Machine Learning Drug Ranking System for Patient-Specific Treatment
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Solution Overview
Problem
Current databases and content repositories lack effective mechanisms for accessing and analyzing specialized information on drug efficacy, potency, and administration, particularly for pharmaceutical drugs and biologics, leading to suboptimal matching of patients with appropriate treatments.
Innovation Solution
A system utilizing machine learning to analyze pre-clinical, clinical, and post-clinical data to rank drugs based on drug characteristics such as efficacy, toxicity, and potency, and predict drug characteristics for drugs lacking available literature information, enabling personalized cancer medicine by targeting specific genes, gene variants, or biological pathways.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If databases and content repositories store large corpus of documents with drug information, then the quantity of available drug information increases, but the ability to access and analyze specialized information on drug efficacy, potency, and administration remains insufficient
Solution Approach 1:
The patent introduces an intermediary system comprising natural language processing modules, machine learning models, and structured data extraction mechanisms that act as mediators between the large corpus of unstructured drug information documents and the user's need for specialized analysis. This intermediary layer processes, extracts, and organizes relevant drug characteristics (efficacy, potency, administration) from the documents, making the information accessible and analyzable without requiring users to manually search through millions of documents.
Solution Approach 2:
The patent replaces manual mechanical searching and analysis methods with automated computational systems. Instead of users manually evaluating figures, graphs, tables and text within research documents, the system employs machine learning models and natural language processing to automatically extract, analyze, and rank drug information from the document corpus, significantly improving ease of operation.
2Reliability
If users manually evaluate figures, graphs, tables and text within research documents to ensure accuracy, then the reliability of data extraction improves, but the time required for data access and analysis increases significantly
Solution Approach 1:
The patent implements preliminary action by pre-processing and structuring drug information from the document corpus in advance. The system pre-extracts drug characteristics, pre-ranks drugs based on efficacy and potency, and pre-organizes information by gene variants and biological pathways. This preliminary processing ensures data reliability through systematic extraction while making the information immediately accessible, eliminating the need for users to spend time manually evaluating documents at the point of need.
Solution Approach 2:
The patent creates structured copies of unstructured drug information from research documents. Instead of requiring users to access and evaluate original documents, the system generates standardized data copies containing extracted drug characteristics, efficacy ratings, potency measurements, and administration details. These copied and structured data representations maintain the reliability of the original information while being much faster to access and analyze.
3Quantity of substance
If content repositories maintain millions of documents without intelligent access methods, then the completeness of information is preserved, but the ability to find relevant information efficiently deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the large corpus of drug information documents into structured categories and segments based on drug characteristics, gene variants, biological pathways, and therapeutic indications. The system segments information hierarchically: from documents to extracted drug profiles, then to ranked drug lists, and finally to patient-specific recommendations. This segmentation preserves the completeness of the original information while enabling efficient retrieval through targeted queries.
Solution Approach 2:
The patent utilizes parameter changes by transforming unstructured document information into structured data with specific parameters (efficacy scores, potency measurements, toxicity profiles, gene variant associations). The system changes the state of information from unstructured text to parameterized data that can be efficiently filtered, sorted, and retrieved based on multiple criteria simultaneously, dramatically improving productivity without losing information completeness.
4Measurement precision
If drugs are ranked based on comprehensive drug characteristics including toxicity, potency, and efficacy, then the precision of drug recommendations improves, but the complexity of the analysis system increases
Solution Approach 1:
The patent implements dynamics by creating an adaptive ranking system that dynamically adjusts drug recommendations based on patient-specific factors. The system dynamically weights different drug characteristics (efficacy, potency, toxicity) based on the patient's gene variants, disease state, and treatment history. This dynamic approach achieves high measurement precision in recommendations while managing system complexity through adaptive algorithms that learn from data rather than requiring static complex rule sets.
Solution Approach 2:
The patent manages complexity through parameter changes by standardizing multiple drug characteristics into comparable quantitative parameters. The system transforms qualitative assessments of efficacy, potency, and toxicity into standardized numerical scores and rankings. This parameterization allows the system to comprehensively evaluate multiple drug characteristics simultaneously while maintaining manageable complexity through consistent measurement scales and integration methods.
Data Source
AI summary
Computer based methods, systems, and computer readable media for intelligently accessing various types of pharmaceutical information in a content repository and ranking drugs at the variant level, gene level, and pathway level. In some cases, drugs that target the same gene, gene variant, or biological pathway may be ranked based upon in vitro, pre-clinical, clinical, or post-clinical evidence. To determine ranking of a plurality of drugs, information pertaining to drug administration is analyzed for the drugs. For a plurality of drugs, attributes corresponding to the drug are determined, wherein the attributes include a variant or a gene targeted by the drug, and a biological pathway comprising the targeted variant or gene. The plurality of drugs are ranked according to a drug effectiveness score based on one or more of a determined efficacy, potency, or toxicity.