Method and appratus for treatment for a medical condition using artficial intelligence

Abstract

The present disclosure presents an apparatus and method for determining a medical condition in a patient, wherein the apparatus comprises at least a computing device, and a memory communicatively connected to the at least a computing, wherein the memory contains instructions configuring the at least a computing device to obtain a test result; identify an imbalance as a function of the test result; and determine a medical condition as a function of the test result and the imbalance.

Description

METHOD AND APPRATUS FOR TREATMENT FOR A MEDICAL CONDITION USING ARTFICIAL INTELLIGENCECROSS-REFERENCE TO RELATED APPLICATIONThis international application claims priority to US Provisional Application No. 63 / 668,110, filed July 5, 2024, and entitled “METHOD, APPRATUS, AND COMPOSITION FOR TREATMENT FOR A MEDICAL CONDITION” which is hereby incorporated by reference in its entirety.FIELD OF THE INVENTIONThe present invention generally relates to the field of disease detection and treatment. In particular, the present invention is directed to a method, apparatus and composition for treatment for a medical condition.BACKGROUND OF THE INVENTIONMedical conditions such as, without limitation, autoimmune conditions, present danger to patients that suffer from them and can be difficult to treat. Existing solutions for detecting and treating medical conditions are not sufficient.SUMMARY OF THE INVENTIONIn an aspect, an apparatus for determining a medical condition in a patient, wherein the apparatus comprises at least a computing device, and a memory communicatively connected to the at least a computing, wherein the memory contains instructions configuring the at least a computing device to obtain a test result; identify an imbalance as a function of the test result; and determine a medical condition as a function of the test result and the imbalance.In yet another non-limiting aspect, a method of determining a medical condition in a patient, wherein the method comprises receiving by at least a computing device, a test result pertaining to the patient; training a first machine learning model operating on the at least a computing with a first training set wherein the first training set comprises a plurality of inputs containing test results correlated to a plurality of outputs containing imbalances; inputting the test result relating to the patient into the trained first machine learning model; outputting the imbalance relating to the patient as a function of the trained first machine learning model; training a second machine learning model operating on the at least a computing device wherein with a second training set wherein the second training set comprises a plurality of inputs containing imbalances correlated to a plurality of outputs containing medical conditions; inputting the imbalance relating to the patient into the trained second machine learning model; and outputting the medical condition as a function of the trained second machine learning model.BRIEF DESCRIPTION OF THE DRAWINGSReferring now to the drawings, wherein like elements are numbered alike in the following figures:FIG. 1 is a block diagram illustrating an exemplary embodiment of an apparatus for determining a medical condition in a patient.FIG. 2A is an exemplary embodiment of a test result.FIG. 2B is an exemplary embodiment of a test result.FIG. 2C is an exemplary embodiment of a test result.FIG. 2D is an exemplary embodiment of a test result.FIG. 2E is an exemplary embodiment of a test result.FIG. 2F is an exemplary embodiment of a test result.FIG. 3 is a block diagram of an exemplary embodiment of a machine learning module.FIG. 4 is a block diagram of an exemplary embodiment of a neural network.FIG. 5 is a block diagram of an exemplary embodiment of a neural network.FIG. 6 is a block diagram of an exemplary embodiment of a computing system that can be used to implement any one or more portions thereof.FIG. 7 is a process flow diagram of an exemplary embodiment of a method for determining a medical condition in a patient.DETAILED DESCRIPTION OF THE INVENTIONAt a high level, aspects of the present disclosure are directed to a method, apparatus and composition for treatment for a medical condition.Referring now to FIG. 1 , Chaetom / um spp., a family of black mold commonly found in water-damaged buildings, are known to cause infection, especially in immunocompromised humans. We report a case of Chaetom / um spp. infection in a 26- year-old female with a history of ulcerative colitis recalcitrant to treatment with immunotherapy.The genus Chaetom / um is a type of black mold found worldwide in soil and one of the most frequently detected fungi in damp or water-damaged buildings. Chaetom / um spp. can trigger infections in humans including onychomycosis, sinusitis, pneumonia and fatal disseminated cerebral disease, especially in immunocompromised individuals. Besides producing spores that negatively affect health upon inhalation, many species also produce toxic metabolites called mycotoxins that damage cellular structures, interfere with cellularprocesses, exert immunosuppressive effects, and damage the gut microbiota and epithelium.Fungal infections can prime the body for autoimmunity through several mechanisms. Fungi reduce Treg cells and increase TH17 cells as well as pro- inflammatory cytokines. They also trigger auto-reactive T cells via molecular mimicry between fungal and human peptides. By altering the gut microbiota and damaging the gut epithelium, mycotoxins also cultivate an environment more susceptible to autoimmunity. Ulcerative colitis is an autoimmune chronic inflammatory bowel disease characterized by relapsing and remitting mucosal inflammation affecting the colon.If a water problem in a building is not repaired and the water dried out within 48 hours, growth of mold and other microorganisms would naturally occur. The types of molds predominant at anyone time would be determined by the level of moisture in the building material. This level of moisture is usually referred to as the water activity and it determines the order in which different categories of molds appear. The first group of molds to appear (at water activities less than 0.85) is referred to as the primary colonizers, the second group (at water activities of 0.85-0.90) is secondary colonizers and the third group (at water activities greater than 0.90) is the tertiary colonizers. The primary colonizers are capable of growing at water activities below 0.85. This group may include Alternar / a c / tr / , Eurot / um amstelodam / , Asperg / llus cand / dus, Asperg / llus glaucus, Asperg / llus n / ger, Asperg / llus pen / c / ll / o / des, Aspergillus repens, Aspergillus restrictus, Aspergillus versicolor, Paecilomyces variotii, Penicillium aurantiogriseum, Penicillium brevicompactum, Penicillium chrysogenum, Penicillium commune, Penicillium expansum, Penicillium griseofulvum, and Wallemia sebi. Secondary colonizers grow best at water activities of 0.85 to 0.90. Secondary colonizers may include Aspergillus flavus, Cladosporium cladosporioides, Cladosporium herbarum, Cladosporium sphaerospermum, Mucor circinelloides, and Rhizopus oryzae. At water activities greater than 0.90, tertiary colonizers appear. These may include Alternaria alternata, Aspergillus fumigatus, Epicoccum spp., Exophiala spp., Fusarium moniliforme, Mucor plumbeus, Phoma herbarum, Phialophora spp., Rhizopus spp., Stachybotrys chartarum, Trichoderma spp., Ulocladium spp., Rhodotorula spp., Sporobolomyces spp., and Actinomycetes.While several studies have identified fungal alterations in the microbiota of patients with ulcerative colitis, we report a case caused by Chaetomium spp. embedded in a 26-year-old female patient’s microbiome after exposure to water-damaged housing. The identification of the causative fungal species was confirmed via stool testing and next-generation DNA sequencing of microbial DNA.This case underscores the need to further elucidate the connection between environmental pathogens, the impact on the micro and mycobiome, and the development of autoimmunity including colitis.Case ReportA 26-year-old female was diagnosed with colitis (unspecified without complications K52.90 ICD-10 code) around the age of 18. Symptoms began at the age of 17. She was initially diagnosed with an unspecified amoeba in her stool for which she was treated with Flagyl for one week. A year later she was diagnosed with Ulcerative Colitis (unspecified without complications K51 .90 I CD-10 code). She began on Asacol but quickly regressed so she was switched to Imuran (125mg a day) for four (4) years which kept her in remission during her college years. The patient follows a gluten-free dairy-free diet low carb paleo keto organic. She is reported no drugs or alcohol consumption.Upon graduating, she decided to taper down Imuran and live medication free. Within a year and a half, she had a flare-up, and the disease spread further up her colon (from 60cm to about 150cm) and symptoms were far worse (public accidents, urgency, blood, mucus, disturbed sleep, and no formed stool).The patient went on Entyvio Biologies (Vedolizumab) in January 2021 , and notes that she only felt good for the first three (3) infusions. Ultimately the Entyvio stopped working and she began to see some blood and mucus with every infusion. She did a colonoscopy September 2022 and found that the disease has spread to her entire colon. She was prescribed Prednisone 40mg and was suggested to start taking Remicade which she delayed in order to consider other treatment options.She began treatment with Dr. Kristine Profeta and nine (9) days after treatment began the patient reported that her bleeding had stopped, and she began to feel a bit better overall. Her stool begam to become formed. Her Prednisone was tapered down to 30mg daily. Twenty-six (26) days after treatment was started, she had normal bowel movements, was thinking clearer, and had more energy. Her Prednisone was tapereddown to 15mg daily. Forty-three (43) days after treatment was started, she still had normal bowel movements, and continued to feel improved. Her Prednisone was tapered down to 10mg daily. Fifty-one (51 ) days after treatment was started, she remained well and she was able to complete two (2) weekends that included travel abroad where she had energy and enjoyed herself.MethodsOne of the laboratory tests that was performed on this patient was the fecal testing using next generation sequencing (NGS). To collect the feces, a feces catcher was used to ensure that the feces does not touch the water in the toilet bowl along with DNA / RNA Shield Fecal (Zymo Research Catalog Number R1101 / R1137) collection tube was used. The patient then collected one spoonful of feces (approximately 1 gram) and placed it into the collection tube. The collection tube was then shaken to ensure that the feces has come in contact with the liquid in the tube to preserve the DNA / RNA. The collection tube was sent to the laboratory where the microbial and bacterial DNA were extracted, and 16S / ITS amplification sequencing was performed.16S sequencing is DNA sequencing encoding small subunit of rRNA of prokaryotes with a length of about 1542 base pairs. This is the most used marker for bacterial or archaea sequencing due to the 16S rRNA gene is a highly conserve component of the transcriptional machinery of all types of life. With manufacturers like Illumina, Qiagen, and others, the universal PCR primers can be used to target the conservative regions of 16S making it possible to amplify the gene in a wide range of different microorganisms. The 16S sequence consists of nine (9) variable regions and ten (10) conservative regions, the conserved regions sequences reflect the genetic relationships between species, while the variable region sequences reflect the difference between species. We can analyze samples as a longitudinal study of a patient(s) to see how their gut microbiome changes over time with Dr. Kristine Profeta’s treatment (The TPM Method) and / or diet changes. With this longitudinal study we can produce a detailed map of the microbial communities that changed throughout the course and potentially identify rare and / or abundant species that are associated with health and disease.The data that are generated from the 16S reads are curated against a database for identification and classification. The curated data are then placed into relatedsequences called clusters and the number of clusters are determined. Similar clusters are called operational taxonomic units (OTUs). OTU counts are summarized in a table of relative abundances for each organism in the sample.In one or more embodiments, collection of 16S sequences and / or reads may be stored in a database. One or more processing steps described herein may involve retrieving data related to one or more 16S sequences and / or reads from the database by querying the database. In a nonlimiting example, database may include a relational database, a key-value retrieval database such as a NoSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Database may alternatively or additionally be implemented using a distributed data storage protocol and / or data structure, such as a distributed hash table or the like. Database may include a plurality of data entries and / or records as described above. Data entries in a database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and / or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and / or reflect data and / or records as used herein, as well as categories and / or populations of data consistently with this disclosure.ITS (Internal Transcribed Spacer) is part of the non-transcriptional region of the fungal RNA gene located between the structural ribosomal RNA (rRNA) of a common precursor transcript, which is useful for elucidating relationships among different species and closely related genera. The ITS sequences used for fungal identification usually include ITS1 In fungi, 5.8S, 18S, and 28S rRNA genes are highly conserved, whereas ITS can tolerate more mutations in the evolutionary process due to less natural selection pressure, and exhibits extremely wide sequence polymorphism in most eukaryotes. At the same time, the conservative type of ITS is relatively consistent within species, and the differences between species or even stains are obvious. ITS sequence fragments are small (350 base pairs and 400 base pairs in length). They have been widely used in phylogenetic analysis of different fungi.In an embodiment, biofilms may be removed, tested, and / or eliminated during the stool testing process. Biofilms are communities of microorganisms that can formon various surfaces including, but is not limited to, patient’s body. In some cases, biofilms may be flushed, and microbiome may be changed on the composition of the stool. In a nonlimiting example, disruption of biofilms and / or alteration of microbial balance may potentially impact the detection of fungal infection by disrupting the protective environment and / or creating a less favorable environment for fungal overgrowth. Testing of biofilms may be performed in any manner described in this disclosure. Alternatively or additionally, testing may be performed to determine whether biofilms are being flushed out or otherwise removed, and / or whether flushing out or removal of biofilms causes changes to microbiome.Additionally, or alternatively, method described herein may also be used to detect additional factors including, without limitation, blood born serological infections (e.g., Bacteria like Lyme disease and Borrelia spp, Bartonella spp, Mycoplasma spp, Chlamydia spp, Rickettsia spp, F. Tularemia, Brucellos spp et al. Viruses like human immunodeficiency virus (HIV), hepatitis B and C, enteroviruses, echoviruses, Coronavirus, Herpes virus, et al. and parasitic activity missed during stool testing (e.g., intestinal parasite activities, malaria, filariasis, toxoplasmosis, hookworm, ascarid parasitosis, leishmaniasis, schistosomiasis, and the like), besides fungi / viruses described herein, including ITS identified fungi.Method described herein may be implemented on any platform and / or using any software deemed suitable by a person of ordinary skill in the art upon reviewing the entirety of this disclosure.DiscussionChaetomium spp. are one of the most common black molds associated with human disease found in water-damaged buildings. As a type of black mold with cell walls that contain melanin, Chaetomium spp. are among the groups of fungi capable of causing phaeohyphomycosis. Among the 105 species identified, C. globosum, C. atrobrunneum, C. strumarium, C. perlucidum, and C. funicolum have been implicated in human illnesses. Because Chaetomium spores are cemented together by mucilage and trapped by hair fibers, few become airborne unless disturbed by building renovations or mold remediation. Inhalation of a significant number of spores can trigger mold allergy and asthma. Many Chaetomium species also produce easily inhaled mycotoxins which are fungal metabolites detrimental to human health.Mycotoxins damage cellular structures (e.g., cell membranes), interfere with cellular processes (e.g., energy production and mitochondria function), exert immunosuppressive effects, and damage the gut microbiota and epithelium. Because of these pathogenic processes, exposure to Chaetomium spp. can cause onychomycosis, sinusitis, pneumonia, and fatal disseminated cerebral disease, especially in immunocompromised individuals.In addition to direct cellular damage, fungal infections (or mycotoxicoses) can prime the body for autoimmunity through several mechanisms. Fungi reduce Treg cells and increase TH17 cells as well as pro-inflammatory cytokines IL-17, IL-22, IL-23. By shifting the immune system into a TH17 dominant response, mycotoxicoses impair pathogen clearance and render the human host vulnerable to subsequent microbial infections. Fungi also trigger auto-reactive T cells via molecular mimicry between fungal and human peptides. Molecular mimicry happens when pathogens form molecules that mimic human molecular structures to avoid immune recognition. In response, the immune system creates autoantibodies that damage human tissues in response to microbial infection. Finally, by dysregulating the Vitamin D receptor, altering the gut microbiota, and damaging the gut epithelium, fungi cultivate an environment conducive to inflammation, persistent infections, and autoimmunity.Ulcerative colitis is an autoimmune chronic inflammatory bowel disease characterized by relapsing and remitting mucosal inflammation affecting the colon. Several studies have identified fungal alterations in the microbiota of patients with ulcerative colitis. The most common fungal infections identified in patients with inflammatory bowel disease were caused by Candida spp , however, no studies to date have implicated Chaetomium spp. in its pathogenesis. Our report illustrates a case of Chaetomium spp. colonizing the gut microbiota of a patient with ulcerative colitis. In our demonstrated example, the patient had a history of living in water-damaged buildings that could be a probable reservoir for Chaetomium spp. While Chaetomium infection sometimes occurs because of immunosuppressive medications such as Vedolizumab, we believe our patient’s exposure coincided with the onset of her ulcerative colitis symptoms several years prior to beginning immunotherapy.This case underscores the potential connection between autoimmunity, gastrointestinal disease, and environmental pathogens. In patients with inflammatory bowel disease, next-generation sequencing of microbial DNA should be considered toidentify potentially infectious environmental exposures, including Chaetomium spp. The relationship between inflammatory bowel disease and pathogenic fungi colonization of the microbiome warrants further investigation. In addition to increasing the prevalence of autoimmune disease, perturbations to the microbiome have been implicated in neurological diseases and other diseases associated with aging; therefore, cases such as this may have broader-reaching implications. Remediating water-damaged buildings and treating fungal infections may help resolve the autoimmune biochemical sequelae underlying inflammatory bowel disease and improve human health.Additionally or alternatively, existence of tularemia and brucellosis may / has been found in subjects Which is a novel testing method. Tularemia and brucellosis are both bacterial infections typically identified through serological testing. See

[0031] , In some cases, tularemia and brucellosis, and / or potential alternative modes of transmission (of the infection) may be detected, using method described herein. Methods described herein may further include testing for Tularemia and / or brucellosis, as well as other bacterial infections.Further, in other nonlimiting embodiments, medical condition may also include vector-borne disease. Vector-borne disease is an illness caused by pathogens (such as viruses, bacteria, or parasites) that are transmitted to humans (e.g., patients) through vectors, wherein the vectors are living organisms that can transmit infectious disease between humans or from animals to humans. In a nonlimiting example, vectors may include mosquitoes, ticks, fleas, and / or the like. In some cases, vector- borne disease may include, without limitation, Malaria, Dengue fever, Lyme disease, Zika virus, West Nile virus, yellow fever, and / or the like. Apparatus and methods described herein may include potential to extend its capabilities to include the study or detection of vector-borne disease.With continued reference to FIG. 1 , apparatus 100 includes a computing device 104. Apparatus 100 includes a processor 108. Processor 108 may include, without limitation, any processor 108 described in this disclosure. Processor 108 may be included in a and / or consistent with computing device 104. Computing device 104 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and / or system on a chip (SoC) as described in this disclosure. Computing device 104 mayinclude, be included in, and / or communicate with a mobile device such as a mobile telephone or smartphone. Computing device 104 may include a single computing device 104 operating independently or may include two or more computing devices operating in concert, in parallel, sequentially orthe like; two or more computing devices may be included together in a single computing device 104 or in two or more computing devices. Computing device 104 may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting computing device 104 to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone / voice provider (e.g., a mobile communications provider data and / or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and / or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and / or from a computer and / or a computing device 104. Computing device 104 may include but is not limited to, for example, a computing device 104 or cluster of computing devices in a first location and a second computing device 104 or cluster of computing devices in a second location. Computing device 104 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Computing device 104 may distribute one or more computing tasks as described below across a plurality of computing devices of computing device 104, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory 112 between computing devices. Computing device 104 may be implemented, as a non-limiting example, using a “shared nothing” architecture.With continued reference to FIG. 1 , computing device 104 may be designed and / or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, computing device 104 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and / or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and / or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and / or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Computing device 104 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and / or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and / or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and / or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and / or parallel processing.With continued reference to FIG. 1 , computing device 104 may perform determinations, classification, and / or analysis steps, methods, processes, or the like as described in this disclosure using machine-learning processes. A “machinelearning process,” as used in this disclosure, is a process that automatedly uses a body of data known as “training data” and / or a “training set” (described further below in this disclosure) to generate an algorithm that will be performed by a Processor module to produce outputs given data provided as inputs; this is in contrast to a nonmachine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. A machinelearning process may utilize supervised, unsupervised, lazy-learning processes and / or neural networks, described further below.With continued reference to FIG. 1 , apparatus 100 includes a memory 112 communicatively connected to processor 108. As used in this disclosure, “communicatively connected” means connected by way of a connection, attachment, or linkage between two or more relata which allows for reception and / or transmittance of information therebetween. For example, and without limitation, this connection may be wired or wireless, direct, or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and / or transmittance of dataand / or signal(s) therebetween. Data and / or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio, and microwave data and / or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital, or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, using a bus or other facility for intercommunication between elements of a computing device 104. Communicative connecting may also include indirect connections via, for example and without limitation, wireless connection, radio communication, low power wide area network, optical communication, magnetic, capacitive, or optical coupling, and the like. In some instances, the terminology “communicatively coupled” may be used in place of communicatively connected in this disclosure.Still referring to FIG. 1 , apparatus 100 may include a database 116. Database 116 may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Database may alternatively or additionally be implemented using a distributed data storage protocol and / or data structure, such as a distributed hash table or the like. Database 116 may include a plurality of data entries and / or records as described above. Data entries in database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and / or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in database may store, retrieve, organize, and / or reflect data and / or records.With continued reference to FIG. 1 , database 116 may be utilized to store one or more data entries. In an embodiment, data entries may relate to a collection of DNA sequencing encodings. In an embodiment, data entries may relate to one or more test results 116. A “test result” as used in this disclosure, is any information and / or data pertaining to a medical procedure performed to detect, diagnose, or monitor diseases,disease processes, susceptibility, and / or to determine a course of treatment. A test result 120 may include patient data. “Patient data” as used in this disclosure, is any information about an individual patient, which may be relevant to decisions about current or future health or illness. Patient data may describe a patient symptom. A “patient symptom” as used in this disclosure, is any sign, physical disturbance, or evidence of disease of an illness, injury, or condition. For example, a patient symptom may indicate that a patient feels lethargic, lack of motivation, and cold all the time. In yet another non-limiting example, a patient symptom may indicate that a patient has brain fog and memory lapses intermittently throughout the day.With continued reference to FIG. 1 , computing device 104 is configured to obtain a test result 120. Computing device 104 may obtain a test result from database 116. In some instances, data entries may be stored and categorized based on patient identifying information. Database 116 may include a searchable database that is allowed to retrieve specific information pertaining to a patient by performing queries or searches relating to a patient. In an embodiment, test result 120 may include a stool test result. A “stool test” as used in this disclosure, is any test that examines a stool sample to detect bacteria, viruses, parasites, blood, mucus, infectious disease and / or other medical conditions such as but not limited to anal fissures, anemia, colitis, colon polyps, colorectal cancer, diverticulosis, exocrine pancreatic insufficiency (EPC), gastrointestinal bleeding, hemorrhoids, inflammatory bowel disease, steatorrhea, stomach ulcers, environmental toxins, and the like. In an embodiment, stool test may determine the existence of one or more infectious diseases such as tularemia and / or brucellosis. Test result 120 may include 16s rRNA sequencing data. “16s rRNA sequencing data” as used in this disclosure, is any data pertaining to the composition and structure of microbial and archaea communities from a test result 120 and / or biological sample. 16s rRNA may specify bacteria and archaea present in clinical samples, such as those collected from the human microbiome or from infected tissue.With continued reference to FIG. 1 , computing device 104 identifies an imbalance 124 as a function of a test result 120. An “imbalance” as used in this disclosure, is any disease or period of sickness affecting the body or mind. An imbalance 124 may include identification of one or more root causes of chronic disease. It may identify what disturbs the equilibrium within the body and prevents it from optimal functioning. In an embodiment, an imbalance 124 may include a moldimbalance. A mold imbalance may include any health condition caused by exposure to mycotoxins or mold toxicity. Patient symptoms relating to a mold imbalance may include but are not limited to chronic inflammation, immune system dysfunction, fatigue and weakness, persistent headaches or migraines, sleep disruptions, neurological and cognitive issues, mental and mood changes, sinus issues, respiratory problems, gastrointestinal distress, muscle aches, joint pain, and allergic reactions. An imbalance 124 may include an environmental toxin. An “environmental toxin” as used in this disclosure, is any substance and / or organism that negatively affects health. An environmental toxin may include poisonous chemicals, chemical compounds, physical materials, endocrine disrupting chemicals, particulate matter, ozone, nitrogen oxides, sulfur dioxide, carbon monoxide, volatile organic compounds, heavy metals, pesticides, pharmaceutical residues, industrial chemicals, microbial contaminants, herbicides, food additives, industrial pollutants, solvents, plasticizers, flame retardants, arsenic, lead, cadmium chromium, and the like.With continued reference to FIG. 1 , computing device 104 may identify an imbalance 124 as a function of a test result 120 using an internal transcribed spacer (ITS). An “internal transcribed spacer” as used in this disclosure, is any spacer DNA situated between the small-subunit ribosomal RNA (rRNA) and large-subunit rRNA genes in the chromosome or the corresponding transcribed region in the polycistronic rRNA precursor transcript. An internal transcribed spacer (ITS) may be used as a molecular marker for species characterization of one or more bacteria, viruses, protozoans, and / or other species that may be found from a test result 120.With continued reference to FIG. 1 , computing device 104 is configured to determine a medical condition 128 as a function of the test result 120 and the imbalance 124. A “medical condition,” as used in this disclosure, is any disease, lesion, disorder, state of mental or physical health, disease, illness or injury. In an embodiment, a medical condition may include an autoimmune condition. An “autoimmune condition,” as used in this disclosure, is a state where an immune system is overactive, causing it to attack and damage the body’s own tissues. An autoimmune condition may include for example, rheumatoid arthritis, systemic lupus erythematosus (SLE), irritable bowel disease (IBD), multiple sclerosis (MS), Type 1 diabetes, Guillain- Barre syndrome (GBS), chronic inflammatory demyelinating polyneuropathy (CIDP),psoriasis, Graves’ disease, Hashimoto’s thyroiditis, Myasthenia gravis, scleroderma, vasculitis, and the like.With continued reference to FIG. 1 , determining a medical condition 128 may include comparing a test result 120 against a database 116 and wherein the database 116 comprises a collection of DNA sequencing encodings. A medical condition 128 may be determined as a function of the comparison. “DNA sequencing encodings” as used in this disclosure, is any sequence of nucleotide bases in a DNA molecule. In an embodiment, DNA sequencing encodings may be stored in database 116 and be updated from expert knowledge in a particular industry and field. Comparing a patient’s test result 120 against data contained within database 116 may include clustering testing results 116 into a plurality of clusters and grouping one or more clusters of the plurality of clusters into an operational taxonomic unit. Clustering may include any unsupervised machine learning technique to group similar data points into clusters based on their similarities or patterns. Clustering may include hard clustering where each data point such as a test result 120 may belong to only one cluster. Clustering may include software clustering where each data point such as a test result 120 may belong to multiple clusters with varying degrees of membership. Clustering may include any steps, methods, processes, and / or techniques as described below in more detail in reference to FIGS. 3-6. An “operational taxonomic unit” as used in this disclosure, is any categorization of closely related organisms based on a set level of similarity, often using DNA sequences. For example, an operational taxonomic unit may classify clusters of organisms grouped by DNA sequence similarity of a specific taxonomic marker gene. In an embodiment, sequences may be clustered according to their similarity to one another, and operational taxonomic units may be defined based on the similarity threshold set by computing device 104.With continued reference to FIG. 1 , determining a medical condition 128 may include determining a plurality of additional infectious factors. “An infectious factor” as used in this disclosure, is any biomarker data relating to and / or indicative of an infectious disease. Biomarker data may include but is not limited to, carbamoyl phosphate synthase-1 (CPS-1 ), chemokines, coagulation system markers, c-reactive protein, CoQ10 level reduction, endotoxin, inducible nitric oxide synthase (iNOS), lactate, leukocytosis, lipoprotein binding protein, moesin, pro-atrial natriuretic peptide, prcalcitonoin, proinflammatory cytokines and the like.With continued reference to FIG. 1 , computing device 104 may be configured to determine a medical condition 128 by training a first machine learning model 132 with a first training set 136, wherein the first training set 136 comprises a plurality of inputs containing test results correlated to a plurality of outputs containing imbalances. First machine learning model 132 may include any machine learning model as described below in more detail in reference to FIGS. 3-6. Computing device 104 may then receive the test result 120 relating to the patient, input the test result 120 relating to the patient into the trained first machine learning model 132, and then output the imbalance 124 relating to the patient as a function of the trained first machine learning model 132. First training set 136 may be stored in database 116 and may contain data submitted and validated by experts endowed with knowledge and expertise to support knowledge based applications. For example, first training set may contain an input containing a test result showing high levels of mercury in the urine correlated to an output of an imbalance showing a heavy metal imbalance caused by dental fillings.With continued reference to FIG. 1 , computing device 104 may training a second machine learning model 140 with a second training set 144 wherein the second training set 144 comprises a plurality of inputs containing imbalances correlated to a plurality of outputs containing medical conditions. Computing device 104 receives the imbalance 124 relating to the patient, inputs the imbalance 124 relating to the patient into the trained second machine learning model 140, and outputs the medical condition 128 as a function of the trained second machine learning model 140. Second machine learning model 140 may include any machine learning model as described below in more detail in reference to FIGS. 3-6. Second training set 144 may be stored in database 116 and may contain data submitted and validated by experts endowed with knowledge and expertise to support knowledge based applications. For example, second training set may contain an input such as an imbalance identifying mineral deficiencies demonstrating low sodium and magnesium levels correlated to an output of a medical condition identifying adrenal fatigue.With continued reference to FIG. 1 , medical condition 128 may identify a treatment plan. A “treatment plan” as used in this disclosure, is any action of treating a patient or medical condition 128 with the intention to prevent, cure, ameliorate or slow progression of a medical condition 128. A treatment plan may include one or more vitamins, supplements, prescription medications, fitness plans, health plans,nutrition plans and the like. In an embodiment, a treatment plan may be generated using one or more machine learning processes, including any process as described below in more detail in reference to FIGS. 3-6. In an embodiment, treatment plan may include a detox protocol. A “detox protocol’’ as used in this disclosure, is a plan designed to support the body’s natural elimination process and restore optimal function. It may focus on diet, lifestyle, microbiome, and environmental interventions. A detox protocol may aim to restore balance and aid the body in eliminating toxins and correcting an imbalance 124.Referring now to FIGS. 2A-2F, exemplary embodiments of a test result 120 is illustrated. In an embodiment, test result 120 may include a stool test as described above in more detail in reference to FIG. 1. In an embodiment, stool test may identify and display one or more bacterial and / or fungal pathogens detected, any antimicrobial resistance genes detected from any bacterial or fungal pathogens that may be present. Stool test may also identify the microbial composition of a patient’s microbiome, including the abundance of various bacterial and fungal species that may be detected. In an embodiment, stool test may recommend antibiotics and / or treatments, specifying various drugs or supplements that may be recommended based on their susceptibility and activity against various bacterial and / or fungal pathogens that may be detected.Referring now to FIG. 3, in one or more embodiments, detection of a medical condition may involve a use of machine learning. An exemplary embodiment of a machine learning module 300 that may perform one or more machine learning processes as described above is illustrated. Machine learning module may perform determinations, classification, and / or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. For the purposes of this disclosure, a “machine learning process” is an automated process that uses training data 304 to generate an algorithm instantiated in hardware or software logic, data structures, and / or functions that will be performed by a computing device / module to produce outputs 308 given data provided as inputs 312; this is in contrast to a nonmachine learning software program where the commands to be executed are predetermined by user and written in a programming language.With continued reference to FIG. 3, “training data”, for the purposes of this disclosure, are data containing correlations that a machine learning process may use to model relationships between two or more categories of data elements. For instance,and without limitation, training data 304 may include a plurality of data entries, also known as “training examples”, each entry representing a set of data elements that were recorded, received, and / or generated together. Data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data 304 may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data 304 according to various correlations; correlations may indicate causative and / or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine learning processes as described in further detail below. Training data 304 may be formatted and / or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As a nonlimiting example, training data 304 may include data entered in standardized forms by persons or processes, such that entry of a given data element within a given field in a given form may be mapped to one or more descriptors of categories.Elements in training data 304 may be linked to descriptors of categories by tags, tokens, or other data elements. For instance, and without limitation, training data 304 may be provided in fixed-length formats, formats linking positions of data to categories such as comma-separated value (CSV) formats and / or self-describing formats such as extensible markup language (XML), JavaScript Object Notation (JSON), or the like, enabling processes or devices to detect categories of data.With continued reference to FIG. 3, alternatively or additionally, training data 304 may include one or more elements that are uncategorized; that is, training data 304 may not be formatted or contain descriptors for some elements of data. Machine learning algorithms and / or other processes may sort training data 304 according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data, and the like; categories may be generated using correlation and / or other processing algorithms.As a nonlimiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person’s name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine learning algorithms, and / or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 304 to be made applicable for two or more distinct machine learning algorithms as described in further detail below. Training data 304 used by machine learning module 300 may correlate any input data as described in this disclosure to any output data as described in this disclosure.With continued reference to FIG. 3, training data 304 may be filtered, sorted, and / or selected using one or more supervised and / or unsupervised machine learning processes and / or models as described in further detail below; such processes and / or models may include without limitation a training data classifier 316. For the purposes of this disclosure, a “classifier” is a machine learning model, such as a data structure representing and / or using a mathematical model, neural net, or a program generated by a machine learning algorithm, known as a “classification algorithm”, that sorts inputs into categories or bins of data, outputting the categories or bins of data and / or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. A distance metric may include any norm, such as, without limitation, a Pythagorean norm. Machine learning module 300 may generate a classifier using a classification algorithm. For the purposes of this disclosure, a “classification algorithm” is a process wherein a computing device and / or any module and / or component operating therein derives a classifier from training data 304. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and / or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, Fisher’s linear discriminant, quadraticclassifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and / or neural network-based classifiers. In one or more embodiments, training data classifier 316 may classify elements of training data to geographic locations, occupations, industries, and / or the like. In one or more embodiments, each geographic location may contain its own regulations and / ordinances. In one or more embodiments, each business specialty may contain its own requirements.With continued reference to FIG. 3, machine learning module 300 may be configured to generate a classifier using a naive Bayes classification algorithm. Naive Bayes classification algorithm generates classifiers by assigning class labels to problem instances, represented as vectors of element values. Class labels are drawn from a finite set. Naive Bayes classification algorithm may include generating a family of algorithms that assume that the value of a particular element is independent of the value of any other element, given a class variable. Naive Bayes classification algorithm may be based on Bayes Theorem expressed as PA / B) = P(B / A) x P(A) + P(B), where P(A / B) is the probability of hypothesis A given data B, also known as posterior probability; P (B / A) is the probability of data B given that the hypothesis was true; P (A) is the probability of hypothesis A being true regardless of data, also known as prior probability of / ; and P(B) is the probability of the data regardless of the hypothesis. A naive Bayes algorithm may be generated by first transforming training data into a frequency table. Machine learning module 300 may then calculate a likelihood table by calculating probabilities of different data entries and classification labels. Machine learning module 300 may utilize a naive Bayes equation to calculate a posterior probability for each class. A class containing the highest posterior probability is the outcome of prediction. Naive Bayes classification algorithm may include a gaussian model that follows a normal distribution. Naive Bayes classification algorithm may include a multinomial model that is used for discrete counts. Naive Bayes classification algorithm may include a Bernoulli model that may be utilized when vectors are binary.With continued reference to FIG. 3, machine learning module 300 may be configured to generate a classifier using a k-nearest neighbors (KNN) algorithm. For the purposes of this disclosure, a “k-nearest neighbors algorithm” is or at least includes a classification method that utilizes feature similarity to analyze how closely out-of- sample features resemble training data 304 and to classify input data to one or moreclusters and / or categories of features as represented in training data 304; this may be performed by representing both training data 304 and input data in vector forms and using one or more measures of vector similarity to identify classifications within training data 304 and determine a classification of input data. K-nearest neighbors algorithm may include specifying a k-value, or a number directing the classifier to select the k most similar entries of training data 304 to a given sample, determining the most common classifier of the entries in the database, and classifying the known sample; this may be performed recursively and / or iteratively to generate a classifier that may be used to classify input data as further samples. For instance, an initial set of samples may be performed to cover an initial heuristic and / or “first guess” at an output and / or relationship, which may be seeded, without limitation, using expert input received according to any process as described herein. As a nonlimiting example, an initial heuristic may include a ranking of associations between inputs 312 and elements of training data 304. Heuristic may include selecting some number of highest-ranking associations and / or training data elements.With continued reference to FIG. 3, generating k-nearest neighbors algorithm may generate a first vector output containing a data entry cluster, generating a second vector output containing input data, and calculate the distance between the first vector output and the second vector output using any suitable norm such as cosine similarity, Euclidean distance measurement, or the like. Each vector output may be represented, without limitation, as an n-tuple of values, where n is at least 2. Each value of n-tuple of values may represent a measurement or other quantitative value associated with a given category of data or attribute, examples of which are provided in further detail below. A vector may be represented, without limitation, in n-dimensional space using an axis per category of value represented in n-tuple of values, such that a vector has a geometric direction characterizing the relative quantities of attributes in the n-tuple as compared to each other. Two vectors may be considered equivalent when their directions and / or relative quantities of values are the same; thus, as a nonlimiting example, a vector represented as [5, 10, 15] may be treated as equivalent, for the purposes of this disclosure, as a vector represented as [1 , 2, 3], Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent. However, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, orany other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for the purposes of loss functions as described in further detail below. Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be “normalized”, or divided by a “length” attribute, such as a length attribute I as derived using a Pythagorean norm: I = where ai is attribute number of vector i. Scalingand / or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes. This may, for instance, be advantageous where cases represented in training data 304 are represented by different quantities of samples, which may result in proportionally equivalent vectors with divergent values.With continued reference to FIG. 3, training examples for use as training data may be selected from a population of potential examples according to cohorts relevant to an analytical problem to be solved, a classification task, or the like. Alternatively or additionally, training data 304 may be selected to span a set of likely circumstances or inputs for a machine learning model and / or process to encounter when deployed. For instance, and without limitation, for each category of input data to a machine learning model and / or process that may exist in a range of values in a population of phenomena such as images, user data, process data, physical data, or the like, a computing device, processor, and / or machine learning module 300 may select training examples representing each possible value on such a range and / or a representative sample of values on such a range. Selection of a representative sample may include selection of training examples in proportions matching a statistically determined and / or predicted distribution of such values according to relative frequency, such that, for instance, values encountered more frequently in a population of data so analyzed are represented by more training examples than values that are encountered less frequently. Alternatively or additionally, a set of training examples may be compared to a collection of representative values in a database and / or presented to user, so that a process can detect, automatically or via user input, one or more values that are not included in the set of training examples. Computing device, processor, and / or module may automatically generate a missing training example. This may be done by receiving and / or retrieving a missing input and / or output value and correlating the missing input and / or output value with a corresponding output and / or input valuecollocated in a data record with the retrieved value, provided by user, another device, or the like.With continued reference to FIG. 3, computing device, processor, and / or module may be configured to preprocess training data 304. For the purposes of this disclosure, “preprocessing” training data is a process that transforms training data from a raw form to a format that can be used for training a machine learning model. Preprocessing may include sanitizing, feature selection, feature scaling, data augmentation and the like.With continued reference to FIG. 3, computing device, processor, and / or module may be configured to sanitize training data. For the purposes of this disclosure, “sanitizing” training data is a process whereby training examples that interfere with convergence of a machine learning model and / or process are removed to yield a useful result. For instance, and without limitation, a training example may include an input and / or output value that is an outlier from typically encountered values, such that a machine learning algorithm using the training example will be skewed to an unlikely range of input 312 and / or output 308; a value that is more than a threshold number of standard deviations away from an average, mean, or expected value, for instance, may be eliminated. Alternatively or additionally, one or more training examples may be identified as having poor-quality data, where “poor-quality” means having a signal- to-noise ratio below a threshold value. In one or more embodiments, sanitizing training data may include steps such as removing duplicative or otherwise redundant data, interpolating missing data, correcting data errors, standardizing data, identifying outliers, and / or the like. In one or more embodiments, sanitizing training data may include algorithms that identify duplicate entries or spell-check algorithms.With continued reference to FIG. 3, in one or more embodiments, images used to train an image classifier or other machine learning model and / or process that takes images as inputs 312 or generates images as outputs 308 may be rejected if image quality is below a threshold value. For instance, and without limitation, computing device, processor, and / or module may perform blur detection. Elimination of one or more blurs may be performed, as a nonlimiting example, by taking Fourier transform or a Fast Fourier Transform (FFT) of image and analyzing a distribution of low and high frequencies in the resulting frequency-domain depiction of the image. Numbers of high-frequency values below a threshold level may indicate blurriness. As a furthernonlimiting example, detection of blurriness may be performed by convolving an image, a channel of an image, or the like with a Laplacian kernel; this may generate a numerical score reflecting a number of rapid changes in intensity shown in the image, such that a high score indicates clarity and a low score indicates blurriness. Blurriness detection may be performed using a gradient-based operator, which measures operators based on the gradient or first derivative of image, based on the hypothesis that rapid changes indicate sharp edges in the image, and thus are indicative of a lower degree of blurriness. Blur detection may be performed using a wavelet-based operator, which uses coefficients of a discrete wavelet transform to describe the frequency and spatial content of images. Blur detection may be performed using statistics-based operators that take advantage of several image statistics as texture descriptors in order to compute a focus level. Blur detection may be performed by using discrete cosine transform (DOT) coefficients in order to compute a focus level of an image from its frequency content.With continued reference to FIG. 3, computing device, processor, and / or module may be configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and / or process has one or more inputs 312 and / or outputs 308 requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more elements of training examples to be used as or compared to inputs 312 and / or outputs 308 may be modified to have such a number of units of data. In one or more embodiments, computing device, processor, and / or module may convert a smaller number of units, such as in a low pixel count image, into a desired number of units by upsampling and interpolating. As a nonlimiting example, a low pixel count image may have 100 pixels, whereas a desired number of pixels may be 132. Processor may interpolate the low pixel count image to convert 100 pixels into 132 pixels. It should also be noted that one of ordinary skill in the art, upon reading the entirety of this disclosure, would recognize the various methods to interpolate a smaller number of data units such as samples, pixels, bits, or the like to a desired number of such units. In one or more embodiments, a set of interpolation rules may be trained by sets of highly detailed inputs 312 and / or outputs 308 and corresponding inputs 312 and / or outputs 308 downsampled to smaller numbers of units, and a neural network or another machine learning model that is trained to predict interpolated pixel values using the training data 304. As a nonlimiting example, asample input 312 and / or output 308, such as a sample picture, with sample-expanded data units (e.g., pixels added between the original pixels) may be input to a neural network or machine learning model and output a pseudo replica sample picture with dummy values assigned to pixels between the original pixels based on a set of interpolation rules. As a nonlimiting example, in the context of an image classifier, a machine learning model may have a set of interpolation rules trained by sets of highly detailed images and images that have been downsampled to smaller numbers of pixels, and a neural network or other machine learning model that is trained using those examples to predict interpolated pixel values in a facial picture context. As a result, an input with sample-expanded data units (the ones added between the original data units, with dummy values) may be run through a trained neural network and / or model, which may fill in values to replace the dummy values. Alternatively or additionally, computing device, processor, and / or module may utilize sample expander methods, a low-pass filter, or both. For the purposes of this disclosure, a “low-pass filter” is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. The exact frequency response of the filter depends on the filter design. Computing device, processor, and / or module may use averaging, such as luma or chroma averaging in images, to fill in data units in between original data units.With continued reference to FIG. 3, in one or more embodiments, computing device, processor, and / or module may downsample elements of a training example to a desired lower number of data elements. As a nonlimiting example, a high pixel count image may contain 256 pixels, however a desired number of pixels may be 132. Processor may downsample the high pixel count image to convert 256 pixels into 132 pixels. In one or more embodiments, processor may be configured to perform downsampling on data. Downsampling, also known as decimation, may include removing every Nthentry in a sequence of samples, all but every Nth entry, or the like, which is a process known as “compression” and may be performed, for instance by an N-sample compressor implemented using hardware or software. Anti-aliasing and / or anti-imaging filters, and / or low-pass filters, may be used to eliminate side effects of compression.With continued reference to FIG. 3, feature selection may include narrowing and / or filtering training data 304 to exclude features and / or elements, or training dataincluding such elements that are not relevant to a purpose for which a trained machine learning model and / or algorithm is being trained, and / or collection of features, elements, or training data including such elements based on relevance to or utility for an intended task or purpose for which a machine learning model and / or algorithm is being trained. Feature selection may be implemented, without limitation, using any process described in this disclosure, including without limitation using training data classifiers, exclusion of outliers, or the like.With continued reference to FIG. 3, feature scaling may include, without limitation, normalization of data entries, which may be accomplished by dividing numerical fields by norms thereof, for instance as performed for vector normalization. Feature scaling may include absolute maximum scaling, wherein each quantitative datum is divided by the maximum absolute value of all quantitative data of a set or subset of quantitative data. Feature scaling may include min-max scaling, wherein a difference between each value, X, and a minimum value, Xmin, in a set or subset of values is divided by a range of values, Xmax - Xmin, in the set or subset: Xnew =X — XlTlin- . Feature scaling may include mean normalization, wherein a difference xmax-xmin between each value, X, and a mean value of a set and / or subset of values, / mean, is divided by a range of values, Xmax - Xmin, in the set or subset: Xnew = - . xmax-xmin Feature scaling may include standardization, wherein a difference between X and Xmean is divided by a standard deviation, cr,of a set or subset of values: Xnew = — - — . Feature scaling may be performed using a median value of a set or subset, Xmedian and / or interquartile range (IQR), which represents the difference between the 25thpercentile value and the 50thpercentile value (or closest values — xmedian thereto by a rounding protocol), such as: Xnew = — — — . A Person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various alternative or additional approaches that may be used for feature scaling.With continued reference to FIG. 3, computing device, processor, and / or module may be configured to perform one or more processes of data augmentation. For the purposes of this disclosure, “data augmentation” is a process that adds data to a training data 304 using elements and / or entries already in the dataset. Data augmentation may be accomplished, without limitation, using interpolation, generation of modified copies of existing entries and / or examples, and / or one or more generativeartificial intelligence (Al) processes, for instance using deep neural networks and / or generative adversarial networks. Generative processes may be referred to alternatively in this context as “data synthesis” and as creating “synthetic data”. Augmentation may include performing one or more transformations on data, such as geometric, color space, affine, brightness, cropping, and / or contrast transformations of images.With continued reference to FIG. 3, machine learning module 300 may be configured to perform a lazy learning process and / or protocol 320. For the purposes of this disclosure, a “lazy learning” process and / or protocol is a process whereby machine learning is conducted upon receipt of input 312 to be converted to output 308 by combining the input 312 and training data 304 to derive the algorithm to be used to produce the output 308 on demand. A lazy learning process may alternatively be referred to as a “lazy loading” or “call-when-needed” process and / or protocol. For instance, an initial set of simulations may be performed to cover an initial heuristic and / or “first guess” at an output 308 and / or relationship. As a nonlimiting example, an initial heuristic may include a ranking of associations between inputs 312 and elements of training data 304. Heuristic may include selecting some number of highest-ranking associations and / or training data 304 elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a k-nearest neighbors algorithm, a lazy naive Bayes algorithm, or the like. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various lazy learning algorithms that may be applied to generate outputs as described in this disclosure, including without limitation lazy learning applications of machine learning algorithms as described in further detail below.With continued reference to FIG. 3, alternatively or additionally, machine learning processes as described in this disclosure may be used to generate machine learning models 324. A “machine learning model”, for the purposes of this disclosure, is a data structure representing and / or instantiating a mathematical and / or algorithmic representation of a relationship between inputs 312 and outputs 308, generated using any machine learning process including without limitation any process described above, and stored in memory. An input 312 is submitted to a machine learning model 324 once created, which generates an output 308 based on the relationship that was derived. For instance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine learning processes to calculate an output datum. As a further nonlimiting example, a machine learning model 324 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created by "training" the network, in which elements from a training data 304 are applied to the input nodes, and a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning, as described in detail below.With continued reference to FIG. 3, machine learning module 300 may perform at least a supervised machine learning process 328. For the purposes of this disclosure, a “supervised” machine learning process is a process with algorithms that receive training data 304 relating one or more inputs 312 to one or more outputs 308, and seek to generate one or more data structures representing and / or instantiating one or more mathematical relations relating input 312 to output 308, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include inputs 312 described above as inputs, and outputs 308 described above as outputs, and a scoring function representing a desired form of relationship to be detected between inputs 312 and outputs 308. Scoring function may, for instance, seek to maximize the probability that a given input 312 and / or combination thereof is associated with a given output 308 to minimize the probability that a given input 312 is not associated with a given output 308. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs 312 to outputs 308, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 304. Supervised machine learning processes may include classification algorithms as defined above. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, willbe aware of various possible variations of at least a supervised machine learning process 328 that may be used to determine a relation between inputs and outputs.With continued reference to FIG. 3, training a supervised machine learning process may include, without limitation, iteratively updating coefficients, biases, and weights based on an error function, expected loss, and / or risk function. For instance, an output 308 generated by a supervised machine learning model 328 using an input example in a training example may be compared to an output example from the training example; an error function may be generated based on the comparison, which may include any error function suitable for use with any machine learning algorithm described in this disclosure, including a square of a difference between one or more sets of compared values or the like. Such an error function may be used in turn to update one or more weights, biases, coefficients, or other parameters of a machine learning model through any suitable process including without limitation gradient descent processes, least-squares processes, and / or other processes described in this disclosure. This may be done iteratively and / or recursively to gradually tune such weights, biases, coefficients, or other parameters. Updates may be performed in neural networks using one or more back-propagation algorithms. Iterative and / or recursive updates to weights, biases, coefficients, or other parameters as described above may be performed until currently available training data 304 are exhausted and / or until a convergence test is passed. For the purposes of this disclosure, a “convergence test” is a test for a condition selected to indicate that a model and / or weights, biases, coefficients, or other parameters thereof has reached a degree of accuracy. A convergence test may, for instance, compare a difference between two or more successive errors or error function values, where differences below a threshold amount may be taken to indicate convergence. Alternatively or additionally, one or more errors and / or error function values evaluated in training iterations may be compared to a threshold.With continued reference to FIG. 3, a computing device, processor, and / or module may be configured to perform method, method step, sequence of method steps, and / or algorithm described in reference to this figure, in any order and with any degree of repetition. For instance, computing device, processor, and / or module may be configured to perform a single step, sequence, and / or algorithm repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and / or recursively using outputs 308 of previous repetitions as inputs 312 to subsequent repetitions, aggregating inputs 312 and / or outputs 308 of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and / or division of a larger processing task into a set of iteratively addressed smaller processing tasks. A computing device, processor, apparatus, or module may perform any step, sequence of steps, or algorithm in parallel, such as simultaneously and / or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and / or processes may be performed according to any protocol suitable for division of tasks between iterations. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and / or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and / or parallel processing.With continued reference to FIG. 3, machine learning process may include at least an unsupervised machine learning process 332. For the purposes of this disclosure, an unsupervised machine learning process is a process that derives inferences in datasets without regard to labels. As a result, an unsupervised machine learning process 332 may be free to discover any structure, relationship, and / or correlation provided in the data. Unsupervised processes 332 may not require a response variable, may be used to find interesting patterns and / or inferences between variables, to determine a degree of correlation between two or more variables, or the like.With continued reference to FIG. 3, machine learning module 300 may be designed and configured to create machine learning model 324 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may includeleast absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include an elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to a person of ordinary skill in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output / actual output fit is sought. Similar methods to those described above may be applied to minimize error functions, as will be apparent to a person of ordinary skill in the art upon reviewing the entirety of this disclosure.With continued reference to FIG. 3, machine learning algorithms may include, without limitation, linear discriminant analysis. Machine learning algorithm may include quadratic discriminant analysis. Machine learning algorithms may include kernel ridge regression. Machine learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine learning algorithms may include nearest neighbors algorithms. Machine learning algorithms may include various forms of latent space regularization such as variational regularization. Machine learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine learning algorithms may include cross-decomposition algorithms, including partial least squares and / or canonical correlation analysis. Machine learning algorithms may include naive Bayes methods. Machine learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine learning algorithms may include ensemble methods such as bagging meta-estimator, forest ofrandomized trees, AdaBoost, gradient tree boosting, and / or voting classifier methods. Machine learning algorithms may include neural net algorithms, including convolutional neural net processes.With continued reference to FIG. 3, a machine learning model and / or process may be deployed or instantiated by incorporation into a program, apparatus, system, and / or module. For instance, and without limitation, a machine learning model, neural network, and / or some or all parameters thereof may be stored and / or deployed in any memory or circuitry. Parameters such as coefficients, weights, and / or biases may be stored as circuit-based constants, such as arrays of wires and / or binary inputs and / or outputs set at logic “1” and “0” voltage levels in a logic circuit, to represent a number according to any suitable encoding system including twos complement or the like, or may be stored in any volatile and / or non-volatile memory. Similarly, mathematical operations and input 312 and / or output 308 of data to or from models, neural network layers, or the like may be instantiated in hardware circuitry and / or in the form of instructions in firmware, machine-code such as binary operation code instructions, assembly language, or any higher-order programming language. Any technology for hardware and / or software instantiation of memory, instructions, data structures, and / or algorithms may be used to instantiate a machine learning process and / or model, including without limitation any combination of production and / or configuration of non- reconfigurable hardware elements, circuits, and / or modules such as without limitation application-specific integrated circuits (ASICs), production and / or configuration of reconfigurable hardware elements, circuits, and / or modules such as without limitation field programmable gate arrays (FPGAs), production and / or configuration of non- reconfigurable and / or non-rewritable memory elements, circuits, and / or modules such as without limitation non-rewritable read-only memory (ROM), other memory technology described in this disclosure, and / or production and / or configuration of any computing device and / or component thereof as described in this disclosure. Such deployed and / or instantiated machine learning model and / or algorithm may receive inputs 312 from any other process, module, and / or component described in this disclosure, and produce outputs 308 to any other process, module, and / or component described in this disclosure.With continued reference to FIG. 3, any process of training, retraining, deployment, and / or instantiation of any machine learning model and / or algorithm maybe performed and / or repeated after an initial deployment and / or instantiation to correct, refine, and / or improve the machine learning model and / or algorithm. Such retraining, deployment, and / or instantiation may be performed as a periodic or regular process, such as retraining, deployment, and / or instantiation at regular elapsed time periods, after some measure of volume such as a number of bytes or other measures of data processed, a number of uses or performances of processes described in this disclosure, or the like, and / or according to a software, firmware, or other update schedule. Alternatively or additionally, retraining, deployment, and / or instantiation may be eventbased, and may be triggered, without limitation, by user inputs indicating sub-optimal or otherwise problematic performance and / or by automated field testing and / or auditing processes, which may compare outputs 308 of machine learning models and / or algorithms, and / or errors and / or error functions thereof, to any thresholds, convergence tests, or the like, and / or may compare outputs 308 of processes described herein to similar thresholds, convergence tests or the like. Event-based retraining, deployment, and / or instantiation may alternatively or additionally be triggered by receipt and / or generation of one or more new training examples; a number of new training examples may be compared to a preconfigured threshold, where exceeding the preconfigured threshold may trigger retraining, deployment, and / or instantiation.With continued reference to FIG. 3, retraining and / or additional training may be performed using any process for training described above, using any currently or previously deployed version of a machine learning model and / or algorithm as a starting point. Training data for retraining may be collected, preconditioned, sorted, classified, sanitized, or otherwise processed according to any process described in this disclosure. Training data 304 may include, without limitation, training examples including inputs 312 and correlated outputs 308 used, received, and / or generated from any version of any system, module, machine learning model or algorithm, apparatus, and / or method described in this disclosure. Such examples may be modified and / or labeled according to user feedback or other processes to indicate desired results, and / or may have actual or measured results from a process being modeled and / or predicted by system, module, machine learning model or algorithm, apparatus, and / or method as “desired” results to be compared to outputs 308 for training processes as described above. Redeployment may be performed using any reconfiguring and / orrewriting of reconfigurable and / or rewritable circuit and / or memory elements; alternatively, redeployment may be performed by production of new hardware and / or software components, circuits, instructions, or the like, which may be added to and / or may replace existing hardware and / or software components, circuits, instructions, or the like.With continued reference to FIG. 3, one or more processes or algorithms described above may be performed by at least a dedicated hardware unit 336. For the purposes of this disclosure, a “dedicated hardware unit” is a hardware component, circuit, or the like, aside from a principal control circuit and / or processor performing method steps as described in this disclosure, that is specifically designated or selected to perform one or more specific tasks and / or processes described in reference to this figure, such as without limitation preprocessing and / or sanitization of training data and / or training a machine learning algorithm and / or model. Dedicated hardware unit 336 may include, without limitation, a hardware unit that can perform iterative or massed calculations, such as matrix-based calculations to update or tune parameters, weights, coefficients, and / or biases of machine learning models and / or neural networks, efficiently using pipelining, parallel processing, or the like; such a hardware unit may be optimized for such processes by, for instance, including dedicated circuitry for matrix and / or signal processing operations that includes, e.g., multiple arithmetic and / or logical circuit units such as multipliers and / oradders that can act simultaneously, in parallel, and / or the like. Such dedicated hardware units 336 may include, without limitation, graphical processing units (GPUs), dedicated signal processing modules, field programmable gate arrays (FPGA), other reconfigurable hardware that has been configured to instantiate parallel processing units for one or more specific tasks, or the like. Computing device, processor, apparatus, or module may be configured to instruct one or more dedicated hardware units 336 to perform one or more operations described herein, such as evaluation of model and / or algorithm outputs, one-time or iterative updates to parameters, coefficients, weights, and / or biases, vector and / or matrix operations, and / or any other operations described in this disclosure.Referring now to FIG. 4, an exemplary embodiment of neural network 400 is illustrated. For the purposes of this disclosure, a neural network or artificial neural network is a network of “nodes” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes maybe organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes 404, at least an intermediate layer of nodes 408, and an output layer of nodes 412. Connections between nodes may be created via the process of "training" neural network 400, in which elements from a training dataset are applied to the input nodes, and a suitable training algorithm (such as Levenberg- Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network 400 to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. Connections may run solely from input nodes toward output nodes in a “feed-forward” network or may feed outputs of one layer back to inputs of the same ora different layer in a “recurrent network”. As a further nonlimiting example, neural network 400 may include a convolutional neural network comprising an input layer of nodes 404, one or more intermediate layers of nodes 408, and an output layer of nodes 412. For the purposes of this disclosure, a “convolutional neural network” is a type of neural network 400 in which at least one hidden layer is a convolutional layer that convolves inputs to that layer with a subset of inputs known as a “kernel”, along with one or more additional layers such as pooling layers, fully connected layers, and the like.Referring now to FIG. 5, an exemplary embodiment of a node 500 of neural network 400 is illustrated. Node 500 may include, without limitation, a plurality of inputs, xi, that may receive numerical values from inputs to neural network 400 containing the node 500 and / or from other nodes 500. Node 500 may perform one or more activation functions to produce its output given one or more inputs, such as without limitation computing a binary step function comparing an input to a threshold value and outputting either a logic 1 or logic 0 output or its equivalent, a linear activation function whereby an output is directly proportional to input, and / or a nonlinear activation function wherein the output is not proportional to the input. Nonlinear activation functions may include, without limitation, a sigmoid function of the form f(x) =x, a tanh (hyperbolic ex_e-x tangent) function of the form - — a tanh derivative function such as f(x) =tanh2(x), a rectified linear unit function such as (x) = max (0, x), a “leaky” and / or “parametric” rectified linear unit function such as f(x) = max (ax, x) for some value of a, an exponential linear units function such as f (x) = {°fa(this function maybe replaced and / orweighted byits own derivative in some embodiments), a softmax function such as where the inputs toan instant layer are xi, a swish function such as f(x) = x * sigmoid(x), a Gaussian error linear unit function such as f (x) = a(1 + tanh (^2]n(x + bxd))) for some values of a, b, and r, and / or a scaled exponential linear unit function such as f (x) Fundamentally, there is no limit tothe nature of functions of inputs xi, that may be used as activation functions. As a nonlimiting and illustrative example, node 500 may perform a weighted sum of inputs using weights, wi, that are multiplied by respective inputs, xi. Additionally or alternatively, a bias b may be added to the weighted sum of the inputs such that an offset is added to each unit in a neural network layer that is independent of the input to the layer. The weighted sum may then be input into a function, <p, which may generate one or more outputs, y. Weight, wi, applied to an input, xi, may indicate whether the input is “excitatory”, indicating that it has strong influence on the one or more outputs, y, for instance by the corresponding weight having a large numerical value, or “inhibitory”, indicating it has a weak influence on the one more outputs, y, for instance by the corresponding weight having a small numerical value. The values of weights, wll, may be determined by training neural network 400 using training data, which may be performed using any suitable process as described above.Referring now to FIG. 6, it is to be noted that any one or more of the aspects and embodiments described herein may be conveniently implemented using one or more machines (e.g., one or more computing devices that are utilized as a user computing device for an electronic document, one or more server devices, such as a document server, etc.) programmed according to the teachings of the present specification, as will be apparent to one of ordinary skill in the computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those of ordinary skill in the software art. Aspects and implementations discussed above employing software and / or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and / or software module. Such software may be a computer program product that employs a machine-readable storage medium. A machine-readable storage medium may be any medium that is capable of storing and / or encoding a sequence of instructions for execution by a machine (e.g., a computing device) and that causes the machine to perform any one of the methodologies and / or embodiments described herein. Examples of a machine- readable storage medium include, but are not limited to, a magnetic disk, an optical disc (e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-only memory “ROM” device, a random-access memory “RAM” device, a magnetic card, an optical card, a solid-state memory device, an EPROM, an EEPROM, and any combinations thereof. A machine-readable medium, as used herein, is intended to include a single medium as well as a collection of physically separate media, such as, for example, a collection of compact discs or one or more hard disk drives in combination with a computer memory. As used herein, a machine-readable storage medium does not include transitory forms of signal transmission. Such software may also include information (e.g., data) carried as a data signal on a data carrier, such as a carrier wave. For example, machine-executable information may be included as a data- carrying signal embodied in a data carrier in which the signal encodes a sequence of instruction, or portion thereof, for execution by a machine (e.g., a computing device) and any related information (e.g., data structures and data) that causes the machine to perform any one of the methodologies and / or embodiments described herein. Examples of a computing device include, but are not limited to, an electronic book reading device, a computer workstation, a terminal computer, a server computer, a handheld device (e.g., a tablet computer, a smartphone, etc.), a web appliance, a network router, a network switch, a network bridge, any machine capable of executing a sequence of instructions that specify an action to be taken by that machine, and any combinations thereof. In one example, a computing device may include and / or be included in a kiosk.With continued reference to FIG. 6, the figure shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computing system 600 within which a set of instructions for causing the computing system 600 to perform any one or more of the aspects and / or methodologies of the present disclosure may be executed. It is also contemplated that multiple computing devices may be utilized to implement a specially configured set of instructions for causing one or more of the devices to perform any one or more of the aspects and / or methodologies of the present disclosure. Computing system 600 may include aprocessor 604 and a memory 608 that communicate with each other, and with other components, via a bus 612. Bus 612 may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures. Processor 604 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit, which may be regulated with a state machine and directed by operational inputs from memory and / or sensors; processor 604 may be organized according to Von Neumann and / or Harvard architecture as a non-limiting example. Processor 604 may include, incorporate, and / or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor, field programmable gate array, complex programmable logic device, graphical processing unit, general-purpose graphical processing unit, tensor processing unit, analog or mixed signal processor, trusted platform module, a floating-point unit, and / or system on a chip.With continued reference to FIG. 6, memory 608 may include various components (e g., machine-readable media) including, but not limited to, a randomaccess memory component, a read only component, and any combinations thereof. In one example, a basic input / output system 616, including basic routines that help to transfer information between elements within computing system 600, such as during start-up, may be stored in memory 608. Memory 608 (e.g., stored on one or more machine-readable media) may also include instructions (e.g., software) 620 embodying any one or more of the aspects and / or methodologies of the present disclosure. In another example, memory 608 may further include any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof.With continued reference to FIG. 6, computing system 600 may also include a storage device 624. Examples of a storage device (e.g., storage device 624) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device 624 may be connected to bus 612 by an appropriate interface (not shown). Example interfaces include, but are not limited to, small computer system interface, advanced technology attachment, serial advancedtechnology attachment, universal serial bus, IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device 624 (or one or more components thereof) may be removably interfaced with computing system 600 (e.g., via an external port connector (not shown)). Particularly, storage device 624 and an associated machine-readable medium 628 may provide nonvolatile and / or volatile storage of machine-readable instructions, data structures, program modules, and / or other data for computing system 600. In one example, software 620 may reside, completely or partially, within machine-readable medium 628. In another example, software 620 may reside, completely or partially, within processor 604.With continued reference to FIG. 6, computing system 600 may also include an input device 632. In one example, a user of computing system 600 may enter commands and / or other information into computing system 600 via input device 632. Examples of input device 632 include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device 632 may be interfaced to bus 612 via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus 612, and any combinations thereof. Input device 632 may include a touch screen interface that may be a part of or separate from display 636, discussed further below. Input device 632 may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.With continued reference to FIG. 6, user may also input commands and / or other information to computing system 600 via storage device 624 (e.g., a removable disk drive, a flash drive, etc.) and / or network interface device 640. A network interface device, such as network interface device 640, may be utilized for connecting computing system 600 to one or more of a variety of networks, such as network 644, and one or more remote devices 648 connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide-area network (e.g., theInternet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone / voice provider (e.g., a mobile communications provider data and / or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network 644, may employ a wired and / or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software 620, etc.) may be communicated to and / orfrom computing system 600 via network interface device 640.With continued reference to FIG. 6, computing system 600 may further include a video display adapter 652 for communicating a displayable image to a display device, such as display device 636. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter 652 and display device 636 may be utilized in combination with processor 604 to provide graphical representations of aspects of the present disclosure. In addition to a display device, computing system 600 may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus 612 via a peripheral interface 656. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.Referring now to FIG. 7, an exemplary embodiment of a method 700 of determining a medical condition is illustrated. At step 705, at least a computing device 104 receives a test result 120 pertaining to a patient. Test result 120 includes any test result 120 as described above in more detail in reference to FIG. 1. Test result 120 may include a stool test as described above in more detail in reference to FIGS. 1 and 2. Test result 120 may include receiving information pertaining to at least a patient symptom.With continued reference to FIG. 7, at step 710, at least a computing device 104 trains a first machine learning model 132 with a first training set 136, wherein the first training set 136 includes a plurality of inputs containing test results correlated to a plurality of outputs containing imbalances. Training the first machine learning model132 may be performed utilizing any methodology as described above in more detail in FIGS. 1-6.With continued reference to FIG. 7, at step 715, at least a computing device 104 inputs a test result 120 relating to a patient into the trained first machine learning model 132. This may be performed utilizing any methodology as described above in more detail in reference to FIGS. 1-6. In an embodiment, test result 120 may identify the existence of bacteria such as tularemia and brucellosis. In an embodiment, test result 120 may include information describing 16s rRNA sequencing data.With continued reference to FIG. 7, at step 720, at least a computing device 104 trains a second machine learning model 140 with a second training set 144 wherein the second training set 144 comprises a plurality of inputs containing imbalances correlated to a plurality of outputs containing medical conditions. This may be performed utilizing any methodology as described above in more detail in reference to FIGS. 1-6. In an embodiment, an imbalance 124 may identify a mold imbalance. In an embodiment, an imbalance 124 may be identified using an internal transcribed spacer (ITS). In an embodiment, imbalance 124 may include an environmental toxin such as a heavy metal, chemical exposure and the like.With continued reference to FIG. 7, at step 725, at least a computing device 104 inputs the imbalance 124 relating to the patient into the trained second machine learning model 140. This may be performed utilizing any methodology as described above in more detail in reference to FIGS. 1-6.With continued reference to FIG. 7, at step 730, at least a computing device 104 outputs the medical condition 128 as a function of the trained second machine learning model 140. In an embodiment, the medical condition may include identification of an autoimmune condition. In an embodiment, medical condition 128 may be identified by comparing a test result 120 pertaining to a patient against database 116 wherein database 116 may contain a collection of DNA sequencing encodings, and determine the medical condition 128 based on the comparison. In an embodiment, computing device 104 may identify a treatment plan and / or a detox plan based on a medical condition 128 and / or an imbalance 124.The foregoing has been a detailed description of illustrative embodiments of the invention. Various modifications and additions can be made without departing from the spirit and scope of this invention. Features of each of the various embodimentsdescribed above may be combined with features of other described embodiments as appropriate in order to provide a multiplicity of feature combinations in associated new embodiments. Furthermore, while the foregoing describes a number of separate embodiments, what has been described herein is merely illustrative of the application of the principles of the present invention. Additionally, although particular methods herein may be illustrated and / or described as being performed in a specific order, the ordering is highly variable within ordinary skill to achieve methods, systems, apparatus, and software according to the present disclosure. Accordingly, this description is meant to be taken only by way of example, and not to otherwise limit the scope of this invention. Exemplary embodiments have been disclosed above and illustrated in the accompanying drawings. It will be understood by those skilled in the art that various changes, omissions and additions may be made to that which is specifically disclosed herein without departing from the spirit and scope of the present invention.Equivalents and ScopeThose skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments in accordance with the invention described herein. The scope of the present invention is not intended to be limited to the above Description, but rather is as set forth in the appended claims.In the claims, articles such as “a,” “an,” and “the” may mean one or more than one unless indicated to the contrary or otherwise evident from the context. Claims or descriptions that include “or” between one or more members of a group are considered satisfied if one, more than one, or all of the group members are present in, employed in, or otherwise relevant to a given product or process unless indicated to the contrary or otherwise evident from the context. The invention includes embodiments in which exactly one member of the group is present in, employed in, or otherwise relevant to a given product or process. The invention includes embodiments in which more than one, or the entire group members are present in, employed in, or otherwise relevant to a given product or process.It is also noted that the term “comprising” is intended to be open and permits but does not require the inclusion of additional elements or steps. When the term “comprising” is used herein, the term “consisting of” is thus also encompassed and disclosed.Where ranges are given, endpoints are included. Furthermore, it is to be understood that unless otherwise indicated or otherwise evident from the context and understanding of one of ordinary skill in the art, values that are expressed as ranges can assume any specific value or subrange within the stated ranges in different embodiments of the invention, to the tenth of the unit of the lower limit of the range, unless the context clearly dictates otherwise.In addition, it is to be understood that any particular embodiment of the present invention that falls within the prior art may be explicitly excluded from any one or more of the claims. Since such embodiments are deemed to be known to one of ordinary skill in the art, they may be excluded even if the exclusion is not set forth explicitly herein. Any particular embodiment of the compositions of the invention (e.g., any antibiotic, therapeutic or active ingredient; any method of production; any method of use; etc.) can be excluded from any one or more claims, for any reason, whether or not related to the existence of prior art.It is to be understood that the words which have been used are words of description rather than limitation, and that changes may be made within the purview of the appended claims without departing from the true scope and spirit of the invention in its broader aspects.While the present invention has been described at some length and with some particularity with respect to the several described embodiments, it is not intended that it should be limited to any such particulars or embodiments or any particular embodiment, but it is to be construed with references to the appended claims so as to provide the broadest possible interpretation of such claims in view of the prior art and, therefore, to effectively encompass the intended scope of the invention.

Claims

CLAIMSWhat is claimed is:

1. An apparatus for determining a medical condition in a patient, wherein the apparatus comprises: at least a computing device, and a memory communicatively connected to the at least a computing, wherein the memory: contains instructions configuring the at least a computing device to: obtain a test result; identify an imbalance as a function of the test result; and determine a medical condition as a function of the test result and the imbalance.

2. The apparatus of claim 1 , wherein the test result further comprises a stool test result.

3. The apparatus of claim 2, wherein the test result further comprises an existence of tularemia and brucellosis.

4. The apparatus of claim 1 , wherein the medical condition comprises an autoimmune condition.

5. The apparatus of claim 1 , wherein obtaining the test result comprises receiving patient data containing at least a patient symptom.

6. The apparatus of claim 1 , wherein the imbalance comprises a mold imbalance.

7. The apparatus of claim 1 , wherein identifying the imbalance comprises identifying the imbalance as a function of the test result using an internal transcribed spacer (ITS).

8. The apparatus of claim 1 , wherein determining the medical condition comprises: comparing the test result against a database, wherein the database comprises a collection of DNA sequencing encodings; and determining the medical condition as a function of the comparison.

9. The apparatus of claim 8, wherein comparing the patient data against the database comprises: clustering the test result into a plurality of clusters; and grouping one or more clusters of the plurality of clusters into an operational taxonomic unit.

10. The apparatus of claim 1 , wherein determining the medical condition further comprises determining a plurality of additional infectious factors.

11. The apparatus of claim 1 , wherein determining the medical condition further comprises: training a first machine learning model operating on the at least a computing device with a first training set wherein the first training set comprises a plurality of inputs containing test results correlated to a plurality of outputs containing imbalances; receiving the test result relating to the patient; inputting the test result relating to the patient into the trained first machine learning model; and outputting the imbalance relating to the patient as a function of the trained first machine learning model.

12. The apparatus of claim 11 , wherein determining the medical condition further comprises: training a second machine learning model operating on the at least a computing device wherein with a second training set wherein the second training set comprises a plurality of inputs containing imbalances correlated to a plurality of outputs containing medical conditions; receiving the imbalance relating to the patient; inputting the imbalance relating to the patient into the trained second machine learning model; and outputting the medical condition as a function of the trained second machine learning model.

13. The apparatus of claim 11 , wherein the test result relating to the patient further comprises 16s rRNA sequencing data.

14. The apparatus of claim 12, wherein the imbalance comprises an environmental toxin.

15. The apparatus of claim 14, wherein the environmental toxin further comprises a heavy metal.

16. The apparatus of claim 14, wherein the environmental toxin further comprises a chemical exposure.

17. The apparatus of claim 12, wherein outputting the medical condition further comprises identifying a treatment plan.

18. The apparatus of claim 17, wherein the treatment plan further comprises a detox protocol.

19. A method of determining a medical condition in a patient, wherein the method comprises: receiving by at least a computing device, a test result pertaining to the patient; training a first machine learning model operating on the at least a computing with a first training set wherein the first training set comprises a plurality of inputs containing test results correlated to a plurality of outputs containing imbalances; inputting the test result relating to the patient into the trained first machine learning model; outputting the imbalance relating to the patient as a function of the trained first machine learning model; training a second machine learning model operating on the at least a computing device wherein with a second training set wherein the second training set comprises a plurality of inputs containing imbalances correlated to a plurality of outputs containing medical conditions; inputting the imbalance relating to the patient into the trained second machine learning model; and outputting the medical condition as a function of the trained second machine learning model.

20. The method of claim 19, wherein the test result further comprises a stool test result.

21. The method of claim 19, wherein the test result further comprises an existence of tularemia and brucellosis.

22. The method of claim 19, wherein the medical condition comprises an autoimmune condition.

23. The method of claim 19, wherein obtaining the test result comprises receiving patient data containing at least a patient symptom.

24. The method of claim 19, wherein the imbalance comprises a mold imbalance.

25. The method of claim 19, wherein identifying the imbalance comprises identifying the imbalance as a function of the test result using an internal transcribed spacer (ITS).

26. The method of claim 19, wherein determining the medical condition comprises: comparing the test result against a database, wherein the database comprises a collection of DNA sequencing encodings; and determining the medical condition as a function of the comparison.

27. The method of claim 26, wherein comparing the test result against the database comprises: clustering the test result into a plurality of clusters; and grouping one or more clusters of the plurality of clusters into an operational taxonomic unit.

28. The method of claim 19, wherein determining the medical condition further comprises determining a plurality of additional infectious factors.

29. The method of claim 19, wherein the test result relating to the patient further comprises 16s rRNA sequencing data.

30. The method of claim 19, wherein the imbalance comprises an environmental toxin.

31. The method of claim 30, wherein the environmental toxin further comprises a heavy metal.

32. The method of claim 31 , wherein the environmental toxin further comprises a chemical exposure.

33. The method of claim 19, wherein outputting the medical condition further comprises identifying a treatment plan.

34. The method of claim 33, wherein the treatment plan further comprises a detox protocol.

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