Hi everyone. Thank you for being here today. It's truly an honor to speak on Conference 42, machine Learning 2025. I'm D Patel, a seasoned data engineer specializing in healthcare data compliance and analytics. Let's begin with a simple caution. What if we could predict a heart attack, not minutes before, but a days before it occurs? Thanks to the breakout in AI and data engineering, that's no longer just a. Healthcare is shifting from a reactive model to a predictive one where we prevent illness before it even occur Today. I'll walk you through this transformation, the technical journey behind it, the real world example, and how predictive healthcare is changing the future of medicine. So let's dive in. All right, so from data to diagnosis, engineering, ai, power predictive healthcare system, we are at a pivotal moment in the evolution of healthcare. One where artificial intelligence combined with robust data engineering, is enabling us to move from reactive care to a predictive system. This technology allow us to anticipate. Patients need in real time, significantly improving early detection, enabling timely intervention, and ultimately enhancing clinical outcomes. In this session, I will walk you through how this system are designed, the challenges and the measurable impact they are already making across the healthcare setting. Alright. So let's begin with the healthcare transformation training. It helps to take a step back and look at how far the field has come. Because the healthcare has been undergoing a significant transformation over the past few decades. This transformation can be broken down into three major phases, traditional healthcare, digital transformation, and AI integration. Let's begin with the first phase, traditional transformation. This was the era most of us are familiar with. Healthcare was reactive by design. You waited until you had a symptom, maybe a chest pain, fever, shortness of breath, and then you went to a doctor. Diagnosis were based on symptom patient memory or physical exam or basis of a test lab test. At this stage, very little was systematically recorded or reused. It was mostly paper-based and the information rarely left the physician office. The role of data was minimal. It showed only an immediate visit and a long-term trend of across patient insight. Lastly, invisible then comes the transformation, digital transformation. This really gained a momentum in 2000. Accelerated by eruption of electronic healthcare record, which is EHR. Suddenly we started capturing everything, vital sign, lab results, medications, diagnosis, progress notes, and all in a digital format. But here's the catch. While we were collecting all this data, most of our of it went unused beyond billing or record keeping. We had a digital infrastructure, but we are weren't yet unlocking the true value hidden in the data. That brings us to a third and the current phase AI integration. This is where things get exciting. We now leverage AI and machine learning to analyze massive amount of health data in a real time instead of waiting for a symptom to appear. This model can predict risk sometimes days or even weeks in air. Advance hospital around the world are now using predictive model to flag patient at risk of cardio event hospital remissions and more even consumers like. Consumer devices like Apple Watch are entering in the picture, capturing users, heart rhythms, changing, change in the mobility or, and feeding that data into a health system for further monitoring. So to summarize this journey, we have more from and reactive care where we responded to an illness after the talker to a data reach where we capture more information than ever before. And now to a predictive care where we can use this information proactively to protect patient health before it compromise. This is the foundation of predictive healthcare, and it's only possible because how we evolve through these three clinical phases. In the next few slides, we will dive deeper into how this is actually implemented from the data pipeline to AI model and real world impact. It's having. All right. First, let's look at the early detection success. Let's explore what predictive healthcare really means in practice, because this is where we begin to see the power of artificial intelligence comes alive in a real clinical setting. AI model with a vast and diverse healthcare data sets are now capable of identifying disease risk. Long before symptoms surface, allowing the healthcare professional to take action proactively rather than reactively. So there are few outcomes that you can find on Marketplace First. Research shows 70% improvement in early cardiovascular risk detection. This means. We are identifying high risk patients significantly, early, often, while they are still feeling comfortably, completely fine. For instance, patient at risk for heart failure or a stroke can now be flagged during a routine checkup or even when a traditional diagnosis won't raise an alert. Second, on average, the predictive system provides about a week or more advanced warning. Before a symptoms actually appear, imagine what can be done with that time scheduling specialist visit, order ordering additional test adjusting medication, or for a cardiac patient, few days could be a difference between a manageable condition to an ER admission. Third, the early intervention based on this insight has resulted in one third reduction of emergency room visits. That's not just a statistics, that's fever. Patient in crisis, less strain in emergency department, and better resource allocation in hospital. It's powerful demonstration of AI driven, one insight translate into a real world efficiency and better care. This is what predictive healthcare looks like. It's about equipping doctors with timely, actionable insight, transforming data into an early warning, and fundamentally changing the tragedies of the patient. Patient's Health Journey Data integration Architects. As we move towards predictive healthcare, one crisis critical truth stands out without a strong data integration architect, none of this works. Predictive AI in healthcare does not begin with an algorithm. It begins with data, specifically data that's timely, accurate, and comprehensive. First, we look at the source integration. This involves bringing together data from various system. EHR. Electronic Health Record contains diagnosis, medications, vitals, clinical notes. Variable devices like Apple Watch or Fitbit contributes continuous monitoring data such as heart rate, sleep patterns, even blood oxygen levels. Genetic testing brings insight into a long-term risk of a patient. And a clinical system such as lab information systems or image archives brings the picture as a format data. So pulling this data in only one is only a step one. The next major phase is data harmonization. Healthcare data comes in widely different format. One hospital might use milligram per decimal while the other use melo mile per liter. Even diagnosis score vary from ICD to snow harmonization, standardized this, align units, terminologies, formats, and even a, and ensure the consistencies. Once harmonized, the data is. Is ready for the AI processing model are timed, trained on this high quality, unified data to learn pattern, generate predictive health, such as who might develop sepsis, who likely to be readmitted, or whose condition is trending towards an acute ation. The final and the perhaps most important stage is a clinical insight delivery. It's not enough to generate a prediction. We must emel them into a clinical workflow, whether it's through an alert in EHR Visual dashboard or mobile app, inside must be accessible, actionable, and timely. The takeaway here is simple. Prediction is the is only possible when we integrate data effectively, and that's only possible when with a robust data engineering architect isn't just an infrastructure, it's a foundation of a better healthcare core. Technical challenges. Now that we have seen how data flow into a predictive system, let's talk about what can go wrong and what makes healthcare data engineering uniquely challenging. Building AI for healthcare isn't as simple as collecting data and train a model. We face four core technical challenges that must be addressed before we even begin modeling first. Security and privacy. Healthcare data is among the most sensitive information we can collect. Patient trust us with detail about their body, their mental health, their genetics, sometimes even their lifestyle. We have a legal and ethical responsibility to protect the data. HIPA compliance isn't an optional, and yet, AI system requires a broad access to data to work well. The challenge is enabling access while maintaining rigorous privacy control, audit will and encryption standards. The technique like de-identification, data masking or federated learning are a part of the solution. Allow us to train model without even exposing the raw patient data. Second interoperability healthcare system are fragmented. One hospital use epic. Other use corner labs use different codes. Peer use ICD 10 codes and devices help proprietary format as well. Without standardization, it's nearly impossible to combine data for these sources. That's where interoperability standard, like actual seven or fire comes in. Third is latency. Predictions aren't useful if they are out too late. In a clinical setting, especially in emergency care or ICU, we need a real time processing. For instance, if a model detect a sign of sepsis, but it takes 20 minutes to send that alert to a nurse, that delay could cost a life. The architect must therefore support a low latency interference with a model running close to the data source, maybe in the EHR itself or at the edge. The fourth is data quality. Even with a perfect infrastructure, poor data quality can derail everything. A single miscoded allergy, or incorrect medication dose, or outdated me diagnosis can lead to an incorrect correction or worse unsafe recommendation. These issues aren't theoretical. They happen even every day, and that's why the next part of this talk focus specifically on building a rigorous data quality framework. One that enables safe, reliable AI in healthcare data quality framework. Let's now turn our focus on data quality. Which is often underestimated, yet it's arguably the most critical piece of predictive healthcare person. AI models are only as good as data they train on. In healthcare. Bad data does not just result, improve a prediction. It can literally put life at risk. That could be a multiple steps. Data quality framework to ensure data used in predictive system is clean, standardized, and clinically meaningful. The first step is cleansing. We correct typos, fix unit errors or remote duplicate records. Common in fast phase clinical setting. For example, temperature has one oh three.zero Fahrenheit if missing a decimal could. Seems like 1,030 Faite second normalization. Healthcare data comes in varied units, terminology, and format. One lab might use glucose in a milligram per decimal, other use in milligram per liter. One physician might use heart failure, another might use CHS, cardiac health failure. Also standardization, need terminology mapping, unit conversion, and consistent DATETIME formatting. Third integration. This involves record lineage matching a patient lab resolved with their EHR, no medication records and even variable device data to build a unified profile that way. Predictions are based on a complete context, not fragmented data points. Fourth validation. Even after clean cleaning and integrating a clinical review, statistic checks or a logic based verification is needed. For example, if a male patient is listed as pregnant or a medication is prescribed outside a safe dose range, those are caught through an automatic check or an expert review. Simply put, better data equals to a better and safe ai. Okay, next is AI model Architect. Now let's sift from a data preparation to how we design and build an AI model in healthcare. This is where data science meets a domain knowledge and where the thoughtful architect can make or break and initiate. There are three major components in EA architecture, feature engineering, model selection, and training pipeline. So let's first take a look at feature engineering. Healthcare data is noisy, irregular, and deeply contextual feature that extract trend over time. Suggest changing of blood pressure or a lab value rather than relying on a single measurement. Also normalizing patient demographic and clinical context, for instance. Resetting resting ha. Heart rate of 90 means something very different to a 20-year-old LIC versus a 70-year-old cardiac patient. Second model selections. There could be a different choice of a model that can be used. Assemble method for complex structured data like EHR, deep learning models for radiology and image pro imaging. Pro imaging data. Based on network, where we need to the model uncertainty, especially in case where data is incomplete or ambiguous, no single algorithm is perfect. The best result often comes from combining them, optimizing for an accuracy. Third, training, a pipeline in healthcare AI model must be continuously trained and updated. Auto ML techniques used to tune parameter efficiently of federated learning. When multiple institutes wants to collect on model training without sharing raw data, it's important to ensure a version control properly so that a clinical audit, trial and regulatory review can be done. The goal of this entire architect is deliver accurate, actionable, continuous ipro, improving prediction. All without compromising clinical safety and privacy, explainability innovation. Now let's talk about a critical and more often overlooked, an aspect of AI explainability, no matter how accurate the model is, it is, if a clinician does not understand or trust its output, it won't be used. This can be addressed through four key explainability techniques. X feature at first is feature attributes. Feature attributes shows which data points influence model decision. For example, if an EF flag, sepsis risk, it might highlight rising white blood cell count, or a recent fever as a top contributor visualization system. Second is visualization system. Convert complex probability model into an interac. Dashboard using color coded alerts, trendlines and confidence bar. The third is contextual explanation. It ties prediction back to the established medical knowledge offering. Clinical the why behind an alert, not just what for this confidence metrics, communication communi, which communicate uncertainty. A prediction with 95% confidence carries more weight than the one with 60%. It helps clinicals gauge how much trust to place in the ai. Ensure transparency builds trust, and trust drives the eruption. A let's bring all together with a real world example. Let's look at a case study from the marketplace. Cardiac care transformation at a regional hospital. Original hospital integrates AI model directly into an EHR system, focusing on health risk, cardiac patient, and the staff training, helping clinician understand how the how to interpret a I one alert and incorporate them into their daily workflow. Okay. Within months, the research shows miserable re results about one third reduction in cardiac related hospital remission. Patients were flagged, almost flagged with a risk almost a week or more earlier for the potential complication. Hospital reported over millions in annual saving in a patient care. This is the power of AI and this properly integrated, not just predictive, but transformative. Let's look at the economic impact beyond clinical outcome. Predictive healthcare delivers significant economic value. Hospitalization is expensive. Emergency care is expensive, but early intervention that's efficient and scalable. Hence healthcare system implementing ai, data engineering solution achieves substantial cost reduction. That's because the risk can be captured away in a advance, and a medication can be applied to a patient. This isn't just about a better medicine, it's about sustainable healthcare economic. Let's look at the implementation roadmap. So how we actually implement a predictive healthcare in a real world. Let's look at the steps that needs to be followed. First assessment phase. Evaluate your current data maturity and a clinical pain points. Second, understand the infrastructure development. Needed to build an AI application. Build pipelines to inject clean or harmonize data. Third, AI model implementation. Understand how to train AI model test, validate model for a key use case. Fourth, clinical integration, Amber predictions into a clinical workflow and train staff. Fifth, continuous improvement. Monitor, model performance, and retain as necessary. Now, let's look at the future of predictive healthcare. Finally, where is predictive healthcare going first? Personalized, predictive model. By integrating gen data, we will move from a population level inside to an individualized risk assessment. UBI case monitoring, continuous input from a smartwatch, blood pressure heart rate monitoring. Even a smart toilet will feed predictive information to a AM model 24 7. The third is system wise integration. AI will operate not in silos, but across entire hospital networks. Optimizing care delivery at scale. AI acumen, Acumenta clinicians, much like pilots rely on autopilot. Clinician will use AI as testosterone partner in the care. In short, predictive healthcare is not just evolving, it's accelerating. Thank you.