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The consequences of hearing loss are many, including social isolation1-3 and depression.10-13 The failure of hearing aids to either prevent or improve depression may stem from a complex relationship between hearing loss, depression, and the social dynamism of hearing loss that we have yet to fully understand. To this end, there are several opportunities to explore the relationship between hearing loss and depression. Moreover, there may be utility in identifying depression in patients with hearing loss at the point of care considering the established link between hearing loss and depression.

Shutterstock/Photographee.eu, technology. Hearing loss, mental health.

Using machine learning to predict depression scores (adapted from Crowson et. al., 2020). Technology. Hearing loss, mental health.

The primary objective of our work was to use a predictive approach using machine learning and audiometric data to determine if these data accurately predict patient-reported depression. We hypothesized that an advanced machine learning model may be useful for identifying depression in patients with audiometric data. We also sought to determine if the addition of other clinical and demographic variables combined with the audiometric data would produce further gains in model accuracy.

In our study, we developed a supervised machine learning model using National Health and Nutrition Examination Survey (NHANES) data, composed of subjective and objective audiometric variables and several other health determinant predictors, to predict scores on a validated depression scalethe Patient Health Questionnaire (PHQ-9). The NHANES is a cross-sectional public health survey designed to assess the health and nutrition status of individuals in the United States. NHANES data has been used in numerous research investigations to incorporate the interplay between determinants of health and specific medical conditions.

In a sample of participants from a survey cycle of the NHANES database, our supervised machine learning approach accurately predicted depression scale scores using audiometric and health determinant predictors. The model's most influential audiometric predictors of higher scores on the depression scale were functional dimensions and not objective audiometric testing variables. Among the most influential predictors, half were related to the social dynamics of hearing loss. The remaining predictors associated with depression in hearing loss were related to noise exposure, tinnitus, and objective audiometric testing. When expanding to include predictors ranging from demographics to other medical and health status content, a social context of hearing loss ranked in the top five most influential.

A strong association between social isolation and hearing loss has been demonstrated in adults.1-3 The observation that hearing loss leads to social isolation is intuitive given that hearing loss leads to impaired efficiency of communication. The connection between social isolation and depression is a natural extension as humans are social creatures.

If hearing care professionals treat hearing loss with conventional hearing aid devices, would it be reasonable to expect social isolation and associated depression to improve? Unfortunately, the relationship appears more complex. Prior work has shown that hearing aids do not result in consistent improvement in social isolation11,14 or depression.10-13 Perhaps the disconnect might be explained by noting that hearing amplification exclusively does not address hearing performance in real-life social situations. Basic sound amplification can and does help individuals with hearing loss. More advanced hearing aids incorporate signal processing technologies to better isolate the relevant sounds in noisy environments. Perhaps future research involving hearing aids with enhanced signal processing technology may lead to further insights into the utility of hearing aids to directly augment the social dynamics of hearing loss.

In summary, we found the NHANES dataset is useful for training machine learning models to accurately predict depression scale scores from audiometric data. As many of the variables collected in the NHANES data are the same clinical data we extract from our patients in real-life clinical encounters, such a predictive model could be useful in predicting depression scale scores at the point of care. We found the most influential audiometric predictors of higher scores on the depression scale were functional dimensions of hearing loss and not simply objective audiometric data like thresholds and word recognition scores. Among these influential functional dimensions, our model indicated the specific effect of hearing loss on social relations was particularly powerful. This is an interesting finding, as prior investigations into the effect of hearing aids on depression and social isolation have failed to show a consistent benefit. Simply giving a patient a hearing aid is not a guarantee that social isolation or depression will improve. Thus, our model output puts forth a new hypothesis that simply amplifying sound alone, in general, fails to address or improve the social dynamism of hearing loss. For today's hearing care clinicians, we suggest that recognizing that social aspects of hearing loss may carry more influence on the development or maintenance of depression than previously thought. Moreover, we may need to reimagine our aural rehabilitation strategies to include specific interventions to optimize social dynamics.

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Predicting Depression from Hearing Loss Using Artificial... : The Hearing Journal - LWW Journals

AI, RPA, and machine learning, you must have heard these words echoing in the tech industry. Be it blogs, websites, videos, or even product descriptions, disruptive technologies have made their presence bold. The fact that we all have AI-powered devices in our homes is a sign that the technology has come so far.

If you are under the impression that AI, robotic process automation, and machine learning have nothing in common, then heres what you need to know, they are all related concepts. Oftentimes, people use these names interchangeably and incorrectly which causes confusion among businesses that are looking for the latest technological solutions.

Understanding the differences between AI, ML, and RPA tools will help you identify and understand where the best opportunities are for your business to make the right technological investment.

According to IBM, Robotic process automation (RPA), also known as software robotics, uses automation technologies to mimic back-office tasks of human workers, such as extracting data, filling in forms, moving files, etc. It combines APIs and user interface (UI) interactions to integrate and perform repetitive tasks between enterprise and productivity applications. By deploying scripts which emulate human processes, RPA tools complete autonomous execution of various activities and transactions across unrelated software systems.

In that sense, RPA tools enable highly logical tasks that dont require human understanding or human interference. For example, if your work revolves around inputting account numbers on a spreadsheet to run a report with a filter category, you can use RPA to fill the numbers on the sheet. Automation will mimic your actions of setting up the filter and generate the report on its own.

With a clear set of instructions, RPA can perform any task. But theres one thing to remember, RPA systems dont have the capabilities to learn as they go. If there is a change in your task, (for example if the filter has changed in the spreadsheet report), you will have to manually input the new set of instructions.

The highest adopters of this technology are banking firms, financial services, insurance, and telecom industries. Federal agencies like NASA have also started using RPA to automate repetitive tasks.

According to Microsoft, Artificial Intelligence is the ability of a computer system to deal with ambiguity, by making predictions using previously gathered data, and learning from errors in those predictions in order to generate newer, more accurate predictions about how to behave in the future.

In that sense, the major difference between RPA and AI is intelligence. While these technologies efficiently perform tasks, only AI can do it with similar capabilities to human intelligence.

Chatbots and virtual assistants are two popular uses of AI in the business world. In the tax industry, AI is making tax forecasting increasingly accurate with its predictive analytics capabilities. AI can also perform thorough data analysis which makes identifying tax deductions and tax credits easier than before.

According to Gartner, Advanced machine learning algorithms are composed of many technologies (such as deep learning, neural networks, and natural language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information.

Machine learning is a part of AI, so the two terms cannot be used interchangeably. And thats the difference between RPA and ML, machine learnings intelligence comes from AI but RPA lacks all intelligence.

To understand better, let us apply these technologies in a property tax scenario. First, you can create an ML model based on a hundred tax bills. The more bills you feed the model, the more accurately it will make predictions for the future bills. But if you want to use the same machine learning model to address an assessment notice, the model will be of no use. You would then have to build a new machine learning model that knows how to work with assessment notices. This is where machine learnings intelligence capabilities draw a line. Where ML fails to recognize the similarities of the document, an AI application would recognize it, thanks to its human-like interpretation skills.

The healthcare industry uses ML to accurately diagnose and treat patients, retailers use ML to make the right products available at the right stores at the right time, and pharmaceutical companies use machine learning to develop new medications. These are just a few use cases of this technology.

No, but they can work together. The combination of AI and RPA is called smart process automation, or SPA.

Also known as intelligent process automation or IPA, this duo facilitates an automated workflow with advanced capabilities than RPA using machine learning. The RPA part of the system works on doing the tasks while the machine learning part focuses on learning. In short, SPA solutions can learn to perform a specific task with the help of patterns.

The three technologies, AI, RPA, and ML, and the duet, SPA hold exciting possibilities for the future. But only when companies make the right choice, the rewards can be reaped. Now that you have an understanding of the various capabilities of these technologies, adapt and innovate.

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AI, RPA, and Machine Learning How are they Similar & Different? - Analytics Insight

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While there have been strides taken in filling up the gender gap across fields, especially engineering and technology-based, there are still miles to go. At times, though, it is due to part societal misconceptions and part lack of knowledge about different fields that we have a gap to fill. That said, what we need is information on all available career and educational prospects that help with choosing the path forward. One such option is machine learning. Machine learning (ML), for the uninitiated like me, is the science of getting computers ie the machines to study and behave like humans, and improve their learning over time automatically, from the fed information and data that comes in the form of observations and real-world interactions. It is a subset of artificial intelligence (AI).

Photo: Vaishali Kasture

With digitisation and AI being a huge part of the future, a career in ML could be successful and rewarding, as Vaishali Kasture, Leader Strategic Projects, AISPL, Amazon Web Services (AWS) India and South Asia, can attest to. Machine learning is one of the most disruptive technologies we will encounter in our generation. Were seeing ML adopted across all industries, verticals, and businesses. For example, Zomato uses machine learning for menu digitisation and enabling consumers to run advanced searches for dishes, and RedBus uses ML to improve click-through rates on their website by 25% and conversion rates by 5%.

Importance Of Machine Learning For The Future

In her over two-decade-old career, one thingKasture has realised is that technology is one of the most important driving factors in any business, be it banking where she started her career or the Knowledge Process Outsourcing (KPO) industry. Even when working at one of Indias prominent credit bureaus, she saw that technology was the key differentiator. There she used the cloud, machine learning and artificial intelligence to drive faster and better outcomes for our banking customers. This really opened my eyes to the power of the cloud and new emerging technologies, she notes, I am convinced that every business will be reimagined using new and emerging technologies, and only those that adapt and embrace this change will survive. She joined AWS in 2019 on the back of this conviction.

The AWS DeepRacer Womens League India 2021 is intentionally designed to create awareness of ML among women students in India, enable them to explore ML, learn collaboratively, and inspire them to take up careers in ML. We were delighted that over 17,000 women students from all corners of India showed interest to participate in the competition, she smiles. DeepRacer as the AWS website states is an autonomous 1/18th scale race car designed to test real-life models by racing them on a physical track. Using cameras to view the track and a reinforcement model to control throttle and steering, the car shows how a model trained in a simulated environment can be transferred to the real-world.

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ML proved to be useful in the current pandemic too! It is playing a key role in better understanding and addressing the COVID-19 pandemic. In the fight against the pandemic, organisations have been quick to apply their machine learning expertise in several areas including scaling customer communications, understanding how COVID-19 spreads and speeding up research and treatment.

Overcoming The Gender Disparity In Technology

Despitethe strides women have made in engineering, IT and beyond, there is still a gender gap in the field. Kasture gives a clear idea on what can be and should be done: At the grassroots level, there is a strong gender stereotype about women in STEM in general. We need to remove this stereotype. Encourage girls from a very young age in schools and colleges to opt for STEM programmes. Once women join the workforce, encourage them to actively raise their hands and ask for roles in hot technologies areas like ML, AI, analytics, augmented and virtual reality, blockchain, and quantum computing. Organisations need to partner with women, support, and reward them for working in new and emerging technologies. A mentoring programme to encourage women to participate in enhancing their knowledge and giving them an edge is also very useful. A knowledge series designed to give women deeper learning in a safe environment will go a long way.

Also read: 5 Indian Women Making Waves In The Field Of Science And Technology

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Future Calling: Machine Learning Is The Next Big Thing! - Femina

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Neurosurg Focus. 2021 May;50(5):E5. doi: 10.3171/2021.2.FOCUS201113.

ABSTRACT

OBJECTIVE: Frailty is recognized as an important consideration in patients with cancer who are undergoing therapies, including spine surgery. The definition of frailty in the context of spinal metastases is unclear, and few have studied such markers and their association with postoperative outcomes and survival. Using national databases, the metastatic spinal tumor frailty index (MSTFI) was developed as a tool to predict outcomes in this specific patient population and has not been tested with external data. The purpose of this study was to test the performance of the MSTFI with institutional data and determine whether machine learning methods could better identify measures of frailty as predictors of outcomes.

METHODS: Electronic health record data from 479 adult patients admitted to the Massachusetts General Hospital for metastatic spinal tumor surgery from 2010 to 2019 formed a validation cohort for the MSTFI to predict major complications, in-hospital mortality, and length of stay (LOS). The 9 parameters of the MSTFI were modeled in 3 machine learning algorithms (lasso regularization logistic regression, random forest, and gradient-boosted decision tree) to assess clinical outcome prediction and determine variable importance. Prediction performance of the models was measured by computing areas under the receiver operating characteristic curve (AUROCs), calibration, and confusion matrix metrics (positive predictive value, sensitivity, and specificity) and was subjected to internal bootstrap validation.

RESULTS: Of 479 patients (median age 64 years [IQR 55-71 years]; 58.7% male), 28.4% had complications after spine surgery. The in-hospital mortality rate was 1.9%, and the mean LOS was 7.8 days. The MSTFI demonstrated poor discrimination for predicting complications (AUROC 0.56, 95% CI 0.50-0.62) and in-hospital mortality (AUROC 0.69, 95% CI 0.54-0.85) in the validation cohort. For postoperative complications, machine learning approaches showed a greater advantage over the logistic regression model used to develop the MSTFI (AUROC 0.62, 95% CI 0.56-0.68 for random forest vs AUROC 0.56, 95% CI 0.50-0.62 for logistic regression). The random forest model had the highest positive predictive value (0.53, 95% CI 0.43-0.64) and the highest negative predictive value (0.77, 95% CI 0.72-0.81), with chronic lung disease, coagulopathy, anemia, and malnutrition identified as the most important predictors of postoperative complications.

CONCLUSIONS: This study highlights the challenges of defining and quantifying frailty in the metastatic spine tumor population. Further study is required to improve the determination of surgical frailty in this specific cohort.

PMID:33932935 | DOI:10.3171/2021.2.FOCUS201113

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Performance assessment of the metastatic spinal tumor frailty index using machine learning algorithms: limitations and future directions - DocWire...

Alzheimer's Disease remains one of the most common brain disorders affecting people, especially the elderly, worldwide - and a new study reports that there's not only one, but there are four different types of these progressive brain disorders.

The currently irreversible brain condition has been characterized by slowly declining memory, cognitive capabilities, which eventually lead to the incapability to perform even the simplest type. As mankind learns more about this disease, the better we can address this condition and hopefully in the near future, develop a cure for it. This makes the new discovery particularly important progress in the study of the disease.

A report appearing in the latest Nature Medicine, published last April 29, presents findings from an international team of researchers - including those from the McGill University in Canada, the King's College London in the UK, Skne University Hospital in Sweden, Yonsei University College of Medicine in South Korea, as well as members of AVID Radiopharmaceuticals and the Alzheimer's Disease Neuroimaging Initiative.

(Photo: ADEAR via Wikimedia Commons)Diagram of the brain of a person with Alzheimer's Disease

ALSO READ: Recent Study Shows Link Between Alzheimer's Disease and Major Surgery

In the study titled "Four distinct trajectories of tau deposition identified in Alzheimer's disease," researchers explain how Alzheimer's disease is "characterized by the spread of tau pathology throughout the cerebral cortex."

The brain has a member of the microtubule-associated family called the "Tau protein," which is involved in a number of neurodegenerative diseases like Parkinson's and Alzheimer's disease.

Tau pathology refers to the existence of a pathological aggregation of these proteins in the neurofibrillary tangles (NFTs). These misshapen proteins and the pattern of how they get tangled have long been previously believed to be more or less similar to people having neurodegenerative disease.

This particular phenomenon, which develops in cases of Alzheimer's disease, was examined by the researchers with help from specially-developed machine learning algorithms. The machine learning tool was trained to analyze brain scans of 1,143 people - a mixed data set of healthy brains and those diagnosed with Alzheimer's disease.

"We identified four clear patterns of tau pathology that became distinct over time," said Oskar Hansson, co-author of the study and a neurologist from the Clinical Memory Unit at the Lund University, in a press release from the Swedish university.

Hansson additionally explains that the prevalence of the subgroups was anywhere from 18 to 30 percent of the cases in the study. This means that all of the subtypes of the disease appear to be almost equally common, with no single subtype dominating over the others.

The first variant, Subtype 1: Limbic, was found in 33 percent of the cases. It was characterized by pathologic tau spread mostly within the brain's temporal lobe and is affecting patient memory. It is followed by the Subtype, MTL-Sparing, which was present in 18 percent of the cases and spreads across other sections of the cerebral cortex. Under these cases, memory problems become less common but are dominated by difficulties in planning and performing actions.

The third, Subtype 3: Posterior, was found in 30 percent of the cases - tau proteins spreading in the visual cortex, which is the brain's region for processing eyesight. In this case, patients experience difficulties in orientation, depth and distance perception, and processing shapes. The last one, Subtype 4: L Temporal, was only detected in 19 percent of cases and is asymmetrically spread in the left hemisphere, affecting speech and language.

"We now have reason to reevaluate the concept of typical Alzheimer's, and in the long run also the methods we use to assess the progression of the disease," commented Jacob Vogel, co-author of the study from McGill University.

RELATED ARTICLE: Study Shows Alzheimer's Could Be Predicted Through Writing Tests

Check out more news and information on Alzheimer's Disease in Science Times.

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Four Types of Alzheimer's Disease and How Machine Learning Helped Identify Them - Science Times