How to Use Predictive Analytics in Medicine

Updating My Latest “AI in Medicine Forecasting”: 🎨 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜’s Biggest Impact: The “Front of the House” 🔮 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗜’s Biggest Impact: The “Back of the House” If you’ve ever worked in the food service industry, you’re familiar with these terms: the “front of the house” includes all areas a customer interacts with while dining, while the “back of the house” refers to the behind-the-scenes kitchen where meals are cooked. In medicine, we have a perfect corollary: the 𝗳𝗿𝗼𝗻𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗵𝗼𝘂𝘀𝗲 includes everything where the patient is actively involved—seeing the doctor, discussing results, and making decisions. But in healthcare, the 𝗯𝗮𝗰𝗸 𝗼𝗳 𝘁𝗵𝗲 𝗵𝗼𝘂𝘀𝗲 isn’t a physical space; it's the doctor's mind. Thinking about diagnoses, developing plans, and pondering prognoses. In medicine, your "meal" is prepared in our heads. Of course GenAI and PredAI are on a spectrum, and both will have profound impacts in healthcare. But generally speaking a lot more of the Front work involves language, communication, documentation, transcription, and translation. Write a clinic note, or translate a language — or even translate from “doctor speak” to patient-friendly language. Coming up with an analogy to help explain something to a patient. Generative AI is well-suited for this work. When it comes to the Back work, we’ve barely scratched the surface of what Predictive AI can do. Predictive AI has the potential to revolutionize diagnostic accuracy, improve prognostic predictions, and refine treatment plans. Currently, much of our approach in medicine feels like we're in a relative stone age compared to the potential predictive AI offers: 👉Diagnostic Accuracy: Moving beyond the guesswork of “Let’s run some tests and see what comes back,” predictive AI could offer more precise diagnostics based on comprehensive data analysis. 👉Treatment Planning: Rather than the trial-and-error approach“Let’s treat everything and see what works”—AI could help tailor treatments with higher success rates and fewer side effects. 👉 Patient Management: Improving decision-making about hospital admissions and discharges, predictive AI could ensure that only those who need intensive care are hospitalized, potentially reducing healthcare costs and improving patient outcomes. There’s so much in medicine that still relies on uncertainty. Phrases like “We don’t know exactly what will happen, so let’s admit you and see” could become less frequent as AI tools help us navigate complex medical landscapes with more precision and confidence. While generative AI focuses on patient-facing tasks, predictive AI opens new possibilities for internal decision-making. Curious to know if other clinicians see a similar dichotomy; there’s a whole portion of medical “work” that no one really knows about unless you’re the one seeing the patient.

Foundation models like GPT have revolutionized how machines understand and generate human language. What if we could apply similar principles to understand the complex language of disease biology to predict which patients will respond best to new cancer treatments?  Our latest research, out now in Nature Communications, titled “Pretrained transformers applied to clinical studies improve predictions of treatment efficacy and associated biomarkers” explores this question. In it, we propose the *Clinical Transformer*, a deep neural network survival prediction framework based on transformers that: 🧠 Leverages transfer learning from large data repositories (like TCGA and GENIE) to build foundation models that can be fine-tuned to tasks like predicting immunotherapy responses in early-stage clinical trials  🔗 Captures complex relationships between molecular, clinical, and demographic data   🔍 Explains its predictions by showing which features drive risk or response.  In our studies, the Clinical Transformer:  📊 Outperformed state-of-the-art methods to predict survival for over 150,000 patients across 12 different cancer types  🔬 Predicted survival in small, early-stage clinical trials for immunotherapy  💡 Identified new biomarkers of immunotherapy response and resistance through in silico perturbation experiments.   We're excited about the potential of foundation models like the Clinical Transformer to drive innovation in precision medicine and help improve patient outcomes.  Read the full paper here: https://lnkd.in/dUFcg_4B   Thanks to all the co-authors: Gustavo Arango, Elly Kipkogei, Ross Stewart, Gerald Sun, Arijit Patra, Ioannis Kagiampakis   #PrecisionMedicine #ClinicalAI #AIinHealthcare #AIinLifeSciences 

It was a pleasure to connect with Dr. David Krummen, who generously shared insights into their EP Lab and how they’re leveraging GEHC CardioLab alongside AI tools to advance care for arrhythmia patients. By unlocking the potential of ECG data, they are improving cardiac ablation outcomes, streamlining workflows, and boosting procedural efficiency.   Cardiac arrhythmias are responsible for 10% of global deaths, with over 25% of adults over 40 likely to develop a serious arrhythmia. Left untreated, arrhythmias significantly increase the risk of death and are linked to severe co-morbidities like stroke and dementia.   The role of AI in Electrophysiology (EP) labs is becoming increasingly vital in enhancing diagnostic accuracy, improving procedural success, and optimizing workflow efficiency. Here’s how AI is making an impact: Arrhythmia Detection and Classification: AI algorithms, particularly deep learning models, are now analyzing ECGs and intracardiac signals with high precision, enabling early and accurate detection of arrhythmias such as atrial fibrillation and ventricular tachycardia. Mapping and Ablation: AI-powered systems are aiding the creation of 3D electroanatomical heart maps, essential for guiding ablation procedures, by integrating and analyzing large datasets from various sources. Workflow Optimization: AI is streamlining EP lab operations by automating routine tasks like data entry and image processing, allowing clinicians to focus on patient care. It also helps predict procedure durations and optimize resource scheduling. Predictive Analytics: AI models are being used to predict procedural outcomes, assess patient risks, and support personalized treatment planning. Decision Support Systems: AI-based tools provide real-time guidance during procedures, helping clinicians make informed decisions by suggesting optimal ablation points or predicting procedure success. Research and Development: AI is accelerating electrophysiology research by analyzing large datasets, uncovering patterns, and generating new hypotheses for innovative treatments.   AI's integration into EP labs is transforming the field, driving greater precision, improving patient outcomes, and making procedures more efficient and accessible.