The work of a data scientist is never done.

The work of crafting top 10 lists for data scientist is also never done.

Top 10 tools a data scientist should use in 2021 The work of a data scientist centers around the process of extraction of meaningful data from unstructured information and analyzing that data for necessary interpretation. This requires a lot of useful tools. The following are the top 10 most […]


The following is a guest post by Ainsley Lawrence, a freelance writer from the Pacific Northwest. She is interested in topics related to better living through education and technology. She is frequently lost in a good book.


It likely hasn’t escaped your notice just how much our contemporary society relies on data. From a business perspective, it is used to help companies better understand their consumers. On a wider social level, data science is informing conservation efforts and assisting organizations dedicated to tackling poverty. In essence, the more we explore the analysis and interpretation of large amounts of information, the better able we are to use it for the benefit of everyone.

This is particularly evident in healthcare fields. Between the advances in technology over the last couple of decades and algorithms for decision making, talented data scientists and developers are making a difference. Indeed, some significant attention as of late is directed toward data science’s application in mental healthcare. We are facing a mental health crisis in this country — a recent poll found 1 in 4 adults aged 18 to 24 have considered suicide — and professionals in the field are desperate for effective tools.

Let’s take a closer look at how data science is being used to help mental healthcare.

Diagnosis and Research

To be able to treat patients correctly and effectively, professionals need to be able to understand how subjective experiences of symptoms fit into a spectrum of known conditions. Mental health workers’ own expertise is vital here, but by partnering with data science tools, they can make more informed and relevant conclusions on what a patient’s challenges are and where to explore further.

The data science role in diagnostic medicine has been on the rise over the past several years. Indeed, the combination of medical records being transferred to electronic methods and the growing accessibility of machine learning software is developing into a useful ecosystem for diagnosis, including in mental health fields. The vast amounts of information gathered on symptoms, triggers, and experiences provide data scientists with vital clues toward common correlations in illnesses. The more data is produced, analyzed, and verified, the better able experts in the field can offer accurate algorithms to quickly and effectively diagnose patients so they can get on the path to treatment sooner.

It’s important to note, though, that at present this is far from perfect. This is partly why some major institutions have established research units to unlock data science’s potential for mental illness. Both the Alan Turing Institute in the U.K. and Columbia University in the U.S. are participating in statistical collaborations with mental health professionals and patients. The intention of this is not just to collect and use data on how illnesses present in patients — although that’s certainly a part of it. Some of the focus is also on improving the methodology of analytics in mental health so it can be appropriately applied in clinical settings.

Improving Treatment

One of the issues in mental health services today is that there has long been a lack of useful tools. Indeed, until current advances in technology, the most recent significant tool for psychiatric patients had been the introduction of selective serotonin reuptake inhibitors (SSRIs) in 1987. Even these are considered to be limited in their efficacy. It’s no surprise, then, that the prospect of utilizing data science in helping to find more impactful and positive solutions to mental illness is an attractive one.

It may be surprising that among the ways that data science is helpful in this regard is from a business perspective. Particularly in the U.S., the development of medications and treatments is wrapped up in economic and even commercial interests. As such, business analytics plays an important role in helping to get facilities on board with emerging methods, and the allocation of funding for new treatments. Professionals in this field are using their data science expertise to monitor the raw information produced by medical facilities — including supply costs, staff salaries, and even the efficiency of tasks involved. They then apply critical thinking to establish what aspects of medical care can be streamlined and produce advisory reports so funding can be diverted toward the relevant needs. This is a vital tool in mental healthcare fields as these reports help to establish gaps in services, and influence where investment and focus for new treatments, processes, and equipment should be directed.

Identifying Need

The status of mental health across the population being at a point of crisis means there is an imperative not just for health professionals to provide treatment. The increase of psychological, behavioral, and emotional unwellness has transformed from an issue that impacts individuals, to a matter of severe public health concern. As such, data science has a role in shaping effective responses.

Primarily, this is from the perspective of predictive modeling. Data from medical records, patient visits, and treatment outcomes are being collected and analyzed to establish what sectors of the population are experiencing mental unwellness. This tells health departments and providers where the shifts in the need for treatment will be in the near future. Indeed, this predictive approach can be life-saving. Utilizing big data gathered from millions of hospital visits has been instrumental in providing accurate indicators that a patient may be at risk of attempting suicide following their discharge. By being able to review the larger picture, public health officials are better able to direct resources to meet the changing needs of the population.

This isn’t something new, either. Data provided by health professionals and analyzed by officials has helped to positively impact policies in other areas. Medical experts contributed statistics that proved members of the population who had access to walkable neighborhoods were healthier and happier. Their evidence highlighted links to reductions in chronic diseases, and illnesses that were both physical and mental. These types of hard data tend to spur public initiatives and influence not just how the health industry responds but also how cities change infrastructure to tackle mental wellness in the population as a whole.

Conclusion

Mental wellness is a considerable concern in our present society. The fact it is becoming a more open part of our general discussions and shared experiences also means there is scope to utilize tools such as data science to make a positive impact. It’s still a developing area, particularly in the realm of diagnosis. But this approach is already helping to predict the areas of need, and improve treatment options.

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Here is the entire AM keynote from the Data + AI Summit 2021.

The pursuit of AI is one of the biggest priorities in data today. The Thursday morning keynote will be led by Databricks Co-founder and CEO Ali Ghodsi and cover advances in data science, machine learning, MLOps and more in both open source and the Databricks Lakehouse Platform. Stay tuned for some exciting announcements.

We’ll also be joined by a co-creator of TensorFlow, data leaders from McDonald’s, as well as the legendary Bill Nye, a scientist, engineer, comedian, and author.