A pilot study for development of a novel tool for clinical decision making to identify fallers among ophthalmic patients

Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches

Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute's SEER Program's November 2017 update, which offers population-based cancer statistics. The dataset included female patients diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). The dataset included nine predictor factors and one predictor variable that were linked to the patients' survival status (alive or dead). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using K-nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression (accuracy = 80.57 percent) and the greatest acquired from the random forest (accuracy = 94.64 percent). Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area.

Ophthalmic In Situ Gelling System Containing Lanosterol Nanoparticles Delays Collapse of Lens Structure in Shumiya Cataract Rats

We attempted to prepare ophthalmic in situ gel formulations containing lanosterol (Lan) nanoparticles (LA-NPs/ISG) and investigated the characteristics, delivery pathway into the lens, and anti-cataract effects of LA-NPs/ISG using SCR-N (rats with slight lens structure collapse) and SCR-C (rats with a combination of remarkable lens structure collapse and opacification). LA-NPs/ISG was prepared by bead milling of the dispersions containing 0.5% Lan powder, 5% 2-hydroxypropyl-β-cyclodextrin, 0.5% methylcellulose, 0.005% benzalkonium chloride, and 0.5% mannitol. The particle size distribution of Lan was 60–250 nm. The LA-NPs/ISG was gelled at 37 °C, and the LA-NPs/ISG was taken into the cornea by energy-dependent endocytosis and then released to the intraocular side. In addition, the Lan contents in the lenses of both SCR-N and SCR-C were increased by the repetitive instillation of LA-NPs/ISG (twice per day). The space and structure collapse in the lens of SCR-N with aging was attenuated by the instillation of LA-NPs/ISG. Moreover, the repetitive instillation of LA-NPs/ISG attenuated the changes in cataract-related factors (the enhancement of nitric oxide levels, calpain activity, lipid peroxidation levels, Ca²⁺ contents, and the decrease of Ca²⁺-ATPase activity) in the lenses of SCR-C, and the repetitive instillation of LA-NPs/ISG delayed the onset of opacification in the SCR-C. It is possible that the LA-NPs/ISG is useful in maintaining lens homeostasis.

Predicting factors for survival of breast cancer patients using machine learning techniques

BACKGROUND

Breast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an alternative, this study used machine learning techniques to build models for detecting and visualising significant prognostic indicators of breast cancer survival rate.

METHODS

A large hospital-based breast cancer dataset retrieved from the University Malaya Medical Centre, Kuala Lumpur, Malaysia (n = 8066) with diagnosis information between 1993 and 2016 was used in this study. The dataset contained 23 predictor variables and one dependent variable, which referred to the survival status of the patients (alive or dead). In determining the significant prognostic factors of breast cancer survival rate, prediction models were built using decision tree, random forest, neural networks, extreme boost, logistic regression, and support vector machine. Next, the dataset was clustered based on the receptor status of breast cancer patients identified via immunohistochemistry to perform advanced modelling using random forest. Subsequently, the important variables were ranked via variable selection methods in random forest. Finally, decision trees were built and validation was performed using survival analysis.

RESULTS

In terms of both model accuracy and calibration measure, all algorithms produced close outcomes, with the lowest obtained from decision tree (accuracy = 79.8%) and the highest from random forest (accuracy = 82.7%). The important variables identified in this study were cancer stage classification, tumour size, number of total axillary lymph nodes removed, number of positive lymph nodes, types of primary treatment, and methods of diagnosis.

CONCLUSION

Interestingly the various machine learning algorithms used in this study yielded close accuracy hence these methods could be used as alternative predictive tools in the breast cancer survival studies, particularly in the Asian region. The important prognostic factors influencing survival rate of breast cancer identified in this study, which were validated by survival curves, are useful and could be translated into decision support tools in the medical domain.

Identifying fallers among ophthalmic patients using classification tree methodology

PURPOSE

To develop and validate a tool aiming to support ophthalmologists in identifying, during routine ophthalmologic visits, patients at higher risk of falling in the following year.

METHODS

A group of 141 subjects (age: 73.2 ± 11.4 years), recruited at our Eye Clinic, underwent a baseline ophthalmic examination and a standardized questionnaire, including lifestyles, general health, social engagement and eyesight problems. Moreover, visual disability was assessed by the Activity of Daily Vision Scale (ADVS). The subjects were followed up for 12 months in order to record prospective falls. A subject who reported at least one fall within one year from the baseline assessment was considered as faller, otherwise as non-faller. Different tree-based algorithms (i.e., C4.5, AdaBoost and Random Forests) were used to develop automatic classifiers and their performances were evaluated by the cross-validation approach.

RESULTS

Over the follow-up, 25 falls were referred by 13 patients. The logistic regression analysis showed the following variables as significant predictors of prospective falls: pseudophakia and use of prescribed eyeglasses as protective factors, recent worsening of visual acuity as risk factor. Random Forest ranked best corrected visual acuity, number of sleeping hours and job type as the most important features. Finally, AdaBoost enabled the identification of subjects at higher risk of falling in the following 12 months with a sensitivity rate of 69.2% and a specificity rate of 76.6%.

CONCLUSIONS

The current study proposes a novel method, based on classification trees applied to self-reported factors and health information assessed by a standardized questionnaire during ophthalmological visits, to identify ophthalmic patients at higher risk of falling in the following 12 months. The findings of the current study pave the way to the validation of the proposed novel tool for fall risk screening on a larger cohort of patients with visual impairment referred to eye clinics.

Pharmacological approaches to restoring lens transparency: Real world applications

Cataract is the most common cause of blindness and a major cause of visual impairment worldwide. As the world's population ages, cataract-induced visual impairment is of increasing prevalence, and treatment is limited to those with access to surgical care. While cataracts are mainly a disease of the elderly, infantile cataracts lead to lifelong visual impairment if untreated. Even in those with surgical treatment early in life, visual prognosis is often guarded. Consequently, there is an increasing impetus for alternative therapeutic modalities. Makley and Zhao utilize two different experimental approaches to identify novel pharmacological substances able to improve lens transparency by reducing aggregation of crystalline proteins. These data support an alternative to surgical correction that may be applied to adult patients without access to surgical care as well as address the unique challenges of infantile cataracts.

Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients

The aim of this paper is to describe the design and the preliminary validation of a platform developed to collect and automatically analyze biomedical signals for risk assessment of vascular events and falls in hypertensive patients. This m-health platform, based on cloud computing, was designed to be flexible, extensible, and transparent, and to provide proactive remote monitoring via data-mining functionalities. A retrospective study was conducted to train and test the platform. The developed system was able to predict a future vascular event within the next 12 months with an accuracy rate of 84 % and to identify fallers with an accuracy rate of 72 %. In an ongoing prospective trial, almost all the recruited patients accepted favorably the system with a limited rate of inadherences causing data losses (<20 %). The developed platform supported clinical decision by processing tele-monitored data and providing quick and accurate risk assessment of vascular events and falls.