Arine’s Post

5 things one should know before starting a career as a healthcare data scientist. 1️⃣ 𝐃𝐚𝐭𝐚 𝐃𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲: The variety and complexity of biomedical data is immense. This includes demographics, vital signs, lab measurements, clinical notes, medical imaging, genomics, and more. Be prepared to encounter data types that may be entirely new to you. 2️⃣ 𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐨𝐟 𝐃𝐚𝐭𝐚: Accessing healthcare data is complex and time-consuming due to stringent privacy and security regulations. Once access is obtained, the next challenge often lies in data annotation, requiring collaboration with clinical or domain experts. This process can potentially delay projects. 3️⃣ 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐤𝐢𝐥𝐥𝐬: As a healthcare data scientist, you'll frequently communicate with non-technical stakeholders, including business leaders, product managers, sales teams, clinicians, and even patients. The ability to tailor your messages for a non-technical audience is crucial – without this skill, their interest can quickly wane. 4️⃣ 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 ≠ 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: Moving a model from development to production doesn't automatically make it ready for clinical deployment. An additional, critical layer of validation and approval is required, involving rigorous clinical trials, validation studies, and often regulatory approvals to ensure the model's safety and efficacy in real-world medical settings. 5️⃣ 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: Your models will directly impact human health and lives, influencing diagnostics, treatment plans, and patient outcomes. It's crucial to place uncompromising emphasis on accuracy, safety, transparency, privacy, and non-discrimination. Healthcare data science is a field unlike any other in data science. It's challenging and demands perseverance, patience, and a highly responsible approach. Yet, the satisfaction of knowing your work can improve human lives is incomparable and deeply rewarding 🙂. #datascience #healthcare #career

🚀 Exciting Journey to Becoming a Data Scientist! 📊 I'm thrilled to share that I'm currently on the path to becoming a data scientist! 🌟 As I take this incredible journey, I wanted to share one of my recent projects that showcases the power of data science in healthcare. 🩺 The project involved creating a decision tree to predict whether a patient has diabetes based on various clinical variables. This crucial step can lead to early diagnosis and better patient care. Let me walk you through the key highlights of this project! 🌳 🔍 Explored Clinical Variables 📊 🔧 Cleaned and Transformed Data 🧹 🌳 Trained Decision Tree with ID3 Algorithm 🌱 📊 Evaluated Model Performance 📈 🌿 Interpreted Crucial Insights 🧠 🎯 Validated with Sensitivity Analysis 📝 Developing this decision tree model has the potential to make a significant impact in healthcare. Early identification of diabetes risk can lead to improved patient outcomes, reduced healthcare costs, and a healthier population overall. ✨ Conclusion As I continue on my data science journey, I'm excited about the endless possibilities to apply data-driven solutions in healthcare and beyond. Data science empowers us to make informed decisions and solve real-world problems. I look forward to sharing more exciting projects and experiences with you as I grow in this dynamic field. If you have any questions or would like to collaborate on data science projects, please feel free to reach out. Let's make a positive impact together! 🚀🌎 #DataScience #Healthcare #DiabetesPrediction #DecisionTreeModel #MachineLearning #HealthTech #DataDrivenDecisions #PatientCare #EarlyDiagnosis #DataScience #MachineLearning #Healthcare #DataDrivenDecisions #HealthTech #AI #Analytics #HealthcareTech #PredictiveModeling #DiabetesPrediction #EarlyDiagnosis #MedicalResearch #DataAnalysis #DataVisualization #DataInsights #SensitivityAnalysis #DecisionTreeModel #HealthcareInnovation #InformedDecisions #PatientCare #HealthcareSolutions #TechForGood #DataScienceEnthusiast #ContinuingTheJourney #LinkedInNetworking #CommunityEngagement #ProfessionalGrowth #IndustryInsights #CareerInDataScience #ImpactfulProjects #PositiveChange #DataForBetterHealthcare #RealWorldApplications #DataDrivenSolutions #DataScienceCommunity #FutureOfHealthcare #DataDrivenHealthcare #LinkedInDataScience #HealthcareData #DataAnalytics #DataGeeks #ArtificialIntelli #DataScienceApplications #HealthTechInnovations #DataScienceCommunity #HealthTechInsights #DataScienceImpact #DataSciencePassion #HealthTechAdvancements #DataScienceInAction #HealthTechDataManagement #DataScienceExperts #HealthTechSuccess #DataScienceForHealthcare #HealthTechFuture #DataScienceApplications #HealthTechInnovations #DataScienceCommunity #HealthTechInsights #DataScienceImpact #HealthTechAdvancements #DataScienceInAction #HealthTechDataManagement #DataScienceExperts #HealthTechSuccess

There's a common misconception that a biomedical degree and experience are a must for becoming a data scientist in healthcare. Let me categorize the different types of healthcare data scientist roles: 1️⃣ The Applied Scientist: These individuals work in research settings. Such positions often require a biomedicine degree and sometimes, relevant experience. Examples include bioinformaticians, biostatisticians, and pharma data scientists. 2️⃣ The Research Engineer: These roles demand technical expertise in data science areas like NLP, computer vision, signals processing, etc. for developing healthcare AI applications. My current role aligns with this category, as I specialize in developing LLM-based applications. 3️⃣ The Product Data Scientist: These professionals focus on enhancing product user experience, revenue, marketing, and distribution through data-driven insights. These positions call for knowledge and experience in statistics and some ML. Good communication skills are required for these types of roles. For research and product data scientist roles, healthcare-specific experience is an advantage but not essential. Fortunately, many opportunities today fall into the second and third categories. These present ideal pathways for those aspiring to enter the rewarding field of healthcare data science. #datascientist #jobs #healthcare #artificialintelligence

Data Engineers are like Medical Laboratory scientist Most of the decisions taken in patient management are usually generated by seasoned professionals in the laboratory, very integral to our healthcare system. Unfortunately, our patients don't get to see this amazing professionals and thank them for the amazing work they do, instead, patients give all the praise and thanks to the nurses and doctors. This also applies to the data management system. The end users will appreciate the report developer, the data science expert, the product guys etc, but rarely the Data Engineers 😊😊 So this is my special thanks to all our amazing professionals behind the scene, you're the real gem 💎 #medicalscientist #dataengineers #medtech

Really cool things happening with this team. Check out the open data science roles in the link

👩⚕️🔬 Bridging Biomedical Engineering and Data Analytics: A World of Possibilities 🔬👨⚕️ Hey LinkedIn community! I’ve been reflecting on the exciting intersection of biomedical engineering and data analytics, and I’m thrilled to share some insights on the boundless opportunities this combination offers. As a biomedical engineer stepping into the world of data, I’ve realized how powerful this blend can be. Here’s why: 🌟 Transforming Healthcare with Data 🌟 1. Healthcare Data Analysis: By diving into electronic health records (EHRs), we can enhance patient care and streamline hospital operations. Predictive analytics can foresee patient outcomes, making healthcare proactive rather than reactive. 2. Medical Imaging: Imagine using machine learning to analyze MRI and CT scans. We’re talking about more accurate diagnostics and better treatment planning! 3. Genomics and Bioinformatics: Genomic data analysis is like unlocking the secrets of our DNA. It helps in personalized medicine, ensuring treatments are tailored to individual genetic profiles. 4. Wearable Health Tech: From fitness trackers to smartwatches, analyzing data from wearables can revolutionize real-time health monitoring and remote patient care. 5. Drug Development: Analyzing clinical trial data ensures new drugs are safe and effective. It’s a critical step in bringing innovative treatments to market. 🛠️ Essential Skills and Tools 🛠️ To thrive in this space, here are some skills and tools that have been game-changers for me: - Programming: Python, R, and MATLAB. - Data Analysis: Pandas, NumPy, SciPy, and SQL. - Machine Learning: Scikit-Learn, TensorFlow, Keras, and PyTorch. - Visualization: Matplotlib, Seaborn, ggplot2, and Plotly. - Statistical Analysis: A solid grasp of statistical methods. 🚀 Career Paths 🚀 The fusion of these fields opens up diverse career paths: - Healthcare Data Scientist - Biomedical Data Analyst - Clinical Data Manager - Health Informatics Specialist - Bioinformatics Analyst - Medical Imaging Analyst #BiomedicalEngineering #DataAnalytics #HealthcareInnovation #MachineLearning #Genomics #HealthTech

𝗪𝗵𝗼 𝗹𝗲𝗮𝗱𝘀 𝘁𝗵𝗲 𝗰𝗹𝗶𝗻𝗶𝗰𝗮𝗹/𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻?   The data scientist! The clinical evaluation is the statistical summary and assessment of all clinical data concerning a medical device, encompassing information from clinical investigations, Post-Market Clinical Follow-up (PMCF), and Post-Market Surveillance (PMS). In essence, the clinical evaluation is a comprehensive meta-analysis that compares device data to state-of-the-art data using well-established techniques, and ultimately, the person who possesses this comprehensive overview effectively controls the Clinical Evaluation Report (CER). However, basic knowledge of R or SPSS is not sufficient. What is needed is someone with a profound background in data analysis – ideally, someone with an academic background in mathematics or physics, combined with extensive experience in data analysis. Data is inherently messy and complex; only individuals with the necessary experience and skill set can analyze it effectively. Thus, while one could try to recruit a data scientist from a tech giant like Google, a more strategic option would be to engage our services. We offer expertise and specialized knowledge in clinical evaluations that are readily available. #MDR #IVDR #ClinicalEvalaution #PerformanceEvaluation

Hello connections ...! Another day back with another useful information. - DATA SCIENTIST ROLE IN HEALTHCARE : Data scientist in the pharmaceutical industry, should have the analytical skills to diagnose illnesses accurately and save lives. The immense amount of data that is sourced from the healthcare sector, from patient information to records stored by government bodies, requires a skilled analyst to manage it all. Regarding national-level healthcare, data scientists could help monitor the spread of the disease within the country and accordingly coordinate with the authorities to send resources to the most impacted areas. The Covid-19 pandemic has recently shown how vital data science in healthcare is , data science improved the collection and sampling of information but it also demonstrated global patterns in the spread of the infection, predicting the next region where Covid would spread and how government policies could be designed to combat the contagious disease effectively. - Responsibilities of data scientist in healthcare: * Preparing reports and dashboards and presenting the findings to the authorities in charge of the overall management of the organization. * Understanding the role of data science in health care is also a vital responsibility for a healthcare data scientist. It involves modifying collected data to align with the goals and objectives of the organization. * Preparing reports and dashboards and presenting the findings to the authorities in charge of the overall management of the organization. #day3 #14dayschallenge #usefulinformation #bversity Bversity Sudharsan Varatharajan

𝗪𝗵𝗮𝘁 𝘄𝗲 𝗹𝗲𝗮𝗿𝗻𝗲𝗱... 𝗳𝗿𝗼𝗺 𝗿𝗲𝗰𝗿𝘂𝗶𝘁𝗶𝗻𝗴 𝗮 𝗱𝗿𝘂𝗴 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗶𝗻 𝟮 𝘄𝗲𝗲𝗸𝘀 Data Scientists are enabling the future of AI and ML powered drug discovery and design, utilising their computational biology and data analysis domain expertise. It's no surprise their skills are in high demand in the world of biotech and drug discovery. Here are three things we learned from helping a VP Data Science at a Bay Area AI-drug discovery biotech recruit a Sr Data Scientist in 2 weeks: 💊𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝗻 𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: despite the HQ been in the Bay, the VP Data Science was pro-remote working, meaning our talent pool increased tenfold, and we were able to conduct streamlined virtual interviews in week 1, and an offer accepted in week 2. It's no surprise their existing team had an impressive 𝘳𝘦𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘳𝘢𝘵𝘦 𝘰𝘧 >95%. 💊𝗕𝗶𝗼𝗺𝗮𝗿𝗸𝗲𝗿 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻: with the surge of biomarker discovery research, it was reassuring to see lots of recent data scientists majoring in deep machine learning with biomarker ID or single cell transcriptomic approaches. 💊𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗲𝗲𝘁𝘀 𝘀𝘆𝗻𝗲𝗿𝗴𝗶𝘀𝘁𝗶𝗰 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗽𝗵𝗮𝗿𝗺𝗮𝗰𝗼𝗹𝗼𝗴𝘆: a lot of prospects were showing a keen interest in the emerging field of multi-drug target research which utilizes deep learning computational algorithms to overcome the analysis of very big biological data sets. Although this particular client had no existing pipeline applications here, it signalled pro-activity and awareness to emerging trends in the field, which the hiring team loved. AI and ML technology will continue to be hot topics across the biotech field for years to come, and the demand for talent isn't slowing down. Get in touch if you're seeking guidance on how to identify and hire the right data scientists for your drug discovery/pipeline goals: E: Adam.butler@mason-harding.com #datascience #machinelearning #drugdiscovery #masonharding

🎄Merry Christmas & Happy New Year 🎄 As we all reflect on the year as it draws to a close, a profound sense of gratitude fills the air. Our journey has been marked by milestones, teamwork, and unwavering commitment to advancing healthcare solutions. Here's to gratitude, growth, and the boundless horizons that await us at #Phastar. Looking ahead, excitement brews as we anticipate the unfolding chapters in Technology advancements, breakthrough discoveries, and a relentless pursuit of excellence define our roadmap for the future. We're committed to pushing boundaries, embracing challenges, and contributing to a healthier and brighter tomorrow. As we bid farewell to the present year, we carry with us a sense of gratitude for the journey we've traversed together. Simultaneously, we stand on the brink of a future that teems with possibilities and opportunities. Together, let us continue our pursuit of a healthier and brighter future. P.S: We are actively hiring Data Scientist (in EU), Senior DVA consultant (in Denmark), Senior Clinical Data Managers (in EU). If you're up for chat connect with me on @apoorva.dupargude@phastar.com. #jobs #hiring #datascientist #datavisualanalytics #seniordatamanagers