Healthcare Patient Outcome Prediction -- 2

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@glanegonsalves

Hey there Glane here, hope you're doing well, i can help you in end to end analysis right from loading sql data into python, data cleaning to exploratory data analysis then building models to focus on performance metrices, storing the results so fhat can be displayed on PowerBi or fhe data can directlt be loaded in PowerBi to create a dashboard. I can also help you in pushing your folder to GitHub repo. Feel free to get in touch.

@shadabkhan92

I already developed several models, With your project aiming to leverage patient data and predictive modeling to improve healthcare outcomes, I’m fully equipped for the task. My name is MOHD SADAB, an AI professional specializing in real-world ML solutions—a perfect fit for your project's medical domain. I understand the value of clean, efficient, and end-to-end pipelines—qualities that are essential to transforming raw electronic health records into a model-ready dataset. Machine Learning using Python (in particular libraries like scikit-learn) is my forte as well. Be it benchmarking XGBoost or delivering well-explained feature importance, rest assured that I can handle the statistical complexity and leverage cross-validation to boost the accuracy of your models. My skills extend beyond just model creation; I excel in data visualization too—a crucial component of your project that I’m highly proficient with. Moreover, deploying on secure platforms like AWS, GCP, and Azure and familiarizing myself with HIPAA compliance would be a smooth process. My natural inclination towards leveraging AI within existing workflows will ensure a seamless integration of your project with clinical staff's day-to-day operations while delivering clear and interactive visualizations empowering them with insights into patient-risks within age group, diagnosis or ward. In summary, I am best-placed to transform your massive dataset into valuable insights that drive positive patient outcomes.

@quan9824

With a 100% Job Completion Rate and Best Review on all the projects I've worked on, I am confident that I can provide you with precisely what you need for your healthcare patient outcome prediction project. My extensive experience of creating web applications and AI systems has given me a deep understanding of the unique challenges that this project poses. In accordance with your expectations, I have a wide range of skills in the field of Artificial Intelligence including but not limited to: Machine Learning, Deep Learning, Reinforcement Learning; Time Series Forecasting and House Price Prediction to name but a few. This rich skillset is tailored precisely to enable me to meet your needs. Data security is a critical concern when dealing with sensitive information such as electronic health records, and something that I take very seriously. My utilization of Python ensures maximum stability for the project and leverages Git for secure collaboration, enabling the hospital IT team to review every line of code. Given my ample experience in handling large datasets like yours, I can confidently provide you with a clean, reproducible pipeline utilizing both Python and SQL to effectively handle missing values and encode categorical variables. Additionally, my knowledge in tools like scikit-learn would be instrumental in developing one or more predictive models, providing well-explained feature importance, all backed up by AUROC measures.

@nrajuu07

I'm a healthcare AI specialist with an MS by Research in Medical Image Analysis from IIIT Bangalore and experience building HIPAA-compliant clinical prediction systems. I'll build a secure, reproducible Python/SQL pipeline ingesting anonymised EHR data — handling missing values, encoding categorical variables, processing free-text fields via NLP where needed — and output a model-ready dataset. I'll develop and benchmark scikit-learn and XGBoost models optimized for AUROC with full cross-validation and held-out test evaluation, plus explainable feature importance analysis. Deliverables include interactive Power BI dashboards showing cohort characteristics, model performance, and a patient-risk explorer filterable by age, diagnosis, and ward; a comprehensive Jupyter Notebook documenting assumptions, statistical tests, and clinical recommendations; and all code pushed to a private Git repo for hospital IT review

@KolmanOuassou

Hi, I am a data analyst/statistician and Economist with more than 6 years of experience. I can do your project, Please take time to check my profile and then you decide to contact me.

@extreamcode

Hi Client, I can see the real challenge isn’t just building a model , it’s making the predictions trustworthy enough for clinical review. I’ve built Python data pipelines and outcome models where missing values, mixed structured/text fields, and auditability all mattered, and I’d approach this with a reproducible SQL-to-Python workflow, scikit-learn/XGBoost benchmarking, and clear AUROC validation. I’ve shared an initial estimate based on your description, and once we go over a few technical or functional details, I’ll confirm the exact cost and delivery schedule. I’ll also keep the workflow review-friendly for your hospital IT team by pushing clean code to a private Git repo and documenting assumptions, tests, and governance notes in a Jupyter Notebook. For the dashboard layer, I can structure the outputs so Power BI can show cohort breakdowns, model performance, and an interactive patient-risk explorer by age group, diagnosis, and ward. Which outcome label should the model predict, and do you already have an approved train/test split or date cutoff? Best regards, Asad

@KrisuTechkul

Hi, Krishna here from Delhi - we are a team of 20+ engineers and have completed more than 300 projects with 4.7 rating. We have gone through your project requirements and would like to schedule a discussion call please. With a keen focus on the healthcare sector, my team and I have extensively worked on projects similar to yours. We possess a deep understanding of advanced data science and machine learning methodologies, ensuring we can create a clean, reproducible pipeline in Python and SQL for your project. This pipeline will efficiently handle missing values, encode categorical variables, and produce a model-ready dataset. The predictive models we’ll build using scikit-learn will be cross-validated, hold-out tested, and their feature importance carefully explained—your AUROC metric needs will be fulfilled. Moreover, our expertise extends to creating clear visualizations using powerful tools like Power BI that cater to the unique requirements of healthcare settings. We’ll provide cohort characteristics analysis that will directly support data-driven decisions and help clinicians understand patient risk factors better. Our final delivery—be it in the form of a concise report or interactive Jupyter Notebooks—will detail all the assumptions made, statistical tests utilized, and actionable next-step recommendations to enhance patient care.

@Srashtasoft

Hi there, I have read your project requirement. You need a secure and reproducible healthcare analytics pipeline that transforms anonymized EHR data into predictive models capable of identifying high-risk patients, along with interactive dashboards, explainable model outputs, and comprehensive documentation suitable for clinical and IT review. We have experience with Python, SQL, machine learning, predictive analytics, Power BI, healthcare data processing, feature engineering, model validation, and data visualization. We can build an end-to-end solution covering data extraction, preprocessing, NLP for de-identified text fields, model development, cross-validation, AUROC evaluation, explainability analysis, Power BI reporting, and deployment-ready documentation while following HIPAA-compliant development practices. A few questions: ============== What specific outcome are you trying to predict (readmission risk, mortality, complications, length of stay, etc.)? Approximately how many patient records and years of historical data are available? Which database platform stores the EHR data (SQL Server, PostgreSQL, MySQL, etc.)? Do you require model deployment for real-time predictions, or is the current scope focused on analytics and risk reporting? Best Regards, Srashtasoft Team

@lalitdeveloper7

Hi I can help build a complete, reproducible patient-risk prediction pipeline using Python and SQL, starting with data preparation, missing-value handling, feature engineering, and model development. I’ll benchmark suitable models such as Random Forest, XGBoost, and other scikit-learn approaches, evaluate them using cross-validation, hold-out testing, and AUROC, and provide clear explanations of feature importance and model behavior. In addition to the modeling work, I can create interactive Power BI dashboards to visualize patient cohorts, model performance, and risk stratification by age group, diagnosis, or ward. The deliverables will include well-documented code, a clear analytical report, and a structured workflow that supports review by your IT and clinical teams while maintaining HIPAA-compliant handling of the data. Please let me know further. Thanks.

@Ritesh11gupta

Turning historical patient data into a predictive model sounds like a great challenge! I've worked with similar datasets using Python to create accurate predictions. What specific outcomes are you most interested in predicting?

@bizvivo

Greetings, With a robust background in statistics and data science, complemented by a prolific academic writing portfolio, I am well-equipped to tackle complex data-driven challenges. My expertise is rooted in the successful completion of numerous PhD-level thesis projects, where I employed advanced statistical methodologies to extract meaningful insights from diverse datasets. My professional journey has been marked by collaborations with various companies, leading to projects that demanded high-level quantitative analysis and data interpretation. These projects enabled me to delve into trend analysis, temporal behaviour studies, and comparative assessments of data variables. I possess proficiency in a suite of analytical tools, including SPSS, R, Python, OpenCV, WEKA, Tableau, Power BI, and Excel. My skill set extends to sophisticated techniques such as image processing, machine learning, deep learning, artificial intelligence, natural language processing, hypothesis testing, forecasting, T-tests, and ANOVA, among others. I am eager to engage in discussions that leverage my comprehensive skill set to provide innovative solutions in AI and ML domains. Warm regards, Radhika

@goranm16

Hi, this fits well with my experience in Python/SQL data pipelines, predictive modeling, and dashboard/reporting workflows. Your main goal is not just building a model, but creating a clinically reviewable risk-scoring process that hospital staff can trust. I’d start with a data audit: schema, missingness, outcome definition, leakage risks, and HIPAA handling rules. Then I’d build a reproducible pipeline, compare baseline and tree-based models using AUROC, cross-validation, and a hold-out set, and present feature importance with clear Power BI views for cohort, performance, and patient-risk filtering. The main risk is misleading results from messy EHR data or hidden leakage, so I’d document assumptions early and keep every transformation reviewable for IT and clinical stakeholders. Thanks!

@perfectwriter002

Hello client, I am excited to submit my proposal for your project. With years of experience in the field, I am confident in my ability to deliver high-quality work that meets your needs. I have carefully reviewed your project description and requirements; I understand that you are looking to achieve your project objectives. My approach will ensure that I deliver exactly what you have requested in the project. I will keep you updated on the project progress and ensure timely delivery. If you are interested in moving forward I’d be happy to discuss the project further and answer any questions you may have. Thanks for considering my proposal; I look forward to the opportunity to work with you. Please open your messenger and send me complete details to discuss it further. Thank you.

@zrupcic

Hello, I have 20 years of experience of software development and Master's Degree in Physics. I'm currently pursuing another Master's Degree in Business Analytics which I think makes me a perfect fit for this project. I have experience with Python, SQL, Statistics, Machine Learning and so on. During my studies, I created a project "Using HR Analytics to Improve Workforce Performance Sustainability", rated with 5/5, which uses Python and ML to predict employee satisfaction factors based on various parameters and I can send you link to project report or GitHub repo, if you want to check on it before you hire me. I have also been part of team working on another project which uses patient data from public AACT database to analyze cancer treatment results by cancer type, facility, country and so on (while still being HIPAA and GDPR compliant) where I was involved with creating relevant SQL queries. I'm hoping that's enough for you to hire me on this project! Looking forward to work with/for you!

@ewava

This is very close to what I do daily in credit risk — predicting outcomes and segmenting by risk level using historical data, features, and binary/multiclass models. The logic is essentially the same: data pipeline + feature engineering + model + risk thresholds. I'd use Python with scikit-learn and XGBoost for the predictive side, and Power BI for an interactive dashboard with SHAP explainability so the clinical team understands what drives each prediction. I can also handle HIPAA compliance requirements. Do you already have the database schema documented, or should we start from the CSV exports?

@Danzy262

Hi, I read your requirements carefully. This project combines healthcare analytics, machine learning, and BI reporting, with a strong emphasis on reproducibility, model transparency, and HIPAA-compliant handling of de-identified patient data. What you’ll get: • End-to-end Python + SQL pipeline for data extraction, cleaning, imputation, encoding, and feature engineering • Reproducible workflow pushed to your private Git repository with clear documentation • Benchmarking of multiple models (Logistic Regression, Random Forest, XGBoost, etc.) using cross-validation and hold-out testing • AUROC-focused evaluation with confusion matrices, calibration metrics, and feature importance analysis • Power BI dashboards covering cohort demographics, model performance, and patient risk exploration • Jupyter Notebook documenting methodology, assumptions, statistical tests, and recommendations • Data governance review to ensure alignment with HIPAA and internal IT requirements I’m a verified freelancer and I’ve completed similar projects with 5⭐ feedback for accuracy and delivery. I estimate 4 to 6 days for a complete solution, with a project budget of 750 inr per hour. Please send me a message so we can get started immediately.

@sjohn117

Hi, I'm Sean, an AI & Full-Stack Developer with over 10 years of experience, specializing in predictive analytics and machine learning. Your project to predict patient outcomes using historical health records aligns perfectly with my expertise. I have successfully developed similar models that utilize Python and SQL, ensuring rigorous data handling and feature engineering. I will create a reliable pipeline that processes your data, manages missing values, encodes categorical variables, and prepares a model-ready dataset. Additionally, I can implement multiple predictive models in scikit-learn and explore XGBoost, emphasizing AUROC as a key metric and employing cross-validation for model reliability. My commitment includes delivering clean code, comprehensive documentation, and well-structured visualizations, preferably through Power BI dashboards. I can provide a detailed report in a Jupyter Notebook, outlining assumptions, statistical tests, and improvement recommendations. I estimate the first milestone can be delivered within one week. What specific features do you want to prioritize in the model's predictive capabilities? Sincerely, Sean

@jagsimranjit

Hi there, I have reviewed your project requirements and understand the critical nature of building a HIPAA-compliant, reproducible prediction pipeline for patient outcomes. I have significant experience in medical data processing, statistical modeling, and creating clinical-grade visualizations. My Approach to Your Project: Data Pipeline: I will build a structured, modular Python/SQL pipeline that handles the ingestion of both structured data and free-text notes (using NLP preprocessing where necessary). I ensure full data sanitization and missing-value imputation protocols that are standard in clinical research. Predictive Modeling: I will implement a robust framework using scikit-learn and XGBoost, focusing on interpretability. You will receive clear metrics (AUROC, Precision/Recall, F1-Score) derived from rigorous k-fold cross-validation to ensure model generalizability. Clinical Dashboards: I specialize in building Power BI dashboards that are tailored for clinical staff—prioritizing 'explainability' so that doctors can understand why a patient is flagged as high-risk, rather than just seeing a score. Governance & Transparency: I am fully aware of the sensitivity of electronic health records. I am available to start immediately and can provide a detailed timeline for the development, validation, and documentation phases once I review the initial sample CSVs. Looking forward to hearing from you