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image:Fig 7. The top 10 most important questions for males vs females. view more

Credit: Weller et al., 2021, PLOS ONE, CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

Researchers have developed a new, machine learning-based algorithm that shows high accuracy in identifying adolescents who are experiencing suicidal thoughts and behavior. Orion Weller of Johns Hopkins University in Baltimore, Maryland, and colleagues present these findings in the open-access journal PLOS ONE on November 3rd, 2021.

Decades of research have identified specific risk factors associated with suicidal thoughts and behavior among adolescents, helping to inform suicide prevention efforts. However, few studies have explored these risk factors in combination with each other, especially in large groups of adolescents. Now, the field of machine learning has opened up new opportunities for such research, which could ultimately improve prevention efforts.

To explore that opportunity, Weller and colleagues applied machine-learning analysis to data from a survey of high school students in Utah that is routinely conducted to monitor issues such as drug abuse and mental health. The data included responses to more than 300 questions each for more than 179,000 high school students who took the survey between 2011 to 2017, as well as demographic data from the U.S. census.

The researchers found that they could use the survey data to predict with 91 percent accuracy which individual adolescents answers indicated suicidal thoughts or behavior. In doing so, they were able to identify which survey questions had the most predictive power; these included questions about digital media harassment or threats, at-school bullying, serious arguments at home, gender, alcohol use, feelings of safety at school, age, and attitudes about marijuana.

The new algorithms accuracy is higher than that of previously developed predictive approaches, suggesting that machine-learning could indeed improve understanding of adolescent suicidal thoughts and behaviorand could thereby help inform and refine preventive programs and policies.

Future research could expand the new findings by using data from other states, as well as data on actual suicide rates.

The authors add: Our paper examines machine learning approaches applied to a large dataset of adolescent questionnaires, in order to predict suicidal thoughts and behaviors from their answers. We find strong predictive accuracy in identifying those at risk and analyze our model with recent advances in ML interpretability. We found that factors that strongly influence the model include bullying and harassment, as expected, but also aspects of their family life, such as being in a family with yelling and/or serious arguments.We hope that this study can provide insight to inform early prevention efforts.

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Predicting suicidal thoughts and behavior among adolescents using the risk and protective factor framework: A large-scale machine learning approach

3-Nov-2021

The authors have declared that no competing interests exist.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Machine learning can provide strong predictive accuracy for identifying adolescents that have experienced suicidal thoughts and behavior - EurekAlert

How can oncologists better predict who will benefit from a widely used class of immunotherapy drugs called checkpoint inhibitors?

In the precision medicine era of cancer care, its a question that has only increased in relevance. To answer it, Luc Morris, a physician-scientist and research laboratory head, together with several colleagues at Memorial Sloan Kettering Cancer Center, are looking beyond a known method to predict immunotherapy response.

Tumor mutational burden, or TMB, refers to the number of mutations a tumor has. High TMB means there are a lot of mutations. Low TMB means there are not many mutations. In the past five years, its been well established that tumors that have high TMB tend to respond better to checkpoint inhibitor therapy compared with tumors that have low TMB. Because checkpoint inhibitors only work in a fraction of people with cancer, the ability to predict response like with TMB is crucial. While TMB can be used to guide treatment decisions for certain patients with cancer for example, the checkpoint inhibitor pembrolizumab (Keytruda) is FDA approved for all tumors with high TMB it remains a crude predictor by itself, according to Dr. Morris.

We know that TMB provides some value in predicting immunotherapy response, but we also know that it is not a perfect predictor. It has limited value in isolation, says Dr. Morris, a senior author on the study, which published November 1, 2021, in Nature Biotechnology.

Oncologists will consider many factors when deciding on the best treatment for a patient with cancer TMB is only one, he says. For example, a melanoma tumor with low TMB may still have a very good chance of responding, just as a breast tumor with high TMB might have a lower chance of responding. We recognize that we need more predictive tools besides just TMB.

The studys co-first authors were Diego Chowell and Steve Yoo, research fellows in the lab of Timothy Chan at MSK, and Cristina Valero, a research fellow inthe Morris Labat MSK. Diego Chowell is currently an assistant professor in the Icahn School of Medicine at Mount Sinai.Nils Weinhold, an MSK cancer researcher and computational biologist, led the study as a co-senior author together with Dr. Chan and Dr. Morris. (Dr. Chan, whose lab first reported the importance of TMB in cancer immunotherapy in 2014, moved to the Cleveland Clinic in 2020.)

The limited value in isolation of TMB was one of the reasons why Dr. Morris and fellow investigators wanted to go beyond the biomarker in their latest analysis, he says. Another reason that Dr. Morris undertook this research was to learn more about a blood marker called neutrophil-to-lymphocyte ratio (NLR). Recent MSK research showed that NLR, especially when combined with TMB and other information such as patient blood markers could improve the ability to predict tumor immunotherapy response.

That opened the door for us to say: Why dont we just gather all of the variables that either have been shown to have predictive value, or that we think might possibly have predictive value, and put them into a machine learning algorithm and see how well we can predict outcomes with a larger pool of information, Dr. Morris says.

The team used a model that integrated 16 genomic, molecular, demographic, and clinical features, including TMB and NLR. By taking a machine learning approach, the investigators would be able to determine which combination of variables had the highest predictive power.

Using this large set of clinical and genomic data from patients treated at MSK, we trained a machine learning model that incorporated a number of different pieces of data, Dr. Morris explains.

Oncologists will consider many factors when deciding on the best treatment for a patient with cancer TMB is only one.

The investigators analyzed the variables in a group of 1,479 patients who were treated with immunotherapy: PD-1/PD-L1 inhibitor immunotherapy, CTLA-4 inhibitor immunotherapy, or a combination of both. Most patients (1,070) did not respond. The group included patients with 16 different types of cancer, of which non-small cell lung cancer and melanoma were the most prevalent. Investigators analyzed patients tumors using MSK-IMPACTTM, a powerful tool that provides detailed information about a tumors mutations.

MSK-IMPACT is an incredible resource for us, both as oncologists treating patients and as scientists trying to understand cancer, says Dr. Morris. For this study, we had a wealth of genomic data for these patients who were treated at MSK, to integrate with clinical data and blood test data.

The results reaffirmed TMBs relevance as a predictor of immunotherapy response; when the variables were studied individually, TMB was associated with the greatest effect of the 16 individual factors.

The next strongest predictors of response to immunotherapy were prior receipt of chemotherapy, albumin levels in the blood, and NLR.

Although each of these four measures could predict immunotherapy response, MSK researchers found that the 16-feature model more accurately predicted response than any one of the individual factors studied alone. Whats more, the 16-feature model was also able to better forecast survival differences among patients who did respond to immune checkpoint blockade and those who did not, further supporting the 16-feature approach over one involving fewer features. Cumulatively, the findings indicate that clinicians can do better than TMB alone by including other available pieces of information about the patient or the tumor genetics, Dr. Morris says.

Importantly, the model also takes into account TMBs varying degrees of predictive value across cancer types, Dr. Morris adds.

Although the predictive value of TMB varies quite a bit across different cancer types, the [16-feature] model had good predictive ability across all cancer types, he says. This is important because TMB is less predictive for some malignancies than for others, and for some types of cancer, it has no value at all. For example, the predictive value of elevated TMB is well established in melanoma and non-small cell lung cancer. In breast and prostate cancers, though, TMB has not been found to accurately predict immunotherapy response.

Broad use is part of Dr. Morris and his colleagues aim: This is a very good predictive biomarker based on genetic data from tumor sequencing, but our next research goal will be to try to determine how much value we can glean from a simpler model that maybe could be more widely implemented around the world.

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Machine Learning Approach Takes MSK Researchers Beyond Known Method to Predict Immunotherapy Response - On Cancer - Memorial Sloan Kettering

According to a study published in the Journal of Sex Research, relationship characteristics like relationship satisfaction, relationship length, and romantic love are among the top predictors of cheating within a relationship. The researchers used a machine learning algorithm to pinpoint the top predictors of infidelity among over 95 different variables.

While a host of studies have investigated predictors of infidelity, the research has largely revealed mixed and often contradictory findings. Study authors Laura M. Vowels and her colleagues aimed to improve on these inconsistencies by using machine learning models. This approach would allow them to compare the relative predictability of various relationship factors within the same analyses.

The research topic was actually suggested by my co-author, Dr. Kristen Mark, who was interested in understanding predictors of infidelity better. She has previously published several articles on infidelity and is interested in the topic, explained Vowels, a principal researcher forBlueheart.ioand postdoctoral researcher at the University of Lausanne.

Vowels and her team pooled data from two different studies. The first data set came from a study of 891 adults, the majority of whom were married or cohabitating with a partner (63%). Around 54% of the sample identified as straight, 21% identified as bisexual, 11% identified as gay, and 7% identified as lesbian. A second data set was collected from both members of 202 mixed-sex couples who had been together for an average of 9 years, the majority of whom were straight (93%).

Data from the two studies included many of the same variables such as demographic measures like age, race, sexual orientation, and education, in addition to assessments of participants sexual behavior, sexual satisfaction, relationship satisfaction, and attachment styles. Both studies also included a measure of in-person infidelity (having interacted sexually with someone other than ones current partner) and online infidelity (having interacted sexually with someone other than ones current partner on the internet).

Using machine learning techniques, the researchers analyzed the data sets together first for all respondents and then separately for men and women. They then identified the top ten predictors for in-person cheating and for online cheating. Across both samples and among both men and women, higher relationship satisfaction predicted a lower likelihood of in-person cheating. By contrast, higher desire for solo sexual activity, higher desire for sex with ones partner, and being in a longer relationship predicted a higher likelihood of in-person cheating. In the second data set only, greater sexual satisfaction and romantic love predicted a lower likelihood of in-person infidelity.

When it came to online cheating, greater sexual desire and being in a longer relationship predicted a higher likelihood of cheating. Never having had anal sex with ones current partner decreased the likelihood of cheating online a finding the authors say likely reflects more conservative attitudes toward sexuality. In the second data set only, higher relationship and sexual satisfaction also predicted a lower likelihood of cheating.

Overall, I would say that there isnt one specific thing that would predict infidelity. However, relationship related variables were more predictive of infidelity compared to individual variables like personality. Therefore, preventing infidelity might be more successful by maintaining a good and healthy relationship rather than thinking about specific characteristics of the person, Vowels told PsyPost.

Consistent with previous studies, relationship characteristics like romantic love and sexual satisfaction surfaced as top predictors of infidelity across both samples. The researchers say this suggests that the strongest predictors for cheating are often found within the relationship, noting that, addressing relationship issues may buffer against the likelihood of one partner going out of the relationship to seek fulfillment.

These results suggest that intervening in relationships when difficulties first arise may be the best way to prevent future infidelity. Furthermore, because sexual desire was one of the most robust predictors of infidelity, discussing sexual needs and desires and finding ways to meet those needs in relationships may also decrease the risk of infidelity, the authors report.

The researchers emphasize that their analysis involved predicting past experiences of infidelity from an array of present-day assessments. They say that this design may have affected their findings, since couples who had previously dealt with cheating within the relationship may have worked through it by the time they completed the survey.

The study was exploratory in nature and didnt include all the potential predictors, Vowels explained. It also predicted infidelity in the past rather than current or future infidelity, so there are certain elements like relationship satisfaction that might have changed since the infidelity occurred. I think in the future it would be useful to look into other variables and also look at recent infidelity because that would make the measure of infidelity more reliable.

The study, Is Infidelity Predictable? Using Explainable Machine Learning to Identify the Most Important Predictors of Infidelity, was authored by Laura M. Vowels, Matthew J. Vowels, and Kristen P. Mark.

Link:
Psychologists use machine learning algorithm to pinpoint top predictors of cheating in a relationship - PsyPost

The amount of data generated on a day-to-day basis is humungous so much so that the term given to identify such a large volume of data is coined as big data. Big data is usually raw and cannot be used to meet business objectives. Thus, transforming this data into a form that is easy to understand is important. This is exactly where machine learning comes into play. With machine learning in place, it is possible to understand the customer demands, their behavioral pattern and a lot more thereby enabling the business to meet its objectives. For this very purpose, companies and experts rely on certain machine learning tools. Here is our find of top machine learning tools used by experts in 2021. Have a look!

Keras is a free and open-source Python library popularly used for machine learning. Designed by Google engineer Franois Chollet, Keras acts as an interface for the TensorFlow library. In addition to being user-friendly, this machine learning tool is quick, easy and runs on both CPU and GPU. Keras is written in Python language and functions as an API for neural networks.

Yet another widely used machine learning tool across the globe is KNIME. It is easy to learn, free and ideal for data reporting, analytics, and integration platforms. One of the many remarkable features of this machine learning tool is that it can integrate codes of programming languages like Java, JavaScript, R, Python, C, and C++.

WEKA, designed at the University of Waikato, in New Zealand is a tried-and-tested solution for open-source machine learning. This machine learning tool is considered ideal for research, teaching I models, and creating powerful applications. This is written in Java and supports platforms like Linux, Mac OS, Windows. It is extensively used for teaching and research purposes and also for industrial applications for the sole reason that the algorithms employed are easy to understand.

Shogun, an open-source and free-to-use software library for machine learning is quite easily accessible for businesses of all backgrounds and sizes. Shoguns solution is entirely in C++. One can access it in other development languages, including R, Python, Ruby, Scala, and more. Everything from regression and classification to Hidden Markov models, this machine learning tool has got you covered.

If you are a beginner then there cannot be a better machine learning tool to start with other than Rapid Miner. It is because of the fact that it doesnt require any programming skills in the first place. This machine learning tool is considered to be ideal for text mining, data preparation, and predictive analytics. Designed for business leaders, data scientists, and forward-thinking organisations, Rapid Miner surely has grabbed attention for all the right reasons.

TensorFlow is yet another machine learning tool that has gained immense popularity in no time. This open-source framework blends both neural network models with other machine learning strategies. With its ability to run on both CPU as well as GPU, TensorFlow has managed to make it to the list of favourite machine learning tools.

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Top Machine Learning Tools Used By Experts In 2021 - Analytics Insight

Calling all data devotees, machine-learning mavens and arbiters of AI. Clear your calendar to make room for the iMerit ML DataOps Summit on December 2, 2021. Join and engage with AI and ML leaders from multiple tech industries, including autonomous mobility, healthcare AI, technology and geospatial to name just a few.

Attend for free: Theres nothing wrong with your vision the iMerit ML DataOps Summit is 100% free, but you must register here to attend.

The summit is in partnership with iMerit, a leading AI data solutions company providing high-quality data across computer vision, natural language processing and content that powers machine learning and artificial intelligence applications. So, what can you expect at this free event?

Great topics require great speakers, and well have those in abundance. Lets highlight just three of the many AI and ML experts who will take the virtual stage.

Radha Basu: The founder and CEO of iMerit leads an inclusive, global workforce of more than 5,300 people 80% of whom come from underserved communities and 54% of whom are women. Basu has raised $23.5 million from investors, led the company to impressive revenue heights and has earned a long list of business achievements, awards and accolades.

Hussein Mehanna: Currently the head of Artificial Intelligence and Machine Learning at Cruise, Mehanna has spent more than 15 years successfully building and leading AI teams at Fortune 500 companies. He led the Cloud AI Platform organization at Google and co-founded the Applied Machine Learning group at Facebook, where his team added billions of revenue dollars.

DJ Patil: The former U.S. Chief Data Scientist, White House Office of Science and Technology Policy, Patils experience in data science and technology runs deep. He has held high-level leadership positions at RelateIQ, Greylock Partners, Color Labs, LinkedIn and eBay.

The iMerit ML DataOps Summit takes place on December 2, 2021. If your business involves data-, AI- and ML-driven technologies, this event is made for you. Learn, network and stay current with this fast-paced sector and do it for free. All you need to do is register. Start clicking.

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A look at some of the AI and ML expert speakers at the iMerit ML DataOps Summit - TechCrunch