasal sarcoma and clear cell carcinoma; (2) novel classifications (21 cases, 0.8%), as seen in HPV-related oropharyngeal squamous cell carcinoma and polymorphous adenocarcinoma; and (3) novel grading schema (35 cases, 1.4%), as seen in keratinizing dysplasia and oropharyngeal malignancies. Conclusion Several nuances emerged in the process of reviewing slides, highlighting the need for continual amendment of any machine learning dataset over time. Curating anatomic pathology cases for machine learning algorithm development requires the recognition of emerging entities, with re-classification and re-grading as needed.

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Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

10.1055/s-0038-1660728 ◽

2018 ◽

Author(s):

C.H.B. van Niftrik ◽

F. van der Wouden ◽

V. Staartjes ◽

J. Fierstra ◽

M. Stienen ◽

...

Keyword(s):

Machine Learning ◽

Brain Tumor ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Tumor Surgery ◽

Early Complications ◽

Brain Tumor Surgery

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Feature extraction and prediction of Dengue Outbreaks

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206544 ◽

2020 ◽

pp. 216-222

Author(s):

Kunal Parikh ◽

Tanvi Makadia ◽

Harshil Patel

Keyword(s):

Public Health ◽

Machine Learning ◽

Developing Countries ◽

Feature Extraction ◽

Predictive Analytics ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Health Concerns ◽

The World ◽

Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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