#Healthcare Datasets

[shaip]

Top 19 Medical Datasets to Supercharge Your Machine Learning Models

[globosetechnologysolutins]

The Role of Healthcare Datasets in Revolutionizing Modern Medicine

In the rapidly evolving world of medicine, data is king. Among the myriad forms of data that drive innovation, healthcare datasets stand out as pivotal tools that are transforming the way healthcare is delivered. From improving patient outcomes to advancing medical research, these datasets are playing an increasingly vital role.

What Are Healthcare Datasets?

Healthcare datasets encompass a wide range of data collected from various sources within the healthcare system. This data can include patient records, treatment histories, diagnostic images, lab results, genetic information, and even real-time monitoring data from wearable devices. The vast amount of data generated in healthcare is often structured in databases designed to store, manage, and analyze health-related information.

Applications of Healthcare Datasets

Personalized Medicine: One of the most promising applications of healthcare datasets is in personalized medicine. By analyzing large datasets of patient information, healthcare providers can tailor treatments to individual patients based on their unique genetic makeup, lifestyle, and medical history. This leads to more effective treatments with fewer side effects.

Predictive Analytics: Healthcare datasets are instrumental in predictive analytics, which involves using data to predict patient outcomes. For example, by analyzing historical patient data, hospitals can identify patients at high risk of developing certain conditions, allowing for early intervention and prevention.

Public Health Management: Public health officials rely on healthcare datasets to track the spread of diseases, monitor the effectiveness of interventions, and allocate resources more effectively. This is particularly important in managing outbreaks of infectious diseases, where timely data can be the difference between containment and epidemic.

Medical Research: Researchers use healthcare datasets to uncover new insights into diseases, treatment efficacy, and patient behavior. Large-scale studies, such as those investigating the long-term effects of medications or the genetic basis of diseases, are made possible by the availability of comprehensive datasets.

Operational Efficiency: Healthcare providers use datasets to streamline operations and improve efficiency. By analyzing data on patient flow, resource utilization, and treatment outcomes, hospitals can optimize their processes, reduce costs, and enhance patient care.

Challenges and Considerations

While the benefits of healthcare datasets are undeniable, they also present challenges. Data privacy and security are paramount, as healthcare data is highly sensitive. Ensuring that patient information is protected while still being accessible for analysis requires robust cybersecurity measures and adherence to regulatory standards like HIPAA.

Another challenge is data interoperability. Healthcare data often comes from multiple sources, including electronic health records (EHRs), laboratory systems, and medical devices, each with its own format and standards. Integrating these disparate data sources into a cohesive dataset that can be easily analyzed is a complex task.

The Future of Healthcare Datasets

The future of healthcare is increasingly data-driven. As technology advances, the volume and variety of healthcare datasets will continue to grow. Artificial intelligence (AI) and machine learning (ML) are expected to play a significant role in analyzing these vast datasets, uncovering patterns and insights that were previously inaccessible.

Moreover, the rise of wearable devices and mobile health apps is adding new dimensions to healthcare datasets, enabling continuous monitoring and real-time health data collection. This influx of data will further enhance personalized medicine and predictive analytics, making healthcare more proactive and preventive.

Conclusion

Healthcare datasets are revolutionizing the medical field, offering new ways to improve patient care, advance research, and optimize operations. While challenges remain, the potential of these datasets to transform healthcare is immense. As we continue to harness the power of data, the future of medicine looks brighter, more personalized, and more effective than ever before.

[globosetechnologysolution]

Medical Datasets represent the cornerstone of healthcare innovation in the AI era. Through careful analysis and interpretation, these datasets empower healthcare professionals to deliver more accurate diagnoses, personalized treatments, and proactive interventions. At Globose Technology Solutions, we are committed to harnessing the transformative power of medical datasets, pushing the boundaries of healthcare excellence, and ushering in a future where every patient will receive the care they deserve.

[3-loch-prinzessin]

glad to know that all my horny posting on here and from my old blog has been used to train AI...

[chemxpertdatabase]

Elevate Your Pharma Strategy with Chemxpert’s ChemProtel

Chemxpert Database, a trusted name among pharmaceutical data providers in India, offers ChemProtel — an advanced product intelligence platform tailored for pharma professionals. Gain actionable product intelligence through our comprehensive pharma suppliers database, enabling smarter sourcing, competitive analysis, and strategic planning. Whether you're in procurement, R&D, or business development, ChemProtel by Chemxpert Database empowers you with real-time insights to stay ahead in the dynamic pharmaceutical landscape. Discover the smarter way to work with pharma data.

[segmed]

Generating Chest X-Rays with AI: Insights from Christian Bluethgen | Episode 24 - Bytes of Innovation

Join Christian Bluethgen in this 32-minute webinar as he delves into RoentGen, an AI model synthesizing chest X-ray images from textual descriptions. Learn about its potential in medical imaging, benefits for rare disease data, and considerations regarding model limitations and ethical concerns.

[nuadox]

Researchers develop unsupervised machine learning method to improve fraud detection in imbalanced datasets

- By Nuadox Crew -

Researchers at Florida Atlantic University have developed a new machine learning method that significantly improves fraud detection by generating accurate class labels from severely imbalanced datasets—common in fraud cases where fraudulent events are rare.

Unlike traditional methods that rely on labeled data, their unsupervised technique works without prior labeling, cutting costs and addressing privacy concerns.

Tested on large real-world datasets (European credit card transactions and Medicare claims), the method outperformed the widely-used Isolation Forest algorithm by minimizing false positives and requiring less human oversight. It combines three unsupervised learning models with a percentile-gradient approach to isolate the most confidently identified fraud cases, enhancing accuracy and efficiency.

Published in the Journal of Big Data, this approach offers scalable, low-cost fraud detection for high-risk industries like finance and healthcare, and was recognized with a Best Student Paper Award at the IEEE ICTAI 2024 conference. Future work will focus on automating optimal label selection to further boost scalability.

Read more at Florida Atlantic University (FAU)

Scientific paper: Mary Anne Walauskis et al, Unsupervised label generation for severely imbalanced fraud data, Journal of Big Data (2025). DOI: 10.1186/s40537-025-01120-x

Related Content

Small Data approaches provide nuance and context to health datasets

Other Recent News

New oral medication shows promise against antibiotic-resistant gonorrhea.

Medical imaging radiation may be responsible for 5% of cancer cases in the U.S.

[saywhat-politics]

A federal judge has ordered federal health agencies to restore websites and datasets that were abruptly pulled down beginning in late January, prompting an outcry from medical and public health communities.

The temporary restraining order was granted in response to a lawsuit filed against the federal government by Doctors for America (DFA), a progressive advocacy group representing physicians, and the nonprofit Public Citizen, a consumer advocacy group.

Trump administration purges websites across federal health agencies

The pages that are now set to be revived include information for patients about HIV testing and HIV prevention medication, guidance on contraceptives, data on adolescent and youth mental health, and an action plan for improving enrollment of underrepresented populations in clinical trials.

Judge John Bates with the U.S. District Court for the District of Columbia, who was appointed by President George W. Bush in 2001, said the sudden loss of these resources had jeopardized the work of clinicians and public health. "It bears emphasizing who ultimately bears the harm of defendants' actions: everyday Americans, and most acutely, underprivileged Americans, seeking healthcare," he wrote in his opinion.

[mindblowingscience]

The same underlying technology powering massively popular generative AI models like from large tech firms like OpenAI is now being used to scan for early signs of lung disease. Google, one of the leaders in new AI models, is partnering with a healthcare startup that’s analyzing vast datasets of coughs and sneezes to detect signs of tuberculous or other respiratory diseases before they get worse. It’s one of numerous ways the quickly evolving technology is rapidly reshaping early detection of disease across the healthcare industry. What happens once that initial diagnosis is made, however, still requires quintessential human clinical expertise. 

Continue Reading.

[nanamineedstherapy]

Things Never Said @ IIM Ahmedabad

Summary: Gangs of Wasseypur x Shark Tank but everyone has rabies. Mainly Slice of life, but aggressive. Previous Chapter - [Tumblr/Ao3] A/N: Just a girl & the men she outgrew at IIM A. She’s their CEO now.

You met Gojo in a seminar on organisational behaviour. He walked in late, wearing sunglasses indoors and quoting Ratan Tata like he had brunch with him last Sunday. You were sitting in the front row, highlighting a Harvard case study like it was your diary. Margins filled with analysis. Head down. Voice quiet. Invisible in a room full of future CEOs who'd inherited their confidence from their CGPAs.

You thought he was a joke.

Then he smiled at you.

And for two years, you mistook that smile for something that belonged to you.

Nanami was in Finance. Quiet. Sharp. Efficient. You once tried to sit next to him in the library. He didn’t look up. Just plugged in his headphones and typed. You don’t think he ever learned your name. But he always borrowed your pen. Always returned it.

Suguru was in HR & Strategy. Always in black. Always alone. The type who read Ghalib in the back of marketing lectures and left in the middle of team projects “to think.” You once got paired with him for a group case study. He submitted it alone. Put his name first. You didn’t say anything. You just stayed up the whole night correcting his citations.

Shoko was in Healthcare Management. You both lived on the same floor. She was always with Utahime. The kind of girl people gravitated to. Soft smiles. Early dinners. Sincere LinkedIn posts. You once asked Shoko if she wanted to go out for chai. She said, “Rain check!” and never followed up. You never asked again.

Sukuna was in Analytics. Cold. Brilliant. Arrogant in a way only kids who were bullied in school could afford to be. He was the one who told the professor their dataset was flawed—during the presentation. Once, you asked him for access to his pivot sheet. He said, “Get good.” You never asked him again, either.

Ino was a junior in Marketing. Got bullied often.

Toji wasn’t a student. He was a Visiting Faculty. Operations Management. Taught like he hated the syllabus. Graded like he wanted to break you down and rebuild you as someone who could survive the market. You once stayed after class to ask about warehouse metrics. He looked at you like you were a spreadsheet that annoyed him. Then gave you a full breakdown on inventory gaps. On the board. Said “Don’t be useless” and left. You got the highest marks in the midterm. He never said a word about it.

You were just the girl with the files.

The one who showed up early, left late.

Over-prepared. Overlooked.

The one who knew her coffee order, her batch rank, and her backup career plan.

But not what it felt like to be wanted.

Except by Gojo.

He’d drag you to late-night chai breaks outside the hostel, feet hanging off the campus wall like you weren’t both drowning in debt and destiny. He used to steal your notes, call you “his brain,” joke about co-founding something big with you one day. Even call you “partner” like it was a promise.

Ask you dumb questions like “What would you name our company if we started one?”

You’d laugh. Give him your time. Your trust. Your softest version.

A part of you believed in maybe... it memorised those moments like scripture.

Until the day he brought Utahime to campus fest.

Held her hand.

Called her his safe space.

So while you were falling for him in between lectures and Excel models, he was falling for Utahime—the wholesome girl with a stable life and a skincare routine.

You skipped the next chai break.

Then the one after that.

He noticed.

But not enough.

You’d watch them walk across campus, Gojo smiling like an idiot, Utahime gently adjusting his collar.

You started carrying headphones. Even when there was no music playing.

After graduation, he called.

Said, “Let’s do this. Let’s build something.”

Said, “I can get the funding. You’ll make it real.”

Said, “You’re the smartest person I know.”

You said yes.

Not because you loved him anymore.

You buried that part of yourself the night he twirled Utahime under the fest lights—just hours before you lost your virginity, drunk, to some freshman whose name never stuck.

You said yes because you were tired of being the smartest person in the room with nothing to show for it.

And because his dad had money. And you had nothing left to prove.

Now, he’s your co-founder. You’re the CEO.

You run the company. The systems. The strategy. The team.

Gojo handles investors. PR. Slides with aesthetics and no numbers.

He still calls you “partner” during all-hands.

You call him a “funded clown” during lunch.

He brings you coffee during crunch time.

You never drink it.

He sends memes in the office WhatsApp.

You mute the chat.

He still asks, “Lunch?” after every board meeting.

You say, “Busy,” and go sit next to Kokichi just to watch his jaw clench.

You’ve never told Gojo the truth.

Never told him you liked him.

Never told him how you looked for excuses to study with him.

Never told him how you downloaded Utahime’s research paper just to hate-read it and realised Gojo had supplied her with your research.

Never told him how much it hurt when he called you safe, then picked someone else to be his home.

You buried those feelings in code, contracts, and caffeine.

And you never looked back.

And sometimes—when he looks at you like he wants to say something, but he never got the timing right for—you want to scream.

Because he could’ve had you.

You were right fucking there.

But he picked the safe girl. The nice girl. The girl who wouldn’t burn the world to build her own.

And now he’s alone in the glass cabin next to yours, rich off his dad’s name and your sleepless nights.

Still charming. Still clueless. Still trying.

While you’re the CEO.

You still hate him.

He doesn’t know what happened between you two.

---

A/N: He could’ve had her. Now do ya'll understand why she hates them all? Let me know in the comments if you’ve ever been the invisible one - who still ran the show? Or If you just want to punch Gojo in the face? That’s valid too.

Next Chapter - Fiscal Year from Hell - [Tumblr/Ao3]

All Works Masterlist

[globosetechnologysolutins]

The Role of Healthcare Datasets in Revolutionizing Modern Medicine

In the rapidly evolving world of medicine, data is king. Among the myriad forms of data that drive innovation, healthcare datasets stand out as pivotal tools that are transforming the way healthcare is delivered. From improving patient outcomes to advancing medical research, these datasets are playing an increasingly vital role.

What Are Healthcare Datasets?

Healthcare datasets encompass a wide range of data collected from various sources within the healthcare system. This data can include patient records, treatment histories, diagnostic images, lab results, genetic information, and even real-time monitoring data from wearable devices. The vast amount of data generated in healthcare is often structured in databases designed to store, manage, and analyze health-related information.

Applications of Healthcare Datasets

Personalized Medicine: One of the most promising applications of healthcare datasets is in personalized medicine. By analyzing large datasets of patient information, healthcare providers can tailor treatments to individual patients based on their unique genetic makeup, lifestyle, and medical history. This leads to more effective treatments with fewer side effects.

Predictive Analytics: Healthcare datasets are instrumental in predictive analytics, which involves using data to predict patient outcomes. For example, by analyzing historical patient data, hospitals can identify patients at high risk of developing certain conditions, allowing for early intervention and prevention.

Public Health Management: Public health officials rely on healthcare datasets to track the spread of diseases, monitor the effectiveness of interventions, and allocate resources more effectively. This is particularly important in managing outbreaks of infectious diseases, where timely data can be the difference between containment and epidemic.

Medical Research: Researchers use healthcare datasets to uncover new insights into diseases, treatment efficacy, and patient behavior. Large-scale studies, such as those investigating the long-term effects of medications or the genetic basis of diseases, are made possible by the availability of comprehensive datasets.

Operational Efficiency: Healthcare providers use datasets to streamline operations and improve efficiency. By analyzing data on patient flow, resource utilization, and treatment outcomes, hospitals can optimize their processes, reduce costs, and enhance patient care.

Challenges and Considerations

While the benefits of healthcare datasets are undeniable, they also present challenges. Data privacy and security are paramount, as healthcare data is highly sensitive. Ensuring that patient information is protected while still being accessible for analysis requires robust cybersecurity measures and adherence to regulatory standards like HIPAA.

Another challenge is data interoperability. Healthcare data often comes from multiple sources, including electronic health records (EHRs), laboratory systems, and medical devices, each with its own format and standards. Integrating these disparate data sources into a cohesive dataset that can be easily analyzed is a complex task.

The Future of Healthcare Datasets

The future of healthcare is increasingly data-driven. As technology advances, the volume and variety of healthcare datasets will continue to grow. Artificial intelligence (AI) and machine learning (ML) are expected to play a significant role in analyzing these vast datasets, uncovering patterns and insights that were previously inaccessible.

Moreover, the rise of wearable devices and mobile health apps is adding new dimensions to healthcare datasets, enabling continuous monitoring and real-time health data collection. This influx of data will further enhance personalized medicine and predictive analytics, making healthcare more proactive and preventive.

Conclusion

Healthcare datasets are revolutionizing the medical field, offering new ways to improve patient care, advance research, and optimize operations. While challenges remain, the potential of these datasets to transform healthcare is immense. As we continue to harness the power of data, the future of medicine looks brighter, more personalized, and more effective than ever before.

[globosetechnologysolution]

Medical Datasets represent the cornerstone of healthcare innovation in the AI era. Through careful analysis and interpretation, these datasets empower healthcare professionals to deliver more accurate diagnoses, personalized treatments, and proactive interventions. At Globose Technology Solutions, we are committed to harnessing the transformative power of medical datasets, pushing the boundaries of healthcare excellence, and ushering in a future where every patient will receive the care they deserve.

[justinspoliticalcorner]

Erin Reed at Erin In The Morning:

Over the past two months, a wave of executive orders has unleashed a coordinated rollback of transgender rights across the United States. Transgender people have been scrubbed from federal websites, health resources erased, and even the Stonewall National Monument—honoring a protest led in large part by trans activists—was rewritten to celebrate only “LGB rights,” erasing the T entirely. At the same time, healthcare access has been stripped away, passport updates halted, teachers threatened with prosecution for affirming trans youth, service members purged from the military, and trans existence erased in federal protections. Now, a new poll reveals overwhelming public opposition to the Trump administration’s sweeping anti-trans agenda—from censorship to criminalizing support for trans students and more.

A new poll from Data for Progress reveals broad public rejection of the Trump administration’s quiet campaign to erase LGBTQ+ health information from federal websites. According to the data, 52 percent of respondents oppose the removal of information on LGBTQ+ health disparities and nondiscrimination protections, while just 26 percent support it. The numbers reflect overwhelming resistance to the administration’s censorship effort—one that has already seen some reversals in court. In one particularly egregious case, an entire national student health dataset was taken down solely because it included questions about gender identity. The poll also found significant public opposition to the erasure of transgender people from federal history and language. Respondents opposed removing mentions of transgender people from the Stonewall National Monument website by a 23-point margin, and opposed altering “LGBTQ+” to “LGB” in government materials by 22 points. These changes came after the Trump administration revised the monument’s website to frame the Stonewall uprising as a fight solely for “LGB rights”—a revision that ignores the central role played by transgender and gender nonconforming leaders. Even Sylvia Rivera, a prominent trans activist who helped lead the uprising, had her biography altered to say she fought for “gay and rights”—a revision that’s not only historically dishonest but grammatically incoherent. The censorship of transgender people from public-facing websites wasn’t the only executive action met with broad public disapproval. The Trump administration’s push to ban transgender healthcare is also deeply unpopular. When asked which statement they agreed with more, a clear majority—55%—said that “families and physicians should be the ones making decisions about transgender youth medical care, not the government,” while just 33% supported government bans on “gender-related care for trans youth.” Among respondents who personally know a transgender person, support for gender affirming care jumped to 65%, with only 30% favoring government intervention.

A Data For Progress poll conducted between March 14th and 21st reveals that the majority of Americans oppose Tyrant 47’s efforts to erase trans people in both online material and in policies, such as the gender-affirming care ban and transgender military ban executive orders.

[chemxpertdatabase]

Biosimilars Market Outlook 2025: Trends, Growth Drivers, and Key Players Shaping the Future

The biosimilars market is gaining strong momentum, fueled by increasing regulatory support, patent expiries, and the growing need for cost-effective therapeutics. With new players entering the field and established companies expanding their pipelines, the landscape is becoming more competitive and data-driven.

Despite ongoing challenges around pricing strategies and global regulatory alignment, the outlook remains highly optimistic. Industry professionals need timely, accurate intelligence to stay ahead of market shifts and innovation curves.

[segmed]

Alzheimer's Disease: Power of Real-World Imaging Data (RWiD)

🧠 New Blog Alert! 🧠 Real-World Imaging Datasets (RWiD) are transforming Alzheimer’s research, offering deeper insights into disease progression, biomarker discovery, and treatment outcomes. In our latest blog, we explore how RWiD enhances early diagnosis, optimizes clinical trials, and accelerates breakthroughs in precision medicine. Read now to see how imaging is shaping the future of Alzheimer’s research! Read more: https://www.segmed.ai/resources/blog/alzheimers-disease-power-of-real-world-imaging-data-rwid

[ariathecalico]

I've tried not to get political on here but this is too important.

What's Happening?

Under trump's administration, government websites are being made to take down any LGBTQ+ papers, research, etc. (including information on mental health, personal care, discrimination, and healthcare).

The existence of LGBTQ+ and the care of queer people is being erased from the government through the removal of DEI policies and funding for those who cover these topics. They're trying to solve the issue by removing it and pretending people don't exist. Children and adults are in danger of not being recognized for who they are and are facing discrimination from those in power. In a nation built on liberty, these policies detract from millions of Americans' quality of life and freedom from oppression.

What Can I Do?

Now more than ever it is important to archive any information you can find. Save pages to the Internet Archive, screenshot, save to hard drives and share it where you still can.

Remember that private companies still are not required to take down their research. Rely on what you can, and archive them in case anything happens in the future.

Methods of protest still exist. Do not protest violently; sometimes it may seem like the only way to get things done, but this only gives the community a reason for blame. You should research how to protest peacefully, but be prepared for anything that may happen. In the coming years, being yourself will be the highest form of protest. Don't conform to what they want.

To My LGBTQ+ Friends And Readers

Now is not the time to give up. Live loudly and be who you know you are. It's going to get rough, but giving in is not a solution. The number of LGBTQ+ Americans is always growing, and nobody can ignore it forever. We are stronger in numbers, and it's with all of us working together that we can make a more free America where innocent people are not discriminated against for being themselves.

Sources/Further Information:

BBC, "US federal websites scrub vaccine data and LGBT references" https://bbc.com/news/articles/cgkj8gx1vy6o

CNN Health, "Epidemiologist reacts to removal of certain health data, information from CDC website" https://www.cnn.com/2025/02/02/health/video/cdc-websites-gender-lgbtq-datasets-dr-nuzzo-foa-digvid